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.

Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust.

The rapid advancement of artificial intelligence (AI) has impacted society in many aspects. Alongside this progress, concerns such as privacy violation, discriminatory bias, and safety risks have also surfaced, highlighting the need for the development of ethical, responsible, and socially beneficial AI. In response, the concept of trustworthy AI has gained prominence, and several guidelines for developing trustworthy AI have been proposed. Against this background, we demonstrate the significance of psychological research in identifying factors that contribute to the formation of trust in AI. Specifically, we review research findings on interpersonal, human-automation, and human-AI trust from the perspective of a three-dimension framework (i.e., the trustor, the trustee, and their interactive context). The framework synthesizes common factors related to trust formation and maintenance across different trust types. These factors point out the foundational requirements for building trustworthy AI and provide pivotal guidance for its development that also involves communication, education, and training for users. We conclude by discussing how the insights in trust research can help enhance AI's trustworthiness and foster its adoption and application.

Genome-wide association studies (GWAS) and post-GWAS analyses of impulsivity: A systematic review.

Impulsivity is related to a host of mental and behavioral problems. It is a complex construct with many different manifestations, most of which are heritable. The genetic compositions of these impulsivity manifestations, however, remain unclear. A number of genome-wide association studies (GWAS) and post-GWAS analyses have tried to address this issue. We conducted a systematic review of all GWAS and post-GWAS analyses of impulsivity published up to December 2023. Available data suggest that single nucleotide polymorphisms (SNPs) in more than a dozen of genes (e.g., CADM2, CTNNA2, GPM6B) are associated with different measures of impulsivity at genome-wide significant levels. Post-GWAS analyses further show that different measures of impulsivity are subject to different degrees of genetic influence, share few genetic variants, and have divergent genetic overlap with basic personality traits such as extroversion and neuroticism, cognitive ability, psychiatric disorders, substance use, and obesity. These findings shed light on controversies in the conceptualization and measurement of impulsivity, while providing new insights on the underlying mechanisms that yoke impulsivity to psychopathology.

Modeling fast-and-frugal heuristics.

Heuristics are simple rules that experts and laypeople rely on to make decisions under uncertainty as opposed to situations with calculable risk. The research program on fast-and-frugal heuristics studies formal models of heuristics and is motivated by Herbert Simon's seminal work on bounded rationality and satisficing. In this article, we first introduce the major theoretical principles (e.g., ecological rationality) and research approaches (e.g., competitive testing) that have been adopted in this research program, and then illustrate these principles and approaches with two heuristics: take-the-best and fast-and-frugal trees. We describe conditions under which simple heuristics predict as accurately as or better than more complex models, despite requiring less effort. We close by pointing out several issues that need to be further studied and better understood in the research on fast-and-frugal heuristics.

Reward, punishment, and prosocial behavior: Recent developments and implications.

Reward and punishment change the payoff structures of social interactions and therefore can potentially play a role in promoting prosocial behavior. Yet, there are boundary conditions for them to be effective. We review recent work that addresses the conditions under which rewards and punishment can enhance prosocial behavior, the proximate and ultimate mechanisms for individuals' rewarding and punishing decisions, and the reputational and behavioral consequences of reward and punishment under noise. The reviewed evidence points to the importance of more field research on how reward and punishment can promote prosocial behavior in real-world settings. We also highlight the need to integrate different methodologies to better examine the effects of reward and punishment on prosocial behavior.

Does risk perception motivate preventive behavior during a pandemic? A longitudinal study in the United States and China.

