It depends: Partisan evaluation of conditional probability importance. Leaf Van Boven et al. Cognition, Mar 2 2019, https://doi.org/10.1016/j.cognition.2019.01.020
Highlights
• Political partisans disagreed about the importance of conditional probabilities.
• Supporters of restricting immigration and banning assault weapons favored uninformative “hit rates”.
• Policy opponents favored normatively informative base rates and inverse conditionals.
• Highly numerate partisans were more polarized than less numerate partisans.
• Adopting an expert’s perspective reduced partisan differences.
Abstract: Policies to suppress rare events such as terrorism often restrict co-occurring categories such as Muslim immigration. Evaluating restrictive policies requires clear thinking about conditional probabilities. For example, terrorism is extremely rare. So even if most terrorist immigrants are Muslim—a high “hit rate”—the inverse conditional probability of Muslim immigrants being terrorists is extremely low. Yet the inverse conditional probability is more relevant to evaluating restrictive policies such as the threat of terrorism if Muslim immigration were restricted. We suggest that people engage in partisan evaluation of conditional probabilities, judging hit rates as more important when they support politically prescribed restrictive policies. In two studies, supporters of expelling asylum seekers from Tel Aviv, Israel, of banning Muslim immigration and travel to the United States, and of banning assault weapons judged “hit rate” probabilities (e.g., that terrorists are Muslims) as more important than did policy opponents, who judged the inverse conditional probabilities (e.g., that Muslims are terrorists) as more important. These partisan differences spanned restrictive policies favored by Rightists and Republicans (expelling asylum seekers and banning Muslim travel) and by Democrats (banning assault weapons). Inviting partisans to adopt an unbiased expert’s perspective partially reduced these partisan differences. In Study 2 (but not Study 1), partisan differences were larger among more numerate partisans, suggesting that numeracy supported motivated reasoning. These findings have implications for polarization, political judgment, and policy evaluation. Even when partisans agree about what the statistical facts are, they markedly disagree about the relevance of those statistical facts.
Check also: Biased Policy Professionals. Sheheryar Banuri, Stefan Dercon, and Varun Gauri. World Bank Policy Research Working Paper 8113. https://www.bipartisanalliance.com/2017/08/biased-policy-professionals-world-bank.html
And: Dispelling the Myth: Training in Education or Neuroscience Decreases but Does Not Eliminate Beliefs in Neuromyths. Kelly Macdonald et al. Frontiers in Psychology, Aug 10 2017. https://www.bipartisanalliance.com/2017/08/training-in-education-or-neuroscience.html
And: Wisdom and how to cultivate it: Review of emerging evidence for a constructivist model of wise thinking. Igor Grossmann. European Psychologist, in press. Pre-print: https://www.bipartisanalliance.com/2017/08/wisdom-and-how-to-cultivate-it-review.html
And: Individuals with greater science literacy and education have more polarized beliefs on controversial science topics. Caitlin Drummond and Baruch Fischhoff. Proceedings of the National Academy of Sciences, vol. 114 no. 36, pp 9587–9592, https://www.bipartisanalliance.com/2017/09/individuals-with-greater-science.html
And: Expert ability can actually impair the accuracy of expert perception when judging others' performance: Adaptation and fallibility in experts' judgments of novice performers. By Larson, J. S., & Billeter, D. M. (2017). Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(2), 271–288. https://www.bipartisanalliance.com/2017/06/expert-ability-can-actually-impair.html
Saturday, March 2, 2019
One conclusion that can be drawn from cognitive psychology is that human beings generally perform poorly when thinking in probabilistic terms; acknowledging these human frailties, how can we compensate?
Collective Intelligence for Clinical Diagnosis—Are 2 (or 3) Heads Better Than 1? Stephan D. Fihn. JAMA Network Open. 2019;2(3):e191071, doi:10.1001/jamanetworkopen.2019.1071
nce upon a time, medical students were taught that the correct approach to diagnosis was to collect a standard, complete set of data and then, based on those data elements, create an exhaustive list of potential diagnoses. The final and most difficult step was then to take this list and engage in a systemic process of deductive reasoning to rule out possibilities until the 1 final diagnosis was established. Master clinicians modeled this process of differential diagnosis in the classic clinicopathologic conferences (CPCs) that were regularly held in most teaching hospitals and published regularly in medical journals. During the past several decades, the popularity of the CPC has faded under criticism that cases discussed were often atypical and the setting was artificial because bits of data were doled out to discussants in a sequential fashion that did not mirror actual clinical practice. Moreover, they came to be seen more as theatrical events than meaningful teaching exercises.
The major reason for the demise of the CPC, however, was that it became apparent that master clinicians did not actually think in this manner at all. Medical educators who carefully observed astute clinicians found that the clinicians began generating hypotheses during the first few moments of an encounter and iteratively updated them while limiting the number of possibilities being entertained to no more than 5 to 7.1 They also found that even the notion of a master clinician is often illusory because diagnostic accuracy is largely a function of knowledge and experience within a specific domain (or set of domains) as opposed to general brilliance as a diagnostician.
This shift in understanding how physicians think developed in parallel with the growth of cognitive psychology, which focuses on how we process and respond to information. As we confront similar situations over time, the brain develops shortcuts known as heuristics that simplify problems and facilitate prompt and efficient responses. Without these heuristics, we would be forced to adopt a CPC approach to the myriad decisions we all face in everyday life, which would be exhausting and paralyzing. Because they are simplifications, these heuristics are subject to error. Research during the past several decades has revealed that although we maintain a Cartesian vision of ourselves as logical creatures, we are all, in fact, subject to a host of biases that distort our perceptions and lead us to make irrational decisions. Many of these have been cataloged by Amos Tversky, PhD, and Daniel Kahneman, PhD, such as recency bias (overweighting recent events compared with distant ones), framing effects (drawing different conclusions from the same information, depending on how it is presented), primacy bias (being influenced more by information presented earlier than later), anchoring (focusing on a piece of information and discounting the rest), and confirmation bias (placing undue emphasis on information consistent with a preconception).2 These perceptual misrepresentations lead to predictable mistakes such as overestimating the frequency of rare events when they are highly visible; underestimating the frequency of common, mundane events; and seeing patterns where none exist. Understanding these quirks underpins the emerging field of behavioral economics, which helps to explain how markets behave but also enables commercial and political entities to manipulate our opinions, sometimes in perverse ways.
One conclusion that can be drawn from cognitive psychology is that human beings generally perform poorly when thinking in probabilistic terms. Naturally, this has grave implications for our ability to function as good diagnosticians. A growing literature suggests that diagnostic error is common and can lead, not unexpectedly, to harm.3
Acknowledging these human frailties, how can we compensate? One potential solution is to harness the power of computers. [...]
