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Rapid individual cognitive phenotyping holds the potential to revolutionize domains as wide-ranging as personalized learning, employment practices, and precision psychiatry. Going beyond limitations imposed by traditional lab-based experiments, new efforts have been underway towards greater ecological validity and participant diversity to capture the full range of individual differences in cognitive abilities and behaviors across the general population. Building on this, we developed Skill Lab, a novel game-based tool that simultaneously assesses a broad suite of cognitive abilities while providing an engaging narrative. Skill Lab consists of six mini-games as well as 14 established cognitive ability tasks. Using a popular citizen science platform (N = 10725), we conducted a comprehensive validation in the wild of a game-based cognitive assessment suite. Based on the game and validation task data, we constructed reliable models to simultaneously predict eight cognitive abilities based on the users' in-game behavior. Follow-'-up validation tests revealed that the models can discriminate nuances contained within each separate cognitive ability as well as capture a shared main factor of generalized cognitive ability. Our game-based measures are five times faster to complete than the equivalent task-based measures and replicate previous findings on the decline of certain cognitive abilities with age in our large cross-sectional population sample (N = 6369). Taken together, our results demonstrate the feasibility of rapid in-the-wild systematic assessment of cognitive abilities as a promising first step towards population-scale benchmarking and individualized mental health diagnostics., 24 pages, 8 figures, and 4 tables. Supplementary Information: osf.io/pnw5z//

In many choice environments, it seems that risks and rewards or probabilities and payoffs are tightly coupled such that high payoffs only occur with low probabilities. This structure may, in the face of uncertainty, afford an adaptive mind an opportunity to exploit it---for instance, by using the size of potential rewards to infer otherwise unknown probabilities of obtaining them. However, a mind can only adapt to and exploit an environmental structure if it is frequent and recurrent. We show that the ecology of competition makes the association of high rewards with low probabilities ubiquitous. It is a consequence of an ecological principle known as the ideal free distribution. This principle posits that the number of competitors in a resource patch is proportional to the gross total amount of resources in it. This principle implies a predictable inverse relationship between probabilities and payoffs, the risk\--reward structure. Harnessing this principle, we develop an ecological theory that identifies important boundary conditions for the structure. For instance, heterogeneity of resources in a given patch, computational limits among competitors, and scarcity of resources, systematically distort the risk-reward structure. Finally, we show that peoples mental representations of the risk--reward structure often conform to the distortions that are associated with these different ecological conditions. Grounding peoples inferences in the theory of the competitive risk-reward ecology makes it possible to predict when an adaptive decision maker should use the risk-reward structure. In sum, our framework demonstrates that the predictability of specific behaviors of a boundedly rational mind will only be possible if theories of the mind and the environment are coupled.

Thaler and Sunstein (2008) advance the concept of ‘nudge’ policies—non-regulatory and non-fiscal mechanisms designed to enlist people's cognitive biases or motivational deficits so as to guide their behaviour in a desired direction. A core assumption of this approach is that policymakers make artful use of people's cognitive biases and motivational deficits in ways that serve the ultimate interests of the nudged individual. We analyse a model of dynamic policymaking in which the policymaker's preferences are not always aligned with those of the individual. One novelty of our set-up is that the policymaker has the option to implement a ‘boost’ policy, equipping the individual with the competence to overcome the nudge-enabling bias once and for all. Our main result identifies conditions under which the policymaker chooses not to boost in order to preserve the option of using the nudge (and its associated bias) in the future—even though boosting is in the immediate best interests of both the policymaker and the individual. We extend our analysis to situations in which the policymaker can be removed (e.g., through an election) and in which the policymaker is similarly prone to bias. We conclude with a discussion of some policy implications of these findings.

Online platforms’ data give advertisers the ability to “microtarget” recipients’ personal vulnerabilities by tailoring different messages for the same thing, such as a product or political candidate. One possible response is to raise awareness for and resilience against such manipulative strategies through psychological inoculation. Two online experiments (total $$N= 828$$ N = 828 ) demonstrated that a short, simple intervention prompting participants to reflect on an attribute of their own personality—by completing a short personality questionnaire—boosted their ability to accurately identify ads that were targeted at them by up to 26 percentage points. Accuracy increased even without personalized feedback, but merely providing a description of the targeted personality dimension did not improve accuracy. We argue that such a “boosting approach,” which here aims to improve people’s competence to detect manipulative strategies themselves, should be part of a policy mix aiming to increase platforms’ transparency and user autonomy.

Loss aversion is often assumed to be a basic and far-reaching psychological regularity in behavior. Yet empirical evidence is accumulating to challenge the assumption of widespread loss aversion in choice. We suggest that a key reason for the apparently elusive nature of loss aversion may be that its manifestation in choice is state-dependent and distinct from a more state-independent principle of heightened attention to losses relative to gains. Using data from process-tracing studies, we show that people invest more attentional resources when evaluating losses than when evaluating gains, even when their choices do not reflect loss aversion. Our evidence converges with previous findings on how losses influence exploratory search as well as physiological, hormonal, and neural responses. Increased attention to losses relative to gains seems to be a necessary but not a sufficient condition for loss aversion in choice.

