Impulsivity is a stable, measurable, and predictive psychological trait.

Impulsivity is a personality construct frequently employed to explain and predict important human behaviors. Major inconsistencies in its definition and measurement, however, have led some researchers to call for an outright rejection of impulsivity as a psychological construct. We address this highly unsatisfactory state with a large-scale, preregistered study (N = 1,676) in which each participant completed 48 measures of impulsivity derived from 10 self-report scales and 10 behavioral tasks and reported frequencies of seven impulsivity-related behaviors (e.g., impulsive buying and social media usage); a subsample (N = 196) then completed a retest session 3 mo later. We found that correlations between self-report measures were substantially higher than those between behavioral tasks and between self-report measures and behavioral tasks. Bifactor analysis of these measures exacted one general factor of impulsivity I, akin to the general intelligence factor g, and six specific factors. Factor I was related mainly to self-report measures, had high test-retest reliability, and could predict impulsivity-related behaviors better than existing measures. We further developed a scale named the adjustable impulsivity scale (AIMS) to measure I. AIMS possesses excellent psychometric properties that are largely retained in shorter versions and could predict impulsivity-related behaviors equally well as I. These findings collectively support impulsivity as a stable, measurable, and predictive trait, indicating that it may be too early to reject it as a valid and useful psychological construct. The bifactorial structure of impulsivity and AIMS, meanwhile, significantly advance the conceptualization and measurement of construct impulsivity.

How cognitive selection affects language change.

Like biological species, words in language must compete to survive. Previously, it has been shown that language changes in response to cognitive constraints and over time becomes more learnable. Here, we use two complementary research paradigms to demonstrate how the survival of existing word forms can be predicted by psycholinguistic properties that impact language production. In the first study, we analyzed the survival of words in the context of interpersonal communication. We analyzed data from a large-scale serial-reproduction experiment in which stories were passed down along a transmission chain over multiple participants. The results show that words that are acquired earlier in life, more concrete, more arousing, and more emotional are more likely to survive retellings. We reason that the same trend might scale up to language evolution over multiple generations of natural language users. If that is the case, the same set of psycholinguistic properties should also account for the change of word frequency in natural language corpora over historical time. That is what we found in two large historical-language corpora (Study 2): Early acquisition, concreteness, and high arousal all predict increasing word frequency over the past 200 y. However, the two studies diverge with respect to the impact of word valence and word length, which we take up in the discussion. By bridging micro-level behavioral preferences and macro-level language patterns, our investigation sheds light on the cognitive mechanisms underlying word competition.

Resolving content moderation dilemmas between free speech and harmful misinformation.

In online content moderation, two key values may come into conflict: protecting freedom of expression and preventing harm. Robust rules based in part on how citizens think about these moral dilemmas are necessary to deal with this conflict in a principled way, yet little is known about people's judgments and preferences around content moderation. We examined such moral dilemmas in a conjoint survey experiment where US respondents (N = 2, 564) indicated whether they would remove problematic social media posts on election denial, antivaccination, Holocaust denial, and climate change denial and whether they would take punitive action against the accounts. Respondents were shown key information about the user and their post as well as the consequences of the misinformation. The majority preferred quashing harmful misinformation over protecting free speech. Respondents were more reluctant to suspend accounts than to remove posts and more likely to do either if the harmful consequences of the misinformation were severe or if sharing it was a repeated offense. Features related to the account itself (the person behind the account, their partisanship, and number of followers) had little to no effect on respondents' decisions. Content moderation of harmful misinformation was a partisan issue: Across all four scenarios, Republicans were consistently less willing than Democrats or independents to remove posts or penalize the accounts that posted them. Our results can inform the design of transparent rules for content moderation of harmful misinformation.

Chimpanzee and Human Risk Preferences Show Key Similarities.

Risk preference impacts how people make key life decisions related to health, wealth, and well-being. Systematic variations in risk-taking behavior can be the result of differences in fitness expectations, as predicted by life-history theory. Yet the evolutionary roots of human risk-taking behavior remain poorly understood. Here, we studied risk preferences of chimpanzees (86 Pan troglodytes; 47 females; age = 2-40 years) using a multimethod approach that combined observer ratings with behavioral choice experiments. We found that chimpanzees' willingness to take risks shared structural similarities with that of humans. First, chimpanzees' risk preference manifested as a traitlike preference that was consistent across domains and measurements. Second, chimpanzees were ambiguity averse. Third, males were more risk prone than females. Fourth, the appetite for risk showed an inverted-U-shaped relation to age and peaked in young adulthood. Our findings suggest that key dimensions of risk preference appear to emerge independently of the influence of human cultural evolution.