Check also: Biased Policy Professionals. Sheheryar Banuri, Stefan Dercon, and Varun Gauri. World Bank Policy Research Working Paper 8113. https://www.bipartisanalliance.com/2017/08/biased-policy-professionals-world-bank.html
And: Dispelling the Myth: Training in Education or Neuroscience Decreases but Does Not Eliminate Beliefs in Neuromyths. Kelly Macdonald et al. Frontiers in Psychology, Aug 10 2017. https://www.bipartisanalliance.com/2017/08/training-in-education-or-neuroscience.html
And: Wisdom and how to cultivate it: Review of emerging evidence for a constructivist model of wise thinking. Igor Grossmann. European Psychologist, in press. Pre-print: https://www.bipartisanalliance.com/2017/08/wisdom-and-how-to-cultivate-it-review.html
And: Individuals with greater science literacy and education have more polarized beliefs on controversial science topics. Caitlin Drummond and Baruch Fischhoff. Proceedings of the National Academy of Sciences, vol. 114 no. 36, pp 9587–9592, https://www.bipartisanalliance.com/2017/09/individuals-with-greater-science.html
And: Expert ability can actually impair the accuracy of expert perception when judging others' performance: Adaptation and fallibility in experts' judgments of novice performers. By Larson, J. S., & Billeter, D. M. (2017). Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(2), 271–288. https://www.bipartisanalliance.com/2017/06/expert-ability-can-actually-impair.html
nce upon a time, medical students were taught that the correct approach to diagnosis was to collect a standard, complete set of data and then, based on those data elements, create an exhaustive list of potential diagnoses. The final and most difficult step was then to take this list and engage in a systemic process of deductive reasoning to rule out possibilities until the 1 final diagnosis was established. Master clinicians modeled this process of differential diagnosis in the classic clinicopathologic conferences (CPCs) that were regularly held in most teaching hospitals and published regularly in medical journals. During the past several decades, the popularity of the CPC has faded under criticism that cases discussed were often atypical and the setting was artificial because bits of data were doled out to discussants in a sequential fashion that did not mirror actual clinical practice. Moreover, they came to be seen more as theatrical events than meaningful teaching exercises.
The major reason for the demise of the CPC, however, was that it became apparent that master clinicians did not actually think in this manner at all. Medical educators who carefully observed astute clinicians found that the clinicians began generating hypotheses during the first few moments of an encounter and iteratively updated them while limiting the number of possibilities being entertained to no more than 5 to 7.1 They also found that even the notion of a master clinician is often illusory because diagnostic accuracy is largely a function of knowledge and experience within a specific domain (or set of domains) as opposed to general brilliance as a diagnostician.
This shift in understanding how physicians think developed in parallel with the growth of cognitive psychology, which focuses on how we process and respond to information. As we confront similar situations over time, the brain develops shortcuts known as heuristics that simplify problems and facilitate prompt and efficient responses. Without these heuristics, we would be forced to adopt a CPC approach to the myriad decisions we all face in everyday life, which would be exhausting and paralyzing. Because they are simplifications, these heuristics are subject to error. Research during the past several decades has revealed that although we maintain a Cartesian vision of ourselves as logical creatures, we are all, in fact, subject to a host of biases that distort our perceptions and lead us to make irrational decisions. Many of these have been cataloged by Amos Tversky, PhD, and Daniel Kahneman, PhD, such as recency bias (overweighting recent events compared with distant ones), framing effects (drawing different conclusions from the same information, depending on how it is presented), primacy bias (being influenced more by information presented earlier than later), anchoring (focusing on a piece of information and discounting the rest), and confirmation bias (placing undue emphasis on information consistent with a preconception).2 These perceptual misrepresentations lead to predictable mistakes such as overestimating the frequency of rare events when they are highly visible; underestimating the frequency of common, mundane events; and seeing patterns where none exist. Understanding these quirks underpins the emerging field of behavioral economics, which helps to explain how markets behave but also enables commercial and political entities to manipulate our opinions, sometimes in perverse ways.
One conclusion that can be drawn from cognitive psychology is that human beings generally perform poorly when thinking in probabilistic terms. Naturally, this has grave implications for our ability to function as good diagnosticians. A growing literature suggests that diagnostic error is common and can lead, not unexpectedly, to harm.3
Acknowledging these human frailties, how can we compensate? One potential solution is to harness the power of computers. [...]
Check also: Biased Policy Professionals. Sheheryar Banuri, Stefan Dercon, and Varun Gauri. World Bank Policy Research Working Paper 8113. https://www.bipartisanalliance.com/2017/08/biased-policy-professionals-world-bank.html
And: Dispelling the Myth: Training in Education or Neuroscience Decreases but Does Not Eliminate Beliefs in Neuromyths. Kelly Macdonald et al. Frontiers in Psychology, Aug 10 2017. https://www.bipartisanalliance.com/2017/08/training-in-education-or-neuroscience.html
And: Wisdom and how to cultivate it: Review of emerging evidence for a constructivist model of wise thinking. Igor Grossmann. European Psychologist, in press. Pre-print: https://www.bipartisanalliance.com/2017/08/wisdom-and-how-to-cultivate-it-review.html
And: Individuals with greater science literacy and education have more polarized beliefs on controversial science topics. Caitlin Drummond and Baruch Fischhoff. Proceedings of the National Academy of Sciences, vol. 114 no. 36, pp 9587–9592, https://www.bipartisanalliance.com/2017/09/individuals-with-greater-science.html
And: Expert ability can actually impair the accuracy of expert perception when judging others' performance: Adaptation and fallibility in experts' judgments of novice performers. By Larson, J. S., & Billeter, D. M. (2017). Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(2), 271–288. https://www.bipartisanalliance.com/2017/06/expert-ability-can-actually-impair.html
Gay men seem more satisfied with their job than other men; lesbians appear less satisfied with their job than other women; reason could be discrimination, who may lead gay men to have low expectations
(I can’t get no) jobsatisfaction? Differences by sexual orientation in Sweden. Lina Alden et al. Linnaeus University, 2018. http://www.diva-portal.org/smash/get/diva2:1291798/FULLTEXT01.pdf
Abstract: We present results from aunique nationwide survey conducted in Sweden on sexual orientation and job satisfaction. Our results show that gay men, on average, seem more satisfied with their job than heterosexual men; lesbians appear less satisfied with their job than heterosexual women. However, the issue of sexual orientation and job satisfaction is complex since gay men, despite their high degree of job satisfaction, like lesbians find their job more mentally straining than heterosexuals. We conclude that gay men and lesbians are facing other stressers at work than heterosexuals do.We also conclude that discrimination and prejudice may lead gay men to have low expectations about their job; these low expectations may translate into high job satisfaction. In contrast, prejudice and discrimination may hinder lesbians from realizing their career plans, resulting in low job satisfaction.
Keywords: Job satisfaction,sexual orientation
Abstract: We present results from aunique nationwide survey conducted in Sweden on sexual orientation and job satisfaction. Our results show that gay men, on average, seem more satisfied with their job than heterosexual men; lesbians appear less satisfied with their job than heterosexual women. However, the issue of sexual orientation and job satisfaction is complex since gay men, despite their high degree of job satisfaction, like lesbians find their job more mentally straining than heterosexuals. We conclude that gay men and lesbians are facing other stressers at work than heterosexuals do.We also conclude that discrimination and prejudice may lead gay men to have low expectations about their job; these low expectations may translate into high job satisfaction. In contrast, prejudice and discrimination may hinder lesbians from realizing their career plans, resulting in low job satisfaction.
Keywords: Job satisfaction,sexual orientation
People believe they value their minds more than other people value theirs, and that they value their bodies less
Jordan, M. R., Gebert, T., & Looser, C. E. (2019). Perspective taking failures in the valuation of mind and body. Journal of Experimental Psychology: General, 148(3), 407-420. http://dx.doi.org/10.1037/xge0000571
Abstract: Accurately inferring the values and preferences of others is crucial for successful social interactions. Nevertheless, without direct access to others’ minds, perspective taking errors are common. Across 5 studies, we demonstrate a systematic perspective taking failure: People believe they value their minds more than others do and often believe they value their bodies less than others do. The bias manifests across a variety of domains and measures, from judgments about the severity of injuries to preferences for new abilities to assessments of how much one is defined by their mind and body. This perspective taking failure was diminished—but still present—when participants thought of a close other. Finally, we assess and find evidence for the notion that this perspective taking failure is a function of the fact that others’ minds are less salient than others’ bodies. It appears to be the case that people believe the most salient cue from a target is also the best indicator of their values and preferences. This bias has implications for the ways in which we create social policy, judge others’ actions, make choices on behalf of others, and allocate resources to the physically and mentally ill.
