“Survival Processing of the Selfish Gene?”: Adaptive Memory and Inclusive Fitness. Patrick Bonin, Margaux Gelin, Betty Laroche, Alain Méot. Evolutionary Psychological Science, December 2 2019. https://link.springer.com/article/10.1007/s40806-019-00220-1
Abstract: The survival processing advantage in memory is the finding that items encoded in survival scenarios are remembered better than words encoded in survival-irrelevant scenarios or in deep encoding situations (e.g., pleasantness). Whether this mnemonic advantage, which is generally found in scenarios involving personal survival, can also be observed in scenarios involving the survival of other people, and in particular, genetically related others, has received little attention. In the present study, we asked nulliparous women to imagine being stranded in the grasslands of a foreign land without any basic survival items and to consider either their personal survival, the survival of their biological child, or the survival of an orphan. Compared to a pleasantness (control) condition, a survival processing advantage was found for the child survival group, which did not differ reliably from personal survival. Both the child and the personal survival conditions yielded better recall than the orphan condition, which did not reliably differ from the pleasantness condition. These findings provide further evidence for the view that memory has been sculpted by evolutionary processes such as inclusive fitness.
Keywords: Adaptive memory Survival processing advantage Inclusive fitness
Discussion
Anecdotal evidence suggests that we do not provide the samelevel of help to strangers as we do to our relatives. It is hard to imagine a world in which parents faced with a life-or-death situation, such as a shipwreck in which the number of life boats is limited, would hesitate to save the life of their own biological child rather than that of an unrelated child. To our knowledge, such a world does not exist: We do not behave altruistically in an undifferentiated manner (Buss2019). Helping has been found to vary as a function of genetic relatedness (Burnstein et al.1 994; Fitzgerald and Whitaker 2009; Stewart-Williams 2007,2008). That being said, human beings belong to a highly cooperative species since we have lived in small and interdependent groups for most of our evolution (Hrdy2009). Social species exhibit an impressive array of altruistic behaviors, some of which are directed to unrelated others (e.g., reciprocal altruism, Trivers1971), and such altruistic behaviors have perhaps evolved to solve issues related to group living (Marsh2016).
Turning to the adaptive memory literature, the survivalprocessing advantage initially discovered by Nairne et al.(2007) is now a well-established finding. However, the ques-tion of whether this memory advantage extends to the survivalof other people (Kostic et al.2012; Krause et al. 2019; Seitz et al. 2018; Weinstein et al. 2008) or is restricted to personal survival (Cunningham et al.2013; LedingandToglia2018) is a matter of debate because of discrepant findings in the literature. In the present study, we put forward the hypothesis thata survival processing advantage should be observed with an imaginary scenario involving the survival of a biological child because, from an evolutionary perspective, offspring are vehi-cles for their parents’ genes (Buss2019). More importantly,we also hypothesized that, in line with the inclusive fitness and kin-selection theories (Hamilton1964), the survival effectin memory, if any, should be smaller when the recipient of helping behaviors is a non-biological child, namely an orphan.The findings were clear-cut. First of all, we were able to replicate the original survival processing advantage (Nairne et al.2007): Encoding words in relation to a personal survival situation yielded better memory performance than rating words for their pleasantness. It should be remembered that pleasantness has often been used as a control condition to evaluate survival processing because it is a deep processing task (e.g., Kazanas and Altarriba 2017; Nairne and Pandeirada 2010;O lds et al. 2014). When survival was directed to a biological child, a survival processing advantage was also found. In linewith Seitz et al.’s (2018) parenting scenario involving the survival of the participants’babies in the grasslands, we found a reproductive advantage in memory that did not differ reli-ably from a personal survival advantage. More importantly, in accordance with the inclusive fitness theory (Hamilton1964)and kin selection theory (Smith1964), the level of recall var-ied as a function of genetic relatedness: More words wererecalled in a survival situation involving a biological childthan in a survival situation involving an orphan, and the latter condition did not differ reliably from the pleasantness condition.
From this pattern of findings, a critical question arises.Why did Krause et al. (2019) fail to find a differential survival processing advantage in their experiments comparing different types of kin vs. non-kin conditions? In Krause et al.’s research, different survival conditions were compared: personal survival, survival of family members,a youngest blood relative, unrelated people. It should be remembered that the authors found that compared to thepleasantness control group, the recall rates were similar across the “kin,” “friend,” and “famous” groups. Thus,the pattern of recall did not show that the survival of kin produced a memory advantage compared to that of non-kin. It remains possible that the social relationships that Krause et al. took into account were not specific enough for differential effects on memory performance to emerge. Finally, it cannot be excluded that our scenarios and Krause et al.’s scenarios are too different to be compared directly. In effect, Krause et al. modified the original survival scenario only slightly, and the different scenarios were similar, whereas in the current study, more modifications were made to the original survival scenario. The strength of our study is that we took care to be specific by comparing children who are biologically relatedto their mothers with children who are not biologically related (we did not therefore include a generic“unrelated other”condition such as“strangers”). Moreover, we decided to include only female participants because it hasbeen found that, in general, women are more interested in children than men are (Cárdenas et al.2013; Charles et al.2013; Maestripieri and Pelka2002), and they also take more time to care for them (Babchuk et al.1985; Buss 2007).
Some readers might be concerned about the finding that nosurvival processing advantage occurred with an orphan. Does this finding mean that people do not behave “altruistically” towards orphans? Certainly not. First of all, our findings concern memory performance and tell us nothing about the emotional responses people may have towards orphans. As suggested by Marsh (2016): “Parental care is such an ordinary phenomenon that we often fail to think of it as altruism. But itclearly meets the definition, which is a behavior that improvesthe welfare of another individual at the expense of the altruist.”(p. 62). Parental care is often provided to distantly relatedchildren or to unrelated children (Hrdy2009), including chil-dren who are adopted. According to Marsh (2016), care-based altruism results from the co-option of systems that initially evolved to support parental care. Here, we have shown that there are differences at a cognitive level in the way things are remembered when they have been processed in a survival situation involving a biological child compared to a biologically unrelated child. Second, if we take a closer look at the memory performance, we observe that the words processed in a survival situation in the orphan condition were recalled well because they were recalled at the same level as the words that were encoded deeply, that is for their pleasantness. One important aspect worth noting is that the words in the orphan condition were not recalled less well than the words in the pleasantness condition—a deep encoding condition. Thus,there is still some level of altruistic behavior at a cognitive level that is deployed in the case of orphans.
Our findings have strong theoretical implications since they show for the first time that the survival processingadvantage has to do with inclusive fitness and kin selection (Hamilton1964;Smith1964). From a general stand-point, they reinforce the evolutionary view of memory according to which our memory is still peculiarly attunedtoward processing issues that our ancestors faced during the distant past, such as finding food, drinking water, andprotection from predators both for themselves and also for their kin, and in particular for their children. It is already clear that more work will be needed to investigate further whether human memory is tuned to encode things better for different types of kin relationships such as sibling, parental, and grandparental relationships. We are aware of the fact that having only nulliparous women as participants constitutes a limitation of our study. Perhaps a different pattern of results would have been found if our participants had been mothers. Furthermore, it remains an avenue for future research to conduct the same studyon young men. It is possible to anticipate that different findings will emerge because men and women faced dif-ferent reproductive issues in the distant past. While women are 100% sure of their parenthood, ancestral men were(and indeed modern men still are) confronted with the problem of paternity uncertainty due to cryptic ovulation(Buss2019). This reproductive problem faced specifically by men would account for their lesser interest (e.g.,Cárdenas et al.2013; Charles et al.2013; Maestripieri and Pelka2002) and investment in children compared to women (e.g., Babchuk et al.1985;Buss2007). Based on these findings, we anticipate that men will recall more words in both the personal survival scenario and childs cenario than in the pleasantness condition, but that even when the risk of not being the biological father is low,men will recall less words in the child condition than will nulliparous women. However, in a survival scenario inwhich men have to imagine that there is a high risk that the child they must take care of is not their biological child, our prediction is that the recall rate will be closeto that found here in the orphan condition. Finally, in th efuture, it would be interesting to test whether grandparents’memory for items in survival situations involving their grandchildren differs as a function of the certainty of genetic relatedness. Likewise, a maternal grandmother is more genetically certain of her grandchildren than a paternal grandfather and, as found by DeKay (1995,reported by Buss2019), maternal grandmothers are closerto and invest more resources in the grandchild than pater-nal grandfathers.
The question of the proximate mechanisms that underpin the survival processing advantage in memory is an issue which has given rise to a large number of studies. Different proximate mechanisms have been put forward and, according to Krause (2015), at least eight candidate mechanisms couldcontribute to the survival memory advantage. Although it was not the aim of our study to address this issue, our findings nevertheless suggest that self-reference, even though it certainly plays a role in this memory effect (Cunningham et al.2013), is not the sole proximate mechanism involved. Indeed,if this were the case, survival-processing effects should have been restricted to the personal survival condition, unless bio-logical children are considered to be part of the parents’self. In line with the latter claim, the literature on the self-reference effect reports that the mnemonic difference between self-reference and other-reference conditions is attenuated or eliminated when the other-reference conditions correspond to aparent or to a best-friend (Bower and Gilligan1979; Symons and Johnson1997).
Elaboration is a basic memory mechanism which certainlyplays a role in the survival processing effect as suggested bycertain studies (e.g., Bell et al.2015;Röeretal.2013; Wilson2016). Nairne et al. (2017a,b) initially reported that survival processing increases not only true memories but also false memories. Here also, we found a higher number of extra-list intrusions in the personal survival scenario than in the pleasantness condition. This type of finding—namely an increase in both true and false memories—was later extended by Howe and Derbish (2010,2014) and Otgaar and Smeets (2010),while other studies have failed to find significant effects ofs urvival processing on extra-list intrusions (Bonin et al.2019b; Gelin et al.2017). According to Howe and Derbish (2014), because elaboration is known to increase both true and false memories, the effect of survival processing sometimes found on both true and false memories may be due to the need for greater levels of elaboration in order to rate words for their survival values. Interestingly, in the current study, we found that the greatest number of extra-list intrusions occurred in theorphan condition. Overall, the current pattern of findings accords with the idea that elaboration underpins (at least inpart) the survival processing advantage.5
As found in some previous studies (e.g., Seitz et al.2018),mean relevance ratings were higher in the pleasantness condi-tion than in any of the other conditions. However, and morespecifically, recall performance was higher for both the per-sonal and the child survival scenario than for the pleasantnesscontrol condition. It is also interesting to note that the ratingsin the child condition were significantly higher than in thepersonal survival, but this difference did not translate into arecall difference between these two conditions. The pattern offindings for relevance ratings and recall rates suggests that theoverall difference in recall rates across conditions was not aresult of differences in depth of processing or in congruity,since it is generally accepted that words that are processed ata deeper level (Craik and Tulving1975) or that are morecongruent in a given encoding context (Butler et al.2009;Craik2002) are often recalled more accurately than words thatare processed more superficially or that are rated as being lesscongruent (Seitz et al.2018).
There are studies which suggest a relationship between empathy and altruistic behaviors (Marsh2016). However,the analyses performed with the scores obtained from the Basic Empathy Scale (Carré et al.2013) did not reveal thatthis dispositional trait played a role in the memory performance observed in the different encoding conditions that we considered. Emotional closeness has been found to be a prox-imal cause of altruism that partially mediates the impact of genetic relatedness on the willingness to act altruistically(Korchmaros and Kenny2001). In the current study, we didnot assess our participants’willingness to help as a function ofdifferent types of social relationship in an ancestral survivalsituation. However, because we were interested in knowingwhether willingness to help an orphan versus a biological child would mirror memory performance as indexed by recallrates, as described in more detail in the Supplementary Material, we designed a questionnaire using LimeSurvey(www.limesurvey.org) and this was completed online by apool of 84 undergraduates (only nulliparous women weretaken into account). We collected ratings using Likert scalesof willingness to help in a survival situation—by providingfood, drinking water, and protection for both an orphan and abiological child who were said to be weak. In addition, wecollected ratings for other types of kin (e.g., mother, sister,cousin) and non-kin (friend, neighbor, stranger) relationships.The findings (see the Results section in the Supplementary Material) turned out to be in line with those reported in theliterature on altruistic behaviors (e.g., Burnstein et al.1994;Fitzgerald and Whitaker2009; Stewart-Williams2007,2008),that is to say, in a hypothetical survival scenario, women were more willing to aid close kin (e.g., child, mother) than distant kin (e.g., cousin), more willing to help distant kin than neighbors,“acquaintances,”or“strangers”(see Figure1A in the Supplementary Material). Interestingly, the level of help for “friend” was comparable to that of“cousin”(Figure1A).More importantly, as far as the comparison between biologicalchild and orphan is concerned, women chose to help theirbiological child more than an orphan. However, the level of help for“orphan”was close to that for“cousin”(Figure1A).To conclude, Krause et al. (2019) made the strong claim that kin selection is one more fitness-relevant scenario that hasbeen found to be either unrelated or irrelevant to the survival processing advantage. The findings from the present study suggest just the contrary, namely that the survival processing advantage varies as a function of genetic relatedness, at least when the kin in question are biological children who, from an evolutionary point of view, ensure the perpetuation of our genes. Thus, our findings are in agreement with the claim put forward by Nairne et al. (2007) that “mnemonic processes likely operate more efficiently when dealing with fitness-relevant problems."
Monday, December 2, 2019
Human beliefs have remarkable robustness in the face of disconfirmation; this author thinks this can arise from purely rational principles when the reasoner has recourse to ad hoc auxiliary hypotheses
How to never be wrong. Samuel J. Gershman. Psychonomic Bulletin & Review, February 2019, Volume 26, Issue 1, pp 13–28. https://link.springer.com/article/10.3758/s13423-018-1488-8
Abstract: Human beliefs have remarkable robustness in the face of disconfirmation. This robustness is often explained as the product of heuristics or motivated reasoning. However, robustness can also arise from purely rational principles when the reasoner has recourse to ad hoc auxiliary hypotheses. Auxiliary hypotheses primarily function as the linking assumptions connecting different beliefs to one another and to observational data, but they can also function as a “protective belt” that explains away disconfirmation by absorbing some of the blame. The present article traces the role of auxiliary hypotheses from philosophy of science to Bayesian models of cognition and a host of behavioral phenomena, demonstrating their wide-ranging implications.
Keywords: Bayesian modeling Computational learning theories Philosophy of science
Introduction
Since the discovery of Uranus in 1781, astronomers were troubled by certain irregularities in its orbit, which appeared to contradict the prevailing Newtonian theory of gravitation. Then, in 1845, Le Verrier and Adams independently completed calculations showing that these irregularities could be entirely explained by the gravity of a previously unobserved planetary body. This hypothesis was confirmed a year later through telescopic observation, and thus an 8th planet (Neptune) was added to the solar system. Le Verrier and Adams succeeded on two fronts: they discovered a new planet, and they rescued the Newtonian theory from disconfirmation.
Neptune is a classic example of what philosophers of science call an ad hoc auxiliary hypothesis (Popper, 1959; Hempel, 1966). All scientific theories make use of auxiliary assumptions that allow them to interpret experimental data. For example, an astronomer makes use of optical assumptions to interpret telescope data, but one would not say that these assumptions are a core part of an astronomical theory; they can be replaced by other assumptions as the need arises (e.g., when using a different measurement device), without threatening the integrity of the theory. An auxiliary assumption becomes an ad hoc hypothesis when it entails unconfirmed claims that are specifically designed to accommodate disconfirmatory evidence.
Ad hoc auxiliary hypotheses have long worried philosophers of science, because they suggest a slippery slope toward unfalsifiability (Harding, 1976). If any theory can be rescued in the face of disconfirmation by changing auxiliary assumptions, how can we tell good theories from bad theories? While Le Verrier and Adams were celebrated for their discovery, many other scientists were less fortunate. For example, in the late 19th century, Michelson and Morley reported experiments apparently contradicting the prevailing theory that electromagnetic radiation is propagated through a space-pervading medium (ether). FitzGerald and Lorentz attempted to rescue this theory by hypothesizing electrical effects of ether that were of exactly the right magnitude to produce the Michelson and Morley results. Ultimately, the ether theory was abandoned, and Popper (1959) derided the FitzGerald–Lorentz explanation as “unsatisfactory” because it “merely served to restore agreement between theory and experiment.”
Ironically, Le Verrier himself was misled by an ad hoc auxiliary hypothesis. The same methodology that had served him so well in the discovery of Neptune failed catastrophically in his “discovery” of Vulcan, a hypothetical planet postulated to explain excess precession in Mercury’s orbit. Le Verrier died convinced that Vulcan existed, and many astronomers subsequently reported sightings of the planet, but the hypothesis was eventually discredited by Einstein’s theory of general relativity, which accounted precisely for the excess precession without recourse to an additional planet.
The basic problem posed by these examples is how to assign credit or blame to central hypotheses vs. auxiliary hypotheses. An influential view, known as the Duhem–Quine thesis (reviewed in the next section), asserts that this credit assignment problem is insoluble—central and auxiliary hypotheses must face observational data “as a corporate body” (Quine, 1951). This thesis implies that theories will be resistant to disconfirmation as long as they have recourse to ad hoc auxiliary hypotheses.
Psychologists recognize such resistance as a ubiquitous cognitive phenomenon, commonly viewed as one among many flaws in human reasoning (Gilovich, 1991). However, as the Neptune example attests, such hypotheses can also be instruments for discovery. The purpose of this paper is to discuss how a Bayesian framework for induction deals with ad hoc auxiliary hypotheses (Dorling, 1979; Earman, 1992; Howson and Urbach, 2006; Strevens, 2001), and then to leverage this framework to understand a range of phenomena in human cognition. According to the Bayesian framework, resistance to disconfirmation can arise from rational belief-updating mechanisms, provided that an individual’s “intuitive theory” satisfies certain properties: a strong prior belief in the central hypothesis, coupled with an inductive bias to posit auxiliary hypotheses that place high probability on observed anomalies. The question then becomes whether human intuitive theories satisfy these properties, and several lines of evidence suggest the answer is yes.1 In this light, humans are surprisingly rational. Human beliefs are guided by strong inductive biases about the world. These biases enable the development of robust intuitive theories, but can sometimes lead to preposterous beliefs.
[...]