EEG-representational geometries and psychometric distortions in approximate numerical judgment.

When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect a relative underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks. We used a simple experimental manipulation (instructions to average a single-stream or to compare dual-streams of samples) to induce compression or anti-compression in behavior when participants judged rapid number sequences. Model-based representational similarity analysis (RSA) replicated the previous finding of neural anti-compression in the dual-stream task, but failed to provide evidence for neural compression in the single-stream task, despite the evidence for compression in behavior. Instead, the results indicated enhanced neural processing of extreme values in either task, regardless of whether extremes were over- or underweighted in subsequent behavioral choice. We further observed more general differences in the neural representation of the sample information between the two tasks. Together, our results indicate a mismatch between sample-level EEG geometries and behavior, which raises new questions about the origin of common psychometric distortions, such as diminishing sensitivity for larger values.

Heterogeneity of rules in Bayesian reasoning: A toolbox analysis.

How do people infer the Bayesian posterior probability from stated base rate, hit rate, and false alarm rate? This question is not only of theoretical relevance but also of practical relevance in medical and legal settings. We test two competing theoretical views: single-process theories versus toolbox theories. Single-process theories assume that a single process explains people's inferences and have indeed been observed to fit people's inferences well. Examples are Bayes's rule, the representativeness heuristic, and a weighing-and-adding model. Their assumed process homogeneity implies unimodal response distributions. Toolbox theories, in contrast, assume process heterogeneity, implying multimodal response distributions. After analyzing response distributions in studies with laypeople and professionals, we find little support for the single-process theories tested. Using simulations, we find that a single process, the weighing-and-adding model, nevertheless can best fit the aggregate data and, surprisingly, also achieve the best out-of-sample prediction even though it fails to predict any single respondent's inferences. To identify the potential toolbox of rules, we test how well candidate rules predict a set of over 10,000 inferences (culled from the literature) from 4,188 participants and 106 different Bayesian tasks. A toolbox of five non-Bayesian rules plus Bayes's rule captures 64% of inferences. Finally, we validate the Five-Plus toolbox in three experiments that measure response times, self-reports, and strategy use. The most important conclusion from these analyses is that the fitting of single-process theories to aggregate data risks misidentifying the cognitive process. Antidotes to that risk are careful analyses of process and rule heterogeneity across people.

Control over sampling boosts numerical evidence processing in human decisions from experience.

When acquiring information about choice alternatives, decision makers may have varying levels of control over which and how much information they sample before making a choice. How does control over information acquisition affect the quality of sample-based decisions? Here, combining variants of a numerical sampling task with neural recordings, we show that control over when to stop sampling can enhance (i) behavioral choice accuracy, (ii) the build-up of parietal decision signals, and (iii) the encoding of numerical sample information in multivariate electroencephalogram patterns. None of these effects were observed when participants could only control which alternatives to sample, but not when to stop sampling. Furthermore, levels of control had no effect on early sensory signals or on the extent to which sample information leaked from memory. The results indicate that freedom to stop sampling can amplify decisional evidence processing from the outset of information acquisition and lead to more accurate choices.

The real cause of our complicity: The preoccupation with human weakness.

Chater & Loewenstein offer an incisive criticism of how behavioral sciences and public policy have become complicit with corporations in blaming public health and societal problems on individual weaknesses, thus deflecting support away from systemic reforms. However, their analysis stops short of holding the field to account in one important respect: its preoccupation with human irrationality and weakness.

Happy and healthy: How family mealtime routines relate to child nutritional health.