Abstract: Accurately inferring the values and preferences of others is crucial for successful social interactions. Nevertheless, without direct access to others’ minds, perspective taking errors are common. Across 5 studies, we demonstrate a systematic perspective taking failure: People believe they value their minds more than others do and often believe they value their bodies less than others do. The bias manifests across a variety of domains and measures, from judgments about the severity of injuries to preferences for new abilities to assessments of how much one is defined by their mind and body. This perspective taking failure was diminished—but still present—when participants thought of a close other. Finally, we assess and find evidence for the notion that this perspective taking failure is a function of the fact that others’ minds are less salient than others’ bodies. It appears to be the case that people believe the most salient cue from a target is also the best indicator of their values and preferences. This bias has implications for the ways in which we create social policy, judge others’ actions, make choices on behalf of others, and allocate resources to the physically and mentally ill.
Asymmetry in individuals’ willingness to venture into cross-cutting spaces, with conservatives more likely to follow media and political accounts classified as left-leaning than the reverse
How Many People Live in Political Bubbles on Social Media? Evidence From Linked Survey and Twitter Data. Gregory Eady et al. SAGE Open, February 28, 2019. https://doi.org/10.1177/2158244019832705
Abstract: A major point of debate in the study of the Internet and politics is the extent to which social media platforms encourage citizens to inhabit online “bubbles” or “echo chambers,” exposed primarily to ideologically congenial political information. To investigate this question, we link a representative survey of Americans with data from respondents’ public Twitter accounts (N = 1,496). We then quantify the ideological distributions of users’ online political and media environments by merging validated estimates of user ideology with the full set of accounts followed by our survey respondents (N = 642,345) and the available tweets posted by those accounts (N ~ 1.2 billion). We study the extent to which liberals and conservatives encounter counter-attitudinal messages in two distinct ways: (a) by the accounts they follow and (b) by the tweets they receive from those accounts, either directly or indirectly (via retweets). More than a third of respondents do not follow any media sources, but among those who do, we find a substantial amount of overlap (51%) in the ideological distributions of accounts followed by users on opposite ends of the political spectrum. At the same time, however, we find asymmetries in individuals’ willingness to venture into cross-cutting spaces, with conservatives more likely to follow media and political accounts classified as left-leaning than the reverse. Finally, we argue that such choices are likely tempered by online news watching behavior.
Keywords: media consumption, media & society, mass communication, communication, social sciences, political communication, new media, communication technologies, political behavior, political science
Abstract: A major point of debate in the study of the Internet and politics is the extent to which social media platforms encourage citizens to inhabit online “bubbles” or “echo chambers,” exposed primarily to ideologically congenial political information. To investigate this question, we link a representative survey of Americans with data from respondents’ public Twitter accounts (N = 1,496). We then quantify the ideological distributions of users’ online political and media environments by merging validated estimates of user ideology with the full set of accounts followed by our survey respondents (N = 642,345) and the available tweets posted by those accounts (N ~ 1.2 billion). We study the extent to which liberals and conservatives encounter counter-attitudinal messages in two distinct ways: (a) by the accounts they follow and (b) by the tweets they receive from those accounts, either directly or indirectly (via retweets). More than a third of respondents do not follow any media sources, but among those who do, we find a substantial amount of overlap (51%) in the ideological distributions of accounts followed by users on opposite ends of the political spectrum. At the same time, however, we find asymmetries in individuals’ willingness to venture into cross-cutting spaces, with conservatives more likely to follow media and political accounts classified as left-leaning than the reverse. Finally, we argue that such choices are likely tempered by online news watching behavior.
Keywords: media consumption, media & society, mass communication, communication, social sciences, political communication, new media, communication technologies, political behavior, political science
Friday, March 1, 2019
Partisans overestimate the negative affect that results from exposure to opposing views; the affective forecasting error derives from underestimation of agreement
Selective exposure partly relies on faulty affective forecasts. Charles A. Dorison, Julia A. Minson, Todd Rogers. Cognition, https://doi.org/10.1016/j.cognition.2019.02.010
Highlights
• Partisans overestimate the negative affect that results from exposure to opposing views.
• The affective forecasting error derives from underestimation of agreement.
• Faulty affective forecasts partially underpin selective exposure.
Abstract: People preferentially consume information that aligns with their prior beliefs, contributing to polarization and undermining democracy. Five studies (collective N = 2455) demonstrate that such “selective exposure” partly stems from faulty affective forecasts. Specifically, political partisans systematically overestimate the strength of negative affect that results from exposure to opposing views. In turn, these incorrect forecasts drive information consumption choices. Clinton voters overestimated the negative affect they would experience from watching President Trump’s Inaugural Address (Study 1) and from reading statements written by Trump voters (Study 2). Democrats and Republicans overestimated the negative affect they would experience from listening to speeches by opposing-party senators (Study 3). People’s tendency to underestimate the extent to which they agree with opponents’ views drove the affective forecasting error. Finally, correcting biased affective forecasts reduced selective exposure by 24–34% (Studies 4 and 5).
Keywords: Selective exposureAffective forecastingFalse polarizationEmotion
---
However, despite the benefits of holding accurate beliefs, the phenomenon of selective exposure to agreeing information has been well documented in social psychology (Frey, 1986), political science (Iyengar & Hahn, 2009; Sears & Freedman, 1967), and communications (Stroud, 2008). For example, one of the earliest studies on selective exposure demonstrated that mothers were more likely to listen to arguments that supported their beliefs regarding hereditary and environmental factors in childrearing than arguments that contradicted their beliefs (Adams, 1961). More recently, in the domain of political communication, conservatives in an experiment preferred to read articles from the conservative site Fox News, whereas liberals preferred to read articles from more liberal sources such as CNN and NPR (Iyengar & Hahn, 2009). These effects persist even with financial incentives on the line (Frimer, Skitka, & Motyl, 2017). Recent research has also examined how presentation order and structure moderate this phenomenon (Fischer et al., 2011; Jonas, Schulz-Hardt, Frey, & Thelen, 2001).
Highlights
• Partisans overestimate the negative affect that results from exposure to opposing views.
• The affective forecasting error derives from underestimation of agreement.
• Faulty affective forecasts partially underpin selective exposure.
Abstract: People preferentially consume information that aligns with their prior beliefs, contributing to polarization and undermining democracy. Five studies (collective N = 2455) demonstrate that such “selective exposure” partly stems from faulty affective forecasts. Specifically, political partisans systematically overestimate the strength of negative affect that results from exposure to opposing views. In turn, these incorrect forecasts drive information consumption choices. Clinton voters overestimated the negative affect they would experience from watching President Trump’s Inaugural Address (Study 1) and from reading statements written by Trump voters (Study 2). Democrats and Republicans overestimated the negative affect they would experience from listening to speeches by opposing-party senators (Study 3). People’s tendency to underestimate the extent to which they agree with opponents’ views drove the affective forecasting error. Finally, correcting biased affective forecasts reduced selective exposure by 24–34% (Studies 4 and 5).
Keywords: Selective exposureAffective forecastingFalse polarizationEmotion
---
However, despite the benefits of holding accurate beliefs, the phenomenon of selective exposure to agreeing information has been well documented in social psychology (Frey, 1986), political science (Iyengar & Hahn, 2009; Sears & Freedman, 1967), and communications (Stroud, 2008). For example, one of the earliest studies on selective exposure demonstrated that mothers were more likely to listen to arguments that supported their beliefs regarding hereditary and environmental factors in childrearing than arguments that contradicted their beliefs (Adams, 1961). More recently, in the domain of political communication, conservatives in an experiment preferred to read articles from the conservative site Fox News, whereas liberals preferred to read articles from more liberal sources such as CNN and NPR (Iyengar & Hahn, 2009). These effects persist even with financial incentives on the line (Frimer, Skitka, & Motyl, 2017). Recent research has also examined how presentation order and structure moderate this phenomenon (Fischer et al., 2011; Jonas, Schulz-Hardt, Frey, & Thelen, 2001).