Uncertainty can arise in interactions with both social partners and nonliving objects. Previous research has shown that humans display higher aversion to uncertainty arising from social interactions than to uncertainty caused by interactions with objects such as gambling machines, and that this difference may be mediated by betrayal aversion. We investigated whether chimpanzees, Pan troglodytes, differentiate between social and nonsocial forms of uncertainty. Subjects participated in two experiments, each involving a social and a nonsocial condition. In both experiments, choosing the safe option resulted in immediate access to low-value food. Choosing the uncertain option could result in access to high-value food, but only if the partner (social condition) or a machine (nonsocial condition) proved trustworthy. In experiment 1, where chimpanzees had no prior information on reciprocation rates (i.e. decided under uncertainty), chimpanzees were less likely to choose the uncertain option when they interacted with a partner than with a machine. When they did choose the uncertain option, chimpanzees also hesitated longer in the social condition. In experiment 2, where chimpanzees had learned the statistical probabilities on reciprocation rates (i.e. decided under risk), they did not distinguish between social and nonsocial situations and were generally risk averse. These results suggest that chimpanzees are more averse to engaging in uncertain choices when the source of uncertainty is a conspecific than when it is a machine; when confronted with risky choices, chimpanzees show no such tendency.

Despite the ubiquity of uncertainty, scientific attention has focused primarily on probabilistic approaches, which predominantly rely on the assumption that uncertainty can be measured and expressed numerically. At the same time, the increasing amount of research from a range of areas including psychology, economics, and sociology testify that in the real world, people’s understanding of risky and uncertain situations cannot be satisfactorily explained in probabilistic and decision-theoretical terms. In this article, we offer a theoretical overview of an alternative approach to uncertainty developed in the framework of the ecological rationality research program. We first trace the origins of the ecological approach to uncertainty in Simon’s bounded rationality and Brunswik’s lens model framework and then proceed by outlining a theoretical view of uncertainty that ensues from the ecological rationality approach. We argue that the ecological concept of uncertainty relies on a systemic view of uncertainty that features it as a property of the organism–environment system. We also show how simple heuristics can deal with unmeasurable uncertainty and in what cases ignoring probabilities emerges as a proper response to uncertainty.

[This article is now published open access in the Psychological Science in the Public Interest https://journals.sagepub.com/doi/full/10.1177/1529100620946707. Please refer to and cite the published paper.] The Internet has evolved into a ubiquitous and indispensable digital environment in which people communicate, seek information, and make decisions. Despite offering various benefits, online environments are also replete with smart, highly adaptive choice architectures designed primarily to maximize commercial interests, capture and sustain users’ attention, monetize user data, and predict and influence future behavior. This online landscape holds multiple negative consequences for society, such as a decline in human autonomy, rising incivility in online conversation, the facilitation of political extremism, and the spread of disinformation. Benevolent choice architects working with regulators may curb the worst excesses of manipulative choice architectures, yet the strategic advantages, resources, and data remain with commercial players. One way to address this imbalance is with interventions that empower Internet users to gain some control over their digital environments, in part by boosting their information literacy and their cognitive resistance to manipulation. Our goal is to present a conceptual map of interventions that are based on insights from psychological science. We begin by systematically outlining how online and offline environments differ despite being increasingly inextricable. We then identify four major types of challenges that users encounter in online environments: persuasive and manipulative choice architectures, AI-assisted information architectures, false and misleading information, and distractive environments. Next, we turn to how psychological science can inform interventions to counteract these challenges of the digital world. After distinguishing between three types of behavioral and cognitive interventions—nudges, technocognition, and boosts—we focus in on boosts, of which we identify two main groups: (1) those aimed at enhancing people’s agency in their digital environments (e.g., self-nudging, deliberate ignorance) and (2) those aimed at boosting competences of reasoning and resilience to manipulation (e.g., simple decision aids, inoculation). These cognitive tools are designed to foster the civility of online discourse and protect reason and human autonomy against manipulative choice architectures, attention-grabbing techniques, and the spread of false information.

Written by Michelle A. Amazeen, Fabrício Benevenuto, Nadia M. Brashier, Robert M. Bond, Lia C. Bozarth, Ceren Budak, Ullrich K. H. Ecker, Lisa K. Fazio, Emilio Ferrara, Andrew J. Flanagin, Ales-sandro Flammini, Deen Freelon, Nir Grinberg, Ralph Hertwig, Kathleen Hall Jamieson, Kenneth Jo-seph, Jason J. Jones, R. Kelly Garrett, Daniel Kreiss, Shannon McGregor, Jasmine McNealy, Drew Margolin, Alice Marwick, FiIippo Menczer, Miriam J. Metzger, Seungahn Nah, Stephan Lewan-dowsky, Philipp Lorenz-Spreen, Pablo Ortellado, Irene Pasquetto, Gordon Pennycook, Ethan Porter, David G. Rand, Ronald Robertson, Briony Swire-Thompson, Francesca Tripodi, Soroush Vosoughi, Chris Vargo, Onur Varol, Brian E. Weeks, John Wihbey, Thomas J. Wood, & Kai-Cheng Yang

People differ in their willingness to take risks. Recent work found that a dominant class of measures, revealed preference tasks (e.g., laboratory lotteries), appear not to tap into stable individual differences, whereas survey-based stated preferences are stable and predict real-world risk taking across different domains. How can stated preferences, often criticised as inconsequential (“cheap talk”), be more valid and predictive than controlled, incentivized lotteries? In our multi-method study, over 3,000 respondents from population samples answered a single widely used and predictive risk preference question. Respondents then explained the reasoning behind their answer. They tended to recount diagnostic behaviours and experiences, focusing on voluntary, consequential acts and experiences from which they seemed to infer their risk preference. We found that third-party readers of respondents’ brief memories and explanations reached similar inferences about respondents preferences, indicating the intersubjective validity of this information. Our results also shed light on the process of preference formation through experience over the lifespan. Finally, stated risk preferences may capture preferences revealed in behaviours in the wild better than the contrived behavioural tasks preferred in economics because they permit people to draw upon their own understanding of what constitutes diagnostic behaviours and experiences.