Children eat most of their meals in a family context, making family meals a key environment in which to learn about healthy food. What makes a family meal "healthy"? This diary study examined the practice of seven family mealtime routines (e.g., positive mealtime atmosphere, parental modeling, and longer meal duration) and their predictive value for children's healthier nutrition focusing on everyday family meal settings. Over 7 consecutive days, parents from N = 310 families (Mage = 42 years) described their most important family meal of the day and food intake for an index child (Mage = 9 years) and indicated what mealtime routines were practiced during the family meal. On average, each parent responded to 5.6 (SD = 1.4) of seven daily surveys. Mean correlations between mealtime routines were small (rs between -0.14 and 0.25), suggesting independent and distinct routines. Creating a positive atmosphere and turning TV and smartphones off were reported most often (on average, 91.2% and 90.5%, respectively). Parent's fruit and vegetable intake and creating a positive mealtime atmosphere were the strongest predictors for children's higher nutritional quality (i.e., higher vegetable and fruit intake; ps < .001). Findings indicate that mealtime routines obtained from independent meta-analyses represent distinct routines. Families practiced these independent and distinct routines to different degrees. Parental modeling and a positive mealtime atmosphere were most predictive of healthier child nutrition in daily family meal settings. More experimental research is needed to better understand causality and provide a better basis for effective interventions.

No effect of birth order on adult risk taking.

Does birth order shape people's propensity to take risks? Evidence is mixed. We used a three-pronged approach to investigate birth-order effects on risk taking. First, we examined the propensity to take risks as measured by a self-report questionnaire administered in the German Socio-Economic Panel, one of the largest and most comprehensive household surveys. Second, we drew on data from the Basel-Berlin Risk Study, one of the most exhaustive attempts to measure risk preference. This study administered 39 risk-taking measures, including a set of incentivized behavioral tasks. Finally, we considered the possibility that birth-order differences in risk taking are not reflected in survey responses and laboratory studies. We thus examined another source of behavioral data: the risky life decision to become an explorer or a revolutionary. Findings from these three qualitatively different sources of data and analytic methods point unanimously in the same direction: We found no birth-order effects on risk taking.

Educating physicians on strong opioids by descriptive versus simulated-experience formats: a randomized controlled trial.

BACKGROUND: Long-term prescriptions of strong opioids for chronic noncancer pain-which are not supported by scientific evidence-suggest miscalibrated risk perceptions among those who prescribe, dispense, and take opioids. Because risk perceptions and behaviors can differ depending on whether people learn about risks through description or experience, we investigated the effects of descriptive versus simulated-experience educative formats on physicians' risk perceptions of strong opioids and their prescription behavior for managing chronic noncancer pain. METHODS: Three hundred general practitioners and 300 pain specialists in Germany-enrolled separately in two independent exploratory randomized controlled online trials-were randomly assigned to either a descriptive format (fact box) or a simulated-experience format (interactive simulation). PRIMARY ENDPOINTS: Objective risk perception (numerical estimates of opioids' benefits and harms), actual prescriptions of seven therapy options for managing chronic pain. SECONDARY ENDPOINT: Implementation of intended prescriptions of seven therapy options for managing chronic pain. RESULTS: Both formats improved the proportion of correct numerical estimates of strong opioids' benefits and harms immediately after intervention, with no notable differences between formats. Compared to description, simulated experience led to significantly lower reported actual prescription rates for strong and/or weak opioids, and was more effective at increasing prescription rates for non-drug-based therapies (e.g., means of opioid reduction) from baseline to follow-up for both general practitioners and pain specialists. Simulated experience also resulted in a higher implementation of intended behavior for some drug-based and non-drug-based therapies. CONCLUSIONS: The two formats, which recruit different cognitive processes, may serve different risk-communication goals: If the goal is to improve exact risk perception, descriptive and simulated-experience formats are likely to be equally suitable. If, however, the goal is to boost less risky prescription habits, simulated experience may be the better choice. TRIAL REGISTRATION: DRKS00020358 (German Clinical Trials Register, first registration: 07/01/2020).

Reach and speed of judgment propagation in the laboratory.

In recent years, a large body of research has demonstrated that judgments and behaviors can propagate from person to person. Phenomena as diverse as political mobilization, health practices, altruism, and emotional states exhibit similar dynamics of social contagion. The precise mechanisms of judgment propagation are not well understood, however, because it is difficult to control for confounding factors such as homophily or dynamic network structures. We introduce an experimental design that renders possible the stringent study of judgment propagation. In this design, experimental chains of individuals can revise their initial judgment in a visual perception task after observing a predecessor's judgment. The positioning of a very good performer at the top of a chain created a performance gap, which triggered waves of judgment propagation down the chain. We evaluated the dynamics of judgment propagation experimentally. Despite strong social influence within pairs of individuals, the reach of judgment propagation across a chain rarely exceeded a social distance of three to four degrees of separation. Furthermore, computer simulations showed that the speed of judgment propagation decayed exponentially with the social distance from the source. We show that information distortion and the overweighting of other people's errors are two individual-level mechanisms hindering judgment propagation at the scale of the chain. Our results contribute to the understanding of social-contagion processes, and our experimental method offers numerous new opportunities to study judgment propagation in the laboratory.

