Monday, May 25, 2020

Role of individual differences for development and manifestation of wisdom, approaches to wisdom development and training, & cultural, subcultural, and social-contextual differences

Grossmann, Igor, Nic M. Weststrate, Monika Ardelt, Justin P. Brienza, Mengxi Dong, Michel Ferrari, Marc A. Fournier, et al. 2020. “The Science of Wisdom in a Polarized World: Knowns and Unknowns.” PsyArXiv. May 26. doi:10.1080/1047840X.2020.1750917

Abstract: Interest in wisdom in the cognitive sciences, psychology, and education has been paralleled by conceptual confusions about its nature and assessment. To clarify these issues and promote consensus in the field, wisdom researchers met in Toronto in July of 2019, resolving disputes through discussion. Guided by a survey of scientists who study wisdom-related constructs, we established a common wisdom model, observing that empirical approaches to wisdom converge on the morally-grounded application of metacognition to reasoning and problem-solving. After outlining the function of relevant metacognitive and moral processes, we critically evaluate existing empirical approaches to measurement and offer recommendations for best practices. In the subsequent sections, we use the common wisdom model to selectively review evidence about the role of individual differences for development and manifestation of wisdom, approaches to wisdom development and training, as well as cultural, subcultural, and social-contextual differences. We conclude by discussing wisdom’s conceptual overlap with a host of other constructs and outline unresolved conceptual and methodological challenges.

