Older adults select different but not simpler strategies than younger adults in risky choice.

Younger and older adults often differ in their risky choices. Theoretical frameworks on human aging point to various cognitive and motivational factors that might underlie these differences. Using a novel computational model based on the framework of resource rationality, we find that the two age groups rely on different strategies. Importantly, older adults did not use simpler strategies than younger adults, they did not select among fewer strategies, they did not make more errors, and they did not put more weight on cognitive costs. Instead, older adults selected strategies that had different risk propensities than those selected by younger adults. Our modeling approach suggests that age differences in risky choice are not necessarily a consequence of cognitive decline; instead, they may reflect motivational differences between age groups.

Linking cognitive and affective heuristic cues to interpersonal risk perceptions and behavior.

People often use cognitive and affective heuristics when judging the likelihood of a health outcome and making health decisions. However, little research has examined how heuristics shape risk perceptions and behavior among people who make decisions on behalf of another person. We examined associations between heuristic cues and caregivers' perceptions of their child's asthma risk, the frequency of caregivers' asthma management behaviors, and child health outcomes. We used Ipsos KnowledgePanel to recruit 814 U.S. adult caregivers of children with asthma of the age <18 years. Participants completed a survey at baseline (T1) and 3 months later (T2). Caregivers who, at T1, reported greater negative affect about their child's asthma (affect heuristic cue), greater ease of imagining their child experiencing asthma symptoms (availability heuristic cue), and greater perceived similarity between their child and a child who has ever experienced asthma symptoms (representativeness heuristic cue) reported statistically significantly (p < 0.05) higher interpersonal perceived risk of their child having an exacerbation or uncontrolled asthma at T1. They also indicated at T2 that their child had poorer asthma control and more frequent exacerbations. Greater T1 negative affect was associated with more frequent T2 actions to reduce inflammation, manage triggers, and manage symptoms, and with poorer T2 child health outcomes. Heuristic cues are likely important for interpersonal-not just personal-risk perceptions. However, the interrelationship between caregivers' ratings of heuristic cues (in particular, negative affect) and risk judgments may signify a struggle with managing their child's asthma and need for extra support from health care providers or systems.

Strategy selection in decisions from givens: Deciding at a glance?

People deciding between alternatives have at their disposal a toolbox containing both compensatory strategies, which take into account all available attributes of those alternatives, and noncompensatory strategies, which consider only some of the attributes. It is commonly assumed that noncompensatory strategies play only a minor role in decisions from givens, where attribute information is openly presented, because all attributes can be processed automatically "at a glance." Based on a literature review, however, I establish that previous studies on strategy selection in decisions from givens have yielded highly heterogeneous findings, including evidence of widespread use of noncompensatory strategies. Drawing on insights from visual attention research on subitizing, I argue that this heterogeneity might be due to differences across studies in the number of attributes and in whether the same or different symbols are used to represent high/low attribute values across attributes. I tested the impact of these factors in two experiments with decisions from givens in which both the number of attributes shown for each alternative and the coding of attribute values was manipulated. An analysis of participants' strategy use with a Bayesian multimethod approach (taking into account both decisions and response-time patterns) showed that a noncompensatory strategy was more frequently selected in conditions with a higher number of attributes; the type of attribute coding scheme did not affect strategy selection. Using a compensatory strategy in the conditions with eight (vs. four) attributes was associated with rather long response times and a high rate of strategy execution errors. The results suggest that decisions from givens can incur cognitive costs that prohibit reliance on automatic compensatory decision making and that can favor the adaptive selection of a noncompensatory strategy.

Developmental differences in description-based versus experience-based decision making under risk in children.

The willingness to take a risk is shaped by temperaments and cognitive abilities, both of which develop rapidly during childhood. In the adult developmental literature, a distinction is drawn between description-based tasks, which provide explicit choice-reward information, and experience-based tasks, which require decisions from past experience, each emphasizing different cognitive demands. Although developmental trends have been investigated for both types of decisions, few studies have compared description-based and experience-based decision making in the same sample of children. In the current study, children (N = 112; 5-9 years of age) completed both description-based and experience-based decision tasks tailored for use with young children. Child temperament was reported by the children's primary teacher. Behavioral measures suggested that the willingness to take a risk in a description-based task increased with age, whereas it decreased in an experience-based task. However, computational modeling alongside further inspection of the behavioral data suggested that these opposite developmental trends across the two types of tasks both were associated with related capacities: older (vs. younger) children's higher sensitivity to experienced losses and higher outcome sensitivity to described rewards and losses. From the temperamental characteristics, higher attentional focusing was linked with a higher learning rate on the experience-based task and a bias to accept gambles in the gain domain on the description-based task. Our findings demonstrate the importance of comparing children's behavior across qualitatively different tasks rather than studying a single behavior in isolation.