Controlling the spread of an infectious disease depends critically on the general public's adoption of preventive measures. Theories of health behavior suggest that risk perceptions motivate preventive behavior. The supporting evidence for this causal link is, however, of questionable validity. The COVID-19 pandemic provides a rare opportunity to examine how risk perceptions, preventive behavior, and the link between them develop in a fast-changing risky environment. In a 4-wave longitudinal study conducted in the United States and China, we found that for Chinese participants, there was little relationship between risk perceptions and preventive behavior. This may be a result of the Chinese government's strict control and containment policies and a collectivistic culture that encourages conforming to norms-both of which limit individuals' nonconformist behavior. For U.S. participants, risk perceptions did motivate preventive behavior in the early stage of the pandemic; however, as time went by and the risk of COVID-19 persisted, preventive behavior also led to perception of higher infection risk, which in turn further motivated preventive behavior. Thus, instead of the presumed unidirectional influence from perception to behavior, our results indicate that the two could mutually reinforce each other. Overall, our findings suggest that risk perceptions-at least in the context of a dynamic health hazard-may only motivate preventive behavior at specific stages and under specific conditions. They also highlight the importance of early interventions in promoting preventive behavior. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Changing emotions in the COVID-19 pandemic: A four-wave longitudinal study in the United States and China.

RATIONALE: Tracking the trajectory of people's emotional and behavioral reactions to the COVID-19 pandemic sheds light on how people cope with the emerging crisis, evaluates the impact of emotional reactions on preventive behaviors, and provides insights into how preventive behaviors can be encouraged and maintained in the long term. OBJECTIVE: We addressed two related questions: How did emotions change across various stages of the COVID-19 pandemic, and to what extent were preventive behaviors predicted by emotional reactions and information acquisition? METHODS: We conducted a four-wave longitudinal study in the United States and China across four stages of the pandemic: prepandemic, onset of viral outbreak, ongoing risk, and contained risk. We measured emotions, life satisfaction, preventive behaviors, acquisition of COVID-19 related information, and risk perceptions. We used the Emotional Recall Task (ERT) to investigate people's emotions. By allowing people to describe their emotional experience in their own words, the ERT evaluates each individual based on emotions relevant to their personal experience, making it more suitable for a wider range of contexts and social groups. RESULTS: Boredom, anxiety, fear, and worry were common emotional reactions to the pandemic as it emerged. Surprisingly, participants' emotional experience did not mirror infection and death rates: Instead of negative emotions growing as the virus spread, emotions soon reverted back to normality. This pattern held regardless of whether the viral spread was contained. Consequently, people's preventive behaviors were predicted by fear, anxiety, and worry only at the onset of the viral outbreak. In contrast, actively acquiring information and knowledge about COVID-19 had a more enduring effect on the engagement of preventive behaviors in both countries. CONCLUSION: Our research suggests a possible life cycle of emotional reactions towards a pandemic and highlights the importance of people acquiring information and knowledge about the threat in containing its spread.

The hierarchical sensitivity to social misalignment during decision-making under uncertainty.

Social misalignment occurs when a person's attitudes and opinions deviate from those of others. We investigated how individuals react to social misalignment in risky (outcome probabilities are known) or ambiguous (outcome probabilities are unknown) decision contexts. During each trial, participants played a forced-choice gamble, and they observed the decisions of four other players after they made a tentative decision, followed by an opportunity to keep or change their initial decision. Behavioral and event-related potential data were collected. Behaviorally, the stronger the participants' initial preference, the less likely they were to switch their decisions, whereas the more their decisions were misaligned with the majority, the more likely they were to switch. Electrophysiological results showed a hierarchical processing pattern of social misalignment. Misalignment was first detected binarily (i.e. match/mismatch) at an early stage, as indexed by the N1 component. During the second stage, participants became sensitive to low levels of misalignment, which were indexed by the feedback-related negativity. The degree of social misalignment was processed in greater detail, as indexed by the P3 component. Moreover, such hierarchical neural sensitivity is generalizable across different decision contexts (i.e. risky and ambiguous). These findings demonstrate a fine-grained neural sensitivity to social misalignment during decision-making under uncertainty.

Public knowledge of stroke and heart attack symptoms in China: a cross-sectional survey.