Character strengths as wisdom in disguise?
The last several decades have not only seen the emergence of the psychometric base to the wisdom construct, but also a proliferation of potentially related constructs. Psychological scientists so far have not considered the conceptual and psychometric relationship of such constructs to wisdom. Below we provide two examples. This list is not exhaustive and questions below may be applied broadly.
Intellectual humility. Several constructs have recently emerged under the umbrella rubric of character strengths and virtues (e.g., Bleidorn & Denissen, 2015; Fleeson, Furr, Jayawickreme, Meindl, & Helzer, 2014; Peterson & Seligman, 2004) , which include a range of characteristics such as courage, gratitude (e.g., DeSteno, Li, Dickens, & Lerner, 2014; McCullough, Kilpatrick, Emmons, & Larson, 2001) , compassion (Goetz, Keltner, & Simon-Thomas, 2010) and forgiveness (McCullough, 2000) . Some of these constructs have their roots in the virtue theory and Christian theology, and have become popular within the positive psychology movement (Seligman & Csikszentmihalyi, 2014) . One character strength that has received a great deal of attention concerns (intellectual) humility (e.g., Leary et al., 2017; Stellar et al., 2018; Van Tongeren, Davis, Hook, & Witvliet, 2019) , defined as the “ability to accurately acknowledge one’s limitations and abilities and (b) an interpersonal stance that is other-oriented rather than self-focused” (Van Tongeren et al., 2019) . Intellectual humility was the focus of Socrates' definition of wisdom and the overlap of this conceptual definition with the common wisdom model is striking, raising the question about the distinctiveness of humility from wisdom, and the discriminant validity of the extant measures. Indeed, several of the most common measures aiming to capture the common wisdom model (Table 1) include intellectual humility as a central facet. Moreover, features of humility and wisdom are listed among the 28 measurable character strengths (Peterson & Seligman, 2004) . Undoubtedly, intellectual humility is an important concept in a time of ever increasing political polarization (Haidt & Lukianoff, 2018; Leary et al., 2017) . The question that remains open concerns its uniqueness beyond the PMC components it shares with the common wisdom model advanced in the present target article. If it is just a facet of the same broader construct, researchers studying humility and wisdom may benefit from insights from respective fields, including concerns with measurement levels and usability of the self-report scales (see Section 3). After all, most humility instruments so far appear to involve abstract self-ratings, even though the process they arguably aim to capture concern is meta-cognitive and thereby is not easily amendable to abstract self-beliefs (Brienza et al., 2018; Grossmann, Dorfman, et al., 2020) . Abstract self-beliefs may be self-deceiving or used for purposes of impression management (Dunning, Heath, & Suls, 2004; Kihlstrom, Eich, Sandbrand, & Tobias, 2000; T. D. Wilson & Bar-Anan, 2008) , raising questions whether a person claiming that they are “much more humble than you would understand” accurately expresses their humility.
Open-mindedness. A related characteristic that has experienced a resurgence in scientific interest concerns open-mindedness. The construct itself is not new and can be traced to theories by Carl Rogers and others (e.g., Rogers, 1954) . Indeed, the initial intent of the openness factor of the OCEAN model of personality has included open-mindedness (for review, John, Naumann, & Soto, 2008 , esp. Table 4.2). However, whereas openness/open-mindedness in personality research has been conceptually defined as a description of “the breadth, depth, originality, and complexity of an individual’s mental and experiential life” (John et al., 2008) , there appears to be no unified definition of open-mindedness neither as a character strength nor as a thinking disposition. Some scholars define and proceed to measure it as intellectual flexibility and openness to diverse viewpoints in the process of making a decision (Fujita, Gollwitzer, & Oettingen, 2007; Price, Ottati, Wilson, & Kim, 2015) . Others define it almost identically to the definition of humility above, as “an intellectual quality displayed by someone who recognizes that her belief could be wrong, so her mind is subject to change” (Spiegel, 2012) . And yet others define open-minded thinking as “the disposition to weigh new evidence against a favored belief heavily (or lightly), the disposition to spend a great deal of time (or very little) on a problem before giving up, or the disposition to weigh heavily the opinions of others in forming one's own” (Baron, 1985 , p. 15; also see Stanovich & West, 1997). The conceptual confusion concerning open-mindedness is particularly evident in the puzzling observations that open-minded thinking appears to be positively related to political polarization about climate change (Kahan & Corbin, 2016) : If the definition of open-mindedness involves openness to diverse viewpoints and a potential of one being wrong, one ought to expect less polarization among more open-minded individuals! Similar to the concept of humility, this conceptual confusion raises questions about the meaning of the term, as well as the overlap with the definition of wisdom as a morally-grounded PMC. Researchers on open-mindedness may benefit from the present discussion of the common wisdom model, which may provide a roadmap for conceptual and methodological clarification of the open-mindedness construct, as well.