The true self
Beliefs about the self provide a particularly powerful example of resistance to disconfirmation. People make a distinction between a “superficial” self and a “true” self, and these selves are associated with distinct patterns of behavior (Strohminger, Knobe, & Newman, 2017). In particular, people hold a strong prior belief that the true self is good (the central hypothesis h in our terminology). This proposition is supported by several lines of evidence. First, positive, desirable personality traits are viewed as more essential to the true self than negative, undesirable traits (Haslam, Bastian, & Bissett, 2004). Second, people feel that they know someone most deeply when given positive information about them Christy et al., (2017). Third, negative changes in traits are perceived as more disruptive to the true self than positive changes (Molouki and Bartels, 2017; De Freitas et al., 2017).
The key question for our purposes is what happens when one observes bad behavior: do people revise their belief in the goodness of the actor’s true self? The answer is largely no. Bad behavior is attributed to the superficial self, whereas good behavior is attributed to the true self (Newman, Bloom, & Knobe, 2014). This tendency is true even of individuals who generally have a negative attitude toward others, such as misanthropes and pessimists (De Freitas et al., 2016). And even if people are told explicitly that an actor’s true self is bad, they are still reluctant to see the actor as truly bad (Newman, De Freitas, & Knobe, 2015). Conversely, observing positive changes in behavior (e.g., becoming an involved father after being a deadbeat) are perceived as indicating “self-discovery” (Bench et al., 2015; De Freitas et al., 2017).
These findings support the view that belief in the true good self shapes the perception of evidence about other individuals: evidence that disconfirms this belief tends to be discounted. The Bayesian framework suggests that this may occur because people infer alternative auxiliary hypotheses, such as situational factors that sever the link between the true self and observed behavior (e.g., he behaved badly because is mother just died). However, this possibility remains to be studied directly.
[...]
Conceptual change in childhood
Children undergo dramatic restructuring of their knowledge during development, inspiring analogies with conceptual change in science (Carey, 2009; Gopnik, 2012). According to this “child-as-scientist” analogy, children engage in many of the same epistemic practices as scientists: probabilistically weighing evidence for different theories, balancing simplicity and fit, inferring causal relationships, carrying out experiments. If this analogy holds, then we should expect to see signs of resistance to disconfirmation early in development. In particular, Gopnik and Wellman (1992) have argued that children form ad hoc auxiliary hypotheses to reason about anomalous data until they can discover more coherent alternative theories.
For example, upon being told that the earth is round, some children preserve their preinstructional belief that the earth is flat by inferring that the earth is disk-shaped (Vosniadou & Brewer, 1992). After being shown two blocks of different weights hitting the ground at the same time when dropped from the same height, some middle-school students inferred that they hit the ground at different times but the difference was too small to observe, or that the blocks were in fact (contrary to the teacher’s claims) the same weight (Champagne et al., 1985). Children who hold a geometric-center theory of balancing believe that blocks must be balanced in the middle; when faced with the failure of this theory applied to uneven blocks, children declare that the uneven blocks are impossible to balance (Karmiloff-Smith & Inhelder, 1975).
Experimental work by Schulz, Goodman, Tenenbaum, and Jenkins (2008) has illuminated the role played by auxiliary hypotheses in children’s causal learning. In these experiments, children viewed contact interactions between various blocks, resulting in particular outcomes (e.g., a train noise or a siren noise). Children then made inferences about novel blocks based on ambiguous evidence. The data suggest that children infer abstract laws that describe causal relations between classes of blocks (see also Schulz and Sommerville, 2006; Saxe et al., 2005). Schulz and colleagues argue for a connection between the rapid learning abilities of children (supported by abstract causal theories) and resistance to disconfirmation: the explanatory scope of abstract causal laws confer a strong inductive bias that enables learning from small amounts of data, and this same inductive bias confers robustness in the face of anomalous data by assigning responsibility to auxiliary hypotheses (e.g., hidden causes). A single anomaly will typically be insufficient to disconfirm an abstract causal theory that explains a wide range of data.
The use of auxiliary hypotheses has important implications for education. In their discussion of the educational literature, Chinn and Brewer (1993) point out that anomalous data are often used in the classroom to spur conceptual change, yet “the use of anomalous data is no panacea. Science students frequently react to anomalous data by discounting the data in some way, thus preserving their preinstructional theories” (p. 2). They provide examples of children employing a variety of discounting strategies, such as ignoring anomalous data, excluding it from the domain of the theory, holding it in abeyance (promising to deal with it later), and reinterpreting it. Careful attention to these strategies leads to pedagogical approaches that more effectively produce theory change. For example, Chinn and Brewer recommend helping children construct necessary background knowledge before introduction of the anomalous data, combined with the presentation of an intelligible and plausible alternative theory. In addition, bolstering the credibility of the anomalous data, avoiding ambiguities, and using multiple lines of evidence can be effective at producing theory change.
Abstract: Human beliefs have remarkable robustness in the face of disconfirmation. This robustness is often explained as the product of heuristics or motivated reasoning. However, robustness can also arise from purely rational principles when the reasoner has recourse to ad hoc auxiliary hypotheses. Auxiliary hypotheses primarily function as the linking assumptions connecting different beliefs to one another and to observational data, but they can also function as a “protective belt” that explains away disconfirmation by absorbing some of the blame. The present article traces the role of auxiliary hypotheses from philosophy of science to Bayesian models of cognition and a host of behavioral phenomena, demonstrating their wide-ranging implications.
Keywords: Bayesian modeling Computational learning theories Philosophy of science
Introduction
Since the discovery of Uranus in 1781, astronomers were troubled by certain irregularities in its orbit, which appeared to contradict the prevailing Newtonian theory of gravitation. Then, in 1845, Le Verrier and Adams independently completed calculations showing that these irregularities could be entirely explained by the gravity of a previously unobserved planetary body. This hypothesis was confirmed a year later through telescopic observation, and thus an 8th planet (Neptune) was added to the solar system. Le Verrier and Adams succeeded on two fronts: they discovered a new planet, and they rescued the Newtonian theory from disconfirmation.
Neptune is a classic example of what philosophers of science call an ad hoc auxiliary hypothesis (Popper, 1959; Hempel, 1966). All scientific theories make use of auxiliary assumptions that allow them to interpret experimental data. For example, an astronomer makes use of optical assumptions to interpret telescope data, but one would not say that these assumptions are a core part of an astronomical theory; they can be replaced by other assumptions as the need arises (e.g., when using a different measurement device), without threatening the integrity of the theory. An auxiliary assumption becomes an ad hoc hypothesis when it entails unconfirmed claims that are specifically designed to accommodate disconfirmatory evidence.
Ad hoc auxiliary hypotheses have long worried philosophers of science, because they suggest a slippery slope toward unfalsifiability (Harding, 1976). If any theory can be rescued in the face of disconfirmation by changing auxiliary assumptions, how can we tell good theories from bad theories? While Le Verrier and Adams were celebrated for their discovery, many other scientists were less fortunate. For example, in the late 19th century, Michelson and Morley reported experiments apparently contradicting the prevailing theory that electromagnetic radiation is propagated through a space-pervading medium (ether). FitzGerald and Lorentz attempted to rescue this theory by hypothesizing electrical effects of ether that were of exactly the right magnitude to produce the Michelson and Morley results. Ultimately, the ether theory was abandoned, and Popper (1959) derided the FitzGerald–Lorentz explanation as “unsatisfactory” because it “merely served to restore agreement between theory and experiment.”
Ironically, Le Verrier himself was misled by an ad hoc auxiliary hypothesis. The same methodology that had served him so well in the discovery of Neptune failed catastrophically in his “discovery” of Vulcan, a hypothetical planet postulated to explain excess precession in Mercury’s orbit. Le Verrier died convinced that Vulcan existed, and many astronomers subsequently reported sightings of the planet, but the hypothesis was eventually discredited by Einstein’s theory of general relativity, which accounted precisely for the excess precession without recourse to an additional planet.
The basic problem posed by these examples is how to assign credit or blame to central hypotheses vs. auxiliary hypotheses. An influential view, known as the Duhem–Quine thesis (reviewed in the next section), asserts that this credit assignment problem is insoluble—central and auxiliary hypotheses must face observational data “as a corporate body” (Quine, 1951). This thesis implies that theories will be resistant to disconfirmation as long as they have recourse to ad hoc auxiliary hypotheses.
Psychologists recognize such resistance as a ubiquitous cognitive phenomenon, commonly viewed as one among many flaws in human reasoning (Gilovich, 1991). However, as the Neptune example attests, such hypotheses can also be instruments for discovery. The purpose of this paper is to discuss how a Bayesian framework for induction deals with ad hoc auxiliary hypotheses (Dorling, 1979; Earman, 1992; Howson and Urbach, 2006; Strevens, 2001), and then to leverage this framework to understand a range of phenomena in human cognition. According to the Bayesian framework, resistance to disconfirmation can arise from rational belief-updating mechanisms, provided that an individual’s “intuitive theory” satisfies certain properties: a strong prior belief in the central hypothesis, coupled with an inductive bias to posit auxiliary hypotheses that place high probability on observed anomalies. The question then becomes whether human intuitive theories satisfy these properties, and several lines of evidence suggest the answer is yes.1 In this light, humans are surprisingly rational. Human beliefs are guided by strong inductive biases about the world. These biases enable the development of robust intuitive theories, but can sometimes lead to preposterous beliefs.
[...]
The true self
Beliefs about the self provide a particularly powerful example of resistance to disconfirmation. People make a distinction between a “superficial” self and a “true” self, and these selves are associated with distinct patterns of behavior (Strohminger, Knobe, & Newman, 2017). In particular, people hold a strong prior belief that the true self is good (the central hypothesis h in our terminology). This proposition is supported by several lines of evidence. First, positive, desirable personality traits are viewed as more essential to the true self than negative, undesirable traits (Haslam, Bastian, & Bissett, 2004). Second, people feel that they know someone most deeply when given positive information about them Christy et al., (2017). Third, negative changes in traits are perceived as more disruptive to the true self than positive changes (Molouki and Bartels, 2017; De Freitas et al., 2017).
The key question for our purposes is what happens when one observes bad behavior: do people revise their belief in the goodness of the actor’s true self? The answer is largely no. Bad behavior is attributed to the superficial self, whereas good behavior is attributed to the true self (Newman, Bloom, & Knobe, 2014). This tendency is true even of individuals who generally have a negative attitude toward others, such as misanthropes and pessimists (De Freitas et al., 2016). And even if people are told explicitly that an actor’s true self is bad, they are still reluctant to see the actor as truly bad (Newman, De Freitas, & Knobe, 2015). Conversely, observing positive changes in behavior (e.g., becoming an involved father after being a deadbeat) are perceived as indicating “self-discovery” (Bench et al., 2015; De Freitas et al., 2017).
These findings support the view that belief in the true good self shapes the perception of evidence about other individuals: evidence that disconfirms this belief tends to be discounted. The Bayesian framework suggests that this may occur because people infer alternative auxiliary hypotheses, such as situational factors that sever the link between the true self and observed behavior (e.g., he behaved badly because is mother just died). However, this possibility remains to be studied directly.
Conceptual change in childhood
Children undergo dramatic restructuring of their knowledge during development, inspiring analogies with conceptual change in science (Carey, 2009; Gopnik, 2012). According to this “child-as-scientist” analogy, children engage in many of the same epistemic practices as scientists: probabilistically weighing evidence for different theories, balancing simplicity and fit, inferring causal relationships, carrying out experiments. If this analogy holds, then we should expect to see signs of resistance to disconfirmation early in development. In particular, Gopnik and Wellman (1992) have argued that children form ad hoc auxiliary hypotheses to reason about anomalous data until they can discover more coherent alternative theories.
For example, upon being told that the earth is round, some children preserve their preinstructional belief that the earth is flat by inferring that the earth is disk-shaped (Vosniadou & Brewer, 1992). After being shown two blocks of different weights hitting the ground at the same time when dropped from the same height, some middle-school students inferred that they hit the ground at different times but the difference was too small to observe, or that the blocks were in fact (contrary to the teacher’s claims) the same weight (Champagne et al., 1985). Children who hold a geometric-center theory of balancing believe that blocks must be balanced in the middle; when faced with the failure of this theory applied to uneven blocks, children declare that the uneven blocks are impossible to balance (Karmiloff-Smith & Inhelder, 1975).
Experimental work by Schulz, Goodman, Tenenbaum, and Jenkins (2008) has illuminated the role played by auxiliary hypotheses in children’s causal learning. In these experiments, children viewed contact interactions between various blocks, resulting in particular outcomes (e.g., a train noise or a siren noise). Children then made inferences about novel blocks based on ambiguous evidence. The data suggest that children infer abstract laws that describe causal relations between classes of blocks (see also Schulz and Sommerville, 2006; Saxe et al., 2005). Schulz and colleagues argue for a connection between the rapid learning abilities of children (supported by abstract causal theories) and resistance to disconfirmation: the explanatory scope of abstract causal laws confer a strong inductive bias that enables learning from small amounts of data, and this same inductive bias confers robustness in the face of anomalous data by assigning responsibility to auxiliary hypotheses (e.g., hidden causes). A single anomaly will typically be insufficient to disconfirm an abstract causal theory that explains a wide range of data.
The use of auxiliary hypotheses has important implications for education. In their discussion of the educational literature, Chinn and Brewer (1993) point out that anomalous data are often used in the classroom to spur conceptual change, yet “the use of anomalous data is no panacea. Science students frequently react to anomalous data by discounting the data in some way, thus preserving their preinstructional theories” (p. 2). They provide examples of children employing a variety of discounting strategies, such as ignoring anomalous data, excluding it from the domain of the theory, holding it in abeyance (promising to deal with it later), and reinterpreting it. Careful attention to these strategies leads to pedagogical approaches that more effectively produce theory change. For example, Chinn and Brewer recommend helping children construct necessary background knowledge before introduction of the anomalous data, combined with the presentation of an intelligible and plausible alternative theory. In addition, bolstering the credibility of the anomalous data, avoiding ambiguities, and using multiple lines of evidence can be effective at producing theory change.
Sunday, December 1, 2019
Sustaining environments hypothesis is the idea that long-term success of early educational interventions depends on the quality of the subsequent learning environment; study finds no such relation
Bailey, Drew H., Jade M. Jenkins, and Daniela Alvarez-Vargas. (2019). Complementarities between Early Educational Intervention and Later Educational Quality? A Systematic Review of the Sustaining Environments Hypothesis. (EdWorkingPaper: 19-99). Annenberg Institute at Brown University, Sep 2019. https://doi.org/10.26300/8tz9-sh62
Abstract: The sustaining environments hypothesis refers to the popular idea, stemming from theories in developmental, cognitive, and educational psychology, that the long-term success of early educational interventions is contingent on the quality of the subsequent learning environment. Several studies have investigated whether specific kindergarten classroom and other elementary school factors account for patterns of persistence and fadeout of early educational interventions. These analyses focus on the statistical interaction between an early educational intervention – usually whether the child attended preschool – and several measures of the quality of the subsequent educational environment. The key prediction of the sustaining environments hypothesis is a positive interaction between these two variables. To quantify the strength of the evidence for such effects, we meta-analyze existing studies that have attempted to estimate interactions between preschool and later educational quality in the United States. We then attempt to establish the consistency of the direction and a plausible range of estimates of the interaction between preschool attendance and subsequent educational quality by using a specification curve analysis in a large, nationally representative dataset that has been used in several recent studies of the sustaining environments hypothesis. The meta-analysis yields small positive interaction estimates ranging from approximately .00 to .04, depending on the specification. The specification curve analyses yield interaction estimates of approximately 0. Results suggest that the current mix of methods used to test the sustaining environments hypothesis cannot reliably detect realistically sized effects. Our recommendations are to combine large sample sizes with strong causal identification strategies, and to study combinations of interventions that have a strong probability of showing large main effects.
Keywords: education, achievement, meta-analysis, persistence and fadeout, intervention
4) Heterogeneity across treatments, contexts, and children.
One possibility is that, although these interactions averaged out to approximately 0, some of them were reliably positive, consistent with complementarity between early educational intervention and later education quality, and others were reliably negative, consistent with substitutability. We find mixed evidence for this, with a statistically significant test for heterogeneity in the meta-analysis and more than 5% statistically significant estimates in the specification curve analysis, but an inferential specification curve consistent with a relatively homogenous effect of approximately 0. Importantly, this occurs despite our inclusion of a heterogeneous set of definitions of early childhood intervention and later educational quality in our analysis, methods that might reasonably be expected to increase the heterogeneity of estimates. Additionally, although the meta-analysis indicated a moderate amount of heterogeneity in interaction estimates, the prediction we thought most directly followed from the sustaining environments hypothesis – namely, that interactions would only be positive when the main effects of early and later quality were positive – was not supported. Still, perhaps the most compelling argument for heterogeneity is that it is real but not well observed in these data, because we did not measure the “right” later educational moderators of early educational intervention effects. We will discuss this possibility below.
Abstract: The sustaining environments hypothesis refers to the popular idea, stemming from theories in developmental, cognitive, and educational psychology, that the long-term success of early educational interventions is contingent on the quality of the subsequent learning environment. Several studies have investigated whether specific kindergarten classroom and other elementary school factors account for patterns of persistence and fadeout of early educational interventions. These analyses focus on the statistical interaction between an early educational intervention – usually whether the child attended preschool – and several measures of the quality of the subsequent educational environment. The key prediction of the sustaining environments hypothesis is a positive interaction between these two variables. To quantify the strength of the evidence for such effects, we meta-analyze existing studies that have attempted to estimate interactions between preschool and later educational quality in the United States. We then attempt to establish the consistency of the direction and a plausible range of estimates of the interaction between preschool attendance and subsequent educational quality by using a specification curve analysis in a large, nationally representative dataset that has been used in several recent studies of the sustaining environments hypothesis. The meta-analysis yields small positive interaction estimates ranging from approximately .00 to .04, depending on the specification. The specification curve analyses yield interaction estimates of approximately 0. Results suggest that the current mix of methods used to test the sustaining environments hypothesis cannot reliably detect realistically sized effects. Our recommendations are to combine large sample sizes with strong causal identification strategies, and to study combinations of interventions that have a strong probability of showing large main effects.
Keywords: education, achievement, meta-analysis, persistence and fadeout, intervention
4) Heterogeneity across treatments, contexts, and children.