Good + Bad = ? Developmental Differences in Balancing Gains and Losses in Value-Based Decisions From Memory.

Value-based decisions often involve comparisons between benefits and costs that must be retrieved from memory. To investigate the development of value-based decisions, 9- to 10-year olds (N = 30), 11- to 12-year olds (N = 30), and young adults (N = 30) first learned to associate gain and loss magnitudes with symbols. In a subsequent decision task, participants rapidly evaluated objects that consisted of combinations of these symbols. All age groups achieved high decision performance and were sensitive to gain-loss magnitudes, suggesting that required core cognitive abilities are developed early. A cognitive-modeling analysis of performance revealed that children were less efficient in object evaluation (drift rate) and had longer nondecision times than adults. Developmental differences, which emerged particularly for objects of high positive net value, were linked to mnemonic and numerical abilities.

How to model the neurocognitive dynamics of decision making: A methodological primer with ACT-R.

Higher cognitive functions are the product of a dynamic interplay of perceptual, mnemonic, and other cognitive processes. Modeling the interplay of these processes and generating predictions about both behavioral and neural data can be achieved with cognitive architectures. However, such architectures are still used relatively rarely, likely because working with them comes with high entry-level barriers. To lower these barriers, we provide a methodological primer for modeling higher cognitive functions and their constituent cognitive subprocesses with arguably the most developed cognitive architecture today-ACT-R. We showcase a principled method of generating individual response time predictions, and demonstrate how neural data can be used to refine ACT-R models. To illustrate our approach, we develop a fully specified neurocognitive model of a prominent strategy for memory-based decisions-the take-the-best heuristic-modeling decision making as a dynamic interplay of perceptual, motor, and memory processes. This implementation allows us to predict the dynamics of behavior and the temporal and spatial patterns of brain activity. Moreover, we show that comparing the predictions for brain activity to empirical BOLD data allows us to differentiate competing ACT-R implementations of take the best.

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.

Social contact patterns can buffer costs of forgetting in the evolution of cooperation.

Analyses of the evolution of cooperation often rely on two simplifying assumptions: (i) individuals interact equally frequently with all social network members and (ii) they accurately remember each partner's past cooperation or defection. Here, we examine how more realistic, skewed patterns of contact-in which individuals interact primarily with only a subset of their network's members-influence cooperation. In addition, we test whether skewed contact patterns can counteract the decrease in cooperation caused by memory errors (i.e. forgetting). Finally, we compare two types of memory error that vary in whether forgotten interactions are replaced with random actions or with actions from previous encounters. We use evolutionary simulations of repeated prisoner's dilemma games that vary agents' contact patterns, forgetting rates and types of memory error. We find that highly skewed contact patterns foster cooperation and also buffer the detrimental effects of forgetting. The type of memory error used also influences cooperation rates. Our findings reveal previously neglected but important roles of contact pattern, type of memory error and the interaction of contact pattern and memory on cooperation. Although cognitive limitations may constrain the evolution of cooperation, social contact patterns can counteract some of these constraints.

Dynamic cognitive models of intertemporal choice.

Traditionally, descriptive accounts of intertemporal choice have relied on static and deterministic models that assume alternative-wise processing of the options. Recent research, by contrast, has highlighted the dynamic and probabilistic nature of intertemporal choice and provided support for attribute-wise processing. Currently, dynamic models of intertemporal choice-which account for both the resulting choice and the time course over which the construction of a choice develops-rely exclusively on the framework of evidence accumulation. In this article, we develop and rigorously compare several candidate schemes for dynamic models of intertemporal choice. Specifically, we consider an existing dynamic modeling scheme based on decision field theory and develop two novel modeling schemes-one assuming lexicographic, noncompensatory processing, and the other built on the classical concepts of random utility in economics and discrimination thresholds in psychophysics. We show that all three modeling schemes can accommodate key behavioral regularities in intertemporal choice. When empirical choice and response time data were fit simultaneously, the models built on random utility and discrimination thresholds performed best. The results also indicated substantial individual differences in the dynamics underlying intertemporal choice. Finally, model recovery analyses demonstrated the benefits of including both choice and response time data for more accurate model selection on the individual level. The present work shows how the classical concept of random utility can be extended to incorporate response dynamics in intertemporal choice. Moreover, the results suggest that this approach offers a successful alternative to the dominating evidence accumulation approach when modeling the dynamics of decision making.

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.

Patients' Values in Clinical Decision-Making.

OBJECTIVE: Shared decision-making involves the participation of patient and dental practitioner. Well-informed decision-making requires that both parties understand important concepts that may influence the decision. This fourth article in a series of 4 aims to discuss the importance of patients' values when a clinical decision is made. METHODS: We report on how to incorporate important concepts for well-informed, shared decision-making. Here, we present patient values as an important issue, in addition to previously established topics such as the risk of bias of a study, cost-effectiveness of treatment approaches, and a comparison of therapeutic benefit with potential side effects. RESULTS: We provide 2 clinical examples and suggestions for a decision tree, based on the available evidence. CONCLUSION: The information reported in this article may improve the relationship between patient and dental practitioner, resulting in more well-informed clinical decisions.

Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making.

Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R., Fincham, J. M., Qin, Y., & Stocco, A. A central circuit of the mind. Trends in Cognitive Sciences, 12, 136-143, 2008] and other neural models of memory, we delineated the neural signatures of two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. To disentangle these processes, we combined a paradigm developed to examine neural correlates of selective and sequential memory retrieval in decision-making with a manipulation of associative fan (i.e., the decision options were associated with one, two, or three attributes). The results show that both the automatic activation of all attributes associated with a decision option and the controlled sequential retrieval of specific attributes can be traced in material-specific brain areas. Moreover, the two facets of memory retrieval were associated with distinct activation patterns within the frontoparietal network: The dorsolateral prefrontal cortex was found to reflect increasing retrieval effort during both automatic and controlled activation of attributes. In contrast, the superior parietal cortex only responded to controlled retrieval, arguably reflecting the sequential updating of attribute information in working memory. This dissociation in activation pattern is consistent with ACT-R and constitutes an important step toward a neural model of the retrieval dynamics involved in memory-based decision-making.

The perception of dramatic risks: Biased media, but unbiased minds.

In their famous study on risk judgments, Lichtenstein, Slovic, Fischhoff, Layman, and Combs (1978) concluded that people tend to overestimate the frequencies of dramatic causes of death (e.g., homicide, tornado) and underestimate the frequencies of nondramatic ones (e.g., diabetes, heart disease). Further, their analyses of newspapers indicated that dramatic risks are overrepresented in the media-suggesting that people's distorted risk perceptions might be driven by distortions in media coverage. Although these patterns were not evaluated statistically in the original analyses, the conclusions have become a staple in the social sciences. How reliable are they? And are they replicable? In a systematic literature search, I identified existing replications of Lichtenstein et al.'s investigation and submitted both the original data and the data from the replications to a Bayesian statistical analysis. All datasets indicated very strong evidence for an overrepresentation of dramatic risks and an underrepresentation of nondramatic risks in media coverage. However, a reliable overestimation (underestimation) of dramatic (nondramatic) risks in people's frequency judgments emerged only in Lichtenstein et al.'s dataset; it did not replicate in the other datasets. In fact, aggregated across all datasets, there was evidence for the absence of a differential distortion of dramatic and nondramatic causes of death in people's risk frequency judgments. Additional analyses suggest that when judging risk frequency, people rely on samples from their personal social networks rather than from the media. The results reveal a limited empirical basis for the common notion that distortions in people's risk judgments echo distortions in media coverage. They also suggest that processes of risk frequency judgments include a metacognitive mechanism that is sensitive to the source of mentally available samples.

Over- and underweighting of extreme values in decisions from sequential samples.

People routinely make decisions based on samples of numerical values. A common conclusion from the literature in psychophysics and behavioral economics is that observers subjectively compress magnitudes, such that extreme values have less sway over people's decisions than prescribed by a normative model (underweighting). However, recent studies have reported evidence for anti-compression, that is, the relative overweighting of extreme values. Here, we investigate potential reasons for this discrepancy in findings and propose that it might reflect adaptive responses to different task requirements. We performed a large-scale study (n = 586) of sequential numerical integration, manipulating (a) the task requirement (averaging a single stream or comparing two interleaved streams of numbers), (b) the distribution of sample values (uniform or Gaussian), and (c) their range (1-9 or 100-900). The data showed compression of subjective values in the averaging task, but anticompression in the comparison task. This pattern held for both distribution types and for both ranges. In model simulations, we show that either compression or anticompression can be beneficial for noisy observers, depending on the sample-level processing demands imposed by the task. This suggests that the empirically observed patterns of over- and underweighting might reflect adaptive responses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Managing children's asthma: what role do caregivers' mental representations of trigger and symptom management behaviors play?

OBJECTIVE: Pediatric asthma management is challenging for parents and guardians (hereafter caregivers). We examined (1) how caregivers mentally represent trigger and symptom management strategies, and (2) how those mental representations are associated with actual management behavior. METHODS: In an online survey, N = 431 caregivers of children with asthma rated 20 trigger management behaviors and 20 symptom management behaviors across 15 characteristics, and indicated how often they engaged in each behavior. RESULTS: Principal components analysis indicated 4 dimensions for trigger management behaviors and 3 for symptom management behaviors. Bayesian mixed-effects models indicated that engagement in trigger management behavior was more likely for behaviors rated as affirming caregiver activities. However, trigger management behavior did not depend on how highly the behavior was rated as challenging for caregiver, burdensome on child, or routine caregiving. Engagement in symptom management behavior was more likely for behaviors rated as affirming and common and harmless to the child, but was unrelated to how highly a behavior was rated as challenging for caregivers. CONCLUSION: These results suggest that interventions might be particularly useful if they focus on the affirming nature of asthma management behaviors. However, such interventions should acknowledge structural factors (e.g. poverty) that constrain caregivers' ability to act.