OBJECTIVE: Stroke and ischaemic heart disease have become the leading causes of death in China. We evaluated recognition of stroke and heart attack symptoms and stroke treatment-seeking behaviour in a large representative sample of the Chinese adult population and explored characteristics associated with recognition rates. DESIGN: Cross-sectional survey. SETTING: Household interviews. PARTICIPANT: 3051 Chinese adults aged between 18 and 69 (50.7% female) were interviewed between January and March 2019. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary measures include recognitions of stroke and heart attack symptoms and stroke treatment-seeking behaviour. Secondary measures include numeracy level, sociodemographics and prior history of cardiovascular diseases and high blood pressure. RESULTS: Participants on average recognised 5.2 out of 14 stroke symptoms and 2.6 out of 6 heart attack symptoms. In the presence of stroke symptoms, three quarters of participants would take immediate action and call an ambulance, yet the second most common action was to advise the person to see a doctor (59%) rather than to consult a doctor immediately (34%). Recognition of atypical heartattack symptoms, such as nausea and feeling of anxiety, was poor. Symptom recognition rates were higher in females, people with a personal or family/friend history of cardiovascular events, those with higher numeracy scores, and for stroke symptoms, participants with high (versus low) education level. Furthermore, symptom recognition rate was negatively correlated with burden of cardiovascular diseases across the four economic regions of China. CONCLUSION: Recognition of stroke and heart-attack symptoms was moderate and there remains a gap between recognising symptoms and taking immediate action. Interventions focusing on simple symptom detection tools and on building numerical competencies may help reduce the burden of cardiovascular diseases in China.

Improving judgment accuracy by sequential adjustment.

Judging an object's value based on relevant cues can be challenging. We propose a simple method to improve judgment accuracy: Instead of estimating a value after seeing all available cues simultaneously, individuals view cues sequentially, one after another, making and adjusting their estimate at each step. The sequential procedure may alleviate computational difficulties in cue integration, leading to higher judgment accuracy. We tested this hypothesis in two real-world tasks in which participants judged either the price of diamonds or the fuel economy of cars. Two studies with professional jewelers and car salespeople show that most participants indeed judged more accurately with a sequential than with a simultaneous procedure. Another two studies with college students further support this finding and show additionally that the sequential procedure could raise the judgment accuracy of inexperienced students to the same level as that of professionals judging with the simultaneous procedure.

The Influence of Adult and Peer Role Models on Children' and Adolescents' Sharing Decisions.

This study explores how the age (adult vs. peer) and the suggestion (to be fair vs. unfair) of models affect the sharing decisions of 9- and 12-year-olds (N = 365) from Italy and Singapore. Results demonstrate a developmental shift in the influence of models on children's and adolescents' sharing decisions in both cultures: Children's decisions were more affected by an adult model's suggestion than by that of a peer model, whereas the opposite was true for adolescents. Regardless of the models' influence, participants considered equal sharing to be the fair choice and reported being happier when their sharing decisions were generous. Our results highlight the crucial importance of social and developmental factors for the promotion of fairness judgments and emotions.

Towards theory integration: Threshold model as a link between signal detection theory, fast-and-frugal trees and evidence accumulation theory.

RATIONALE, AIMS AND OBJECTIVES: Theories of decision making are divided between those aiming to help decision makers in the real, 'large' world and those who study decisions in idealized 'small' world settings. For the most part, these large- and small-world decision theories remain disconnected. METHODS: We linked the small-world decision theoretic concepts of signal detection theory (SDT) and evidence accumulation theory (EAT) to the threshold model and the large world of heuristic decision making that rely on fast-and-frugal decision trees (FFT). RESULTS: We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent. In doing so, we were able (1) to link the threshold model to EAT and FFT, thereby creating decision criteria that take into account both the classification accuracy of FFT and the consequences built in the threshold model; (2) to demonstrate how threshold criteria can be used as a strategy for optimal selection of cues when constructing FFT; and (3) to show that the compensatory strategy expressed in the threshold model can be linked to a non-compensatory FFT approach to decision making. We also showed how construction and performance of FFT depend on having reliable information - the results were highly sensitive to the estimates of benefits and harms of health interventions. We illustrate the practical usefulness of our analysis by describing an FFT we developed for prescribing statins for primary prevention of cardiovascular disease. CONCLUSIONS: By linking SDT and EAT to the compensatory threshold model and to non-compensatory heuristic decision making (FFT), we showed how these two decision strategies are ultimately linked within a broader theoretical framework and thereby respond to calls for integrating decision theory paradigms.