Overall, bodies of research on character strength and thinking dispositions have not sufficiently clarified the relationship to psychological characteristics of wisdom. Is wisdom one of many virtues linked to creativity, open-mindedness, perspective, and innovation, and distinct from humility, prudence, or justice (Peterson & Seligman, 2004) ? Both the common wisdom model advanced here and the relevant psychometric evidence are inconsistent with this perspective: The common wisdom model involves psychological characteristics of humility, prudence, as well as moral aspirations concerning justice and fairness. An alternative possibility advanced in Aristotelian and Thomistic virtue ethics is that wisdom (or prudence) represents a cardinal meta-virtue allowing one to discern which actions to pursue in concrete circumstances one may be experiencing (e.g., Darnell et al., 2019; Schwartz & Sharpe, 2006) . In relationship to thinking dispositions or character strengths, wisdom may be represented as a tool allowing one to figure out which of the character traits are more relevant in a given situation. This perspective is consistent with the common wisdom framework advanced here, with PMC oriented towards discerning the fit between one’s dispositions, one’s goals, and the features of a given situation. However, this “cardinal virtue” perspective requires measurement of the fit between one’s tendencies and the situational demands across multiple diagnostic situations, to estimate whether PMC indeed allows one to flexibly switch between different behaviors to optimally fit the context of a new situation. No psychometric instruments for the assessment of character strengths (e.g., Fleeson, Furr, Jayawickreme, Meindl, & Helzer, 2014) , thinking dispositions (e.g., Baron, 2019; Perkins, Jay, & Tishman, 1993; Stanovich & West, 2002) or wisdom (Staudinger & Glück, 2011) have so far even attempted to assess relevant characteristics within such “strategy-situation fit” framework.
Toward a psychological and cognitive science of wisdom
Beyond individual differences in character strengths and thinking dispositions, the common wisdom model as morally-grounded PMC also has implications for research on consciousness and artificial intelligence. We review possible connections and a range of unanswered questions below.
Consciousness. Within the body of research on consciousness, some researchers have suggested three different levels of awareness (e.g., Pinard, 1992; Schooler, 2002), including unconscious processes, basic conscious processes, and meta-conscious processes. At the implicit or enacted level of consciousness, experience is embedded in the actions one is experiencing. At the explicit level of consciousness, the person engages in conscious processing of the phenomena. At the third, meta-conscious level, the person deliberately and consciously takes charge of their cognitive functioning (Damasio, 1999; Pinard, 1992; Proust, 2013; Winkielman & Schooler, 2011) -- i.e., one becomes aware of the content of one’s conscious processes in ways that become increasingly transparent. The concept of meta-consciousness shares a great deal in common with the meta-cognitive components central to the empirical conceptualizations of wisdom (see Figure 1). Both require a perspectival appreciation of one’s conscious experience (e.g., experience of the inadequacy of one’s explanations of a given concept). Moreover, theoretical and empirical work on meta-consciousness suggests that the processes triggering meta-consciousness (Winkielman & Schooler, 2011) may be relevant for boosting wisdom as well. At the same time, the lack of moral grounding for the concept of meta-consciousness, compared to its centrality to the common wisdom model, raises the questions about the limits of the conceptual overlap. Is meta-consciousness a necessary, but not sufficient aspect of wisdom? If meta-consciousness is necessary for wisdom, can wisdom ever become habitual or automatic? Are the individuals prone to mind-wandering more or less likely to think wisely? Answers to these questions can help better understand the concept of wisdom from a cognitive science perspective, including the role of implicit, automatic, and physiological processes.
Artificial intelligence. Artificial intelligence (AI) research typically aims to develop “rational,” intelligent agents, a quality attributed to devices that adaptively react to the environment and take actions that maximize their chances of successfully achieving their goals (Russell & Norvig, 2016) . Notably, AI is often attributed to those qualities that machine devices have not yet mastered – an observation often described as the Tesler’s Theorem (Hofstadter, 1980 , p. 601) or the AI effect (McCorduck, 2004) . Given the steady AI advances in the domains of speech comprehension, language translations, and human-superior performance on strategic games such as chess or Go, a question arises: Will AI at some point be able to acquire wisdom? The development of “wise” AI systems is of special relevance in the time of ethical debates concerning the use of AI and machine-learning approaches to human-machine interactions, autonomous cars, political advertising, and criminal court decisions. For some time, AI researchers have recognized the hierarchy of Data, Information, Knowledge, and Wisdom (DIKW), in which data represent measurements or symbols, information is the application of data to answer questions, knowledge depends on the context of question and answer, and wisdom depends on values. Indeed, there is a growing recognition that values need to be incorporated into the development of AI (Conn, 2017) . On the surface, advances in AI-based knowledge representation, expert systems, and planning suggest that some aspects of human wisdom may be approximated via the AI. At the same time, the ideal of a goal-oriented, rational agent central to the AI research (Russell & Norvig, 2016) can be idiosyncratic to the common characteristics of a wise person: Under many circumstances, a wise person may choose a socially conscious, reasonable option, rather than preference-maximizing rational option (Rawls, 1971; Toulmin, 2001) . This characterization of a wise person is not idiosyncratic, as it is shared among laypeople across a range of contemporary societies (Grossmann, Eibach, et al., 2020) . Whether AI researchers can go beyond the maxim of goal-oriented optimization and to simulate psychological characteristics of wisdom in the context of ill-defined problems remains an open question. How will AI be able to integrate the influx of multi-model streams of information with moral aspirations as suggested by Asimov’s zeroth law or robotics (1986) ? Without doubt, “wise” AI requires discerning where, when, and to what degree to apply different rules for processing information, but it also requires optimization towards resolution of certain trade-offs (e.g., trade-offs between general and context-specific strategies). Perhaps a first step in testing the viability of a “wise” AI will be contingent on the development of systems capable to effectively simulate PMC common to psychological wisdom in the context of complex, social issues.