For many people, it is morally impermissible to put kidneys, jury duty exemptions, or permits for having children on the free market. All of these are examples of repugnant transactions—market transactions that third parties want to prevent. In two studies (N = 1,554), using respondents’ judgements of 51 different market transactions across 21 characteristics, we show that repugnance can be characterized along five higher-order dimensions: moral outrage, need for regulation, incommensurability, exploitation, and unknown risk. Repugnance toward the 51 market transactions was highly consistent across two samples. Our results can help identify mismatches between public sentiments and current regulations (selling carbon emissions is currently legal but considered repugnant), anticipate responses to novel markets that have not been publicly scrutinized (often arising from technological advances, such as markets for “designer babies”), and help design less repugnant markets (e.g., by making the risks involved in a transaction known to sellers).

People’s risk preferences are thought to be central to many consequential real-life decisions, making it important to identify robust correlates of this construct. Various psychological theories have put forth a series of candidate correlates, yet the strength and robustness of their associations remain unclear because of disparate operationalizations of risk preference and analytic limitations in past research. We addressed these issues with a study involving several operationalizations of risk preference (all collected from each participant in a diverse sample of the German population; N = 916), and by adopting an exhaustive modeling approach—specification curve analysis. Our analyses of 6 candidate correlates (household income, sex, age, fluid intelligence, crystallized intelligence, years of education) suggest that sex and age have robust and consistent associations with risk preference, whereas the other candidate correlates show weaker and more (domain-) specific associations (except for crystallized intelligence, for which there were no robust associations). The results further demonstrate the important role of construct operationalization when assessing people’s risk preferences: Self-reported propensity measures picked up various associations with the proposed correlates, but (incentivized) behavioral measures largely failed to do so. In short, the associations between the 6 candidate correlates and risk preference depend mostly on how risk preference is measured, rather than whether and which control variables are included in the model specifications. The present findings inform several theories that have suggested candidate correlates of risk preference, and illustrate how personality research may profit from exhaustive modeling techniques to improve theory and measurement of essential constructs. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

As climate change unfolds, extreme weather events are on the rise worldwide. According to experts, extreme weather risks already outrank those of terrorism and migration in likelihood and impact. But how well does the public understand weather risks and forecast uncertainty and thus grasp the amplified weather risks that climate change poses for the future? In a nationally representative survey (N = 1004; Germany), we tested the public’s weather literacy and awareness of climate change using 62 factual questions. Many respondents misjudged important weather risks (e.g., they were unaware that UV radiation can be higher under patchy cloud cover than on a cloudless day) and struggled to connect weather conditions to their impacts (e.g., they overestimated the distance to a thunderstorm). Most misinterpreted a probabilistic forecast deterministically, yet they strongly underestimated the uncertainty of deterministic forecasts. Respondents with higher weather literacy obtained weather information more often and spent more time outside but were not more educated. Those better informed about climate change were only slightly more weather literate. Overall, the public does not seem well equipped to anticipate weather risks in the here and now and may thus also fail to fully grasp what climate change implies for the future. These deficits in weather literacy highlight the need for impact forecasts that translate what the weather may be into what the weather may do and for transparent communication of uncertainty to the public. Boosting weather literacy may help to improve the public’s understanding of weather and climate change risks, thereby fostering informed decisions and mitigation support.

Abstract. The layperson’s view associates aging with biological and cognitive losses, which could be associated with decrements in work productivity and overall contributions to society. In turn, ecological approaches to life span development suggest that successful performance can result from an adaptive employment of an individual’s physical, cognitive, or social capital in the appropriate environment. This ecological framework suggests that one must understand the demands of particular ecologies (i.e., niches) to predict whether aging is associated with failure, maintenance, or even improvements in performance. We provide examples that illustrate the importance of an ecological approach to understanding adaptation to challenging decision tasks both in the laboratory and in the wild. Overall, we propose that there are specific strategies and niches that can help older adults thrive and that more work is needed to understand the exact characteristics that lead to good performance in old age.

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.

A few years ago, the world experienced the most severe economic crisis since the Great Depression. According to the depression baby hypothesis, people who live through such macroeconomic shocks take less financial risk in their future lives (e.g., lower stock market participation). This hypothesis has previously been tested against survey data. Here, we tested it in a simulated experimental stock market (based on the Spanish stock index, IBEX-35), varying both the length of historical data available to participants (including or excluding a macroeconomic shock) and the mode of learning about macroeconomic events (through sequential experience or symbolic descriptions). Investors who learned about the market from personal experience took less financial risk than did those who learned from graphs, thus echoing the description–experience gap observed in risky choice. In a second experiment, we reversed the market, turning the crisis into a boom. The description–experience gap persisted, with investors who experienced the boom taking more risk than those who did not. The results of a third experiment suggest that the observed gap is not driven by a wealth effect, and modeling suggests that the description–experience gap is explained by the fact that participants who learn from experience are more risk averse after a negative shock. Our findings highlight the crucial role of the mode of learning for financial risk taking and, by extension, in the legally required provision of financial advice.