One possibility is that, although these interactions averaged out to approximately 0, some of them were reliably positive, consistent with complementarity between early educational intervention and later education quality, and others were reliably negative, consistent with substitutability. We find mixed evidence for this, with a statistically significant test for heterogeneity in the meta-analysis and more than 5% statistically significant estimates in the specification curve analysis, but an inferential specification curve consistent with a relatively homogenous effect of approximately 0. Importantly, this occurs despite our inclusion of a heterogeneous set of definitions of early childhood intervention and later educational quality in our analysis, methods that might reasonably be expected to increase the heterogeneity of estimates. Additionally, although the meta-analysis indicated a moderate amount of heterogeneity in interaction estimates, the prediction we thought most directly followed from the sustaining environments hypothesis – namely, that interactions would only be positive when the main effects of early and later quality were positive – was not supported. Still, perhaps the most compelling argument for heterogeneity is that it is real but not well observed in these data, because we did not measure the “right” later educational moderators of early educational intervention effects. We will discuss this possibility below.
Affirmative Action, Major Choice, and Long-Run Impacts: California after ending college AA admissions policy
Bleemer, Zachary, Affirmative Action, Major Choice, and Long-Run Impacts (2019). SSRN, Nov 20: http://dx.doi.org/10.2139/ssrn.3484530
Abstract: Estimation of the impact of race-based affirmative action (AA) on the medium- and long-run outcomes of underrepresented minority (URM) university applicants has been frustrated by limited data availability. This study presents a highly-detailed novel database of University of California (UC) applications in the years before and after the end of its AA admissions policy, linked to national educational records and a California employment database. Using a difference-in-difference design to compare URM and non-URM freshman applicants' outcomes two years before and after UC's affirmative action policies ended in 1998, I identify substantial and persistent educational and labor market deterioration after 1998 among URM applicants: each of UC's 10,000-per-year URM freshman applicants' likelihood of earning a Bachelor's degree within six years declined by 1.3 percentage points, their likelihood of earning any graduate degree declined 1.4 p.p., and their likelihood of earning at least $100,000 annual between ages 30 and 37 declined by about 1 p.p. per year. These results suggest that affirmative action's end decreased the number of age 30-to-34 URM Californians earning over $100,000 by at least 2.5 percent. Turning to targeted students' major choice, I link the application records to five universities' detailed course transcript data and find no evidence –despite considerable statistical power– that more-selective university enrollment under AA lowered URM students' performance or persistence in core physical, biological, or mathematical science courses. These findings suggest that state prohibitions on university affirmative action policies have modestly exacerbated American socioeconomic inequities.
Keywords: Higher Education; Affirmative Action; University Selectivity; Major Choice
JEL Classification: I24, J24, J31, H75
Abstract: Estimation of the impact of race-based affirmative action (AA) on the medium- and long-run outcomes of underrepresented minority (URM) university applicants has been frustrated by limited data availability. This study presents a highly-detailed novel database of University of California (UC) applications in the years before and after the end of its AA admissions policy, linked to national educational records and a California employment database. Using a difference-in-difference design to compare URM and non-URM freshman applicants' outcomes two years before and after UC's affirmative action policies ended in 1998, I identify substantial and persistent educational and labor market deterioration after 1998 among URM applicants: each of UC's 10,000-per-year URM freshman applicants' likelihood of earning a Bachelor's degree within six years declined by 1.3 percentage points, their likelihood of earning any graduate degree declined 1.4 p.p., and their likelihood of earning at least $100,000 annual between ages 30 and 37 declined by about 1 p.p. per year. These results suggest that affirmative action's end decreased the number of age 30-to-34 URM Californians earning over $100,000 by at least 2.5 percent. Turning to targeted students' major choice, I link the application records to five universities' detailed course transcript data and find no evidence –despite considerable statistical power– that more-selective university enrollment under AA lowered URM students' performance or persistence in core physical, biological, or mathematical science courses. These findings suggest that state prohibitions on university affirmative action policies have modestly exacerbated American socioeconomic inequities.
Keywords: Higher Education; Affirmative Action; University Selectivity; Major Choice
JEL Classification: I24, J24, J31, H75
Did Early Twintieth-Century Alcohol Prohibition Laws Reduce Mortality?
Did Early Twintieth-Century Alcohol Prohibition Affect Mortality? Marc T. Law Mindy S. Marks. Economic Inquiry, November 28 2019. https://doi.org/10.1111/ecin.12868
Abstract: We investigate the contemporaneous mortality consequences of alcohol prohibition laws introduced in America between 1900 and 1920. We improve on existing studies by constructing a time‐varying measure of prohibition at the state level that corrects for the timing of prohibition enforcement and accounts for the presence of dry counties. Using summary indices that aggregate alcohol‐related mortality due to disease and poor decisions, we find that prohibition significantly reduced mortality rates. These findings are corroborated with an area‐level analysis that exploits data on deaths in urban areas that were wet prior to statewide or federal prohibition and nonurban areas that were partially dry. (JEL I18, N4, K2)
Abstract: We investigate the contemporaneous mortality consequences of alcohol prohibition laws introduced in America between 1900 and 1920. We improve on existing studies by constructing a time‐varying measure of prohibition at the state level that corrects for the timing of prohibition enforcement and accounts for the presence of dry counties. Using summary indices that aggregate alcohol‐related mortality due to disease and poor decisions, we find that prohibition significantly reduced mortality rates. These findings are corroborated with an area‐level analysis that exploits data on deaths in urban areas that were wet prior to statewide or federal prohibition and nonurban areas that were partially dry. (JEL I18, N4, K2)
Moral Outrage Porn, vanity projects, competition to be outraged
Moral Outrage Porn. C. Thi Nguyen & Bekka Williams. Forthcoming in Journal of Ethics and Social Philosophy. Nov 2019. https://philarchive.org/archive/NGUMOP
We offer an account of the generic use of the term “porn”, as seen in recent usages such as “food porn” and “real estate porn”. We offer a definition adapted from earlier accounts of sexual pornography. On our account, a representation is used as generic porn when it is engaged with primarily for the sake of a gratifying reaction, freed from the usual costs and consequences of engaging with the represented content. We demonstrate the usefulness of the concept of generic porn by using it to isolate a new type of such porn: moral outrage porn. Moral outrage porn is, as we understand it, representations of moral outrage, engaged with primarily for the sake of the resulting gratification, freed from the usual costs and consequences of engaging with morally outrageous content. Moral outrage porn is dangerous because it encourages the instrumentalization of one’s empirical and moral beliefs, manipulating their content for the sake of gratification. Finally, we suggest that when using porn is wrong, it is often wrong because it instrumentalizes what ought not to be instrumentalized.
We offer an account of the generic use of the term “porn”, as seen in recent usages such as “food porn” and “real estate porn”. We offer a definition adapted from earlier accounts of sexual pornography. On our account, a representation is used as generic porn when it is engaged with primarily for the sake of a gratifying reaction, freed from the usual costs and consequences of engaging with the represented content. We demonstrate the usefulness of the concept of generic porn by using it to isolate a new type of such porn: moral outrage porn. Moral outrage porn is, as we understand it, representations of moral outrage, engaged with primarily for the sake of the resulting gratification, freed from the usual costs and consequences of engaging with morally outrageous content. Moral outrage porn is dangerous because it encourages the instrumentalization of one’s empirical and moral beliefs, manipulating their content for the sake of gratification. Finally, we suggest that when using porn is wrong, it is often wrong because it instrumentalizes what ought not to be instrumentalized.
---
This worry parallels, in some significant ways, Tosi and Warmke’s (2016) complaint
against moral grandstanding.19 Moral grandstanding is morally problematic, they argue, in
large part because grandstanders are treating moral discourse as a “vanity project”:
In using public moral discourse to promote an image of themselves to others, grandstanders turn their contributions to moral discourse into a vanity project. Consider the incongruity between, say, the moral gravity of a world-historic injustice, on the one hand, and a group of acquaintances competing for the position of being most morally offended by it, on the other. Such behavior, we think, is not the sort of thing we should expect from a virtuous person (215- 16).
Note, crucially, that the problem asserted by Tosi and Warmke in this instance isn’t that moral
grandstanding has bad results.20 Instead, the problem is that using moral discourse for selfpromotion is problematically egotistical.
Tosi and Warmke focus on moral problems associated with using moral outrage for interpersonal jockeying. That is the essence of the notion of moral grandstanding — the use of
moral expression for social signaling. Similarly, it seems plausible that the use of moral outrage porn in many cases involves a failure to respect the fundamental role of moral expression. Notice that, where the problem with moral grandstanding is essentially interpersonal
and social, the problem with moral outrage porn is personal and hedonistic.21 The problem
of moral grandstanding is that we use morality for status; the problem of moral outrage porn
is that we are using morality for pleasure. When one indulges in moral outrage porn, one uses what by one’s own lights is morally outrageous for one’s own enjoyment.22 It is, loosely
speaking, to make morality about oneself, when it clearly is not.
Furthermore, it is no accident, we think, that the features of moral outrage porn relevant to the “bad faith” problem mirror Michael Tanner’s (1976/77) account of the problems
of sentimentality. In his discussion of Oscar Wilde and the sentimental, Tanner says, “the
feelings which constitute [the sentimental] are in some important way unearned, being had
on the cheap, come by too easily…” (1976/77: 128). The use of moral outrage porn, if one
accepts our definition, involves an attempt to be gratified by a representation of the endresult of moral engagement without taking on the consequences or effort of actually engaging. This seems a paradigmatic case of getting a feeling on the cheap.
What we’ve sketched thus far are a number of considerations that weigh in favor of a
serious moral strike against the use of moral outrage porn. There are also a number of consequentialist considerations that we might adduce. Tanner (1976/77: 134) argues that the
intrinsically sentimental tends toward passivity (139). Sentimental emotions, Tanner suggests, can themselves encourage inaction.
[I]t also seems to me that some of my feelings are of a kind that inhibit action, because they themselves are enjoyable to have, but if acted upon, one would cease to have them, and one doesn’t want to. Such a feeling does seem to me intrinsically sentimental… (Tanner 1976/77: 139).
Saturday, November 30, 2019
Beijing females growing up during the communist regime are more competitively inclined than males, females growing up during the market regime, & Taipei females
Gender Differences in Willingness to Compete: The Role of Culture and Institutions. Alison Booth, Elliott Fan, Xin Meng, Dandan Zhang. The Economic Journal, February 2 2018. https://doi.org/10.1111/ecoj.12583
Abstract: Our Beijing‐based laboratory experiment investigated gender differences in competitive choices across different birth‐cohorts experiencing – during their crucial developmental‐age – different institutions and social norms. To control for general time trends, we use Taipei counterpart subjects with identical original Confucian traditions. Our findings confirm that exposure to different institutions/norms during crucial developmental‐ages significantly changes individuals’ behaviour. In particular, Beijing females growing up during the communist regime are more competitively inclined than their male counterparts; their female counterparts growing up during the market regime; and Taipei females. For Taipei, there are no statistically significant cohort or gender differences in willingness to compete.
4 Mechanisms
Alesina and Fuchs-Schundeln (2007) discussed the endogenous role political regimes play
in forming people’s tastes for public social policies. They provide strong evidence that
there is a feedback effect from the regime on people’s attitudes. In our experiment, the
finding that women in the 1958 Beijing cohort are more willing to compete may well be
related to this feedback effect. In this section we further explore the mechanisms through
which women growing up under different regimes form different preferences.
To examine if there is a long lasting communist indoctrination effect, we follow Alesina
and Fuchs-Schundeln (2007) and check what other evidence there is in our data that
indicate attitudes towards government intervention in social and economic affairs differs
across cohorts and regions. In our exit-survey, we asked subjects if they believe that
government should play a role in reducing income inequality in general and if they support
the view that the less intervention from the government to the economy the better. The
answers are given on a scale of 1 to 5, with 1 being strongly disagree and 5 being strongly
agree. Using these data as the dependent variables we estimate using OLS a modified
version of Equation 2 without the game controls and added in log individual income into
the vector Xij . The selected results are reported in the first two columns of Table 7, where
we report three panels: the top panel reports the selected estimated coefficients from the
regressions of modified Equation 2, the middle panel reports the implied differences across
cohorts and regions based on the top panel coefficients, and the bottom panel reports
differences between Beijing women/men and Taipei women/men based on results from
the regressions that look only at aggregated differences and disregard cohort variations.
The results for the state-intervention preference variables suggest that, in general, the
Mainland Chinese female cohorts, relative to the Taiwanese female cohorts, are much more
likely to support the view that the government should try to reduce income inequality and
much less likely to think that the less government intervention in the economy the better.
On a scale of 1 to 5 points, the three female cohorts are 1.4 to 1.9 points more likely
to support government reducing inequality and 1.6 to 1.8 points less likely to think that
less government intervention in the economy the better. These are very large differences,
between 28% to 38% of the total scores. These results are largely consistent across the
three female cohorts.
If we examine the bottom panel, which treats male and female subjects in Beijing
and Taipei as single groups, we see that this conclusion applies to males as well. Thus,
after 40 years of market oriented economic reform, the society is still largely geared towards equality and government intervention. This finding is in line with that of Alesina
and Fuchs-Schundeln (2007) – that communist propaganda has a long lasting effect. In
addition, our results indicate that individuals who grew up subject to the heavy dose of
indoctrination, and individuals who largely grew up with new ideology, all seem to prefer
more state intervention in social and economic affairs. This latter finding differs slightly
from that of Alesina and Fuchs-Schundeln (2007) which revealed that in East Germany
those born after 1975 have a much weaker preference for government intervention relative
to their older counterparts. This difference between their results and ours may relate to
two broad sets of factors. First, after German reunification Communism was discredited
and there was a large migration from East to West Germany. Second, in mainland China
the Communist Party is still the ruling party, even though the economy largely operates
under market economic rules. One other interesting issue related to our finding is the
following issue. According to our results, only the behaviour of the cohort which grew
up during the communist regime is in line with the communist indoctrination, while the
behaviour has changed for cohorts growing up in the economic reform era. Yet the attitudes difference across cohorts seems to be limited. This may be related to the lack of
continuation in propaganda on gender issues which affect gender willingness to compete as
we will discuss below,13 whereas government role in the economy persisted in the reform
era.
Is the difference in gender willingness to compete and in the preference for government
intervention for social and economic issues really a reflection of indoctrination? There is
a small literature that discusses intergenerational transmission of cultural norms (see for
example, Bisin and Verdier, 2000; Tabellini, 2010; and Nunn and Wantchekon, 2011).14 In
the post-experiment survey, we follow their ideas and ask respondents to pick, from a list
of qualities or attributes, those that their parents and school encouraged when they were
young. We report here the results from our examination of three attributes that are related
to the competitive inclination and preference for inequality in general: 1. Encourage to
believe that men and women are equal; 2. Encourage to be competitive; and 3. Encourage
to be unselfish. We generate two dummy variables for each of these qualities: whether
mother or school encouraged individuals to have these qualities (yes=1, 0 otherwise). The
modified version of Equation 2 is estimated using a linear probability model (LPM) for
ease of interpretation of the coefficients. The results for mothers’ encouragement and
schools’ encouragement are reported in columns [3] to [8] of Table 7. The three panels
separately report the regression coefficients; the implied differences across gender, cohorts
and region; and the aggregated differences between Beijing and Taipei for females and
males.
The results on gender equality (both from mothers’ and schools’ teaching) show that
there is no consistent gender difference within each Beijing cohort. Relative to the 1958
Beijing females, those who were born later are consistently less likely to have either
mother or school encourage them to believe in gender equality. Although the size of the
differences are large (9% to 13%), none of the differences are precisely estimated. Relative
to Taipei women, however, Beijing women in all three cohorts are more likely to have their
parents or school to encourage them to have the view of gender equality. The difference
in probability ranges from 70% to 74% for mothers’ encouragement and 59% to 82% for
schools’ encouragement. This is not only the case for females: the bottom panel shows
that Beijing males were equally more likely to have their mothers and schools encourage
them to adopt the view of gender equality than their Taipei counterparts.
Regarding being competitive, mainland parents do not seem to have prepared their
children any more than their Taiwan counterparts. However, schools in Beijing did,
but this seems to be more so for the later cohorts than for the 1958 cohort. Mainland
parents and schools are more likely to teach their children to be unselfish than their
Taiwan counterparts—further evidence of the communist indoctrination. This is true
for all female and male cohorts. It is also true that the probability is higher for the
1958 Beijing cohort than that for the later Beijing cohort, but the differences are not
statistically significant.
How did propaganda affect individuals’ behaviour and preferences 40 years after the
change of the regime? Psychologists have long been discussing how social norms about
sex roles may affect children’s personality characteristics and behavioural competencies
to prepare them to fulfil the societal expectations so that they can perform those roles
(see, for example, Horner, 1972; Fitzgerald and Betz, 1983).
Our post-experiment survey implemented the “Big Five inventory” (BFI), which consists of 44 questions designed to elicit individual’s personality traits. The psychological
literature has identified the overlap between extroversion and competitiveness (Hogan,
1986; Digman, 1990; Chen et al., 2011). Recent empirical studies on online game players
also identified that individuals who have higher scores on openness, extroversion, and
conscientiousness are more likely to be players (Teng, 2008). We examine across gendercohort-region differences in the ‘Big Five’ personality by estimating Equation 2 without
controlling for Wij and Xij .
We find that females in all Beijing cohorts are more extroverted than their male counterparts, though only the 1977 cohort exhibit statistically significant difference. More importantly, all three cohorts Beijing women are statistically significantly more extroverted than their Taipei counterparts. The aggregated estimation (the bottom panel of the table) also confirms that Beijing females are more extroverted than Taipei males and
females and they are more extroverted than Beijing males. However, it does not seem to
be the case that there are significant cohort difference in extroversion. If anything, the
1958 cohort seems to be slightly less extroverted than their younger counterparts but the
differences are not statistically significant.
With regard to openness, for the 1958 and 1977 birth cohorts we observed statistically
significant gender differences within the Beijing sample. Women are more open than
men. Beijing women are also more open than Taipei women in every cohort, and the
size of the difference is the largest for the 1958 cohort. Further, at the aggregated level,
Beijing women have higher openness scores than their Beijing male, Taipei male, and
Taipei female counterparts. Once again, we fail to detect across-cohort variations among
Beijing females which may help to shed light on why the 1958 Beijing women are more
For agreeableness, neuroticism, and conscientiousness we find similar but less statistically significant patterns. The Beijing women seem to be more agreeable, less neurotic, and more conscientious than Beijing males, Taipei females and Taipei males.
These results provide some weak evidences that perhaps indoctrination at young age could affect an individual’s personality and subsequently affect individuals’ behaviour. To this end, more research is needed.