Women overestimated the thinness that men prefer in a partner and men overestimated the heaviness and muscularity that women prefer in a partner

Misperceptions of opposite‐sex preferences for thinness and muscularity. Xue Lei  David Perrett. British Journal of Psychology, May 25 2020.

Abstract: Thin and muscular have been characterized as ideals for women and men, respectively. Little research has investigated whether men and women have accurate perceptions of opposite‐sex preferences of thinness and muscularity. Further, no study has explored whether opposite‐sex perceptions of thinness and muscularity preferences differ for short‐term and long‐term relationships. The present study set out to address these questions. We used interactive 3D human models to represent bodies varying in size (body mass index/BMI weight scaled by height) and body composition. University‐aged (18–31) White European heterosexual men and women were asked to choose their own and ideal body shape, the ideal body shape for a short‐ and a long‐term partner, and the body shape they thought the opposite‐sex would most like for short‐ and long‐term partners. Women overestimated the thinness that men prefer in a partner and men overestimated the heaviness and muscularity that women prefer in a partner. These misperceptions were more exaggerated for short‐term relationships than for long‐term relationships. The results illustrate the importance of investigating misperceptions of opposite‐sex preferences and raise the possibility that correcting misperceptions might have utility in reducing body dissatisfaction or eating disorders.


It is worth noting that although the BMI and Fat% of the own ideal and preferred partner bodies are within the healthy range, the values may not truly represent realistic human figures. The body models used in the current study were generated through a mobile app. The extent to which the models accurately reflect the body weight and muscularity of real human bodies remains unclear. Compared with previous findings (Stephen & Perera, 2014; Tovée, Reinhardt, Emery, & Cornelissen, 1998), the ideal female body figure found in the current study is heavier in terms of BMI. For example, the associated BMI of the attractive female bodies or faces found in previous studies was as low as 17 (Stephen & Perera, 2014), and the highest was around 20 (Tovée et al. , 1998). The BMI of women’s ideal body figure in the present study is around 21 and this figure is even higher for men’s preference, which is around 22. Clearly, the ideal female body size is higher in the current study than in previous studies. By the same token, the ideal male body size might also be higher compared to previous work. The discrepancy of the most attractive BMI between the current and previous studies might be due to the different stimuli used. The 3D human body models used in the current study might appear thinner than 2D human body’s photographs used in previous studies given the same BMI. In fact, one previous study has found that BMI of the most desirable 3D male faces is higher than that of the 2D male faces (Lei et al. , 2018). Future study comparing the accuracy of perception of weight from 2D and 3D images might provide more understanding.
Previously, studies examining preferences of body size mainly used line drawings or photographs of different individuals; few used controlled interactive photographs or model images. Even though some studies used real individual body images, body composition was not taken into consideration. Future studies exploring the body weight and shape that are attractive in men and women should use realistic photographs of human bodies and control for other body parameters that influence physical attractiveness like waist to hip ratio. Nonetheless, even though the absolute values of BMI and Fat% of the preferred male and female bodies may not truly represent the most attractive figures of men and women in real life, the aim of the present study was to compare preferences between the two sexes. Thus, the accuracy of the representations of human body models should not affect the misperceptions of opposite‐sex preferences found here.
In addition, the current study used body fat percentage rather than muscle mass as the measurement of muscularity (Holzleitner & Perrett, 2016; Sturman, Stephen, Mond, Stevenson, & Brooks, 2017). We note that a low body fat percentage does not necessarily equate to a high muscularity, particularly when BMI is low (e.g. <20). Nevertheless, the results suggest that men’s underestimation of the body fat percentage women desire accompanies the overestimation of heaviness (BMI ~ 27) that men believe women prefer. Hence, it is appropriate to conclude that men overestimate muscularity that women prefer. We hope that future studies will be able to use of muscle mass to measure preferences for male muscularity.


In conclusion, using models of human bodies with various levels of BMI and Fat%, the current study revealed that misperceptions of opposite‐sex preferences exist in young men and women. In particular, women tend to overestimate the thinness of female bodies that men prefer, and men tend to overestimate the muscularity of male bodies that women prefer. Moreover, these misperceptions are more exaggerated for short‐term relationships. Women mistakenly believe that men would like thinner women for short‐term than for long‐term relationships, while men misperceive that women would like more muscular men for short‐term than for long‐term relationships. Future research on body image should evaluate the influence that misperceptions of opposite‐sex preference have on body dissatisfaction and other body image related psychological problems.