This article argues that nudges can often be turned into self-nudges: empowering interventions that enable people to design and structure their own decision environments – that is, to act as citizen choice architects. Self-nudging applies insights from behavioral science in a way that is practicable and cost-effective, but that sidesteps concerns about paternalism or manipulation. It has the potential to expand the scope of application of behavioral insights from the public to the personal sphere (e.g., homes, offices, families). It is a tool for reducing failures of self-control and enhancing personal autonomy; specifically, self-nudging can mean designing one's proximate choice architecture to alleviate the effects of self-control problems, engaging in education to understand the nature and causes of self-control problems and employing simple educational nudges to improve goal attainment in various domains. It can even mean self-paternalistic interventions such as winnowing down one's choice set by, for instance, removing options. Policy-makers could promote self-nudging by sharing knowledge about nudges and how they work. The ultimate goal of the self-nudging approach is to enable citizen choice architects’ efficient self-governance, where reasonable, and the self-determined arbitration of conflicts between their mutually exclusive goals and preferences.

The wisdom-of-crowds effect describes how aggregating judgments of multiple individuals can lead to a more accurate judgment than that of the typical—or even best—individual. However, what if there are no other individuals’ judgments at one’s disposal? We investigated when an individual can avail theirself of the wisdom of their “inner crowd” to improve the quality of their confidence judgments by either (a) averaging their two confidence judgments or (b) selecting the higher of the two (i.e., maximizing) in two-alternative choice tasks. In a simulation analysis based on a signal detection model of confidence, we investigated how the “kindness” versus “wickedness” of items (i.e., the degree to which the majority of people chooses the correct or wrong answer) affect the performance of averaging and maximizing. Analytical and simulation results show that irrespective of the type of item, averaging consistently improves confidence judgments, but maximizing is risky: It outperformed averaging only once items were answered correctly 50–60% of the time or more—a result, which has not been established in prior work. We investigated the relevance of these effects in three empirical datasets since a person’s actual confidence judgments are redundant (median correlations ranged between .50 and .85). Averaging two confidence judgments from the same person was superior to maximizing, with Cohen’s d’s effect sizes ranging from 0.67–1.44. As people typically have no insight about the wickedness of the individual item, our results suggest that averaging—due to its robustness—should be the default strategy to harness one’s conflicting confidence judgments.

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.

The canonical conclusion from research on age differences in risky choice is that older adults are more risk averse than younger adults, at least in choices involving gains. Most of the evidence for this conclusion derives from studies that used a specific type of choice problem: choices between a safe and a risky option. However, safe and risky options differ not only in the degree of risk but also in the amount of information to be processed-that is, in their complexity. In both an online and a lab experiment, we demonstrate that differences in option complexity can be a key driver of age differences in risk attitude. When the complexity of the safe option is increased, older adults no longer seem more risk averse than younger adults (in gains). Using computational modeling, we test mechanisms that potentially underlie the effect of option complexity. The results show that participants are not simply averse to complexity, and that increasing the complexity of safe options does more than simply make responses more noisy. Rather, differences in option complexity affect the processing of attribute information: whereas the availability of a simple safe option is associated with the distortion of probability weighting and lower outcome sensitivity, these effects are attenuated when both options are more similar in complexity. We also dissociate these effects of option complexity from an effect of certainty. Our findings may also have implications for age differences in other decision phenomena (e.g., framing effect, loss aversion, immediacy effect). (PsycInfo Database Record (c) 2020 APA, all rights reserved).

We select high-performing individuals and groups by looking at how similar individuals’ decisions are to the decisions of others., Distinguishing between high- and low-performing individuals and groups is of prime importance in a wide range of high-stakes contexts. While this is straightforward when accurate records of past performance exist, these records are unavailable in most real-world contexts. Focusing on the class of binary decision problems, we use a combined theoretical and empirical approach to develop and test a approach to this important problem. First, we use a general mathematical argument and numerical simulations to show that the similarity of an individual’s decisions to others is a powerful predictor of that individual’s decision accuracy. Second, testing this prediction with several large datasets on breast and skin cancer diagnostics, geopolitical forecasting, and a general knowledge task, we find that decision similarity robustly permits the identification of high-performing individuals and groups. Our findings offer a simple, yet broadly applicable, heuristic for improving real-world decision-making systems.

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.

One of the most discussed issues in psychology-presently and in the past-is how to define and measure the extent to which human cognition is rational. The rationality of human cognition is often evaluated in terms of normative standards based on a priori intuitions. Yet this approach has been challenged by two recent developments in psychology that we review in this article: ecological rationality and descriptivism. Going beyond these contributions, we consider it a good moment for psychologists and philosophers to join forces and work toward a new foundation for the definition of rational cognition. We take a first step in this direction by proposing that the rationality of both cognitive and normative systems can be measured in terms of their cognitive success. Cognitive success can be defined and gauged in terms of two factors: ecological validity (the system's validity in conditions in which it is applicable) and the system's applicability (the scope of conditions under which it can be applied). As we show, prominent systems of reasoning-deductive reasoning, Bayesian reasoning, uncertain conditionals, and prediction and choice-perform rather differently on these two factors. Furthermore, we demonstrate that conceptualizing rationality according to its cognitive success offers a new perspective on the time-honored relationship between the descriptive ("is") and the normative ("ought") in psychology and philosophy.

Risk preference is one of the most important building blocks of choice theories in the behavioural sciences. In economics, it is often conceptualized as preferences concerning the variance of monetary payoffs, whereas in psychology, risk preference is often thought to capture the propensity to engage in behaviour with the potential for loss or harm. Both concepts are associated with distinct measurement traditions: economics has traditionally relied on behavioural measures, while psychology has often relied on self-reports. We review three important gaps that have emerged from work stemming from these two measurement traditions: first, a description–experience gap which suggests that behavioural measures do not speak with one voice and can give very different views on an individual's appetite for risk; second, a behaviour–self-report gap which suggests that different self-report measures, but not behavioural measures, show a high degree of convergent validity; and, third, a temporal stability gap which suggests that self-reports, but not behavioural measures, show considerable temporal stability across periods of years. Risk preference, when measured through self-reports—but not behavioural tests—appears as a moderately stable psychological trait with both general and domain-specific components. We argue that future work needs to address the gaps that have emerged from the two measurement traditions and test their differential predictive validity for important economic, health and well-being outcomes. This article is part of the theme issue ‘Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications’.