What the Future Holds and When: A Description-Experience Gap in Intertemporal Choice.

Uncertainty about the waiting time before obtaining an outcome is integral to intertemporal choice. Here, we showed that people express different time preferences depending on how they learn about this temporal uncertainty. In two studies, people chose between pairs of options: one with a single, sure delay and the other involving multiple, probabilistic delays (a lottery). The probability of each delay occurring either was explicitly described (timing risk) or could be learned through experiential sampling (timing uncertainty; the delay itself was not experienced). When the shorter delay was rare, people preferred the lottery more often when it was described than when it was experienced. When the longer delay was rare, this pattern was reversed. Modeling analyses suggested that underexperiencing rare delays and different patterns of probability weighting contribute to this description-experience gap. Our results challenge traditional models of intertemporal choice with temporal uncertainty as well as the generality of inverse-S-shaped probability weighting in such choice.

Boosting medical diagnostics by pooling independent judgments.

Collective intelligence refers to the ability of groups to outperform individual decision makers when solving complex cognitive problems. Despite its potential to revolutionize decision making in a wide range of domains, including medical, economic, and political decision making, at present, little is known about the conditions underlying collective intelligence in real-world contexts. We here focus on two key areas of medical diagnostics, breast and skin cancer detection. Using a simulation study that draws on large real-world datasets, involving more than 140 doctors making more than 20,000 diagnoses, we investigate when combining the independent judgments of multiple doctors outperforms the best doctor in a group. We find that similarity in diagnostic accuracy is a key condition for collective intelligence: Aggregating the independent judgments of doctors outperforms the best doctor in a group whenever the diagnostic accuracy of doctors is relatively similar, but not when doctors' diagnostic accuracy differs too much. This intriguingly simple result is highly robust and holds across different group sizes, performance levels of the best doctor, and collective intelligence rules. The enabling role of similarity, in turn, is explained by its systematic effects on the number of correct and incorrect decisions of the best doctor that are overruled by the collective. By identifying a key factor underlying collective intelligence in two important real-world contexts, our findings pave the way for innovative and more effective approaches to complex real-world decision making, and to the scientific analyses of those approaches.

Parents' considerable underestimation of sugar and their child's risk of overweight.

High sugar intake is associated with an increased risk of overweight. For parents, as their children's nutritional gatekeepers, knowledge about sugar is a prerequisite for regulating sugar consumption. Yet little is known about parental ability to estimate the sugar content of foods and beverages and how this ability is associated with children's body mass index (BMI). In 305 parent-child pairs, we investigated to what extent parents systematically under- or overestimate the sugar content of foods and beverages commonly found in children's diets as well as potential associations with children's z-BMI. Parents considerably underestimated the sugar content of most foods and beverages (e.g., 92% of parents underestimated the sugar content of yogurt by, on average, seven sugar cubes). After controlling for parental education and BMI, parental sugar underestimation was significantly associated with a higher risk of their child being overweight or obese (odds ratio = 2.01). There was a small dose-response relationship between the degree of underestimation and the child's z-BMI. These findings suggest that providing easily accessible and practicable knowledge about sugar content through, for instance, nutritional labeling may improve parents' intuition about sugar. This could help curtail sugar intake in children and thus be a preventive measure for overweight.

Public Beliefs About Obesity Relative to Other Major Health Risks: Representative Cross-Sectional Surveys in the USA, the UK, and Germany.