Logically incompatible conspiracy beliefs may show a high correlation in subjects

Imhoff, Roland, and Pia Lamberty. 2020. “A Bioweapon or a Hoax? The Link Between Distinct Conspiracy Beliefs About the Coronavirus Disease (COVID-19) Outbreak and Pandemic Behavior.” PsyArXiv. April 14. doi:10.31234/

Abstract: Exploring the association of distinct conspiracy beliefs (COVID-19 is a hoax; SARS-Cov-2 was human-made) with pandemic-related behavior.

General Discussion

We observed similarly problematic correlates of two distinct conspiracy beliefs concerning the coronavirus pandemic. Depending on whether COVID-19 was believed to be a hoax or the SARS-Cov-2 human-made participants indicated less compliance with selfreported infection-reducing containment-related behavior and more engagement in selfreported self-centered prepping behavior targeting not a reduction of the infection rate but personal benefits in the crisis. Although these associations seem relatively straight-forward, it is important to note that previous research has pointed to the danger of conspiracy beliefs but has dedicated less attention to potentially distinct relations of different kinds of conspiracy beliefs. These distinct associations notwithstanding, our results also provide strong support for the general notion that even logically incompatible conspiracy beliefs show a high correlation and are both positively associated with a general mindset of conspiracy mentality.

Adding to the robustness of the findings, another study conducted within the German context (reported in the Supplement) closely replicated this general pattern. This seems noteworthy as another dataset from the German context failed to find strong relations between conspiracy endorsement and hygiene measures (Pummerer & Sassenberg, 2020).

Limitations and further research

As arguably the most important limitation of our research, all findings are crosssectional correlations and thus mute with regard to causality. Although it seems plausible that people adapt their behavior according to how they see and perceive the world, it is also conceivable that people behave in a certain way (for no or other reasons) and adapt their worldview as a justification after the fact. Another clear limitation is that these studies were conducted in a time of rapidly changing world events and thus might not have undergone the amount of planning and detailed pre-registration as generally desirable for any kind of research question (Scheel, 2020). Applying a stricter alpha level (e.g., p = .005; for a discussion see Benjamin et al., 2018) would yield the negative impact of hoax belief on selfreported containment-related behavior in the UK non-significant after including all control variables. Trusting in the readers’ intuition to interpret the results we refrain here from forcing a binary significant- non-significant decision on these data but merely point to the substantially weaker data pattern in the UK compared to the US.

Conditionability of 'voluntary' and 'reflexive-like' behaviors, with special reference to elimination behavior: It seems promising to promote studies on latrine training in cattle

Conditionability of 'voluntary' and 'reflexive-like' behaviors, with special reference to elimination behavior in cattle. Neele Dirksen. Neuroscience & Biobehavioral Reviews. May 24 2020.

• Indiscriminate excretion by cattle is a source of environmental pollution.
• No successful toilet training in cattle yet.
• Various brain centres, the ANS and the SNS are involved in the control of elimination.
• Toilet training of cattle should be possible using operant conditioning techniques.

Abstract: Typically, cattle urinate and defecate with little or no control over time and place. The resulting excreta contributes to a range of adverse effects on the environment and the animals themselves. These adverse effects could be substantially ameliorated if livestock could be toilet trained. Toilet training requires an animal to suppress impending voiding (a reflexive-like behavior), move to a latrine (voluntary behavior) and reinitiate voiding. Here, we review the neurophysiological processes and learning mechanisms regulating toileting. The suppression and initiation of voiding occur primarily via the coordinated activity of smooth and striated anal and urinary sphincter muscles. The autonomic and somatic nervous systems, along with central processes, regulate these muscles. In several mammalian species, voluntary control of the sphincters has been demonstrated using classical and/or operant conditioning. In this review, we demonstrate that the neurophysiological and behavioral regulation of voiding in cattle is likely to be similarly conditionable. The management of excreta deposition in cattle could have major benefits for reducing livestock greenhouse gas emissions and improving animal health/welfare.