Past empirical work suggests that aging is associated with decreases in risk taking. But are such effects universal? Life-history theory suggests that the link between age and risk taking is a function of specific reproductive strategies that can be more or less risky depending on the ecology. We assessed variation in the age-risk curve using World Values Survey data from 77 countries ( N = 147,118). The results suggest that propensity for risk taking tends to decline across the life span in the vast majority of countries. In addition, there is systematic variation among countries: Countries in which hardship (e.g., high infant mortality) is higher are characterized by higher levels of risk taking and flatter age-risk curves. These findings suggest that hardship may function as a cue to guide life-history strategies. Age-risk relations thus cannot be understood without reference to the demands and affordances of the environment.

For centuries, decision scholars paid little attention to emotions: Decisions were modeled in normative and descriptive frameworks with little regard for affective processes. Recently, however, an “emotions revolution” has taken place, particularly in the neuroscientific study of decision making, putting emotional processes on an equal footing with cognitive ones. Yet disappointingly little theoretical progress has been made. The concepts and processes discussed often remain vague, and conclusions about the implications of emotions for rationality are contradictory and muddled. We discuss three complementary ways to move the neuroscientific study of emotion and decision making from agenda setting to theory building. The first is to use reverse inference as a hypothesis-discovery rather than a hypothesis-testing tool, unless its utility can be systematically quantified (e.g., through meta-analysis). The second is to capitalize on the conceptual inventory advanced by the behavioral science of emotions, testing those concepts and unveiling the underlying processes. The third is to model the interplay between emotions and decisions, harnessing existing cognitive frameworks of decision making and mapping emotions onto the postulated computational processes. To conclude, emotions (like cognitive strategies) are not rational or irrational per se: How (un)reasonable their influence is depends on their fit with the environment.

Psychology offers conceptual and analytic tools that can advance the discussion on the nature of risk preference and its measurement in the behavioral sciences. We discuss the revealed and stated preference measurement traditions, which have coexisted in both psychology and economics in the study of risk preferences, and explore issues of temporal stability, convergent validity, and predictive validity with regard to measurement of risk preferences. As for temporal stability, do risk preference as a psychological trait show a degree of stability over time that approximates what has been established for other major traits, such as intelligence, or, alternatively, are they more similar in stability to transitory psychological states, such as emotional states? Convergent validity refers to the degree to which different measures of a psychological construct capture a common underlying characteristic or trait. Do measures of risk preference all capture a unitary psychological trait that is indicative of risky behavior across various domains, or do they capture various traits that independently contribute to risky behavior in specific areas of life, such as financial, health, and recreational domains? Predictive validity refers to the extent to which a psychological trait has power in forecasting behavior. Intelligence and major personality traits have been shown to predict important life outcomes, such as academic and professional achievement, which suggests there could be studies of the short- and long-term outcomes of risk preference— something lacking in current psychological (and economic) research. We discuss the current empirical knowledge on risk preferences in light of these considerations.

Millions of volunteers take part in clinical trials every year. This is unsurprising, given that clinical trials are often much more lucrative than other types of unskilled work. When clinical trials offer very high pay, however, some people consider them repugnant. To understand why, we asked 1,428 respondents to evaluate a hypothetical medical trial for a new Ebola vaccine offering three different payment amounts. Some respondents (27%) used very high pay (£10,000) as a cue to infer the potential risks the clinical trial posed. These respondents were also concerned that offering £10,000 was coercive—simply too profitable to pass up. Both perceived risk and coercion in high-paying clinical trials shape how people evaluate these trials. This result was robust within and between respondents. The link between risk and repugnance may generalize to other markets in which parties are partially remunerated for the risk they take and contributes to a more complete understanding of why some market transactions appear repugnant.

There is a disconnect in the literature between analyses of risky choice based on cumulative prospect theory (CPT) and work on predecisional information processing. One likely reason is that for expectation models (e.g., CPT), it is often assumed that people behaved only as if they conducted the computations leading to the predicted choice and that the models are thus mute regarding information processing. We suggest that key psychological constructs in CPT, such as loss aversion and outcome and probability sensitivity, can be interpreted in terms of attention allocation. In two experiments, we tested hypotheses about specific links between CPT parameters and attentional regularities. Experiment 1 used process tracing to monitor participants' predecisional attention allocation to outcome and probability information. As hypothesized, individual differences in CPT's loss-aversion, outcome-sensitivity, and probability-sensitivity parameters (estimated from participants' choices) were systematically associated with individual differences in attention allocation to outcome and probability information. For instance, loss aversion was associated with the relative attention allocated to loss and gain outcomes, and a more strongly curved weighting function was associated with less attention allocated to probabilities. Experiment 2 manipulated participants' attention to losses or gains, causing systematic differences in CPT's loss-aversion parameter. This result indicates that attention allocation can to some extent cause choice regularities that are captured by CPT. Our findings demonstrate an as-if model's capacity to reflect characteristics of information processing. We suggest that the observed CPT-attention links can be harnessed to inform the development of process models of risky choice. (PsycINFO Database Record

Worldwide, more than one million people die on the roads each year. A third of these fatal accidents are attributed to speeding, with properties of the individual driver and the environment regarded as key contributing factors. We examine real-world speeding behavior and its interaction with illuminance, an environmental property defined as the luminous flux incident on a surface. Drawing on an analysis of 1.2 million vehicle movements, we show that reduced illuminance levels are associated with increased speeding. This relationship persists when we control for factors known to influence speeding (e.g., fluctuations in traffic volume) and consider proxies of illuminance (e.g., sight distance). Our findings add to a long-standing debate about how the quality of visual conditions affects drivers’ speed perception and driving speed. Policy makers can intervene by educating drivers about the inverse illuminance‒speeding relationship and by testing how improved vehicle headlights and smart road lighting can attenuate speeding.