Implicit Association Test showed a self-other asymmetry, that people perceived a desirable IAT result to be more valid when it applied to themselves than to others, & the opposite held for undesirable IAT results
Mendonça, C., Mata, A., & Vohs, K. D. (2019). Self-other asymmetries in the perceived validity of the implicit association test. Journal of Experimental Psychology: Applied, http://dx.doi.org/10.1037/xap0000214
Abstract: The Implicit Association Test (IAT) is the most popular instrument in implicit social cognition, with some scholars and practitioners calling for its use in applied settings. Yet, little is known about how people perceive the test’s validity as a measure of their true attitudes toward members of other groups. Four experiments manipulated the desirability of the IAT’s result and whether that result referred to one’s own attitudes or other people’s. Results showed a self-other asymmetry, such that people perceived a desirable IAT result to be more valid when it applied to themselves than to others, whereas the opposite held for undesirable IAT results. A fifth experiment demonstrated that these self-other differences influence how people react to the idea of using the IAT as a personnel selection tool. Experiment 6 tested whether the self-other effect was driven by motivation or expectations, finding evidence for motivated reasoning. All told, the current findings suggest potential barriers to implementing the IAT in applied settings.
Abstract: The Implicit Association Test (IAT) is the most popular instrument in implicit social cognition, with some scholars and practitioners calling for its use in applied settings. Yet, little is known about how people perceive the test’s validity as a measure of their true attitudes toward members of other groups. Four experiments manipulated the desirability of the IAT’s result and whether that result referred to one’s own attitudes or other people’s. Results showed a self-other asymmetry, such that people perceived a desirable IAT result to be more valid when it applied to themselves than to others, whereas the opposite held for undesirable IAT results. A fifth experiment demonstrated that these self-other differences influence how people react to the idea of using the IAT as a personnel selection tool. Experiment 6 tested whether the self-other effect was driven by motivation or expectations, finding evidence for motivated reasoning. All told, the current findings suggest potential barriers to implementing the IAT in applied settings.
Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than an average human driver is not acceptably safe performance for most
Safer than the average human driver (who is less safe than me)? Examining a popular safety benchmark for self-driving cars. Michael A. Nees. Journal of Safety Research, https://doi.org/10.1016/j.jsr.2019.02.002
Highlights
• The criterion of being safer than a human driver has become pervasive in the discourse on vehicle automation.
• Most drivers perceive themselves to be safer than the average driver (the better-than-average effect).
• This study replicated the better than average effect and showed that most drivers stated a desire for self-driving cars that are safer than their own perceived ability to drive safely.
• Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than a human driver (on average) may not represent acceptably safe performance of self-driving cars for most drivers.
Abstract: Although the level of safety required before drivers will accept self-driving cars is not clear, the criterion of being safer than a human driver has become pervasive in the discourse on vehicle automation. This criterion actually means “safer than the average human driver,” because it is necessarily defined with respect to population-level data. At the level of individual risk assessment, a body of research has shown that most drivers perceive themselves to be safer than the average driver (the better-than-average effect). Using an online sample of U.S. drivers, this study replicated the better than average effect and showed that most drivers stated a desire for self-driving cars that are safer than their own perceived ability to drive safely before they would: (1) feel reasonably safe riding in a self-driving vehicle; (2) buy a self-driving vehicle, all other things (cost, etc.) being equal; and (3) allow self-driving vehicles on public roads. Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than a human driver (on average) may not represent acceptably safe performance of selfdriving cars for most drivers.
Highlights
• The criterion of being safer than a human driver has become pervasive in the discourse on vehicle automation.
• Most drivers perceive themselves to be safer than the average driver (the better-than-average effect).
• This study replicated the better than average effect and showed that most drivers stated a desire for self-driving cars that are safer than their own perceived ability to drive safely.
• Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than a human driver (on average) may not represent acceptably safe performance of self-driving cars for most drivers.
Abstract: Although the level of safety required before drivers will accept self-driving cars is not clear, the criterion of being safer than a human driver has become pervasive in the discourse on vehicle automation. This criterion actually means “safer than the average human driver,” because it is necessarily defined with respect to population-level data. At the level of individual risk assessment, a body of research has shown that most drivers perceive themselves to be safer than the average driver (the better-than-average effect). Using an online sample of U.S. drivers, this study replicated the better than average effect and showed that most drivers stated a desire for self-driving cars that are safer than their own perceived ability to drive safely before they would: (1) feel reasonably safe riding in a self-driving vehicle; (2) buy a self-driving vehicle, all other things (cost, etc.) being equal; and (3) allow self-driving vehicles on public roads. Since most drivers believe they are better than average drivers, the benchmark of achieving automation that is safer than a human driver (on average) may not represent acceptably safe performance of selfdriving cars for most drivers.
Thursday, February 28, 2019
Effect of olfactory disgust: Disgust might hamper behavioral actions motivated by sexual arousal (e.g., poor judgment, coercive sexual behavior)
The influence of olfactory disgust on (Genital) sexual arousal in men. Charmaine Borg, Tamara A. Oosterwijk, Dominika Lisy, Sanne Boesveldt, Peter J. de Jong. PLOS, February 28, 2019. https://doi.org/10.1371/journal.pone.0213059
Abstract
Background: The generation or persistence of sexual arousal may be compromised when inhibitory processes such as negative emotions, outweigh sexual excitation. Disgust particularly, has been proposed as one of the emotions that may counteract sexual arousal. In support of this view, previous research has shown that disgust priming can reduce subsequent sexual arousal. As a crucial next step, this experimental study tested whether disgust (by means of odor) can also diminish sexual arousal in individuals who are already in a state of heightened sexual excitation.
Methodology: In this study, participants were all men (N = 78). To elicit sexual arousal, participants watched a pornographic video. Following 4.30 minutes from the start of the video clip, they were exposed to either a highly aversive/disgusting odor (n = 42), or an odorless diluent/solvent (n = 36), that was delivered via an olfactometer, while the pornographic video continued. In both conditions the presentation of the odor lasted 1 second and was repeated 11 times with intervals of 26 seconds. Sexual arousal was indexed by both self-reports and penile circumference.
Principal findings: The disgusting odor (released when the participants were already sexually aroused) resulted in a significant decrease of both subjective and genital sexual arousal compared to the control (odorless) condition.
Significance: The finding that the inhibitory effect of disgust was not only expressed in self-report but also expressed on the penile response further strengthens the idea that disgust might hamper behavioral actions motivated by sexual arousal (e.g., poor judgment, coercive sexual behavior). Thus, the current findings indicate that exposure to an aversive odor is sufficiently potent to reduce already present (subjective and) genital sexual arousal. This finding may also have practical relevance for disgust to be used as a tool for self-defence (e.g., Invi Bracelet).
Abstract
Background: The generation or persistence of sexual arousal may be compromised when inhibitory processes such as negative emotions, outweigh sexual excitation. Disgust particularly, has been proposed as one of the emotions that may counteract sexual arousal. In support of this view, previous research has shown that disgust priming can reduce subsequent sexual arousal. As a crucial next step, this experimental study tested whether disgust (by means of odor) can also diminish sexual arousal in individuals who are already in a state of heightened sexual excitation.