Abstract: Our Beijing‐based laboratory experiment investigated gender differences in competitive choices across different birth‐cohorts experiencing – during their crucial developmental‐age – different institutions and social norms. To control for general time trends, we use Taipei counterpart subjects with identical original Confucian traditions. Our findings confirm that exposure to different institutions/norms during crucial developmental‐ages significantly changes individuals’ behaviour. In particular, Beijing females growing up during the communist regime are more competitively inclined than their male counterparts; their female counterparts growing up during the market regime; and Taipei females. For Taipei, there are no statistically significant cohort or gender differences in willingness to compete.
4 Mechanisms
Alesina and Fuchs-Schundeln (2007) discussed the endogenous role political regimes play
in forming people’s tastes for public social policies. They provide strong evidence that
there is a feedback effect from the regime on people’s attitudes. In our experiment, the
finding that women in the 1958 Beijing cohort are more willing to compete may well be
related to this feedback effect. In this section we further explore the mechanisms through
which women growing up under different regimes form different preferences.
To examine if there is a long lasting communist indoctrination effect, we follow Alesina
and Fuchs-Schundeln (2007) and check what other evidence there is in our data that
indicate attitudes towards government intervention in social and economic affairs differs
across cohorts and regions. In our exit-survey, we asked subjects if they believe that
government should play a role in reducing income inequality in general and if they support
the view that the less intervention from the government to the economy the better. The
answers are given on a scale of 1 to 5, with 1 being strongly disagree and 5 being strongly
agree. Using these data as the dependent variables we estimate using OLS a modified
version of Equation 2 without the game controls and added in log individual income into
the vector Xij . The selected results are reported in the first two columns of Table 7, where
we report three panels: the top panel reports the selected estimated coefficients from the
regressions of modified Equation 2, the middle panel reports the implied differences across
cohorts and regions based on the top panel coefficients, and the bottom panel reports
differences between Beijing women/men and Taipei women/men based on results from
the regressions that look only at aggregated differences and disregard cohort variations.
The results for the state-intervention preference variables suggest that, in general, the
Mainland Chinese female cohorts, relative to the Taiwanese female cohorts, are much more
likely to support the view that the government should try to reduce income inequality and
much less likely to think that the less government intervention in the economy the better.
On a scale of 1 to 5 points, the three female cohorts are 1.4 to 1.9 points more likely
to support government reducing inequality and 1.6 to 1.8 points less likely to think that
less government intervention in the economy the better. These are very large differences,
between 28% to 38% of the total scores. These results are largely consistent across the
three female cohorts.
If we examine the bottom panel, which treats male and female subjects in Beijing
and Taipei as single groups, we see that this conclusion applies to males as well. Thus,
after 40 years of market oriented economic reform, the society is still largely geared towards equality and government intervention. This finding is in line with that of Alesina
and Fuchs-Schundeln (2007) – that communist propaganda has a long lasting effect. In
addition, our results indicate that individuals who grew up subject to the heavy dose of
indoctrination, and individuals who largely grew up with new ideology, all seem to prefer
more state intervention in social and economic affairs. This latter finding differs slightly
from that of Alesina and Fuchs-Schundeln (2007) which revealed that in East Germany
those born after 1975 have a much weaker preference for government intervention relative
to their older counterparts. This difference between their results and ours may relate to
two broad sets of factors. First, after German reunification Communism was discredited
and there was a large migration from East to West Germany. Second, in mainland China
the Communist Party is still the ruling party, even though the economy largely operates
under market economic rules. One other interesting issue related to our finding is the
following issue. According to our results, only the behaviour of the cohort which grew
up during the communist regime is in line with the communist indoctrination, while the
behaviour has changed for cohorts growing up in the economic reform era. Yet the attitudes difference across cohorts seems to be limited. This may be related to the lack of
continuation in propaganda on gender issues which affect gender willingness to compete as
we will discuss below,13 whereas government role in the economy persisted in the reform
era.
Is the difference in gender willingness to compete and in the preference for government
intervention for social and economic issues really a reflection of indoctrination? There is
a small literature that discusses intergenerational transmission of cultural norms (see for
example, Bisin and Verdier, 2000; Tabellini, 2010; and Nunn and Wantchekon, 2011).14 In
the post-experiment survey, we follow their ideas and ask respondents to pick, from a list
of qualities or attributes, those that their parents and school encouraged when they were
young. We report here the results from our examination of three attributes that are related
to the competitive inclination and preference for inequality in general: 1. Encourage to
believe that men and women are equal; 2. Encourage to be competitive; and 3. Encourage
to be unselfish. We generate two dummy variables for each of these qualities: whether
mother or school encouraged individuals to have these qualities (yes=1, 0 otherwise). The
modified version of Equation 2 is estimated using a linear probability model (LPM) for
ease of interpretation of the coefficients. The results for mothers’ encouragement and
schools’ encouragement are reported in columns [3] to [8] of Table 7. The three panels
separately report the regression coefficients; the implied differences across gender, cohorts
and region; and the aggregated differences between Beijing and Taipei for females and
males.
The results on gender equality (both from mothers’ and schools’ teaching) show that
there is no consistent gender difference within each Beijing cohort. Relative to the 1958
Beijing females, those who were born later are consistently less likely to have either
mother or school encourage them to believe in gender equality. Although the size of the
differences are large (9% to 13%), none of the differences are precisely estimated. Relative
to Taipei women, however, Beijing women in all three cohorts are more likely to have their
parents or school to encourage them to have the view of gender equality. The difference
in probability ranges from 70% to 74% for mothers’ encouragement and 59% to 82% for
schools’ encouragement. This is not only the case for females: the bottom panel shows
that Beijing males were equally more likely to have their mothers and schools encourage
them to adopt the view of gender equality than their Taipei counterparts.
Regarding being competitive, mainland parents do not seem to have prepared their
children any more than their Taiwan counterparts. However, schools in Beijing did,
but this seems to be more so for the later cohorts than for the 1958 cohort. Mainland
parents and schools are more likely to teach their children to be unselfish than their
Taiwan counterparts—further evidence of the communist indoctrination. This is true
for all female and male cohorts. It is also true that the probability is higher for the
1958 Beijing cohort than that for the later Beijing cohort, but the differences are not
statistically significant.
How did propaganda affect individuals’ behaviour and preferences 40 years after the
change of the regime? Psychologists have long been discussing how social norms about
sex roles may affect children’s personality characteristics and behavioural competencies
to prepare them to fulfil the societal expectations so that they can perform those roles
(see, for example, Horner, 1972; Fitzgerald and Betz, 1983).
Our post-experiment survey implemented the “Big Five inventory” (BFI), which consists of 44 questions designed to elicit individual’s personality traits. The psychological
literature has identified the overlap between extroversion and competitiveness (Hogan,
1986; Digman, 1990; Chen et al., 2011). Recent empirical studies on online game players
also identified that individuals who have higher scores on openness, extroversion, and
conscientiousness are more likely to be players (Teng, 2008). We examine across gendercohort-region differences in the ‘Big Five’ personality by estimating Equation 2 without
controlling for Wij and Xij .
We find that females in all Beijing cohorts are more extroverted than their male counterparts, though only the 1977 cohort exhibit statistically significant difference. More importantly, all three cohorts Beijing women are statistically significantly more extroverted than their Taipei counterparts. The aggregated estimation (the bottom panel of the table) also confirms that Beijing females are more extroverted than Taipei males and
females and they are more extroverted than Beijing males. However, it does not seem to
be the case that there are significant cohort difference in extroversion. If anything, the
1958 cohort seems to be slightly less extroverted than their younger counterparts but the
differences are not statistically significant.
With regard to openness, for the 1958 and 1977 birth cohorts we observed statistically
significant gender differences within the Beijing sample. Women are more open than
men. Beijing women are also more open than Taipei women in every cohort, and the
size of the difference is the largest for the 1958 cohort. Further, at the aggregated level,
Beijing women have higher openness scores than their Beijing male, Taipei male, and
Taipei female counterparts. Once again, we fail to detect across-cohort variations among
Beijing females which may help to shed light on why the 1958 Beijing women are more
For agreeableness, neuroticism, and conscientiousness we find similar but less statistically significant patterns. The Beijing women seem to be more agreeable, less neurotic, and more conscientious than Beijing males, Taipei females and Taipei males.
These results provide some weak evidences that perhaps indoctrination at young age could affect an individual’s personality and subsequently affect individuals’ behaviour. To this end, more research is needed.
An intentionally slower response to an interpersonal request (time-taking) leads to inferences of higher status, but lower judgments of competence and warmth
Ziano, Ignazio, and Vanessa Patrick. 2019. ““Time-takers” Are Important Jerks: Mixed Reputational Consequences of Speed of Response to Requests” PsyArXiv. November 28. doi:10.31234/osf.io/2rjy4
Abstract: How does speed of response influence an individual’s standing and reputation? Five experiments (n = 1,544) demonstrate that “time-takers” are perceived to be high in status but low in competence and warmth, making them important jerks. Despite being a socially dispreferred norm violation, an intentionally slower response to an interpersonal request (time-taking) leads to inferences of higher status, but lower judgments of competence and warmth. We show that observers infer status from response time only when other status information is not accessible. The effects of response speed on status, competence, and warmth are mediated by perceptions of heightened self-orientation: the slow response of time-takers signals higher self-orientation – a feature that observers associate with higher status, but also with lower competence and warmth. We discuss theoretical implications for speed of response, status signalling and inferences of competence and warmth, and practical implications for interpersonal communications in organizations and employee well-being.
General Discussion
Should you respond immediately to a text or email, or, should you take your own sweet time? The focus of this paper is to identify the social consequences of time-taking, the intentionally slow response to an interpersonal request. In a world that is becoming increasingly fast paced, people and organizations are increasingly under pressure to respond as fast as possible. Prompt responses have benefits: a fast responder is seen as both warm and competent. However, a key insight uncovered by the current research is that if a person or an organization swims against the current and responds slowly, there is a silver lining: higher status perceptions, unfortunately however at the cost of competence and warmth perceptions.
Using within-participants and between-participants experiments, we explored the role of response time in a variety of situations, involving parties (people, companies, and institutions) of different nature. In study 1A, we show that people that are intentionally slower in responding to requests are liked less, yet considered of higher status, but of lower competence and warmth. In study 1B, we replicate the results of study 1A using institutions as the signaling parties. In study 2 we show that decision time is only used as a status cue if status information is not accessible. In study 3, we explore the mediating role of heightened self-orientation (lowered other-orientation) in generating impressions of status, competence, and warmth, in both personal and institutional communications.
Contributions, Implications and Directions for Future Research.
We elaborate on the three theoretical contributions previously identified and discuss the implications for theory and practice. Status signaling.
While previous research has looked at the effect of many cues on perception of status (busyness, Bellezza et al. 2017; expensive clothes, Nelissen and Meijers 2011; product size, Dubois et al., 2012), competence (alcohol consumption, Rick and Schweitzer, 2013; non-conforming behavior, Bellezza et al. 2014; 2017) and warmth (luxury consumption, Garcia et al. 2018; moral behavior, Brambilla et al. 2012), response time – despite its prevalence in everyday life and its emergence in the signaling literature (Van de Calseyde et al., 2014)– has been neglected. We fill this gap in the literature and connect these two streams of literature by showing that time taking influences status, competence, and warmth perceptions.
Speed of response. Previous literature demonstrated that observers draw rich inferences from speed of response, for instance in the domains of cooperation (Evans, Dillon, & Rand, 2015; Van de Calseyde et al., 2014) and morality (Critcher et al., 2013). We extend this literature to reputational consequences in three important domains of other perception: status, competence, and warmth. Further, we are the first – to our knowledge - to consider the mixed reputational effects of time taking: the positive effect of slower response times (slower = high status) but the negative perceptions of competence and warmth. This nuance provides increased understanding about how observers interpret speed of response in interpersonal contexts where people may observe response times.
Competence, warmth, and status. By exploring the reactions to slower responses, we show that slow response time is a dominance-based status-gaining strategy (Cheng et al., 2013), best summarized by the quote “It is better to be feared than loved, if you cannot both” (Machiavelli, 1532). In most of the status literature, status and valued personal features such as competence and warmth (Fiske et al., 2007) go hand-in-hand (Anderson & Kilduff, 2009). Manipulations that cause an increase in perceptions of status often also cause an increase in perception of competence and warmth. Strikingly, we find that competence and status can move independently: competence – along with warmth - is lower for slower respondents and status is higher. Our results are in line with this perspective suggested by Cheng and colleagues (2013): dominance does not imply the signaling of desirable qualities in order to signal status, but simply the willingness and the attitude to use coercion.
This paper has substantial practical implications for communications amongst individuals and for companies. When people interact with others, they may draw inferences from response time even where the responding party did not intend to signal it. Therefore, an unexplained delay may be interpreted as intentional and trigger positive (status) but also negative (warmth, competence) inferences. Organizations and workers may be informed about how to intentionally and strategically use response time, in order to enhance what they wish to signal. A key issue is the intentionally of the delayed response which signals greater self-orientation and consequently the neglect to value other people’s time. Future research would need to identify, how long the high status can be maintained, if competence and warmth perceptions are constantly being eroded. Relatedly, prior research has shown that (Tost, Gino, & Larrick, 2013) power can have a negative impact on team performance. One might argue that the dominant route to status that we have documented as a result of timetaking may similarly negatively influence team performance in the long-run.
Second, we show that people interpret status and use it as an inference-making tool only when other status cues are not accessible in the environment. This implies that observers are more likely to infer status from response time in situations such as interactions between colleagues from different departments or employees from different companies, when the status of the responding party is not apparent. Respondents (such as managers and employees) may therefore modulate other people impressions of them by giving (or neglecting to give) the other party status information about them (e.g., managerial function).
Further, employees report that they are under huge pressures to reply faster and faster to an increasing number of emails. This situation is exemplified by an academic worker on Twitter, who reports that they “can’t physically respond to all legitimate email sent to me without spending 3 hours a day on email (which would both cause me to go insane and also get fired)”. Certainly, workers are worried about how their delayed emails are going to make them look to others. We highlight a potential benefit of slow response speed: higher status inferences. Overwhelmed workers may therefore take solace in the fact that their slow speed of response does not have exclusively negative consequences.
However, by the same token, future research might also investigate the silver lining to greater self-orientation and lower other orientation. Indeed, in discussing the laws of power, the author Robert Greene points to self-orientation as essential to gaining and maintaining a position of power and status. For instance, he urges, “Do not accept the roles that society foists on you… Be the master of your own image rather than letting others define if for you. Incorporate dramatic devices into your public gestures and actions – your power will be enhanced and your character will seem larger than life” (p. 191). In this research, the “dramatic device” we identify that enhances status inferences is slower response times. Future research might identify other similar devices that reflect self-orientation and thereby confer status on individuals and organizations.
In our studies, we have operationalized time on a well-defined, unidirectional line that goes from the past to the future. However, the difference between present and future may be blurrier depending on situational (e.g., whether the language spoken possesses a future tense or not; Pérez and Tavits 2017) and individual factors (e.g., self-continuity through time; Hershfield et al. 2009). Future research may therefore investigate ways to moderate the signaling effect of response time by making the time difference between fast and slow seem less definite.
Abstract: How does speed of response influence an individual’s standing and reputation? Five experiments (n = 1,544) demonstrate that “time-takers” are perceived to be high in status but low in competence and warmth, making them important jerks. Despite being a socially dispreferred norm violation, an intentionally slower response to an interpersonal request (time-taking) leads to inferences of higher status, but lower judgments of competence and warmth. We show that observers infer status from response time only when other status information is not accessible. The effects of response speed on status, competence, and warmth are mediated by perceptions of heightened self-orientation: the slow response of time-takers signals higher self-orientation – a feature that observers associate with higher status, but also with lower competence and warmth. We discuss theoretical implications for speed of response, status signalling and inferences of competence and warmth, and practical implications for interpersonal communications in organizations and employee well-being.
General Discussion
Should you respond immediately to a text or email, or, should you take your own sweet time? The focus of this paper is to identify the social consequences of time-taking, the intentionally slow response to an interpersonal request. In a world that is becoming increasingly fast paced, people and organizations are increasingly under pressure to respond as fast as possible. Prompt responses have benefits: a fast responder is seen as both warm and competent. However, a key insight uncovered by the current research is that if a person or an organization swims against the current and responds slowly, there is a silver lining: higher status perceptions, unfortunately however at the cost of competence and warmth perceptions.
Using within-participants and between-participants experiments, we explored the role of response time in a variety of situations, involving parties (people, companies, and institutions) of different nature. In study 1A, we show that people that are intentionally slower in responding to requests are liked less, yet considered of higher status, but of lower competence and warmth. In study 1B, we replicate the results of study 1A using institutions as the signaling parties. In study 2 we show that decision time is only used as a status cue if status information is not accessible. In study 3, we explore the mediating role of heightened self-orientation (lowered other-orientation) in generating impressions of status, competence, and warmth, in both personal and institutional communications.
Contributions, Implications and Directions for Future Research.
We elaborate on the three theoretical contributions previously identified and discuss the implications for theory and practice. Status signaling.
While previous research has looked at the effect of many cues on perception of status (busyness, Bellezza et al. 2017; expensive clothes, Nelissen and Meijers 2011; product size, Dubois et al., 2012), competence (alcohol consumption, Rick and Schweitzer, 2013; non-conforming behavior, Bellezza et al. 2014; 2017) and warmth (luxury consumption, Garcia et al. 2018; moral behavior, Brambilla et al. 2012), response time – despite its prevalence in everyday life and its emergence in the signaling literature (Van de Calseyde et al., 2014)– has been neglected. We fill this gap in the literature and connect these two streams of literature by showing that time taking influences status, competence, and warmth perceptions.
Speed of response. Previous literature demonstrated that observers draw rich inferences from speed of response, for instance in the domains of cooperation (Evans, Dillon, & Rand, 2015; Van de Calseyde et al., 2014) and morality (Critcher et al., 2013). We extend this literature to reputational consequences in three important domains of other perception: status, competence, and warmth. Further, we are the first – to our knowledge - to consider the mixed reputational effects of time taking: the positive effect of slower response times (slower = high status) but the negative perceptions of competence and warmth. This nuance provides increased understanding about how observers interpret speed of response in interpersonal contexts where people may observe response times.
Competence, warmth, and status. By exploring the reactions to slower responses, we show that slow response time is a dominance-based status-gaining strategy (Cheng et al., 2013), best summarized by the quote “It is better to be feared than loved, if you cannot both” (Machiavelli, 1532). In most of the status literature, status and valued personal features such as competence and warmth (Fiske et al., 2007) go hand-in-hand (Anderson & Kilduff, 2009). Manipulations that cause an increase in perceptions of status often also cause an increase in perception of competence and warmth. Strikingly, we find that competence and status can move independently: competence – along with warmth - is lower for slower respondents and status is higher. Our results are in line with this perspective suggested by Cheng and colleagues (2013): dominance does not imply the signaling of desirable qualities in order to signal status, but simply the willingness and the attitude to use coercion.