Keywords: Neurophysiological control of eliminationtoilet trainingcattleclassical conditioningoperant conditioning

6. Summarizing conclusion and practical outlook

We have described the neurophysiology of voiding reflexes and have shown that there are both voluntary and involuntary responses involved in initiation and control of voiding. Regarding the conditionability of voiding, we have described how operant methodologies are mainly applicable to modifying voluntary behavior and classical methodologies are mainly applicable to changing involuntary behavior although there is potential for these to crossover. Further, we have argued that because many voluntary behaviors need to be trained in successful toileting and because involuntary behavior is also susceptible to operant conditioning, operant conditioning is likely to be the main tool for training latrine use in cattle.
For successful latrine training, animals must learn to recognize the impending need to void (Element a), withhold urination/defecation until reaching the latrine, void (Element b) and then leave the toilet.
It is well known that cattle must learn many tasks in farming systems, and these are often self-taught (or by social learning) or trained with little input from the animal carers. For example, cattle learn autonomously the location of essential resources in the living environment, and how to operate equipment such as automatic feeders, water dispensers and entry to automatic milking systems (Wechsler and Lea, 2007). Furthermore, various studies have shown that it is possible to use operant conditioning to teach cows to move reliably to a certain place after prompting with visual (Kiley-Worthington and Savage, 1978), acoustic (Kiley-Worthington and Savage, 1978Wredle et al., 20042006) or vibrational signal (Seo et al., 2002) that serve as discriminative stimuli.
Therefore, cattle have a ready propensity to learn to move to specific locations, which is an important element in training latrine use. Cattle appear to be able to learn to associate urination with a specific location (Vaughan et al., 2014a), and another study found that cattle appear to have an awareness of an association between their elimination behavior and rewards (Whistance et al., 2009). These observations mean that the second part of latrine training (voiding in a specific location) should also be achievable. In practical situations, it would be important for an animal to leave the latrine area after voiding. Leaving the toileting area is an operant response, and, therefore, it should be readily trainable, as for movement into the latrine. Although reflexes are often classically conditioned, the results with other species suggest that latrine training with operant conditioning would seem to be more promising. As continence is controlled not only by the detrusor muscle and internal (smooth muscle) urethral sphincter of the urinary bladder (both under involuntary control) but also by the external urogenital sphincter (striated muscles), which is under voluntary control (Arya and Weissbart, 2017), it is likely that operant rather than classical conditioning procedures will be more useful for latrine training. Therefore, it seems promising to promote studies on latrine training in cattle, particularly using operant conditioning methodology.
This review is mainly concerned with the feasibility of latrine training of cattle from a neurophysiological and learning theory point of view. The next obvious steps are to test the ability of cattle to learn the full latrine training process and the potential for practical implementation. To ensure ready applicability of latrine training on farms, ideally, the training should be undertaken with minimal input from the animal caretakers. One possibility would be to link remotely-detected signs of voiding initiation (e.g., changes in tail position), movement toward a latrine and voiding in a latrine with remotely activated reward presentation. A number of technologies could be utilized for such purposes including thermal imaging, machine vision and machine learning which have been used, for example, in the automated detection of parturient and other behaviors in a range of species including cattle (Miller et al. 2020Wurtz et al., 2019) and for the automated training of rodents (e.g., Poddar et al., 2013).
If feces and urine were voided into latrines, the alleys and cubicles in the barn would much less soiled, which means that the hooves and udder would have reduced exposure to high levels of bacteria on the floor surfaces, thereby contributing to improved animal health (Santman-Berends et al., 2016Sarjokari et al., 2013). In addition, alleys would no longer need to be cleaned as much, providing labor and cost savings (Brantas, 1968Chapinal et al., 2013Somers et al., 2005). Furthermore, the environmental aspect of this approach should not be forgotten. The use of latrines would help to mitigate the emission of greenhouse gases such as N2O and NH3 by making it possible to collect and separate feces and urine (van Dixhoorn et al., 2017).
We have provided solid evidence that toilet training is within the learning capacities of cattle. Our motivation for writing the review is to provide a resource to inspire researchers to explore innovative methods to reduce some of the deleterious effects of cattle farming on climate change and animal health and welfare.