Methodology: In this study, participants were all men (N = 78). To elicit sexual arousal, participants watched a pornographic video. Following 4.30 minutes from the start of the video clip, they were exposed to either a highly aversive/disgusting odor (n = 42), or an odorless diluent/solvent (n = 36), that was delivered via an olfactometer, while the pornographic video continued. In both conditions the presentation of the odor lasted 1 second and was repeated 11 times with intervals of 26 seconds. Sexual arousal was indexed by both self-reports and penile circumference.
Principal findings: The disgusting odor (released when the participants were already sexually aroused) resulted in a significant decrease of both subjective and genital sexual arousal compared to the control (odorless) condition.
Significance: The finding that the inhibitory effect of disgust was not only expressed in self-report but also expressed on the penile response further strengthens the idea that disgust might hamper behavioral actions motivated by sexual arousal (e.g., poor judgment, coercive sexual behavior). Thus, the current findings indicate that exposure to an aversive odor is sufficiently potent to reduce already present (subjective and) genital sexual arousal. This finding may also have practical relevance for disgust to be used as a tool for self-defence (e.g., Invi Bracelet).
Nations that scored higher on democracy indices, especially emerging ones, experienced increased mortality due to violence; women possessed higher rates of homicide & suicide in democracies
Government political structure and gender differences in violent death: A longitudinal analysis of forty-three countries, 1960–2008. Morkeh Blay-Tofey et al. Aggression and Violent Behavior, Feb 28 2019. https://doi.org/10.1016/j.avb.2019.02.011
Highlights
• The purpose of this study is to examine the effect of democracy on violent death rates (homicide, suicide, and combined) by gender (men and women).
• Multi-level regression analyses examined associations between regime-type characteristics and logged rates of violent deaths using homicide and suicide. Models were adjusted for unemployment and economic inequality
• Violent deaths appear to be more prevalent even in stable democracies, and women are more affected than men.
• Although the analysis provided depicts a strong picture anchored in regime type changes and violent death rates, violence is inherently complex and more research is needed to determine what aspects within democracies may lead to increased violent death rates.
Abstract
Objectives: Little global and longitudinal scholarship exists on the relationship between regime type and mortality on a global level. The purpose of this study is to examine the effect of democracy on violent death rates (homicide, suicide, and combined) by gender (men and women).
Methods: Three measures of democracy were used to quantify regime type. Homicide and suicide rates were obtained from the World Health Organization. Multi-level regression analyses examined associations between regime characteristics and logged rates of homicide, suicide, and violent deaths. Models were adjusted for unemployment and economic inequality.
Results: Nations that scored higher on democracy indices, especially emerging democracies, experienced increased mortality due to violence. Women possessed higher rates of homicide and suicide in democracies compared to men.
Conclusions: Violent deaths appear to be more prevalent even in stable democracies, and women are more affected than men. This overturns the common assumption that democracies bring greater equality, and therefore lower death rates over long-term. Future analyses might examine the aspects of democracies that lead to higher rates of violent death so as to help mitigate them.
Keywords: Homicide suicide violence democracy autocracy regime gender
Highlights
• The purpose of this study is to examine the effect of democracy on violent death rates (homicide, suicide, and combined) by gender (men and women).
• Multi-level regression analyses examined associations between regime-type characteristics and logged rates of violent deaths using homicide and suicide. Models were adjusted for unemployment and economic inequality
• Violent deaths appear to be more prevalent even in stable democracies, and women are more affected than men.
• Although the analysis provided depicts a strong picture anchored in regime type changes and violent death rates, violence is inherently complex and more research is needed to determine what aspects within democracies may lead to increased violent death rates.
Abstract
Objectives: Little global and longitudinal scholarship exists on the relationship between regime type and mortality on a global level. The purpose of this study is to examine the effect of democracy on violent death rates (homicide, suicide, and combined) by gender (men and women).
Methods: Three measures of democracy were used to quantify regime type. Homicide and suicide rates were obtained from the World Health Organization. Multi-level regression analyses examined associations between regime characteristics and logged rates of homicide, suicide, and violent deaths. Models were adjusted for unemployment and economic inequality.
Results: Nations that scored higher on democracy indices, especially emerging democracies, experienced increased mortality due to violence. Women possessed higher rates of homicide and suicide in democracies compared to men.
Conclusions: Violent deaths appear to be more prevalent even in stable democracies, and women are more affected than men. This overturns the common assumption that democracies bring greater equality, and therefore lower death rates over long-term. Future analyses might examine the aspects of democracies that lead to higher rates of violent death so as to help mitigate them.
Keywords: Homicide suicide violence democracy autocracy regime gender
Males from Drosophila m. populations with higher competitive mating success produce sons with lower fitness; male investment in enhanced mating success comes at the cost of reduced offspring quality
Males from populations with higher competitive mating success produce sons with lower fitness. Trinh T. X. Nguyen Amanda J. Moehring. Journal of Evolutionary Biology, Feb 27 2019, https://doi.org/10.1111/jeb.13433
Abstract: Female mate choice can result in direct benefits to the female or indirect benefits through her offspring. Females can increase their fitness by mating with males whose genes encode increased survivorship and reproductive output. Alternatively, male investment in enhanced mating success may come at the cost of reduced investment in offspring fitness. Here, we measure male mating success in a mating arena that allows for male‐male, male‐female, and female‐female interactions in Drosophila melanogaster. We then use isofemale line population measurements to correlate male mating success with sperm competitive ability, the number of offspring produced, and the indirect benefits of the number of offspring produced by daughters and sons. We find that males from populations that gain more copulations do not increase female fitness through increased offspring production, nor do these males fare better in sperm competition. Instead, we find that these populations have a reduced reproductive output of sons, indicating a potential reproductive trade‐off between male mating success and offspring quality.
Abstract: Female mate choice can result in direct benefits to the female or indirect benefits through her offspring. Females can increase their fitness by mating with males whose genes encode increased survivorship and reproductive output. Alternatively, male investment in enhanced mating success may come at the cost of reduced investment in offspring fitness. Here, we measure male mating success in a mating arena that allows for male‐male, male‐female, and female‐female interactions in Drosophila melanogaster. We then use isofemale line population measurements to correlate male mating success with sperm competitive ability, the number of offspring produced, and the indirect benefits of the number of offspring produced by daughters and sons. We find that males from populations that gain more copulations do not increase female fitness through increased offspring production, nor do these males fare better in sperm competition. Instead, we find that these populations have a reduced reproductive output of sons, indicating a potential reproductive trade‐off between male mating success and offspring quality.
The wrong belief in the exceptionalism of human cortex has caused to prematurely assign functions distributed widely in the brain to the cortex, & to fail to explore subcortical sources of brain evolution, inter alia
Human exceptionalism, our ordinary cortex and our research futures. Barbara L. Finlay. Developmental Psychobiology, February 27 2019, https://doi.org/10.1002/dev.21838
Abstract: The widely held belief that the human cortex is exceptionally large for our brain size is wrong, resulting from basic errors in how best to compare evolving brains. This misapprehension arises from the comparison of only a few laboratory species, failure to appreciate differences in brain scaling in rodents versus primates, but most important, the false assumption that linear extrapolation can be used to predict changes from small to large brains. Belief in the exceptionalism of human cortex has propagated itself into genomic analysis of the cortex, where cortex has been studied as if it were an example of innovation rather than predictable scaling. Further, this belief has caused both neuroscientists and psychologists to prematurely assign functions distributed widely in the brain to the cortex, to fail to explore subcortical sources of brain evolution, and to neglect genuinely novel features of human infancy and childhood.