Why do grandparents invest so heavily in their grandchildren and what impact does this investment have on families? A multitude of factors influence the roles grandparents play in their families. Here, we present an interdisciplinary perspective of grandparenting incorporating theory and research from evolutionary biology, sociology and economics. Discriminative grandparental solicitude, biological relatedness and the impact of resource availability are three phenomena used to illustrate how these perspectives, within such a multi-level approach, add value by complementing not competing with each other. Changing demographics mean there is greater demand and opportunity for actively engaged grandparents to help their families, especially in times of need. Grandparents have been filling this emerging niche because in some societies the role of community and government never has, or increasingly cannot, meet the diverse needs of families. Built on an empirical foundation of descriptive and correlational research, grandparent research has rapidly entered a phase where the potential causal relationships between grandparents’ roles and family health, well-being and structure can be scrutinised. Together, these investigations are producing high-quality evidence that ultimately can support informed public policy and service delivery decisions. We finish by detailing two examples of such research efforts that highlight opportunities for future research.

Regenwetter and Robinson (2017) discuss a challenging construct-behavior gap in psychological research. It can emerge when testing hypotheses that pertain to a theoretical construct (e.g., preferences) on the basis of observed behavior (e.g., actual choices). The problem is that the different heuristic methods that are sometimes used to link overt choices to covert preferences may ignore heterogeneity between and within individuals, rendering inferences drawn from choices to preferences invalid. Regenwetter and Robinson's remedy is to make heterogeneity an explicit part of the theory. They illustrate the problem and a remedy to it with the description-experience gap (D-E gap), the systematic gap in choices based on described versus 'experienced' probabilities. We welcome their sophisticated reanalysis of some early data sets, which, by taking heterogeneity into account, finds strong evidence for a D-E gap in probability weighting. Yet we see three issues with the remedy, which we likewise highlight using the D-E gap. First, the D-E gap cannot be reduced solely to probability weighting but rather unfolds across several different psychological constructs suggesting that part of the construct-behavior gap may stem from trying to reduce multidimensional behavior to a single construct. Second, the authors' modeling of heterogeneity leaves aside the heterogeneity of people's sampled experience in decisions from experience, which highlights the importance of also considering the potential causes of heterogeneity. Third, we identify potential sources of heterogeneity in choice behavior that go beyond probabilistic responses and preferences and advocate for a pluralistic approach to modeling it. Last but not least, we emphasize that, notwithstanding the importance of rigor and logical coherence in scientific theories, simplifications and (false) generalizations are indispensable in the pursuit of scientific knowledge. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Experience and description are powerful ways of learning and adaptation. Recently, evidence has shown that these can imply systematically distinct cognitions and behaviors. However, there has been ...

People can learn about the probabilistic consequences of their actions in two ways: One is by consulting descriptions of an action's consequences and probabilities (e.g., reading up on a medication's side effects). The other is by personally experiencing the probabilistic consequences of an action (e.g., beta testing software). In principle, people taking each route can reach analogous states of knowledge and consequently make analogous decisions. In the last dozen years, however, research has demonstrated systematic discrepancies between description- and experienced-based choices. This description-experience gap has been attributed to factors including reliance on a small set of experience, the impact of recency, and different weighting of probability information in the two decision types. In this meta-analysis focusing on studies using the sampling paradigm of decisions from experience, we evaluated these and other determinants of the decision-experience gap by reference to more than 70,000 choices made by more than 6,000 participants. We found, first, a robust description-experience gap but also a key moderator, namely, problem structure. Second, the largest determinant of the gap was reliance on small samples and the associated sampling error: free to terminate search, individuals explored too little to experience all possible outcomes. Third, the gap persisted when sampling error was basically eliminated, suggesting other determinants. Fourth, the occurrence of recency was contingent on decision makers' autonomy to terminate search, consistent with the notion of optional stopping. Finally, we found indications of different probability weighting in decisions from experience versus decisions from description when the problem structure involved a risky and a safe option. (PsycINFO Database Record

People's choices between prospects with relatively affect-rich outcomes (e.g., medical side effects) can diverge markedly from their choices between prospects with relatively affect-poor outcomes (e.g., monetary losses). We investigate the cognitive mechanisms underlying this 'affect gap' in risky choice. One possibility is that affect-rich prospects give rise to more distortion in probability weighting. Another is that they lead to the neglect of probabilities. To pit these two possibilities against each other, we fitted cumulative prospect theory (CPT) to the choices of individual participants, separately for choices between options with affect-rich outcomes (adverse medical side effects) and options with affect-poor outcomes (monetary losses); additionally, we tested a simple model of probability neglect, the minimax rule. The results indicated a qualitative difference in cognitive mechanisms between the affect-rich and affect-poor problems. Specifically, in affect-poor problems, the large majority of participants were best described by CPT; in affect-rich problems, the proportion of participants best described by the minimax rule was substantially higher. The affect gap persisted even when affect-rich outcomes were supplemented by numerical information, thus providing no support for the thesis that choices in affect-rich and affect-poor problems diverge because the information provided in the former is nonnumerical. Our findings suggest that the traditional expectation-based framework for modeling risky decision making may not readily generalize to affect-rich choices. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