This paper has substantial practical implications for communications amongst individuals and for companies. When people interact with others, they may draw inferences from response time even where the responding party did not intend to signal it. Therefore, an unexplained delay may be interpreted as intentional and trigger positive (status) but also negative (warmth, competence) inferences. Organizations and workers may be informed about how to intentionally and strategically use response time, in order to enhance what they wish to signal. A key issue is the intentionally of the delayed response which signals greater self-orientation and consequently the neglect to value other people’s time. Future research would need to identify, how long the high status can be maintained, if competence and warmth perceptions are constantly being eroded. Relatedly, prior research has shown that (Tost, Gino, & Larrick, 2013) power can have a negative impact on team performance. One might argue that the dominant route to status that we have documented as a result of timetaking may similarly negatively influence team performance in the long-run.
Second, we show that people interpret status and use it as an inference-making tool only when other status cues are not accessible in the environment. This implies that observers are more likely to infer status from response time in situations such as interactions between colleagues from different departments or employees from different companies, when the status of the responding party is not apparent. Respondents (such as managers and employees) may therefore modulate other people impressions of them by giving (or neglecting to give) the other party status information about them (e.g., managerial function).
Further, employees report that they are under huge pressures to reply faster and faster to an increasing number of emails. This situation is exemplified by an academic worker on Twitter, who reports that they “can’t physically respond to all legitimate email sent to me without spending 3 hours a day on email (which would both cause me to go insane and also get fired)”. Certainly, workers are worried about how their delayed emails are going to make them look to others. We highlight a potential benefit of slow response speed: higher status inferences. Overwhelmed workers may therefore take solace in the fact that their slow speed of response does not have exclusively negative consequences.
However, by the same token, future research might also investigate the silver lining to greater self-orientation and lower other orientation. Indeed, in discussing the laws of power, the author Robert Greene points to self-orientation as essential to gaining and maintaining a position of power and status. For instance, he urges, “Do not accept the roles that society foists on you… Be the master of your own image rather than letting others define if for you. Incorporate dramatic devices into your public gestures and actions – your power will be enhanced and your character will seem larger than life” (p. 191). In this research, the “dramatic device” we identify that enhances status inferences is slower response times. Future research might identify other similar devices that reflect self-orientation and thereby confer status on individuals and organizations.
In our studies, we have operationalized time on a well-defined, unidirectional line that goes from the past to the future. However, the difference between present and future may be blurrier depending on situational (e.g., whether the language spoken possesses a future tense or not; Pérez and Tavits 2017) and individual factors (e.g., self-continuity through time; Hershfield et al. 2009). Future research may therefore investigate ways to moderate the signaling effect of response time by making the time difference between fast and slow seem less definite.
Inbreeding avoidance in primates
A Cul-de-sac of Sexual Evolution. Takeshi Furuichi. Chapter 2 of Bonobo and Chimpanzee: The Lessons of Social Coexistence; pp 37-62. November 23 2019. https://link.springer.com/chapter/10.1007/978-981-13-8059-4_2
Abstract: Incest. It is a word that has an extremely unpleasant echo. Incest taboo (or inbreeding avoidance) between close kin in relation to the societies of numerous ethnic cultures has occupied a significant place in anthropological study. Many people might think that incest taboo is a uniquely human quality, but inbreeding avoidance, in general, is found in almost all animal species. Interestingly, the word “inbreeding avoidance” resonates healthier to our ears than “incest avoidance.”
---
2.1.5 Male-philopatric Society in which Females Leave
Not only the Japanese monkey and other primate species [53] but also many groupliving mammals form matrilineal societies where males transfer between groups. Elephants, lions, and the Japanese Sika deers have similar systems [54]. For this, we consider why females do not transfer.
Animals forage for grasses, fruits and leaves of trees, and other foods in the habitats where they are born. Carnivores must hunt prey animals. Prey animals survive by protecting themselves from predators. To do so, they need to have a thorough knowledge of when and where to go to find which kinds of food, understand where prey animals are located and are easy to hunt, learn where predators roam, and where to escape to defend themselves. There are close relatives in their group who have supported them since they were born, but when they move to another group, they have to be accepted by members of that group and build relationships all over again. While moving, they may have to, temporarily, spend a dangerous period alone. Given all of that, it is better to stay in their natal groups throughout their lives.
However, males and females differ in terms of survival strategies. Females can live a normal life and bear a fixed number of offspring during their lifetimes as long as they respond to males that want to mate with them. Therefore, what is important for females is not the number of offspring they produce, but rather how they obtain a stable food supply and effectively protect themselves from predators to rear offspring with certainties. This is why it is more beneficial for them to stay in their natal group than transferring out. In contrast, the number of offspring males can sire during their lifetime varies from zero to almost infinity depending on their efforts and degrees of success. For them, it is more important to obtain as many mating opportunities as possible and impregnate as many females as they can. To do so, it might be better to find another group that offers possibilities of mating with more females rather than staying in a group with a compromised low-rank position in which they are born. Moreover, females are sexually attracted to novel males coming from outside, rather than closely related males or males that are familiar from their childhoods. Consequently, males that actively move between groups, rather than those males that attempt to stay in natal groups, will have a greater chance of obtaining breeding opportunities.
To live in a group and avoid inbreeding, either a male or a female must transfer between groups. If both sexes transfer, this could incur a possibility that brothers and sisters would encounter in a newly transferred group unbeknownst to them. Therefore, if one sex transfers, individuals of the opposite sex do not normally need to transfer. If males are more likely to greatly benefit from transferring, it seems natural for many species to live in female-philopatric societies where males transfer.
---
However, immigrant females are desperate. They somehow have to build relationships with males in the group, and the only thing they can depend on is sex.
One day I witnessed an interesting scene. Until 1996, to carefully observe the behavior of the E1 group, we sometimes brought their favorite sugarcanes, cut them into about 30 cm in length, and scattered them on the forest floor beneath the trees where they spent the night. The scene happened when I was observing the bonobos that were coming down to take the sugarcanes.
In the center of the feeding area, the male bonobo named Ten that had quite recently climbed up to the top rank position was eating a sugarcane. He had some in his hands and was eating slowly. A young female named Nao that had recently immigrated into the E1 group came closer to him. As Nao approached Ten, so closely as if their faces would stick together, she peered at Ten’s mouth and she reached for his mouth with her hand. This is a behavior displayed when a bonobo begs for food. However, Ten pretended not to notice Nao and continued to eat slowly.
Then Nao changed her tactic. She changed the direction of her body to protrude her buttocks to Ten and began inviting him to copulate. Even though males are usually not interested in young females, there was no reason for Ten to refuse copulation if the buttocks are pressed to his nose. Ten stood up with a few sugarcanes in his hand and copulated with Nao as he clasped her from behind.
Then the power relationship between them completely changed. Nao reached out for the sugarcanes that Ten was holding in his hands and took the longest piece from him as if to say that reciprocating with the sugarcanes was an appropriate thing to do. She finally got the piece and started eating it alongside him.
Readers may think that this is an episode in which food was exchanged for sex, like prostitution. However, if we look at this situation more closely, there was no need for Nao to get sugarcane from Ten, because there were a lot of sugarcane stalks still lying on the forest floor. If she really wanted a sugarcane, she could have just picked any one up.
Nao took the trouble to approach Ten and beg for a sugarcane, but when he ignored her, she made a series of negotiations to get a sugarcane to eat together simply because she wanted to begin a relationship with him. It is analogous to being in the university library and asking to borrow stationery that you do not really need from a person of the opposite sex whom you are interested in getting a chance to talk to.
Abstract: Incest. It is a word that has an extremely unpleasant echo. Incest taboo (or inbreeding avoidance) between close kin in relation to the societies of numerous ethnic cultures has occupied a significant place in anthropological study. Many people might think that incest taboo is a uniquely human quality, but inbreeding avoidance, in general, is found in almost all animal species. Interestingly, the word “inbreeding avoidance” resonates healthier to our ears than “incest avoidance.”
---
2.1.5 Male-philopatric Society in which Females Leave
Not only the Japanese monkey and other primate species [53] but also many groupliving mammals form matrilineal societies where males transfer between groups. Elephants, lions, and the Japanese Sika deers have similar systems [54]. For this, we consider why females do not transfer.
Animals forage for grasses, fruits and leaves of trees, and other foods in the habitats where they are born. Carnivores must hunt prey animals. Prey animals survive by protecting themselves from predators. To do so, they need to have a thorough knowledge of when and where to go to find which kinds of food, understand where prey animals are located and are easy to hunt, learn where predators roam, and where to escape to defend themselves. There are close relatives in their group who have supported them since they were born, but when they move to another group, they have to be accepted by members of that group and build relationships all over again. While moving, they may have to, temporarily, spend a dangerous period alone. Given all of that, it is better to stay in their natal groups throughout their lives.
However, males and females differ in terms of survival strategies. Females can live a normal life and bear a fixed number of offspring during their lifetimes as long as they respond to males that want to mate with them. Therefore, what is important for females is not the number of offspring they produce, but rather how they obtain a stable food supply and effectively protect themselves from predators to rear offspring with certainties. This is why it is more beneficial for them to stay in their natal group than transferring out. In contrast, the number of offspring males can sire during their lifetime varies from zero to almost infinity depending on their efforts and degrees of success. For them, it is more important to obtain as many mating opportunities as possible and impregnate as many females as they can. To do so, it might be better to find another group that offers possibilities of mating with more females rather than staying in a group with a compromised low-rank position in which they are born. Moreover, females are sexually attracted to novel males coming from outside, rather than closely related males or males that are familiar from their childhoods. Consequently, males that actively move between groups, rather than those males that attempt to stay in natal groups, will have a greater chance of obtaining breeding opportunities.
To live in a group and avoid inbreeding, either a male or a female must transfer between groups. If both sexes transfer, this could incur a possibility that brothers and sisters would encounter in a newly transferred group unbeknownst to them. Therefore, if one sex transfers, individuals of the opposite sex do not normally need to transfer. If males are more likely to greatly benefit from transferring, it seems natural for many species to live in female-philopatric societies where males transfer.
---
However, immigrant females are desperate. They somehow have to build relationships with males in the group, and the only thing they can depend on is sex.
One day I witnessed an interesting scene. Until 1996, to carefully observe the behavior of the E1 group, we sometimes brought their favorite sugarcanes, cut them into about 30 cm in length, and scattered them on the forest floor beneath the trees where they spent the night. The scene happened when I was observing the bonobos that were coming down to take the sugarcanes.
In the center of the feeding area, the male bonobo named Ten that had quite recently climbed up to the top rank position was eating a sugarcane. He had some in his hands and was eating slowly. A young female named Nao that had recently immigrated into the E1 group came closer to him. As Nao approached Ten, so closely as if their faces would stick together, she peered at Ten’s mouth and she reached for his mouth with her hand. This is a behavior displayed when a bonobo begs for food. However, Ten pretended not to notice Nao and continued to eat slowly.
Then Nao changed her tactic. She changed the direction of her body to protrude her buttocks to Ten and began inviting him to copulate. Even though males are usually not interested in young females, there was no reason for Ten to refuse copulation if the buttocks are pressed to his nose. Ten stood up with a few sugarcanes in his hand and copulated with Nao as he clasped her from behind.
Then the power relationship between them completely changed. Nao reached out for the sugarcanes that Ten was holding in his hands and took the longest piece from him as if to say that reciprocating with the sugarcanes was an appropriate thing to do. She finally got the piece and started eating it alongside him.
Readers may think that this is an episode in which food was exchanged for sex, like prostitution. However, if we look at this situation more closely, there was no need for Nao to get sugarcane from Ten, because there were a lot of sugarcane stalks still lying on the forest floor. If she really wanted a sugarcane, she could have just picked any one up.
Nao took the trouble to approach Ten and beg for a sugarcane, but when he ignored her, she made a series of negotiations to get a sugarcane to eat together simply because she wanted to begin a relationship with him. It is analogous to being in the university library and asking to borrow stationery that you do not really need from a person of the opposite sex whom you are interested in getting a chance to talk to.
Beliefs need to be pragmatic, grounded in reality, but there are other incentives influencing our beliefs and behavior, like being a good group member, the need to appear as beneficent & effective
On the Function of Beliefs in Strategic Social Interactions. Arnaud Wolff. Bureau d’Économie
Théorique et Appliquée, Document de Travail n° 2019 – 41, Oct 2019. http://www.beta-umr7522.fr/productions/publications/2019/2019-41.pdf
Abstract: We review the way beliefs have traditionally been formalized in game-theoretic settings, and argue that this formalization has its limits, especially in the realm of strategic social interactions. Normative game theory, with its emphasis on equilibrium concepts and its concern about how rational and intelligent players should play, has left little room for a formal characterization of the role of players’ beliefs. Given that beliefs determine play, we argue that a case can be made for a deeper understanding of their nature. We draw on the literature in evolutionary psychology and biology to decipher underlying, not readily apparent, incentives that might influence belief adoption. In fact, we take the view that beliefs are themselves subject to incentives, and that agents’ beliefs may therefore take on a predictable form if we are able to decipher the underlying incentives that they face. This predictable form might then be used to justify specific modelling assumptions, and accordingly improve the models’ predictive power.
Keywords: Beliefs, Game Theory, Social Incentives, Evolution, Coordination
JEL: B40, C70
Discussion
The key point we want to underline is that in strategic interactions, we might be able to predict the direction of people’s beliefs based on the underlying incentives that they face. Nonetheless, it is important to remember that all these incentives probably act in concert. For instance, one might want to be seen as acting consistently with respect to a particular (core) belief adopted by one’s own group. That is, the core belief, around which other related beliefs are tailored, is endogenously determined by one’s group belonging. Also, one might want to stay consistent with respect to a cherished belief, in order to appear as effective to others. A politician, for instance, might want to stay true to her principles in order to appear as confident, or in charge, thereby persuading others of her convictions.
The primary task of the theorist should be to decipher which incentives are the most relevant in the particular context studied (and why?). In fact, very little is known about the interplay of the different incentives individuals face, and about which ones are more binding (and when?). Pecuniary incentives might well be as important as social incentives (think about oil company managers that are convinced that their activities are not harmful for the environment). When, for instance, do pecuniary incentives take the ascendant over social incentives? Or, conversely, in which cases do social incentives (for instance, the incentive to appear as a good coalition member), trump financial incentives? It will likely be very important to determine how individuals deal with the numerous (hidden, underlying) trade-offs that they face, if we want to reap more interesting insights about human behavior. But to be successful, this endeavor requires that we take more seriously the evolutionary approach to human motivations.
Conclusion
Aumann (1976) has shown that if individuals start with the same priors beliefs, and their posteriors about the occurrence of some event are common knowledge, then these posteriors must be equal (i.e., their beliefs must be the same). One however often observes persistent disagreement. Is it because the respective posteriors only rarely fully become common knowledge, or is it because people don’t start off with the same priors? Geanakoplos and Polemarchakis (1982) have shown that in a finite period of time, honest, truth-seeking individuals will reach an agreement by communicating back and forth their posteriors, even if the event was not common knowledge to start with. It must therefore be the priors. Or is it because people are not honest, truth-seekers? We believe that the answer is a mix of both, and that the key to understanding why people are not honest, truth-seekers, and therefore do not converge towards a common posterior, is to decipher the underlying incentives they face to adopt their respective beliefs.
It has traditionally been assumed in economics and game theory that the agents in our models want to be right. They strive to get closer to truth, and they will sometimes undertake costly actions to reach their objective. This assumption is evidently uncontroversial when we consider agents taking decisions whose outcomes depend on the true state of nature. A trader investing in the stock market will want the best available information, while environmentally-friendly consumers will want to know everything about the products they buy. The beliefs need to be pragmatic, grounded in reality. But social life is complex, and there are other (maybe even more important) incentives influencing our beliefs and behavior. We have argued, based on the literature in evolutionary psychology and biology, that some additional, not readily apparent motivations presumably play a large role. These motivations comprise the need to appear as a good coalition (group) member, the need to appear as consistent, and the need to appear as beneficent and effective to others. These motivations are distinct (and without any doubt not exhaustive), but they probably act in concert. In all these cases, pragmatic beliefs are not very useful, because agents have no incentives to be right. Their decisions in these areas do not directly depend on the true state of nature. Instead, their incentives are social, and their beliefs will bear this sign.
Social beliefs have not been much studied, and they are not well understood. They do not respond to evidence as pragmatic beliefs do, and the reason is that they shouldn’t. We have conjectured that much of the apparent disagreement on largely factual matters is due to the above mentioned motivations (together with pecuniary incentives), and that without a deeper appreciation of the underlying (hidden) incentives that agents face, we will not be able to improve our understanding of how to tackle pressing issues such as climate denial, conspiracy theories, or anti-science movements. We believe that the tools of game theory can be successfully applied in those areas, helping us decipher which incentives are the more stringent and binding in different contexts. Nonetheless, this new endeavor requires a deeper comprehension of human motivations and of the particular (often hidden) incentives that we face, at the risk of being stuck in the study of proximate mechanisms, without grasping what the ultimate motives are.
Théorique et Appliquée, Document de Travail n° 2019 – 41, Oct 2019. http://www.beta-umr7522.fr/productions/publications/2019/2019-41.pdf
Abstract: We review the way beliefs have traditionally been formalized in game-theoretic settings, and argue that this formalization has its limits, especially in the realm of strategic social interactions. Normative game theory, with its emphasis on equilibrium concepts and its concern about how rational and intelligent players should play, has left little room for a formal characterization of the role of players’ beliefs. Given that beliefs determine play, we argue that a case can be made for a deeper understanding of their nature. We draw on the literature in evolutionary psychology and biology to decipher underlying, not readily apparent, incentives that might influence belief adoption. In fact, we take the view that beliefs are themselves subject to incentives, and that agents’ beliefs may therefore take on a predictable form if we are able to decipher the underlying incentives that they face. This predictable form might then be used to justify specific modelling assumptions, and accordingly improve the models’ predictive power.