Abstract: The widely held belief that the human cortex is exceptionally large for our brain size is wrong, resulting from basic errors in how best to compare evolving brains. This misapprehension arises from the comparison of only a few laboratory species, failure to appreciate differences in brain scaling in rodents versus primates, but most important, the false assumption that linear extrapolation can be used to predict changes from small to large brains. Belief in the exceptionalism of human cortex has propagated itself into genomic analysis of the cortex, where cortex has been studied as if it were an example of innovation rather than predictable scaling. Further, this belief has caused both neuroscientists and psychologists to prematurely assign functions distributed widely in the brain to the cortex, to fail to explore subcortical sources of brain evolution, and to neglect genuinely novel features of human infancy and childhood.
“Dysrationalia” Among University Students: Intelligence & rational thinking, although related, represent two fundamentally different constructs; the intelligent have the same inability to think rationally
“Dysrationalia” Among University Students: The Role of Cognitive Abilities, Different Aspects of Rational Thought and Self-Control in Explaining Epistemically Suspect Beliefs
Nikola Erceg, Zvonimir Galić, Andreja Bubić. Europe's Journal of Psychology, Vol 15, No 1 (2019), https://ejop.psychopen.eu/article/view/1696
Abstract: The aim of the study was to investigate the role that cognitive abilities, rational thinking abilities, cognitive styles and self-control play in explaining the endorsement of epistemically suspect beliefs among university students. A total of 159 students participated in the study. We found that different aspects of rational thought (i.e. rational thinking abilities and cognitive styles) and self-control, but not intelligence, significantly predicted the endorsement of epistemically suspect beliefs. Based on these findings, it may be suggested that intelligence and rational thinking, although related, represent two fundamentally different constructs. Thus, deviations from rational thinking could be well described by the term “dysrationalia”, meaning the inability to think rationally despite having adequate intelligence. We discuss the implications of the results, as well as some drawbacks of the study.
Keywords: dysrationalia; epistemically suspect beliefs; cognitive abilities; rational thinking; self-control
Nikola Erceg, Zvonimir Galić, Andreja Bubić. Europe's Journal of Psychology, Vol 15, No 1 (2019), https://ejop.psychopen.eu/article/view/1696
Abstract: The aim of the study was to investigate the role that cognitive abilities, rational thinking abilities, cognitive styles and self-control play in explaining the endorsement of epistemically suspect beliefs among university students. A total of 159 students participated in the study. We found that different aspects of rational thought (i.e. rational thinking abilities and cognitive styles) and self-control, but not intelligence, significantly predicted the endorsement of epistemically suspect beliefs. Based on these findings, it may be suggested that intelligence and rational thinking, although related, represent two fundamentally different constructs. Thus, deviations from rational thinking could be well described by the term “dysrationalia”, meaning the inability to think rationally despite having adequate intelligence. We discuss the implications of the results, as well as some drawbacks of the study.
Keywords: dysrationalia; epistemically suspect beliefs; cognitive abilities; rational thinking; self-control
Low replicability damages public trust in psychology; neither information about increased transparency nor explanations for low replicability, nor recovered replicability repaired public trust
Wingen, Tobias, Jana Berkessel, and Birte Englich. 2019. “No Replication, No Trust? How Low Replicability Influences Trust in Psychology.” OSF Preprints. February 22. doi:10.31219/osf.io/4ukq5
Abstract: In the current psychological debate, low replicability of psychological findings is the central topic. While this discussion about the replication crisis has a huge impact on psychological research, we know less about how it impacts lay people’s trust in psychology. In the current paper, we examine whether low replicability damages public trust in psychology and whether this damaged trust can be repaired. Study 1 and 2 provide correlational and experimental evidence that low replicability reduces public trust in psychological science. Additionally, Studies 3, 4, and 5 evaluate whether and how damaged trust in psychological science could be repaired. Critically, neither information about increased transparency (Study 3), nor explanations for low replicability (either QRPs or hidden moderators; Study 4), nor recovered replicability (Study 5) repaired public trust. Overall, our studies highlight the crucial importance of replicability for public trust, as well as the importance of balanced communication of low replicability.
Abstract: In the current psychological debate, low replicability of psychological findings is the central topic. While this discussion about the replication crisis has a huge impact on psychological research, we know less about how it impacts lay people’s trust in psychology. In the current paper, we examine whether low replicability damages public trust in psychology and whether this damaged trust can be repaired. Study 1 and 2 provide correlational and experimental evidence that low replicability reduces public trust in psychological science. Additionally, Studies 3, 4, and 5 evaluate whether and how damaged trust in psychological science could be repaired. Critically, neither information about increased transparency (Study 3), nor explanations for low replicability (either QRPs or hidden moderators; Study 4), nor recovered replicability (Study 5) repaired public trust. Overall, our studies highlight the crucial importance of replicability for public trust, as well as the importance of balanced communication of low replicability.
It is unlikely that we will find strong relationships between what individuals are reporting about themselves and how they objectively behave
The Challenges and Opportunities of Small EffectsThe New Normal in Academic Psychiatry. Martin P. Paulus, Wesley K. Thompson. JAMA Psychiatry. February 27, 2019. doi:10.1001/jamapsychiatry.2018.4540
Full text in the link above.
Explanations and accurate predictions are the fundamental deliverables for a mechanistic or pragmatic approach that academic psychiatric research can provide to stakeholders. Starting with this issue, we are publishing a series of Viewpoints describing the research boundaries and challenges to progress in our field. In this issue, Simon1 raises the need for better explanatory model using data from electronic health records. This Viewpoint acknowledges an important issue: variables or constructs that are used to help explain the current state of individuals or to generate predictions need to account for a substantial proportion of the variance of the dependent variable or outcome measure to be clinically useful. However, similar to findings from genetics literature, systems neuroscience approaches using brain imaging are beginning to show that variability in structural and functional brain imaging only accounts for a small percentage of the explained variance when considering a variety of clinical phenotypes, especially in large population-representative samples.2 For example, in a 2016 analysis of UK Biobank data,3 the functional activation related to a face processing task, which activated the fusiform gyrus and amygdala, accounted for a maximum of 1.8% of the variance of 1100 nonimaging variables. These findings are in line with emerging results from the Adolescent Brain Cognitive Development study4 focused on the association between screen media behavior and structural MRI characteristics. Importantly, these large-scale studies have used robust and reliable estimators to reduce false-positive discoveries. Thus, similar to genetics literature, it appears that individual processing differences as measured by neuroimaging account for little symptomatic or behavioral variance.
There is evidence that the association between individual variation on self-assessed symptoms and behavioral performance on neurocognitive tasks is weak.5,6 Moreover, many behavioral tasks show limited test-retest reliability and little agreement between task conceptualization and actual agreement with emerging latent variables of these tasks. Therefore, it is unlikely that we will find strong relationships between what individuals are reporting about themselves and how they objectively behave. It seems that the individual experience of a person with a mental health condition, which has been proposed to be an important end point for explanatory approaches,7 is not well approximated by the behavioral probes that are currently available.
These and other findings have profound implications for our theoretical understanding of psychiatric diseases. Specifically, small effect sizes make it unlikely that psychiatric disorders can be explained by unicausal or oligocausal theories. In other words, there is not going to be a unifying glutamatergic or inflammatory disease model of mood disorders. What’s more, even if there is a relationship between markers for these disease processes and the state of a psychiatric disorder, as currently conceived, it may not be sufficiently strong to be used by itself to make useful person-level predictions. This is not to say that these processes are not contributing to the etiology or pathophysiology of the disorder but rather that their impact is likely to be small so as to not be individually useful in helping patients and other stakeholders explain their current disease state. As a consequence, there is a low probability of a generic disease process for a group of psychiatric disorders or a final common pathway for a disease.