If citizens' behavior threatens to harm others or seems not to be in their own interest (e.g., risking severe head injuries by riding a motorcycle without a helmet), it is not uncommon for governments to attempt to change that behavior. Governmental policy makers can apply established tools from the governmental toolbox to this end (e.g., laws, regulations, incentives, and disincentives). Alternatively, they can employ new tools that capitalize on the wealth of knowledge about human behavior and behavior change that has been accumulated in the behavioral sciences (e.g., psychology and economics). Two contrasting approaches to behavior change are nudge policies and boost policies. These policies rest on fundamentally different research programs on bounded rationality, namely, the heuristics and biases program and the simple heuristics program, respectively. This article examines the policy---theory coherence of each approach. To this end, it identifies the necessary assumptions underlying each policy and analyzes to what extent these assumptions are implied by the theoretical commitments of the respective research program. Two key results of this analysis are that the two policy approaches rest on diverging assumptions and that both suffer from disconnects with the respective theoretical program, but to different degrees: Nudging appears to be more adversely affected than boosting does. The article concludes with a discussion of the limits of the chosen evaluative dimension, policy---theory coherence, and reviews some other benchmarks on which policy programs can be assessed.

The description–experience gap refers to the robust finding that learning about uncertain options via description or experience results in systematically different choices. This gap has previously been studied primarily with monetary gambles. Here, we examine search and choice processes in decisions from experience involving medical outcomes (side effects of medication). We compare these processes both to decisions from experience involving monetary gambles and to decisions from description involving the same medical outcomes. As in the monetary domain, we found a description–experience gap in medical choices. Yet we also found four striking differences between medical and monetary choices. First, medical choices were significantly less consistent with the maximization of expected value than were monetary choices from description or experience. Second, medical choices gave rise to more strongly inverse S-shaped probability weighting functions in decisions from description and experience, suggesting considerably lower probability sensitivity in the medical than the monetary domain. Third, participants gathered considerably less information in medical than in monetary decisions from experience. Finally, we found that minimax—a simple decision rule that aims to minimize the maximum possible loss—predicted medical choices substantially better than monetary choices, in decisions from both description and experience. Copyright © 2015 John Wiley & Sons, Ltd.

In nonmonotonic decision problems, the magnitude of outcomes can both increase and decrease over time depending on the state of the decision problem. These increases and decreases may occur repeatedly and result in a variety of possible outcome distributions. In many previously investigated sequential decision problems, in contrast, outcomes (or the probabilities of obtaining specific outcomes) change monotonically in 1 direction. To investigate how and to what extent people learn in nonmonotonic decision problems, we developed a new task, the Sequential Investment Task (SIT), in which people sequentially decide whether or not to sell shares at several selling points over the course of virtual days. Across trials, they can learn which selling point yields the highest payoff in a specific market. The results of 2 experiments suggest that a reinforcement-learning model generally describes participants’ learning processes best. Learning largely depends on an interaction of the complexity of the stochastic process that generates the outcome distribution (i.e., whether the peak selling point is early or late in the selling period and whether there are single or multiple payoff maxima) and the amount of feedback that is available for learning. Although the risk profile in nonmonotonic decision problems renders exploration relatively safe, a clear gap persisted between the choices of people receiving partial feedback (thus facing an exploration–exploitation trade-off) and those of people receiving full feedback: Only the choices of the latter consistently approximated the peak selling points.

In recent years, policy makers worldwide have begun to acknowledge the potential value of insights from psychology and behavioral economics into how people make decisions. These insights can inform the design of nonregulatory and nonmonetary policy interventions—as well as more traditional fiscal and coercive measures. To date, much of the discussion of behaviorally informed approaches has emphasized “nudges,” that is, interventions designed to steer people in a particular direction while preserving their freedom of choice. Yet behavioral science also provides support for a distinct kind of nonfiscal and noncoercive intervention, namely, “boosts.” The objective of boosts is to foster people’s competence to make their own choices—that is, to exercise their own agency. Building on this distinction, we further elaborate on how boosts are conceptually distinct from nudges: The two kinds of interventions differ with respect to (a) their immediate intervention targets, (b) their roots in different research programs, (c) the causal pathways through which they affect behavior, (d) their assumptions about human cognitive architecture, (e) the reversibility of their effects, (f) their programmatic ambitions, and (g) their normative implications. We discuss each of these dimensions, provide an initial taxonomy of boosts, and address some possible misconceptions.

The predictive power of cumulative prospect theory and expected utility theory is typically compared using decisions from description, where lotteries' outcome values and probabilities are explicitly stated. In decisions from experience, individuals sample (in the sampling paradigm without cost) from the return distributions to learn outcome values and their relative frequencies; here cumulative prospect theory and expected utility theory require the calculation of probabilities from experience. Individuals, however, may be more attuned to the experienced moments of outcome distributions, rather than the probabilities. We therefore test the mean-variance-skewness model, and retrieve the proportion of expected utility theory (over income), cumulative prospect theory, and mean-variance-skewness populations using a latent-class hierarchical Bayesian model across six large datasets. For simple lotteries (with 1-2 outcomes), we find a mixture of cumulative prospect theory and mean-variance-skewness populations in decisions from both description and experience. For more complex lotteries (with 2-3 outcomes), all participants are classified as cumulative prospect theory types in decisions from description, but as mean-variance-skewness types in decisions from experience. This suggests that in decisions from experience with more complex return distributions, preferences for skewness are more predictive than nonlinear probability weighting.