Keywords: Beliefs, Game Theory, Social Incentives, Evolution, Coordination
JEL: B40, C70
Discussion
The key point we want to underline is that in strategic interactions, we might be able to predict the direction of people’s beliefs based on the underlying incentives that they face. Nonetheless, it is important to remember that all these incentives probably act in concert. For instance, one might want to be seen as acting consistently with respect to a particular (core) belief adopted by one’s own group. That is, the core belief, around which other related beliefs are tailored, is endogenously determined by one’s group belonging. Also, one might want to stay consistent with respect to a cherished belief, in order to appear as effective to others. A politician, for instance, might want to stay true to her principles in order to appear as confident, or in charge, thereby persuading others of her convictions.
The primary task of the theorist should be to decipher which incentives are the most relevant in the particular context studied (and why?). In fact, very little is known about the interplay of the different incentives individuals face, and about which ones are more binding (and when?). Pecuniary incentives might well be as important as social incentives (think about oil company managers that are convinced that their activities are not harmful for the environment). When, for instance, do pecuniary incentives take the ascendant over social incentives? Or, conversely, in which cases do social incentives (for instance, the incentive to appear as a good coalition member), trump financial incentives? It will likely be very important to determine how individuals deal with the numerous (hidden, underlying) trade-offs that they face, if we want to reap more interesting insights about human behavior. But to be successful, this endeavor requires that we take more seriously the evolutionary approach to human motivations.
Conclusion
Aumann (1976) has shown that if individuals start with the same priors beliefs, and their posteriors about the occurrence of some event are common knowledge, then these posteriors must be equal (i.e., their beliefs must be the same). One however often observes persistent disagreement. Is it because the respective posteriors only rarely fully become common knowledge, or is it because people don’t start off with the same priors? Geanakoplos and Polemarchakis (1982) have shown that in a finite period of time, honest, truth-seeking individuals will reach an agreement by communicating back and forth their posteriors, even if the event was not common knowledge to start with. It must therefore be the priors. Or is it because people are not honest, truth-seekers? We believe that the answer is a mix of both, and that the key to understanding why people are not honest, truth-seekers, and therefore do not converge towards a common posterior, is to decipher the underlying incentives they face to adopt their respective beliefs.
It has traditionally been assumed in economics and game theory that the agents in our models want to be right. They strive to get closer to truth, and they will sometimes undertake costly actions to reach their objective. This assumption is evidently uncontroversial when we consider agents taking decisions whose outcomes depend on the true state of nature. A trader investing in the stock market will want the best available information, while environmentally-friendly consumers will want to know everything about the products they buy. The beliefs need to be pragmatic, grounded in reality. But social life is complex, and there are other (maybe even more important) incentives influencing our beliefs and behavior. We have argued, based on the literature in evolutionary psychology and biology, that some additional, not readily apparent motivations presumably play a large role. These motivations comprise the need to appear as a good coalition (group) member, the need to appear as consistent, and the need to appear as beneficent and effective to others. These motivations are distinct (and without any doubt not exhaustive), but they probably act in concert. In all these cases, pragmatic beliefs are not very useful, because agents have no incentives to be right. Their decisions in these areas do not directly depend on the true state of nature. Instead, their incentives are social, and their beliefs will bear this sign.
Social beliefs have not been much studied, and they are not well understood. They do not respond to evidence as pragmatic beliefs do, and the reason is that they shouldn’t. We have conjectured that much of the apparent disagreement on largely factual matters is due to the above mentioned motivations (together with pecuniary incentives), and that without a deeper appreciation of the underlying (hidden) incentives that agents face, we will not be able to improve our understanding of how to tackle pressing issues such as climate denial, conspiracy theories, or anti-science movements. We believe that the tools of game theory can be successfully applied in those areas, helping us decipher which incentives are the more stringent and binding in different contexts. Nonetheless, this new endeavor requires a deeper comprehension of human motivations and of the particular (often hidden) incentives that we face, at the risk of being stuck in the study of proximate mechanisms, without grasping what the ultimate motives are.
Positive “good” information is more frequent compared to negative “bad” information despite our great focusing in bad behaviors/events; bad events/traits/etc. have more diversity of descriptions
The evaluative information ecology: On the frequency and diversity of “good” and “bad”. Christian Unkelbach, Alex Koch & Hans Alves. European Review of Social Psychology, Volume 30, 2019 - Issue 1, Pages 216-270. Nov 24 2019. https://doi.org/10.1080/10463283.2019.1688474
Abstract: We propose the Evaluative Information Ecology (EvIE) model as a model of the social environment. It makes two assumptions: Positive “good” information is more frequent compared to negative “bad” information and positive information is more similar and less diverse compared to negative information. We review support for these two properties based on psycho-lexical studies (e.g., negative trait words are used less frequently but they are more diverse), studies on affective reactions (e.g., people experience positive emotions more frequently but negative emotions are more diverse), and studies using direct similarity assessments (i.e., people rate positive information as more similar/less diverse compared to negative information). Next, we suggest explanations for the two properties building on potential adaptive advantages, reinforcement learning, hedonistic sampling processes, similarity from co-occurrence, and similarity from restricted ranges. Finally, we provide examples of how the EvIE model refines well-established effects (e.g., intergroup biases; preferences for groups without motivation or intent) and how it leads to the discovery of novel phenomena (e.g., the common good phenomenon; people share positive traits but negative traits make them distinct). We close by discussing the benefits relative to the drawbacks of ecological approaches in social psychology and how an ecological and cognitive level of analysis may complement each other.
Keywords: Evaluation, ecology, halo effects, person perception, intergroup biases
Excerpts. Check the full paper for figures, tables, references, etc.
Implications
So far, we provided evidence and explanations for the higher frequency of
positive information relative to negative information (i.e., traits, experiences,
behaviours), and the higher similarity of positive information to other positive
information. In the remainder, we aim to back up our claim that the interaction of these properties with well-established social-cognitive principles within
the organism may lead to the discovery of novel phenomena and alternative
explanations for classic social psychological findings. We will address halo
effects, the relation of similarity and liking, the relation of frequency and liking,
as well as the field of intergroup biases. In the following review of our empirical
findings, anything that is reported as a difference is significant (i.e., probability
of the test statistic under the H0 is p < . 05), unless indicated otherwise; all
reported experiments had proper power considerations and reported all conditions, all data exclusions, and all variables. In addition, we predicted the
empirical findings from the assumed properties and did not derive the EvIE’s
properties from these studies; thus, the following experimental work supports
the EvIE as a general model for people’s social reality.
Halo effects: being honest makes you industrious, but lying does not make you lazy
Halo effects are among the best-established findings in psychology. Thorndike
(1920) coined the term when he observed a “constant error in psychological
ratings”: When army officers were evaluated by their superiors, theoretically
independent dimensions constantly correlated more highly than they should.
Thus, raters either used information on one dimension to rate another dimension or made inferences from a global impression about the to-be-rated target
(Cooper, 1981). Probably the most famous halo effect is from ratings of
physical attractiveness to ratings of intelligence or morality, famous under
the “What is beautiful is good” label (Dion, Berscheid, & Walster, 1972).
Based on our assumptions about the EvIE, an intriguing prediction
follows from the similarity property, namely that halo effects should be
most apparent given positive traits and rating dimensions, but less pronounced given negative traits. This is a strong prediction insofar as there is
consensus in the literature that negative information has more impact than
positive information on social evaluations (e.g., Kanouse & Hanson, 1972;
Peeters & Czapinski, 1990; Skowronski & Carlston, 1989).
To test this idea, Gräf and Unkelbach (2016) presented participants with
targets’ positive or negative traits as well as behaviours from two dimensions
of social perception (Bakan, 1966; see also Abele & Wojciszke, 2018), namely
communion (e.g., being honest) and agency (e.g., being industrious), and
asked participants to rate the targets on other traits either from the same or
the other dimension. Across three experiments, Gräf and Unkelbach investigated halo effects on 30 traits and 48 different behaviours. Participants
observed a target showing either a trait label or a behavioural description and
were asked how likely it was that the target would possess another trait
(Experiments 1 and 2) or would show another behaviour (Experiment 3).
Importantly, they varied the valence and the social perception dimension.
For example, participants saw a lying target (i.e., a negative communion trait)
and answered how likely this person was to also be lazy (i.e., a negative
agency trait), or in another trial, how likely this person was also to be egoistic
(i.e., negative communion trait). Similarly, they would see an honest target
and answer how likely this person was to also be industrious (or, in another
trial: helpful). Thus, the trials tested whether halo effects, an inference from
one behaviour/trait to another behaviour/trait, vary as a function of trait/
behaviour valence and as a function of within/between dimension inferences
on the two fundamental dimensions of social perception.
Figure 5 shows the data from these three experiments. As predicted from
the EvIE’s similarity property, positive traits and behaviours lead to substantially stronger halo effects, both within and across the dimensions of communion and agency (Gräf & Unkelbach, 2016; Exp. 1 to 3; see also, 2018, for
a conceptual replication). These findings are difficult to reconcile with classic
assumptions about the unconditional higher impact of negative information
on social evaluations, but they follow from the EvIE’s similarity property. The
results may also explain apparent features in the literature, namely why there
are few published studies showing “negative” halo effects (i.e., “horn” effects),
simply because they usually do not exist (i.e., lying does not make you lazy).
The EvIE’s frequency property also suggests an intriguing point; namely,
that the observed halo effects might not be an error in ratings (Thorndike,
1920), but a true property of the ecology. Similar to our argument concerning
how higher similarity follows from a higher frequency, the higher frequency
of occurrence of positive traits and behaviours also implies that any positive
trait or behaviour is more likely to co-occur. People should, therefore, learn
that positive traits and positive behaviours appear together on a person-level.
If our assumption about the EvIE’s frequency property is correct, then the
personality profile of being both honest and industrious is factually more
likely than the profile of being dishonest and lazy. From an ecological view,
the constant error in ratings observed by Thorndike might not be entirely an
error after all, but a generalisation of observed ecological co-occurrence to
a task involving trait ratings in a psychology experiment. Investigating this
alternative source for halo effects provides a fascinating venue for future
research.
Similarity and liking: your friends are all alike
The EvIE model states that positive information is more similar and less
diverse compared to negative information; as Figure 4 illustrates, there is
only one way (or fewer ways) to be good compared to the many ways
someone might be bad. One implication of this ecological property is that
liked people (i.e., someone’s friends) should be more similar to one another
compared to disliked people.
This is an interesting prediction, because, based on the hedonic sampling
principle discussed above, people should spend more time with other people
they like compared to people they do not like (Denrell, 2005). This increase
in spent time should lead to more knowledge about liked people, and thereby
to a more differentiated representation of these liked others. Smallman and
Roese (2008) explicitly stated this as follows: “to cherish a loved one is to
relish the fine nuances of his or her personality” while “the rejected and
forsaken are construed on a relatively surface level” (p. 1228). However, if we
assume that people like each other because they possess positive traits,
attributes, or qualities which makes them likeable, the EvIE’s assumed
similarity property predicts that these people should be very similar, particularly in comparison to disliked people. Their mental representation might
be highly differentiated as proposed by Smallman and Roese, but this differentiation does not make them dissimilar, just because the properties (i.e.,
traits and behaviours) that lead to liking are factually highly alike.
Alves, Koch, and Unkelbach (2016) conducted seven experiments to test
whether people see other people they like as more similar to one another
compared to people they dislike. We discuss five of these experiments in the
following. The basic paradigm was straightforward. Participants generated
names of target persons they liked and of targets they disliked. Then, they
used the spatial arrangement method described above (see Figure 1‘s right
panel; Hout et al., 2013) or pairwise similarity ratings (see Figure 1‘s left
panel) to arrange these targets on the screen according to the similarities of
their personalities. They also provided ratings of the time spent together with
these people and of how much they knew about them. As expected, participants reported having spent more time with liked compared to disliked
targets, and they reported knowing more about the liked compared to the
disliked targets. Yet, in line with the prediction from the EvIE, participants
consistently reported higher similarity for liked and disliked targets.
Figure 6 provides a summary of the similarity judgements from
Experiments 1, 3 and 5. Experiment 1 used target persons participants knew
personally with spatial arrangement to assess similarity. Experiment 3 used
target persons participants knew personally with pairwise comparisons to
assess similarity. Experiment 5 used celebrity targets with pairwise comparisons. As Figure 6 shows, participants consistently reported liked targets to be
more similar than disliked targets, despite spending more time with them. We
omit Experiments 2 and 6 here; Experiment 2 replicated Experiment 1 with
target valence manipulated between participants and Experiment 6 replicated
Experiment 5 with a larger set of celebrity targets.
Experiment 4 tested the underlying EvIE structure directly. Participants
generated as many traits as they could for each of the four liked and disliked
targets they named. First, in line with the assumed greater knowledge for liked
targets, participants generated on average 6.9 traits for liked, but only 3.9 traits
for disliked targets. Second, we computed the probability that a trait was shared
among the targets. Figure 7‘s left panel shows the relevant data. The probability
that participants generated shared traits among liked targets was substantially
higher compared to disliked targets. This was true within participants’ eight
targets, but also across participants; that is, even across participants, liked targets
were more likely to share traits and therefore be more similar, providing support
for the assumption that there are ecologically fewer ways to be liked than to be
disliked. This difference in shared traits also held when controlling for the
number of generated traits in a regression analysis.
Experiment 7 then flipped the paradigm and asked participants to generate the names of two people they personally knew without specifying
whether they had to be liked or disliked. Instead, we asked them to generate
either positive traits or negative traits that described each of the two targets.
After providing as many traits as they could, we asked participants to rate the
similarity of the two targets. First, as expected, participants showed the
reversed effect as well – generating positive traits made the two targets appear
more similar compared to generating negative traits. As the targets were
selected in both conditions before we asked for positive or negative traits, any
alternative explanation in terms of differential target generation is taken care
of. In addition, participants generated more traits in the positive traits
condition, 6.4 on average, compared to the negative traits condition, where
they generated only 3.8 traits on average. Replicating Experiment 4, as shown
in Figure 7‘s right panel, the probability that participants generated shared
traits among positive traits was substantially higher compared to negative
traits. This was again true within and also across participants, and also when
controlling for the absolute number of traits generated.
Across seven experiments, of which we summarised five here, we found that
positive traits are more frequently generated and these generated traits also are
more likely to be found across targets, leading to the conclusion that liked
people tend to be seen as alike. In particular, the within-participant comparisons might partially follow from intra-psychic mechanisms (e.g., motivated
reasoning to see your friends as similar and good); however, the effects acrossparticipants are difficult to explain without the presented EvIE model (see
Figure 7).
Frequency and valence: the common good in person perception
In another series of experiments (Alves, Koch, & Unkelbach, 2017b), we
tested a prediction from the frequency property discussed above: If positive
information is more frequent, then it should more likely co-occur with other
positive information compared to negative information. Across people, this
implies that people have positive traits in common, but their negative traits
make them distinct: “Those attributes that connect different people and that
define their similarities are usually good attributes. Those attributes that
distinguish different people and make them unique are often bad attributes.”
(p. 512). This prediction follows solely from the frequency property and does
not depend on the similarity of the information.
For illustration, let us again consider the formal relation of shared and
unshared positive and negative attributes, as we did above for personality
traits. For example, positive attributes may have the probability of being
present in any person of p(pos) = 0.6, and negative attributes may have
a probability of being present of p(neg) = 0.2. The probability of a shared
attribute (i.e., being simultaneously present in two persons) being positive is
then p(positive|shared) = p(pos)*p(pos) = 0.36, while the probability for the
negative attribute is p(negative|shared) = p(neg)*p(neg) = 0.04. In other
words, if a positive trait is three times more likely in the ecology than
a negative trait, it is nine times more likely to be shared than a negative
trait. This leads to two hypotheses: positive traits should be more likely to be
shared amongst targets compared to negative traits, p(shared|positive) > p
(shared|negative), and shared traits should be more likely to be positive
compared to negative traits, p(positive|shared) > p(negative|shared).
To test these hypotheses, Alves et al. (2017b) asked participants to
sample traits of target persons. Experiments 1a and 1b tested the first
prediction, p(shared|positive) > p(shared|negative). In Experiment 1a
(n = 41), participants generated two people they knew personally and
then generated four positive traits and four negative traits for one of the
two. Then, we asked them which of the eight traits also described the other
person. In line with our first prediction, participants assigned on average
3.4 positive traits (i.e., almost all) two both targets. Out of the four negative
traits, they assigned only 1.1 to both targets. Figure 8‘s left panel reports the
respective conditional probabilities for positive and negative traits.
To generalise this result, Experiment 1b (n = 82) asked participants to
generate 10 target persons. Then, we randomly sampled a given set of four
positive and four negative traits from Experiment 1a and participants had to
indicate to which of the 10 targets each of the traits applied. Replicating 1a,
participants assigned on average 3.1 of the positive traits to a target from
their own sample, but only 1.2 of the negative traits. Figure 8 shows the
resulting conditional probabilities. As the left panel shows, positive traits
were much more likely to be shared across participants compared to negative
traits. And as the trait and target generation were separated in Experiment
1b, this replication provides support for our ecological argument.
Experiment 2 in this series of “common good” experiments (Alves et al.,
2017b) tested the second prediction: if a trait is shared as opposed to
unshared, it should be more likely positive, and thus, p(positive|shared) > p
(negative|shared). Participants again generated two target names; then, we
asked them for either shared or unshared traits. We asked for four shared
traits in the former, and two traits that belonged uniquely to the first target,
and two traits that belonged uniquely to the second target, in the latter
condition. Then, participants rated the valence of the generated traits.
Figure 8‘s right panel shows the probabilities: Overall, participants generated
more positive traits than negative traits in both conditions, reflecting the
general positivity prevalence. Yet, in the shared condition, 3.5 traits were
positive on average, and only 0.2 traits were negative. In the unshared
condition, 2.3 traits were positive and 1.3 traits were negative. Thus, the traits
people have in common are usually positive.
Experiment 4a (n = 176) in Alves et al. (2017b) aimed to show that
searching for similarities (i.e., shared traits) amplifies the ecological default,
and searching for differences (i.e., unique traits) attenuates it. Thus, the
experiment replicated Experiment 2 but included a “natural” condition, in
addition to the “shared” and “unshared” conditions. The “natural” condition
asked participants to generate traits for two target persons without specifying
whether these should be shared or unshared traits. Again, across conditions,
participants generated substantially more positive traits: about 4.8 traits out
of six were positive. However, the probability of generating a positive trait
varied as a function of the traits being generated as “shared”, “unshared”, or
“natural” (i.e., without specific instructions). Figure 9 shows these probabilities of a trait being positive. The probability of a trait being positive was
smaller in the natural condition compared to the “shared” condition, and
smaller in the “unshared” condition compared to the “natural” condition.