One possible reason for the lack of a strong relationship between units of analyses, ie, between brain circuits and behavior or behavior and symptoms, is many-to-one and one-to-many mapping. In other words, the brain has many ways of producing the same symptoms, and very similar brain dysfunctions can produce a number of different clinical symptoms. An example of one-to-many mapping is the phenotypic heterogeneity of Huntington disease, which, as an autosomal dominant disorder, has a simple genetic basis but enormous clinical variability via the modulation of multiple biochemical pathways.8 In comparison, the clinical homogeneity of motor neuron disease is betrayed by a significant genetic variability, leading to similar symptoms.9 Therefore, it is quite possible that phenotypically similar groups result from different processes and phenotypically heterogeneous individuals actually share broadly similar underlying pathophysiology.
These many-to-one and one-to-many mappings put a profound strain on case-control studies, ie, comparing individuals diagnosed with a particular psychiatric disease with controls that are matched on a limited number of variables. Case-control designs have very limited explanatory depth and are fundamentally uninformative of the disease process because they are correlational, provide little specificity and questionable sensitivity, and have questionable generalizability to populations.10 Single-case designs together with hierarchical inferential procedures might provide a reasonable alternative.11 Single-case designs use individuals as their own control, can use controlled interventions to examine causality, and are well suited to uncover individual differences across phenotypically similar participants. However, care must be taken not to subdivide studies so finely that defects of small sample sizes, including elevated rates of type I and II errors, become problematic even for large epidemiologically informed samples.
Latent variable approaches, such as principal components or factor analyses, can be useful unsupervised statistical methods to uncover relationships between variables within and across units of analyses. However, the underlying assumption is that these latent variables reflect common relationships among all individuals. Instead, it is more likely that relationships differ across individuals and may even differ across states within an individual. Recent approaches to this problem use both latent variable and mixture approaches to differentiate different subgroups of individuals with depression.12 Others have used deviation from normative regression models to identify heterogeneity in schizophrenia and bipolar disorder.13 Both sets of approaches support the hypothesis that there are no generic depressive, bipolar, or schizophrenia diseases. At the other extreme, considering that psychiatric diseases emerge from causal factors that vary across units of analyses ranging from molecular to social,7 one might hypothesize that each individual patient with a mental health condition is an exemplar of a rare disease model. In this case, no generalizable model might be possible, and useful individual-level predictions would be elusive.
Thus, we are facing the classical problem of variance-bias trade-off,14 which has been examined in great detail in the statistical literature. Specifically, how do we arbitrate between generating a few generic models with useful explanatory or predictive values vs multiple models that may tend to overexplain and overfit individual patient’s disease etiology, pathophysiology, and clinical course? This decision cannot be arbitrated solely on statistical grounds but will need to judiciously incorporate expert knowledge about the disease and candidate processes on different units of analyses because the permutational complexity of the variables to be considered is so large that even data sets with thousands of individuals may not provide a sufficient sample size to approach this using exploratory techniques resistant to overfitting.
At this time, we are standing at a precipice: our explanatory disease models are woefully insufficient, and our predictive approaches have not yielded robust individual-level predictions that can be used by clinicians. Yet there is room for hope. Larger data sets will be widely available, multilevel data sets that span assessments from genes to social factors are being released, new statistical tools are being developed, within-subject statistical designs are being rediscovered, and attempts to include expert knowledge into latent variable approaches might help arbitrating the variance-bias trade-off. Fundamentally, academic psychiatry cannot continue to move forward with small n case-control studies to provide tangible results to stakeholders.
References
1.
Simon GE. Big data from health records in mental health care: hardly clairvoyant but already useful [published online February 27, 2019]. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2018.4510ArticleGoogle Scholar
2.
Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell. 2017;169(7):1177-1186. doi:10.1016/j.cell.2017.05.038PubMedGoogle ScholarCrossref
3.
Miller KL, Alfaro-Almagro F, Bangerter NK, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19(11):1523-1536. doi:10.1038/nn.4393PubMedGoogle ScholarCrossref
4.
Paulus MP, Squeglia LM, Bagot K, et al. Screen media activity and brain structure in youth: evidence for diverse structural correlation networks from the ABCD study. Neuroimage. 2019;185:140-153. doi:10.1016/j.neuroimage.2018.10.040PubMedGoogle ScholarCrossref
5.
Eisenberg IW, Bissett PG, Enkavi AZ, et al. Uncovering mental structure through data-driven ontology discovery [published online December 12, 2018]. PsyArXiv. doi:10.31234/osf.io/fvqejGoogle Scholar
6.
Thompson WK, Barch DM, Bjork JM, et al. The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: findings from the ABCD study’s baseline neurocognitive battery [published online December 13, 2018]. Dev Cogn Neurosci. doi:10.1016/j.dcn.2018.12.004PubMedGoogle Scholar
7.
Kendler KS. Levels of explanation in psychiatric and substance use disorders: implications for the development of an etiologically based nosology. Mol Psychiatry. 2012;17(1):11-21. doi:10.1038/mp.2011.70PubMedGoogle ScholarCrossref
8.
Ross CA, Aylward EH, Wild EJ, et al. Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat Rev Neurol. 2014;10(4):204-216. doi:10.1038/nrneurol.2014.24PubMedGoogle ScholarCrossref
9.
Dion PA, Daoud H, Rouleau GA. Genetics of motor neuron disorders: new insights into pathogenic mechanisms. Nat Rev Genet. 2009;10(11):769-782. doi:10.1038/nrg2680PubMedGoogle ScholarCrossref
10.
Sedgwick P. Case-control studies: advantages and disadvantages. BMJ. 2014;348:f7707. doi:10.1136/bmj.f7707Google ScholarCrossref
11.
Smith JD. Single-case experimental designs: a systematic review of published research and current standards. Psychol Methods. 2012;17(4):510-550. doi:10.1037/a0029312PubMedGoogle ScholarCrossref
12.
Drysdale AT, Grosenick L, Downar J, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23(1):28-38. doi:10.1038/nm.4246PubMedGoogle ScholarCrossref
13.
Wolfers T, Doan NT, Kaufmann T, et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry. 2018;75(11):1146-1155. doi:10.1001/jamapsychiatry.2018.2467ArticlePubMedGoogle ScholarCrossref
14.
James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning With Applications in R. New York, NY: Springer-Verlag New York; 2013.
Full text in the link above.
Explanations and accurate predictions are the fundamental deliverables for a mechanistic or pragmatic approach that academic psychiatric research can provide to stakeholders. Starting with this issue, we are publishing a series of Viewpoints describing the research boundaries and challenges to progress in our field. In this issue, Simon1 raises the need for better explanatory model using data from electronic health records. This Viewpoint acknowledges an important issue: variables or constructs that are used to help explain the current state of individuals or to generate predictions need to account for a substantial proportion of the variance of the dependent variable or outcome measure to be clinically useful. However, similar to findings from genetics literature, systems neuroscience approaches using brain imaging are beginning to show that variability in structural and functional brain imaging only accounts for a small percentage of the explained variance when considering a variety of clinical phenotypes, especially in large population-representative samples.2 For example, in a 2016 analysis of UK Biobank data,3 the functional activation related to a face processing task, which activated the fusiform gyrus and amygdala, accounted for a maximum of 1.8% of the variance of 1100 nonimaging variables. These findings are in line with emerging results from the Adolescent Brain Cognitive Development study4 focused on the association between screen media behavior and structural MRI characteristics. Importantly, these large-scale studies have used robust and reliable estimators to reduce false-positive discoveries. Thus, similar to genetics literature, it appears that individual processing differences as measured by neuroimaging account for little symptomatic or behavioral variance.