Background: Overweight and obesity are among the leading risk factors for death worldwide. Scientists believe that the increase in obesity is primarily due to environmental changes and thus favor obesity prevention measures targeting the environment. However, it is less clear what lay people perceive as causes of obesity, and which measures they deem acceptable and promising in fighting it. Purpose: This article compares lay beliefs about obesity with beliefs about other major health risks sharing certain similarities with obesity (alcohol and tobacco dependence, depression) in three countries with high obesity rates. Methods: Computer-assisted face-to-face interviews with representative samples in the UK (N = 1,216) and Germany (N = 973) and an online survey in the USA (N = 982) tapping beliefs about locus of responsibility, liability for treatment costs, and effectiveness of policy measures. Results: In each country, respondents attributed responsibility for obesity primarily to the individual; the same pattern emerged for alcohol and tobacco dependence, but not for depression (ps < .01). The higher the attribution of personal responsibility, the more strongly respondents endorsed individual liability for treatment costs (ps < .01). Respondents judged information and fiscal policies as most and least effective, respectively, in obesity prevention. Conclusions: Respondents' views about obesity are similar to those about addictions; however, they regard fiscal and regulatory policies as less effective for obesity than for addictions. Raising awareness about environmental drivers of obesity and framing policy measures by reference to the fight against tobacco and alcohol could increase public support of obesity-targeted policies.

Who Dares, Who Errs? Disentangling Cognitive and Motivational Roots of Age Differences in Decisions Under Risk.

We separate for the first time the roles of cognitive and motivational factors in shaping age differences in decision making under risk. Younger and older adults completed gain, loss, and mixed-domain choice problems as well as measures of cognitive functioning and affect. The older adults' decision quality was lower than the younger adults' in the loss domain, and this age difference was attributable to the older adults' lower cognitive abilities. In addition, the older adults chose the more risky option more often than the younger adults in the gain and mixed domains; this difference in risk aversion was attributable to less pronounced negative affect among the older adults. Computational modeling with a hierarchical Bayesian implementation of cumulative prospect theory revealed that the older adults had higher response noise and more optimistic decision weights for gains than did the younger adults. Moreover, the older adults showed no loss aversion, a finding that supports a positivity-focus (rather than a loss-prevention) view of motivational reorientation in older age.

How the twain can meet: Prospect theory and models of heuristics in risky choice.

Two influential approaches to modeling choice between risky options are algebraic models (which focus on predicting the overt decisions) and models of heuristics (which are also concerned with capturing the underlying cognitive process). Because they rest on fundamentally different assumptions and algorithms, the two approaches are usually treated as antithetical, or even incommensurable. Drawing on cumulative prospect theory (CPT; Tversky & Kahneman, 1992) as the currently most influential instance of a descriptive algebraic model, we demonstrate how the two modeling traditions can be linked. CPT's algebraic functions characterize choices in terms of psychophysical (diminishing sensitivity to probabilities and outcomes) as well as psychological (risk aversion and loss aversion) constructs. Models of heuristics characterize choices as rooted in simple information-processing principles such as lexicographic and limited search. In computer simulations, we estimated CPT's parameters for choices produced by various heuristics. The resulting CPT parameter profiles portray each of the choice-generating heuristics in psychologically meaningful ways-capturing, for instance, differences in how the heuristics process probability information. Furthermore, CPT parameters can reflect a key property of many heuristics, lexicographic search, and track the environment-dependent behavior of heuristics. Finally, we show, both in an empirical and a model recovery study, how CPT parameter profiles can be used to detect the operation of heuristics. We also address the limits of CPT's ability to capture choices produced by heuristics. Our results highlight an untapped potential of CPT as a measurement tool to characterize the information processing underlying risky choice.

Information structuring improves recall of emergency discharge information: a randomized clinical trial.

This article examines the extent to which structuring Emergency Department discharge information improves the ability to recall that information, and whether such benefits interact with relevant prior knowledge. Using three samples of students with different levels of prior medical knowledge, we investigated the amount of information recalled after structured vs. non-structured presentation of information. Across all student samples, the structured discharge information led to a relative increase in recalled items of 17% compared to non-structured discharge information (M = 9.70, SD = 4.96 vs. M = 8.31, SD = 4.93). In the sample with least medical knowledge, however, the structured discharge information resulted in a relative increase in recall by 42% (M = 8.12 vs. M = 5.71). These results suggest that structuring discharge information can be a useful tool to improve recall of information and is likely to be most beneficial for patient populations with lower levels of medical knowledge.

Social nature of eating could explain missing link between food insecurity and childhood obesity.

We suggest that social factors are key to explain the missing link between food insecurity and obesity in children. Parents and public institutions are children's nutritional gatekeepers. They protect children from food insecurity by trimming down their consumption or by institutional support. To gauge children's food insecurity, evaluations across the different nutritional gatekeepers need to be integrated.