Thus, as predicted, looking for similarities amplifies the prevalence of positive traits, while looking for differences attenuates it.
A basic drawback in the reported “common good” studies so far is that
participants self-generated targets, which makes the observed “common good”
effect less surprising, as most people might generate people they know and also
like, and the phenomenon might follow from the “my friends are all alike”
effect described above. However, the present approach is different as it is solely
based on the proposed EvIE’s frequency property. The similarity property
implies that positive information should always be more similar to other
positive information (again; there is only one way to be good), and thus, as
long as people have friends they like, these should be alike.
The present “common good” effect, however, follows only if the available
information is predominantly positive. This leads to the reverse prediction if
the available information is predominantly negative. Thus, in Experiments 5
and 6 in Alves et al. (2017b) “common good” series, participants did not
generate targets, but we provided liked and disliked targets for which the
available trait information should be either predominantly positive or negative,
respectively. To do so, Experiment 5 took advantage of the US’s bipartisan
political structure of Democrats and Republicans and recruited 310 US participants online. Half of the participants generated either shared or unshared
traits for Mitt Romney and George W. Bush, two well-known republicans, and
the other half did the same for Bill Clinton and Barack Obama, two wellknown democrats. To divide the sample, we asked participants how much they
liked these political figures; 160 participants reported liking the politicians in
their respective conditions, and 143 participants reported disliking them.
Seven participants reported neither liking nor disliking them and were
excluded from the analysis.
In Experiment 6 (n = 307), we sampled the target persons from a list of the
10 most popular and most unpopular people other participants generated.
The 10 most popular people for US citizens were Abraham Lincoln,
John F. Kennedy, Elvis Presley, Martin Luther King, Oprah Winfrey, Taylor
Swift, George Washington, Michael Jordan, Beyoncé Knowles, and Jesus
Christ. The 10 most unpopular people were Adolf Hitler, Donald Trump,
George W. Bush, Osama Bin Laden, Saddam Hussein, Joseph Stalin, Kim
Jong Un, Justin Bieber, Fidel Castro, and Kanye West. For example, participants generated four traits that Abraham Lincoln and Elvis Presley shared or
two traits that were unique to Lincoln and Presley, respectively. Each pairing
was randomly created for each participant. In the negative targets condition,
for example, participants generated traits that Adolf Hitler and Justin Biber
shared, or two traits that were unique to each of these targets.
Figure 10 shows the results for these two studies, plotting the frequency of
traits being positive and negative as a function of being shared or unshared
among the target persons. For liked targets, the trait frequencies replicate the
previous studies. Both for liked political figures of that time as well as
consensually liked persons, looking for similarities yielded many positive
traits, and few negative traits. Looking for differences yielded fewer positive
traits and more negative traits. However, when participants disliked the
targets, that is, when operating in an ecology of predominantly negative
information, they generated more negative traits in the shared compared to
the unshared condition. Conversely, they provided fewer positive traits in the
shared compared to the unshared condition. This pattern of results provided
distinct evidence for the “common good” implication of the EvIE’s assumed
frequency property. Looking for similarities between targets amplifies, and
looking for differences between targets attenuates, the underlying base-rate;
and this base-rate is, in most cases, marked by a high frequency of positive
information, leading to a “Common Good” phenomenon.
Thus, based on the assumption that positive information is more frequent,
we predicted and found a novel phenomenon in person perception – the
common good effect. The attributes people have in common are usually good
attributes, and negative attributes are rather unique. In addition, searching
for similarities leads to the discovery of the common good, while searching
for differences subjectively attenuates the prevalence of positive information.
Intergroup biases: a cognitive-ecological explanation
Having shown implications of positive information’s higher similarity
(strong halo effects from positive traits; friends are more alike than enemies)
and positive information’s higher frequency (the common good phenomenon), our final example provides a genuinely new explanation for intergroup biases (Alves, Koch, & Unkelbach, 2018), by combining basic
cognitive processes with our assumptions about the EvIE.
One of the most prominent effects in social psychology is that people tend to
devalue minorities (e.g., refugees, immigrants) and out-groups (e.g., rival sport
teams, other states). There is a wealth of models and theories to explain these
biases (e.g., Tajfel and Turner’s Social Identity Theory, 1979; or Brewer’s
theory of optimal distinctiveness, 1991). However, taking the assumed EvIE
properties offers a novel explanation.
For this explanation, we only need the assumption that out-groups and
minorities are “novel” groups in comparison to ingroups and majorities. This
is highly plausible, as people usually come in contact first with their ingroups
(e.g., family, fellow citizens) and majorities (e.g., Whites, Christians); they
learn about outgroups and minorities later and these groups are then novel
in comparison to the former.
On the cognitive side, novel groups are defined in relation to existing groups
(i.e., ingroups, majorities) by the attributes that make them unique, rather than
by the attributes they have in common with existing groups (Hodges, 2005;
Sherman et al., 2009; Tversky & Gati, 1978). On the ecological side, as the
presented evidence suggests, positive attributes are less diverse or more similar
than negative information, and positive information is more frequent than
negative information. Consequently, unique attributes that differentiate
a novel group from already-known groups are likely to be negative.
Thus, the argument is as follows: Minorities and outgroups are most likely
novel groups to social perceivers, compared to majorities and ingroups.
Novel groups are defined by their unique attributes (i.e., the cognitive part)
and unique attributes are most likely negative (i.e., the ecological part),
leading to an association between outgroups and minorities with negative
attributes, which in turn may cause negative stereotypes and prejudice.
To test this explanation, we invited participants to take the role of space
explorers. On a novel planet, they would encounter members of two alien
tribes. We used the neutral aliens provided by Gupta et al. (2004) as stimuli.
Participants would encounter one member of the first tribe and receive
information about one of the alien’s trait; that is, they saw a picture of the
alien and the alien’s respective trait (e.g., helpful, intelligent, anxious, or
aggressive). After participants had encountered six members of the alien
tribe, we instructed participants to imagine that they would now continue
their travels and encounter another alien tribe. Then, they would learn about
the traits of six members of the second tribe. In the real world, people should
probabilistically learn first about members of their ingroup before learning
about members of outgroups. Similarly, they are more likely to meet majority
group members before meeting minority group members. Thus, the first
tribe is functionally similar to a majority or ingroup, and the second tribe is
functionally similar to minorities or out-groups. After these learning phases,
participants chose which group they preferred.
The central manipulation across three experiments was the trait pool from
which we assigned the two tribes’ traits. After learning, we asked participants
which tribe they prefer; that is, we elicited a binary preference choice
between the first and the second tribe as the central dependent variable.
Experiment 1 manipulated whether the positive or whether the negative
attributes were shared or unshared among the two groups. That is, in one
condition, the groups’ positive attributes were identical, while their negative
attributes differed, and this was reversed in the other condition. Table 4‘s left
section presents the resulting preference frequencies. As predicted from our
cognitive-ecological explanation, participants preferred the first group when
the positive attributes were shared and negative attributes were unique, but
preferred the second group when positive attributes were unique and negative attributes were shared. In other words, although the distribution of
positive and negative traits was identical, there was a bias against the novel
group in a standard ecology (i.e., where negative information is unique),
which reversed as a function of the trait ecology.
Experiment 2 then manipulated the similarity of evaluative information in
the ecology. We created two attribute ecologies. In the standard ecology,
positive attributes were less diverse compared to negative attributes. In the
reversed ecology, negative attributes were less diverse. We manipulated
diversity by the number of unique traits in a given ecology. In the standard
ecology condition, we randomly sampled each alien tribe’s three positive
traits from a set of four traits, while we sampled the three negative traits from
a set of 16 traits (i.e., there were more ways to be negative). In the reversed
ecology condition, we sampled the alien tribes’ three negative traits from a set
of four traits, and their positive traits from a set of 16 traits (i.e., there were
more ways to be positive). Consequently, in the standard ecology, the
positive traits were likely to be shared and the first tribe should be preferred.
In the reversed ecology, the negative traits were likely to be shared and
the second tribe should be preferred. As Table 4‘s middle panel shows, the
preference frequencies replicated Experiment 1. Participants preferred the
first group in the standard ecology (i.e., when negative attributes were likely
unique), but in the reversed ecology they preferred the second group (i.e.,
when positive attributes were likely unique).
Experiment 3 then manipulated the EvIE’s second property, the frequency
of evaluative information. In the standard ecology, both groups possessed
more positive than negative attributes, while in the reversed ecology, negative attributes were more frequent. Specifically, in the standard ecology, both
tribes displayed four positive traits and one negative trait. Both positive and
negative traits were randomly sampled from a set of six positive and six
negative traits. In the reversed ecology, both tribes displayed four positive
and one negative trait. Consequently, in the standard ecology (positive
frequent), unique attributes were likely to be negative, while in the reversed
ecology, unique attributes were likely to be negative.
Table 4‘s right section shows the respective preference frequencies.
Replicating Experiments 1 and 2, participants preferred the first group in
the standard ecology, but they preferred the second group in the reversed
ecology. One apparent feature of Table 4 is that the standard ecologies (i.e.,
when negative information is unique) yield stronger differences between the
tribes, while the preference differential is less strong when positive information is unique. This is actually in line with our overall assumptions about the
EvIE. We did not control for the connotative similarity of the positive and
negative traits, but research on the similarity of personality traits
(Bruckmüller & Abele, 2013; Gräf & Unkelbach, 2016; Leising et al., 2012)
shows that positive traits are more similar to each other compared to
negative traits. By implication, the positive unique traits were, less “unique”
compared to the negative unique traits. This differential valence asymmetry
explains at least part of the differential impact of the ordering.
Thus, across three experiments, participants associated a novel group
with its unique attributes, which differentiate the group from previously
encountered groups. Depending on the ecology’s properties, unique attributes were more likely to be positive or negative, and participants’ preferences followed accordingly. As the general structural properties of the EvIE
make unique attributes more likely negative, p(negative|unique) > p(positive|unique), an evaluative disadvantage for novel groups, and thereby for
minorities and outgroups, follows. In other words, people do not need
a real conflict (Sherif, Harvey, White, Hood, & Sherif, 1961), motivated
reasoning (Kunda, 1990), or a hostile personality structure to show differential preferences for minorities and outgroups (Altemeyer, 1998). Rather,
all they need is a cognitive system that tries to differentiate different groups
in an ecology that is marked by high similarity and a high frequency of
positive information
Summary of the implications
We have provided two examples of how our EvIE model refines our knowledge
about classic and important social psychological phenomena. First, halo effects;
we have delineated and shown that halo effects appear predominantly for
positive traits, but are largely absent for negative traits, despite the typically
assumed stronger impact of negative information (Baumeister et al., 2001; Ito,
Larsen, Smith, & Cacioppo, 1998). Second, intergroup biases; we have provided a cognitive-ecological explanation for intergroup biases that do not rely
on motivated reasoning (Kunda, 1990; Tajfel & Turner, 1979), but builds solely
on cognitive processes that interact with the EvIE’s properties.
We have also provided two examples that illustrate the discovery of genuinely new phenomena. First, people’s friends are all alike. Based on the proposed
similarity property, we have shown that people perceive others they know and
like as more similar to one another, just because there is not much room for
variety on the positive side. Second, the common good phenomenon; based on
the proposed frequency property, we have shown that what people have in
common are usually positive attributes, just because negative attributes are
infrequent, and their joint occurrence is therefore unlikely.
Abstract: We propose the Evaluative Information Ecology (EvIE) model as a model of the social environment. It makes two assumptions: Positive “good” information is more frequent compared to negative “bad” information and positive information is more similar and less diverse compared to negative information. We review support for these two properties based on psycho-lexical studies (e.g., negative trait words are used less frequently but they are more diverse), studies on affective reactions (e.g., people experience positive emotions more frequently but negative emotions are more diverse), and studies using direct similarity assessments (i.e., people rate positive information as more similar/less diverse compared to negative information). Next, we suggest explanations for the two properties building on potential adaptive advantages, reinforcement learning, hedonistic sampling processes, similarity from co-occurrence, and similarity from restricted ranges. Finally, we provide examples of how the EvIE model refines well-established effects (e.g., intergroup biases; preferences for groups without motivation or intent) and how it leads to the discovery of novel phenomena (e.g., the common good phenomenon; people share positive traits but negative traits make them distinct). We close by discussing the benefits relative to the drawbacks of ecological approaches in social psychology and how an ecological and cognitive level of analysis may complement each other.
Keywords: Evaluation, ecology, halo effects, person perception, intergroup biases
Excerpts. Check the full paper for figures, tables, references, etc.
Implications
So far, we provided evidence and explanations for the higher frequency of
positive information relative to negative information (i.e., traits, experiences,
behaviours), and the higher similarity of positive information to other positive
information. In the remainder, we aim to back up our claim that the interaction of these properties with well-established social-cognitive principles within
the organism may lead to the discovery of novel phenomena and alternative
explanations for classic social psychological findings. We will address halo
effects, the relation of similarity and liking, the relation of frequency and liking,
as well as the field of intergroup biases. In the following review of our empirical
findings, anything that is reported as a difference is significant (i.e., probability
of the test statistic under the H0 is p < . 05), unless indicated otherwise; all
reported experiments had proper power considerations and reported all conditions, all data exclusions, and all variables. In addition, we predicted the
empirical findings from the assumed properties and did not derive the EvIE’s
properties from these studies; thus, the following experimental work supports
the EvIE as a general model for people’s social reality.
Halo effects: being honest makes you industrious, but lying does not make you lazy
Halo effects are among the best-established findings in psychology. Thorndike
(1920) coined the term when he observed a “constant error in psychological
ratings”: When army officers were evaluated by their superiors, theoretically
independent dimensions constantly correlated more highly than they should.
Thus, raters either used information on one dimension to rate another dimension or made inferences from a global impression about the to-be-rated target
(Cooper, 1981). Probably the most famous halo effect is from ratings of
physical attractiveness to ratings of intelligence or morality, famous under
the “What is beautiful is good” label (Dion, Berscheid, & Walster, 1972).
Based on our assumptions about the EvIE, an intriguing prediction
follows from the similarity property, namely that halo effects should be
most apparent given positive traits and rating dimensions, but less pronounced given negative traits. This is a strong prediction insofar as there is
consensus in the literature that negative information has more impact than
positive information on social evaluations (e.g., Kanouse & Hanson, 1972;
Peeters & Czapinski, 1990; Skowronski & Carlston, 1989).
To test this idea, Gräf and Unkelbach (2016) presented participants with
targets’ positive or negative traits as well as behaviours from two dimensions
of social perception (Bakan, 1966; see also Abele & Wojciszke, 2018), namely
communion (e.g., being honest) and agency (e.g., being industrious), and
asked participants to rate the targets on other traits either from the same or
the other dimension. Across three experiments, Gräf and Unkelbach investigated halo effects on 30 traits and 48 different behaviours. Participants
observed a target showing either a trait label or a behavioural description and
were asked how likely it was that the target would possess another trait
(Experiments 1 and 2) or would show another behaviour (Experiment 3).
Importantly, they varied the valence and the social perception dimension.
For example, participants saw a lying target (i.e., a negative communion trait)
and answered how likely this person was to also be lazy (i.e., a negative
agency trait), or in another trial, how likely this person was also to be egoistic
(i.e., negative communion trait). Similarly, they would see an honest target
and answer how likely this person was to also be industrious (or, in another
trial: helpful). Thus, the trials tested whether halo effects, an inference from
one behaviour/trait to another behaviour/trait, vary as a function of trait/
behaviour valence and as a function of within/between dimension inferences
on the two fundamental dimensions of social perception.
Figure 5 shows the data from these three experiments. As predicted from
the EvIE’s similarity property, positive traits and behaviours lead to substantially stronger halo effects, both within and across the dimensions of communion and agency (Gräf & Unkelbach, 2016; Exp. 1 to 3; see also, 2018, for
a conceptual replication). These findings are difficult to reconcile with classic
assumptions about the unconditional higher impact of negative information
on social evaluations, but they follow from the EvIE’s similarity property. The
results may also explain apparent features in the literature, namely why there
are few published studies showing “negative” halo effects (i.e., “horn” effects),
simply because they usually do not exist (i.e., lying does not make you lazy).
The EvIE’s frequency property also suggests an intriguing point; namely,
that the observed halo effects might not be an error in ratings (Thorndike,
1920), but a true property of the ecology. Similar to our argument concerning
how higher similarity follows from a higher frequency, the higher frequency
of occurrence of positive traits and behaviours also implies that any positive
trait or behaviour is more likely to co-occur. People should, therefore, learn
that positive traits and positive behaviours appear together on a person-level.
If our assumption about the EvIE’s frequency property is correct, then the
personality profile of being both honest and industrious is factually more
likely than the profile of being dishonest and lazy. From an ecological view,
the constant error in ratings observed by Thorndike might not be entirely an
error after all, but a generalisation of observed ecological co-occurrence to
a task involving trait ratings in a psychology experiment. Investigating this
alternative source for halo effects provides a fascinating venue for future
research.
Similarity and liking: your friends are all alike
The EvIE model states that positive information is more similar and less
diverse compared to negative information; as Figure 4 illustrates, there is
only one way (or fewer ways) to be good compared to the many ways
someone might be bad. One implication of this ecological property is that
liked people (i.e., someone’s friends) should be more similar to one another
compared to disliked people.
This is an interesting prediction, because, based on the hedonic sampling
principle discussed above, people should spend more time with other people
they like compared to people they do not like (Denrell, 2005). This increase
in spent time should lead to more knowledge about liked people, and thereby
to a more differentiated representation of these liked others. Smallman and
Roese (2008) explicitly stated this as follows: “to cherish a loved one is to
relish the fine nuances of his or her personality” while “the rejected and
forsaken are construed on a relatively surface level” (p. 1228). However, if we
assume that people like each other because they possess positive traits,
attributes, or qualities which makes them likeable, the EvIE’s assumed
similarity property predicts that these people should be very similar, particularly in comparison to disliked people. Their mental representation might
be highly differentiated as proposed by Smallman and Roese, but this differentiation does not make them dissimilar, just because the properties (i.e.,
traits and behaviours) that lead to liking are factually highly alike.