There is evidence that the association between individual variation on self-assessed symptoms and behavioral performance on neurocognitive tasks is weak.5,6 Moreover, many behavioral tasks show limited test-retest reliability and little agreement between task conceptualization and actual agreement with emerging latent variables of these tasks. Therefore, it is unlikely that we will find strong relationships between what individuals are reporting about themselves and how they objectively behave. It seems that the individual experience of a person with a mental health condition, which has been proposed to be an important end point for explanatory approaches,7 is not well approximated by the behavioral probes that are currently available.
These and other findings have profound implications for our theoretical understanding of psychiatric diseases. Specifically, small effect sizes make it unlikely that psychiatric disorders can be explained by unicausal or oligocausal theories. In other words, there is not going to be a unifying glutamatergic or inflammatory disease model of mood disorders. What’s more, even if there is a relationship between markers for these disease processes and the state of a psychiatric disorder, as currently conceived, it may not be sufficiently strong to be used by itself to make useful person-level predictions. This is not to say that these processes are not contributing to the etiology or pathophysiology of the disorder but rather that their impact is likely to be small so as to not be individually useful in helping patients and other stakeholders explain their current disease state. As a consequence, there is a low probability of a generic disease process for a group of psychiatric disorders or a final common pathway for a disease.
One possible reason for the lack of a strong relationship between units of analyses, ie, between brain circuits and behavior or behavior and symptoms, is many-to-one and one-to-many mapping. In other words, the brain has many ways of producing the same symptoms, and very similar brain dysfunctions can produce a number of different clinical symptoms. An example of one-to-many mapping is the phenotypic heterogeneity of Huntington disease, which, as an autosomal dominant disorder, has a simple genetic basis but enormous clinical variability via the modulation of multiple biochemical pathways.8 In comparison, the clinical homogeneity of motor neuron disease is betrayed by a significant genetic variability, leading to similar symptoms.9 Therefore, it is quite possible that phenotypically similar groups result from different processes and phenotypically heterogeneous individuals actually share broadly similar underlying pathophysiology.
These many-to-one and one-to-many mappings put a profound strain on case-control studies, ie, comparing individuals diagnosed with a particular psychiatric disease with controls that are matched on a limited number of variables. Case-control designs have very limited explanatory depth and are fundamentally uninformative of the disease process because they are correlational, provide little specificity and questionable sensitivity, and have questionable generalizability to populations.10 Single-case designs together with hierarchical inferential procedures might provide a reasonable alternative.11 Single-case designs use individuals as their own control, can use controlled interventions to examine causality, and are well suited to uncover individual differences across phenotypically similar participants. However, care must be taken not to subdivide studies so finely that defects of small sample sizes, including elevated rates of type I and II errors, become problematic even for large epidemiologically informed samples.
Latent variable approaches, such as principal components or factor analyses, can be useful unsupervised statistical methods to uncover relationships between variables within and across units of analyses. However, the underlying assumption is that these latent variables reflect common relationships among all individuals. Instead, it is more likely that relationships differ across individuals and may even differ across states within an individual. Recent approaches to this problem use both latent variable and mixture approaches to differentiate different subgroups of individuals with depression.12 Others have used deviation from normative regression models to identify heterogeneity in schizophrenia and bipolar disorder.13 Both sets of approaches support the hypothesis that there are no generic depressive, bipolar, or schizophrenia diseases. At the other extreme, considering that psychiatric diseases emerge from causal factors that vary across units of analyses ranging from molecular to social,7 one might hypothesize that each individual patient with a mental health condition is an exemplar of a rare disease model. In this case, no generalizable model might be possible, and useful individual-level predictions would be elusive.
Thus, we are facing the classical problem of variance-bias trade-off,14 which has been examined in great detail in the statistical literature. Specifically, how do we arbitrate between generating a few generic models with useful explanatory or predictive values vs multiple models that may tend to overexplain and overfit individual patient’s disease etiology, pathophysiology, and clinical course? This decision cannot be arbitrated solely on statistical grounds but will need to judiciously incorporate expert knowledge about the disease and candidate processes on different units of analyses because the permutational complexity of the variables to be considered is so large that even data sets with thousands of individuals may not provide a sufficient sample size to approach this using exploratory techniques resistant to overfitting.
At this time, we are standing at a precipice: our explanatory disease models are woefully insufficient, and our predictive approaches have not yielded robust individual-level predictions that can be used by clinicians. Yet there is room for hope. Larger data sets will be widely available, multilevel data sets that span assessments from genes to social factors are being released, new statistical tools are being developed, within-subject statistical designs are being rediscovered, and attempts to include expert knowledge into latent variable approaches might help arbitrating the variance-bias trade-off. Fundamentally, academic psychiatry cannot continue to move forward with small n case-control studies to provide tangible results to stakeholders.
References
1.
Simon GE. Big data from health records in mental health care: hardly clairvoyant but already useful [published online February 27, 2019]. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2018.4510ArticleGoogle Scholar
2.
Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell. 2017;169(7):1177-1186. doi:10.1016/j.cell.2017.05.038PubMedGoogle ScholarCrossref
3.
Miller KL, Alfaro-Almagro F, Bangerter NK, et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat Neurosci. 2016;19(11):1523-1536. doi:10.1038/nn.4393PubMedGoogle ScholarCrossref
4.
Paulus MP, Squeglia LM, Bagot K, et al. Screen media activity and brain structure in youth: evidence for diverse structural correlation networks from the ABCD study. Neuroimage. 2019;185:140-153. doi:10.1016/j.neuroimage.2018.10.040PubMedGoogle ScholarCrossref
5.
Eisenberg IW, Bissett PG, Enkavi AZ, et al. Uncovering mental structure through data-driven ontology discovery [published online December 12, 2018]. PsyArXiv. doi:10.31234/osf.io/fvqejGoogle Scholar
6.
Thompson WK, Barch DM, Bjork JM, et al. The structure of cognition in 9 and 10 year-old children and associations with problem behaviors: findings from the ABCD study’s baseline neurocognitive battery [published online December 13, 2018]. Dev Cogn Neurosci. doi:10.1016/j.dcn.2018.12.004PubMedGoogle Scholar
7.
Kendler KS. Levels of explanation in psychiatric and substance use disorders: implications for the development of an etiologically based nosology. Mol Psychiatry. 2012;17(1):11-21. doi:10.1038/mp.2011.70PubMedGoogle ScholarCrossref
8.
Ross CA, Aylward EH, Wild EJ, et al. Huntington disease: natural history, biomarkers and prospects for therapeutics. Nat Rev Neurol. 2014;10(4):204-216. doi:10.1038/nrneurol.2014.24PubMedGoogle ScholarCrossref
9.
Dion PA, Daoud H, Rouleau GA. Genetics of motor neuron disorders: new insights into pathogenic mechanisms. Nat Rev Genet. 2009;10(11):769-782. doi:10.1038/nrg2680PubMedGoogle ScholarCrossref
10.
Sedgwick P. Case-control studies: advantages and disadvantages. BMJ. 2014;348:f7707. doi:10.1136/bmj.f7707Google ScholarCrossref
11.
Smith JD. Single-case experimental designs: a systematic review of published research and current standards. Psychol Methods. 2012;17(4):510-550. doi:10.1037/a0029312PubMedGoogle ScholarCrossref
12.
Drysdale AT, Grosenick L, Downar J, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23(1):28-38. doi:10.1038/nm.4246PubMedGoogle ScholarCrossref
13.
Wolfers T, Doan NT, Kaufmann T, et al. Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry. 2018;75(11):1146-1155. doi:10.1001/jamapsychiatry.2018.2467ArticlePubMedGoogle ScholarCrossref
14.
James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning With Applications in R. New York, NY: Springer-Verlag New York; 2013.
Subscribe to:
Posts (Atom)
Posts