Although actuarial data indicate that risk-taking behavior peaks in adolescence, laboratory evidence for this developmental spike remains scarce. One possible explanation for this incongruity is that in the real world adolescents often have only vague information about the potential consequences of their behavior and the likelihoods of those consequences, whereas in the lab these are often clearly stated. How do adolescents behave under such more realistic conditions of ambiguity and uncertainty? We asked 105 participants aged from 8 to 22 years to make three types of choices: (1) choices between options whose possible outcomes and probabilities were fully described (choices under risk); (2) choices between options whose possible outcomes were described but whose probability information was incomplete (choices under ambiguity), and (3) choices between unknown options whose possible outcomes and probabilities could be explored (choices under uncertainty). Relative to children and adults, two adolescent-specific markers emerged. First, adolescents were more accepting of ambiguity; second, they were also more accepting of uncertainty (as indicated by shorter pre-decisional search). Furthermore, this tolerance of the unknown was associated with motivational, but not cognitive, factors. These findings offer novel insights into the psychology of adolescent risk taking.

Grandparenting has been proposed as an ultimate evolutionary mechanism that has contributed to the increase in human life expectancy (see the grandmother hypothesis). The neural and hormonal system – originally rooted in parenting and thus grandparenting – that is activated in the process of caregiving has been suggested as a potential proximate mechanism that promotes engagement in prosocial behavior towards kin and non-kin alike. Evidence and theory suggest that activating this caregiving system positively impacts health and may reduce the mortality of the helper. Although some studies have found grandparental care to have beneficial effects on grandparents' health outcomes, most studies have focused on the detrimental health consequences of providing custodial care for grandchildren. Little is known about how non-custodial grandparental and other forms of caregiving relate to mortality hazards for the care provider. Using an evolutionary framework, we examined whether caregiving within and beyond the family is related to mortality in older adults. Survival analyses based on data from the Berlin Aging Study revealed that mortality hazards for grandparents who provided non-custodial childcare were 37% lower than for grandparents who did not provide childcare and for non-grandparents. These associations held after controlling for physical health, age, socioeconomic status and various characteristics of the children and grandchildren. Furthermore, the effect of caregiving extended to non-grandparents and to childless older adults who helped beyond their families. Potential ultimate and proximate mechanisms underlying these effects are discussed.

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.

On the basis of 39 risk-taking measures, this study finds evidence for a general and stable factor of risk preference., To what extent is there a general factor of risk preference, R, akin to g, the general factor of intelligence? Can risk preference be regarded as a stable psychological trait? These conceptual issues persist because few attempts have been made to integrate multiple risk-taking measures, particularly measures from different and largely unrelated measurement traditions (self-reported propensity measures assessing stated preferences, incentivized behavioral measures eliciting revealed preferences, and frequency measures assessing actual risky activities). Adopting a comprehensive psychometric approach (1507 healthy adults completing 39 risk-taking measures, with a subsample of 109 participants completing a retest session after 6 months), we provide a substantive empirical foundation to address these issues, finding that correlations between propensity and behavioral measures were weak. Yet, a general factor of risk preference, R, emerged from stated preferences and generalized to specific and actual real-world risky activities (for example, smoking). Moreover, R proved to be highly reliable across time, indicative of a stable psychological trait. Our findings offer a first step toward a general mapping of the construct risk preference, which encompasses both general and domain-specific components, and have implications for the assessment of risk preference in the laboratory and in the wild.

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.

Until recently, loss aversion has been inferred exclusively from choice asymmetries in the loss and gain domains. This study examines the impact of the prospect of losses on exploratory search in a situation in which exploration is costly. Taking advantage of the largest available data set of decisions from experience, analyses showed that most people explore payoff distributions more under the threat of a loss than under the promise of a gain. This behavioral regularity thus occurs in both costly search and cost-free search (see Lejarraga, Hertwig, & Gonzalez, Cognition, 124, 334–342, 2012). Furthermore, a model comparison identified the simple win-stay-lose-shift heuristic as a likely candidate mechanism behind the loss–gain exploration asymmetry observed. In contrast, models assuming loss aversion failed to reproduce the asymmetry. Moreover, the asymmetry was not simply a precursor of loss aversion but a phenomenon separate from it. These findings are consistent with the recently proposed notion of intensified vigilance in the face of potential losses. Electronic supplementary material The online version of this article (doi:10.3758/s13423-016-1158-7) contains supplementary material, which is available to authorized users.

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 a novel 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.

In recent years, public officials have shown a growing interest in using evidence from the behavioural sciences to promote policy goals. Much of the discussion of behaviourally informed approaches has focused on ‘nudges’; that is, non-fiscal and non-regulatory interventions that steer (nudge) people in a specific direction while preserving choice. Less attention has been paid to boosts, an alternative evidence-based class of non-fiscal and non-regulatory intervention. The goal of boosts is to make it easier for people to exercise their own agency in making choices. For instance, when people are at risk of making poor health, medical or financial choices, the policy-maker – rather than steering behaviour through nudging – can take action to foster or boost individuals’ own decision-making competences. Boosts range from interventions that require little time and cognitive effort on the individual's part to ones that require substantial amounts of training, effort and motivation. This article outlines six rules that policy-makers can apply in order to determine under which conditions boosts, relative to nudges, are the preferable form of non-fiscal and non-regulatory intervention. The objective is not to argue that boosts are better than nudges or vice versa, but to begin to spell out the two approaches’ respective conditions for success.