Alves, Koch, and Unkelbach (2016) conducted seven experiments to test
whether people see other people they like as more similar to one another
compared to people they dislike. We discuss five of these experiments in the
following. The basic paradigm was straightforward. Participants generated
names of target persons they liked and of targets they disliked. Then, they
used the spatial arrangement method described above (see Figure 1‘s right
panel; Hout et al., 2013) or pairwise similarity ratings (see Figure 1‘s left
panel) to arrange these targets on the screen according to the similarities of
their personalities. They also provided ratings of the time spent together with
these people and of how much they knew about them. As expected, participants reported having spent more time with liked compared to disliked
targets, and they reported knowing more about the liked compared to the
disliked targets. Yet, in line with the prediction from the EvIE, participants
consistently reported higher similarity for liked and disliked targets.
Figure 6 provides a summary of the similarity judgements from
Experiments 1, 3 and 5. Experiment 1 used target persons participants knew
personally with spatial arrangement to assess similarity. Experiment 3 used
target persons participants knew personally with pairwise comparisons to
assess similarity. Experiment 5 used celebrity targets with pairwise comparisons. As Figure 6 shows, participants consistently reported liked targets to be
more similar than disliked targets, despite spending more time with them. We
omit Experiments 2 and 6 here; Experiment 2 replicated Experiment 1 with
target valence manipulated between participants and Experiment 6 replicated
Experiment 5 with a larger set of celebrity targets.
Experiment 4 tested the underlying EvIE structure directly. Participants
generated as many traits as they could for each of the four liked and disliked
targets they named. First, in line with the assumed greater knowledge for liked
targets, participants generated on average 6.9 traits for liked, but only 3.9 traits
for disliked targets. Second, we computed the probability that a trait was shared
among the targets. Figure 7‘s left panel shows the relevant data. The probability
that participants generated shared traits among liked targets was substantially
higher compared to disliked targets. This was true within participants’ eight
targets, but also across participants; that is, even across participants, liked targets
were more likely to share traits and therefore be more similar, providing support
for the assumption that there are ecologically fewer ways to be liked than to be
disliked. This difference in shared traits also held when controlling for the
number of generated traits in a regression analysis.
Experiment 7 then flipped the paradigm and asked participants to generate the names of two people they personally knew without specifying
whether they had to be liked or disliked. Instead, we asked them to generate
either positive traits or negative traits that described each of the two targets.
After providing as many traits as they could, we asked participants to rate the
similarity of the two targets. First, as expected, participants showed the
reversed effect as well – generating positive traits made the two targets appear
more similar compared to generating negative traits. As the targets were
selected in both conditions before we asked for positive or negative traits, any
alternative explanation in terms of differential target generation is taken care
of. In addition, participants generated more traits in the positive traits
condition, 6.4 on average, compared to the negative traits condition, where
they generated only 3.8 traits on average. Replicating Experiment 4, as shown
in Figure 7‘s right panel, the probability that participants generated shared
traits among positive traits was substantially higher compared to negative
traits. This was again true within and also across participants, and also when
controlling for the absolute number of traits generated.
Across seven experiments, of which we summarised five here, we found that
positive traits are more frequently generated and these generated traits also are
more likely to be found across targets, leading to the conclusion that liked
people tend to be seen as alike. In particular, the within-participant comparisons might partially follow from intra-psychic mechanisms (e.g., motivated
reasoning to see your friends as similar and good); however, the effects acrossparticipants are difficult to explain without the presented EvIE model (see
Figure 7).
Frequency and valence: the common good in person perception
In another series of experiments (Alves, Koch, & Unkelbach, 2017b), we
tested a prediction from the frequency property discussed above: If positive
information is more frequent, then it should more likely co-occur with other
positive information compared to negative information. Across people, this
implies that people have positive traits in common, but their negative traits
make them distinct: “Those attributes that connect different people and that
define their similarities are usually good attributes. Those attributes that
distinguish different people and make them unique are often bad attributes.”
(p. 512). This prediction follows solely from the frequency property and does
not depend on the similarity of the information.
For illustration, let us again consider the formal relation of shared and
unshared positive and negative attributes, as we did above for personality
traits. For example, positive attributes may have the probability of being
present in any person of p(pos) = 0.6, and negative attributes may have
a probability of being present of p(neg) = 0.2. The probability of a shared
attribute (i.e., being simultaneously present in two persons) being positive is
then p(positive|shared) = p(pos)*p(pos) = 0.36, while the probability for the
negative attribute is p(negative|shared) = p(neg)*p(neg) = 0.04. In other
words, if a positive trait is three times more likely in the ecology than
a negative trait, it is nine times more likely to be shared than a negative
trait. This leads to two hypotheses: positive traits should be more likely to be
shared amongst targets compared to negative traits, p(shared|positive) > p
(shared|negative), and shared traits should be more likely to be positive
compared to negative traits, p(positive|shared) > p(negative|shared).
To test these hypotheses, Alves et al. (2017b) asked participants to
sample traits of target persons. Experiments 1a and 1b tested the first
prediction, p(shared|positive) > p(shared|negative). In Experiment 1a
(n = 41), participants generated two people they knew personally and
then generated four positive traits and four negative traits for one of the
two. Then, we asked them which of the eight traits also described the other
person. In line with our first prediction, participants assigned on average
3.4 positive traits (i.e., almost all) two both targets. Out of the four negative
traits, they assigned only 1.1 to both targets. Figure 8‘s left panel reports the
respective conditional probabilities for positive and negative traits.
To generalise this result, Experiment 1b (n = 82) asked participants to
generate 10 target persons. Then, we randomly sampled a given set of four
positive and four negative traits from Experiment 1a and participants had to
indicate to which of the 10 targets each of the traits applied. Replicating 1a,
participants assigned on average 3.1 of the positive traits to a target from
their own sample, but only 1.2 of the negative traits. Figure 8 shows the
resulting conditional probabilities. As the left panel shows, positive traits
were much more likely to be shared across participants compared to negative
traits. And as the trait and target generation were separated in Experiment
1b, this replication provides support for our ecological argument.
Experiment 2 in this series of “common good” experiments (Alves et al.,
2017b) tested the second prediction: if a trait is shared as opposed to
unshared, it should be more likely positive, and thus, p(positive|shared) > p
(negative|shared). Participants again generated two target names; then, we
asked them for either shared or unshared traits. We asked for four shared
traits in the former, and two traits that belonged uniquely to the first target,
and two traits that belonged uniquely to the second target, in the latter
condition. Then, participants rated the valence of the generated traits.
Figure 8‘s right panel shows the probabilities: Overall, participants generated
more positive traits than negative traits in both conditions, reflecting the
general positivity prevalence. Yet, in the shared condition, 3.5 traits were
positive on average, and only 0.2 traits were negative. In the unshared
condition, 2.3 traits were positive and 1.3 traits were negative. Thus, the traits
people have in common are usually positive.
Experiment 4a (n = 176) in Alves et al. (2017b) aimed to show that
searching for similarities (i.e., shared traits) amplifies the ecological default,
and searching for differences (i.e., unique traits) attenuates it. Thus, the
experiment replicated Experiment 2 but included a “natural” condition, in
addition to the “shared” and “unshared” conditions. The “natural” condition
asked participants to generate traits for two target persons without specifying
whether these should be shared or unshared traits. Again, across conditions,
participants generated substantially more positive traits: about 4.8 traits out
of six were positive. However, the probability of generating a positive trait
varied as a function of the traits being generated as “shared”, “unshared”, or
“natural” (i.e., without specific instructions). Figure 9 shows these probabilities of a trait being positive. The probability of a trait being positive was
smaller in the natural condition compared to the “shared” condition, and
smaller in the “unshared” condition compared to the “natural” condition.
Thus, as predicted, looking for similarities amplifies the prevalence of positive traits, while looking for differences attenuates it.
A basic drawback in the reported “common good” studies so far is that
participants self-generated targets, which makes the observed “common good”
effect less surprising, as most people might generate people they know and also
like, and the phenomenon might follow from the “my friends are all alike”
effect described above. However, the present approach is different as it is solely
based on the proposed EvIE’s frequency property. The similarity property
implies that positive information should always be more similar to other
positive information (again; there is only one way to be good), and thus, as
long as people have friends they like, these should be alike.
The present “common good” effect, however, follows only if the available
information is predominantly positive. This leads to the reverse prediction if
the available information is predominantly negative. Thus, in Experiments 5
and 6 in Alves et al. (2017b) “common good” series, participants did not
generate targets, but we provided liked and disliked targets for which the
available trait information should be either predominantly positive or negative,
respectively. To do so, Experiment 5 took advantage of the US’s bipartisan
political structure of Democrats and Republicans and recruited 310 US participants online. Half of the participants generated either shared or unshared
traits for Mitt Romney and George W. Bush, two well-known republicans, and
the other half did the same for Bill Clinton and Barack Obama, two wellknown democrats. To divide the sample, we asked participants how much they
liked these political figures; 160 participants reported liking the politicians in
their respective conditions, and 143 participants reported disliking them.
Seven participants reported neither liking nor disliking them and were
excluded from the analysis.
In Experiment 6 (n = 307), we sampled the target persons from a list of the
10 most popular and most unpopular people other participants generated.
The 10 most popular people for US citizens were Abraham Lincoln,
John F. Kennedy, Elvis Presley, Martin Luther King, Oprah Winfrey, Taylor
Swift, George Washington, Michael Jordan, Beyoncé Knowles, and Jesus
Christ. The 10 most unpopular people were Adolf Hitler, Donald Trump,
George W. Bush, Osama Bin Laden, Saddam Hussein, Joseph Stalin, Kim
Jong Un, Justin Bieber, Fidel Castro, and Kanye West. For example, participants generated four traits that Abraham Lincoln and Elvis Presley shared or
two traits that were unique to Lincoln and Presley, respectively. Each pairing
was randomly created for each participant. In the negative targets condition,
for example, participants generated traits that Adolf Hitler and Justin Biber
shared, or two traits that were unique to each of these targets.
Figure 10 shows the results for these two studies, plotting the frequency of
traits being positive and negative as a function of being shared or unshared
among the target persons. For liked targets, the trait frequencies replicate the
previous studies. Both for liked political figures of that time as well as
consensually liked persons, looking for similarities yielded many positive
traits, and few negative traits. Looking for differences yielded fewer positive
traits and more negative traits. However, when participants disliked the
targets, that is, when operating in an ecology of predominantly negative
information, they generated more negative traits in the shared compared to
the unshared condition. Conversely, they provided fewer positive traits in the
shared compared to the unshared condition. This pattern of results provided
distinct evidence for the “common good” implication of the EvIE’s assumed
frequency property. Looking for similarities between targets amplifies, and
looking for differences between targets attenuates, the underlying base-rate;
and this base-rate is, in most cases, marked by a high frequency of positive
information, leading to a “Common Good” phenomenon.
Thus, based on the assumption that positive information is more frequent,
we predicted and found a novel phenomenon in person perception – the
common good effect. The attributes people have in common are usually good
attributes, and negative attributes are rather unique. In addition, searching
for similarities leads to the discovery of the common good, while searching
for differences subjectively attenuates the prevalence of positive information.
Intergroup biases: a cognitive-ecological explanation
Having shown implications of positive information’s higher similarity
(strong halo effects from positive traits; friends are more alike than enemies)
and positive information’s higher frequency (the common good phenomenon), our final example provides a genuinely new explanation for intergroup biases (Alves, Koch, & Unkelbach, 2018), by combining basic
cognitive processes with our assumptions about the EvIE.
One of the most prominent effects in social psychology is that people tend to
devalue minorities (e.g., refugees, immigrants) and out-groups (e.g., rival sport
teams, other states). There is a wealth of models and theories to explain these
biases (e.g., Tajfel and Turner’s Social Identity Theory, 1979; or Brewer’s
theory of optimal distinctiveness, 1991). However, taking the assumed EvIE
properties offers a novel explanation.
For this explanation, we only need the assumption that out-groups and
minorities are “novel” groups in comparison to ingroups and majorities. This
is highly plausible, as people usually come in contact first with their ingroups
(e.g., family, fellow citizens) and majorities (e.g., Whites, Christians); they
learn about outgroups and minorities later and these groups are then novel
in comparison to the former.
On the cognitive side, novel groups are defined in relation to existing groups
(i.e., ingroups, majorities) by the attributes that make them unique, rather than
by the attributes they have in common with existing groups (Hodges, 2005;
Sherman et al., 2009; Tversky & Gati, 1978). On the ecological side, as the
presented evidence suggests, positive attributes are less diverse or more similar
than negative information, and positive information is more frequent than
negative information. Consequently, unique attributes that differentiate
a novel group from already-known groups are likely to be negative.
Thus, the argument is as follows: Minorities and outgroups are most likely
novel groups to social perceivers, compared to majorities and ingroups.
Novel groups are defined by their unique attributes (i.e., the cognitive part)
and unique attributes are most likely negative (i.e., the ecological part),
leading to an association between outgroups and minorities with negative
attributes, which in turn may cause negative stereotypes and prejudice.
To test this explanation, we invited participants to take the role of space
explorers. On a novel planet, they would encounter members of two alien
tribes. We used the neutral aliens provided by Gupta et al. (2004) as stimuli.
Participants would encounter one member of the first tribe and receive
information about one of the alien’s trait; that is, they saw a picture of the
alien and the alien’s respective trait (e.g., helpful, intelligent, anxious, or
aggressive). After participants had encountered six members of the alien
tribe, we instructed participants to imagine that they would now continue
their travels and encounter another alien tribe. Then, they would learn about
the traits of six members of the second tribe. In the real world, people should
probabilistically learn first about members of their ingroup before learning
about members of outgroups. Similarly, they are more likely to meet majority
group members before meeting minority group members. Thus, the first
tribe is functionally similar to a majority or ingroup, and the second tribe is
functionally similar to minorities or out-groups. After these learning phases,
participants chose which group they preferred.
The central manipulation across three experiments was the trait pool from
which we assigned the two tribes’ traits. After learning, we asked participants
which tribe they prefer; that is, we elicited a binary preference choice
between the first and the second tribe as the central dependent variable.
Experiment 1 manipulated whether the positive or whether the negative
attributes were shared or unshared among the two groups. That is, in one
condition, the groups’ positive attributes were identical, while their negative
attributes differed, and this was reversed in the other condition. Table 4‘s left
section presents the resulting preference frequencies. As predicted from our
cognitive-ecological explanation, participants preferred the first group when
the positive attributes were shared and negative attributes were unique, but
preferred the second group when positive attributes were unique and negative attributes were shared. In other words, although the distribution of
positive and negative traits was identical, there was a bias against the novel
group in a standard ecology (i.e., where negative information is unique),
which reversed as a function of the trait ecology.
Experiment 2 then manipulated the similarity of evaluative information in
the ecology. We created two attribute ecologies. In the standard ecology,
positive attributes were less diverse compared to negative attributes. In the
reversed ecology, negative attributes were less diverse. We manipulated
diversity by the number of unique traits in a given ecology. In the standard
ecology condition, we randomly sampled each alien tribe’s three positive
traits from a set of four traits, while we sampled the three negative traits from
a set of 16 traits (i.e., there were more ways to be negative). In the reversed
ecology condition, we sampled the alien tribes’ three negative traits from a set
of four traits, and their positive traits from a set of 16 traits (i.e., there were
more ways to be positive). Consequently, in the standard ecology, the
positive traits were likely to be shared and the first tribe should be preferred.
In the reversed ecology, the negative traits were likely to be shared and
the second tribe should be preferred. As Table 4‘s middle panel shows, the
preference frequencies replicated Experiment 1. Participants preferred the
first group in the standard ecology (i.e., when negative attributes were likely
unique), but in the reversed ecology they preferred the second group (i.e.,
when positive attributes were likely unique).
Experiment 3 then manipulated the EvIE’s second property, the frequency
of evaluative information. In the standard ecology, both groups possessed
more positive than negative attributes, while in the reversed ecology, negative attributes were more frequent. Specifically, in the standard ecology, both
tribes displayed four positive traits and one negative trait. Both positive and
negative traits were randomly sampled from a set of six positive and six
negative traits. In the reversed ecology, both tribes displayed four positive
and one negative trait. Consequently, in the standard ecology (positive
frequent), unique attributes were likely to be negative, while in the reversed
ecology, unique attributes were likely to be negative.
Table 4‘s right section shows the respective preference frequencies.
Replicating Experiments 1 and 2, participants preferred the first group in
the standard ecology, but they preferred the second group in the reversed
ecology. One apparent feature of Table 4 is that the standard ecologies (i.e.,
when negative information is unique) yield stronger differences between the
tribes, while the preference differential is less strong when positive information is unique. This is actually in line with our overall assumptions about the
EvIE. We did not control for the connotative similarity of the positive and
negative traits, but research on the similarity of personality traits
(Bruckmüller & Abele, 2013; Gräf & Unkelbach, 2016; Leising et al., 2012)
shows that positive traits are more similar to each other compared to
negative traits. By implication, the positive unique traits were, less “unique”
compared to the negative unique traits. This differential valence asymmetry
explains at least part of the differential impact of the ordering.
Thus, across three experiments, participants associated a novel group
with its unique attributes, which differentiate the group from previously
encountered groups. Depending on the ecology’s properties, unique attributes were more likely to be positive or negative, and participants’ preferences followed accordingly. As the general structural properties of the EvIE
make unique attributes more likely negative, p(negative|unique) > p(positive|unique), an evaluative disadvantage for novel groups, and thereby for
minorities and outgroups, follows. In other words, people do not need
a real conflict (Sherif, Harvey, White, Hood, & Sherif, 1961), motivated
reasoning (Kunda, 1990), or a hostile personality structure to show differential preferences for minorities and outgroups (Altemeyer, 1998). Rather,
all they need is a cognitive system that tries to differentiate different groups
in an ecology that is marked by high similarity and a high frequency of
positive information
Summary of the implications
We have provided two examples of how our EvIE model refines our knowledge
about classic and important social psychological phenomena. First, halo effects;
we have delineated and shown that halo effects appear predominantly for
positive traits, but are largely absent for negative traits, despite the typically
assumed stronger impact of negative information (Baumeister et al., 2001; Ito,
Larsen, Smith, & Cacioppo, 1998). Second, intergroup biases; we have provided a cognitive-ecological explanation for intergroup biases that do not rely
on motivated reasoning (Kunda, 1990; Tajfel & Turner, 1979), but builds solely
on cognitive processes that interact with the EvIE’s properties.
We have also provided two examples that illustrate the discovery of genuinely new phenomena. First, people’s friends are all alike. Based on the proposed
similarity property, we have shown that people perceive others they know and
like as more similar to one another, just because there is not much room for
variety on the positive side. Second, the common good phenomenon; based on
the proposed frequency property, we have shown that what people have in
common are usually positive attributes, just because negative attributes are
infrequent, and their joint occurrence is therefore unlikely.
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