From perception to preference and on to inference: an approach-avoidance analysis of thresholds.

In a lexicographic semiorders model for preference, cues are searched in a subjective order, and an alternative is preferred if its value on a cue exceeds those of other alternatives by a threshold Δ, akin to a just noticeable difference in perception. We generalized this model from preference to inference and refer to it as Δ-inference. Unlike with preference, where accuracy is difficult to define, the problem a mind faces when making an inference is to select a Δ that can lead to accurate judgments. To find a solution to this problem, we applied Clyde Coombs's theory of single-peaked preference functions. We show that the accuracy of Δ-inference can be understood as an approach-avoidance conflict between the decreasing usefulness of the first cue and the increasing usefulness of subsequent cues as Δ grows larger, resulting in a single-peaked function between accuracy and Δ. The peak of this function varies with the properties of the task environment: The more redundant the cues and the larger the differences in their information quality, the smaller the Δ. An analysis of 39 real-world task environments led to the surprising result that the best inferences are made when Δ is 0, which implies relying almost exclusively on the best cue and ignoring the rest. This finding provides a new perspective on the take-the-best heuristic. Overall, our study demonstrates the potential of integrating and extending established concepts, models, and theories from perception and preference to improve our understanding of how the mind makes inferences.

When does diversity trump ability (and vice versa) in group decision making? A simulation study.

It is often unclear which factor plays a more critical role in determining a group's performance: the diversity among members of the group or their individual abilities. In this study, we addressed this "diversity vs. ability" issue in a decision-making task. We conducted three simulation studies in which we manipulated agents' individual ability (or accuracy, in the context of our investigation) and group diversity by varying (1) the heuristics agents used to search task-relevant information (i.e., cues); (2) the size of their groups; (3) how much they had learned about a good cue search order; and (4) the magnitude of errors in the information they searched. In each study, we found that a manipulation reducing agents' individual accuracy simultaneously increased their group's diversity, leading to a conflict between the two. These conflicts enabled us to identify certain conditions under which diversity trumps individual accuracy, and vice versa. Specifically, we found that individual accuracy is more important in task environments in which cues differ greatly in the quality of their information, and diversity matters more when such differences are relatively small. Changing the size of a group and the amount of learning by an agent had a limited impact on this general effect of task environment. Furthermore, we found that a group achieves its highest accuracy when there is an intermediate amount of errors in the cue information, regardless of the environment and the heuristic used, an effect that we believe has not been previously reported and warrants further investigation.

A signal-detection analysis of fast-and-frugal trees.

Models of decision making are distinguished by those that aim for an optimal solution in a world that is precisely specified by a set of assumptions (a so-called "small world") and those that aim for a simple but satisfactory solution in an uncertain world where the assumptions of optimization models may not be met (a so-called "large world"). Few connections have been drawn between these 2 families of models. In this study, the authors show how psychological concepts originating in the classic signal-detection theory (SDT), a small-world approach to decision making, can be used to understand the workings of a class of simple models known as fast-and-frugal trees (FFTs). Results indicate that (a) the setting of the subjective decision criterion in SDT corresponds directly to the choice of exit structure in an FFT; (b) the sensitivity of an FFT (measured in d') is reflected by the order of cues searched and the properties of cues in an FFT, including the mean and variance of cues' individual d's, the intercue correlation, and the number of cues; and (c) compared with the ideal and the optimal sequential sampling models in SDT and a majority model with an information search component, FFTs are extremely frugal (i.e., do not search for much cue information), highly robust, and well adapted to the payoff structure of a task. These findings demonstrate the potential of theory integration in understanding the common underlying psychological structures of apparently disparate theories of cognition.