Attribute attention and option attention in risky choice.

Probability weighting is one of the most powerful theoretical constructs in descriptive models of risky choice and constitutes a central component of cumulative prospect theory (CPT). Probability weighting has been shown to be related to two facets of attention allocation: one analysis showed that differences in the shape of CPT's probability-weighting function are linked to differences in how attention is allocated across attributes (i.e., probabilities vs. outcomes); another analysis (that used a different measure of attention) showed a link between probability weighting and differences in how attention is allocated across options. However, the relationship between these two links is unclear. We investigate to what extent attribute attention and option attention independently contribute to probability weighting. Reanalyzing data from a process-tracing study, we first demonstrate links between probability weighting and both attribute attention and option attention within the same data set and the same measure of attention. We then find that attribute attention and option attention are at best weakly related and have independent and distinct effects on probability weighting. Moreover, deviations from linear weighting mainly emerged when attribute attention or option attention were imbalanced. Our analyses enrich the understanding of the cognitive underpinnings of preferences and illustrate that similar probability-weighting patterns can be associated with very different attentional policies. This complicates an unambiguous psychological interpretation of psycho-economic functions. Our findings indicate that cognitive process models of decision making should aim to concurrently account for the effects of different facets of attention allocation on preference. In addition, we argue that the origins of biases in attribute attention and option attention need to be better understood.

Knowledge updating in real-world estimation: Connecting hindsight bias and seeding effects.

When people estimate the quantities of objects (e.g., country populations), are then presented with the objects' actual quantities, and subsequently asked to remember their initial estimates, responses are often distorted towards the actual quantities. This hindsight bias-traditionally considered to reflect a cognitive error-has more recently been proposed to result from adaptive knowledge updating. But how to conceptualize such knowledge-updating processes and their potentially beneficial consequences? Here, we provide a framework that conceptualizes knowledge updating in the context of hindsight bias in real-world estimation by connecting it with research on seeding effects-improvements in people's estimation accuracy after exposure to numerical facts. This integrative perspective highlights a previously neglected facet of knowledge updating, namely, recalibration of metric domain knowledge, which can be expected to lead to transfer learning and thus improve estimation for objects from a domain more generally. We develop an experimental paradigm to investigate the association of hindsight bias with improved estimation accuracy. In Experiment 1, we demonstrate that the classical approach to induce hindsight bias indeed produces transfer learning. In Experiment 2, we provide evidence for the novel prediction that hindsight bias can be triggered via transfer learning; this establishes a direct link from knowledge updating to hindsight bias. Our work integrates two prominent but previously unconnected research programs on the effects of knowledge updating in real-world estimation and supports the notion that hindsight bias is driven by adaptive learning processes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

How are mental representations of asthma triggers and symptoms related to interpersonal risk perceptions? A psychometric investigation of caregivers of children with asthma.

OBJECTIVE: Parents and guardians (hereafter caregivers) make decisions for their children's medical care. However, many caregivers of children with asthma struggle to understand their child's illness. We used the psychometric paradigm to investigate how caregivers conceptualize, or mentally represent, asthma triggers and symptoms and how these representations are linked to perceived asthma exacerbation risk. METHODS: We asked 377 caregivers of children with asthma across the U.S. to rate 20 triggers or 20 symptoms along 15 characteristics. Caregivers also indicated their perceived risk of their child having an asthma exacerbation (hereafter interpersonal risk perceptions). Using principal components analysis, we extracted key dimensions underlying caregivers' ratings on the characteristics. Then we related the triggers' and symptoms' scores on the dimensions to caregivers' interpersonal risk perceptions. RESULTS: Interpersonal risk perceptions were higher for triggers with high ratings for the dimensions severe and relevant, and negative affect-yet manageable, but not chronic-yet unpredictable. Risk perceptions were also higher for symptoms with high ratings for the dimensions severe and unpredictable, and relevant and common, but not self-blame or manageable despite unknown cause. CONCLUSION: By identifying key dimensions underlying caregivers' mental representations of asthma triggers and symptoms, these findings can inform a new approach to asthma education.

Type of learning task impacts performance and strategy selection in decision making.

In order to be adaptive, cognition requires knowledge about the statistical structure of the environment. We show that decision performance and the selection between cue-based and exemplar-based inference mechanisms can depend critically on how this knowledge is acquired. Two types of learning tasks are distinguished: learning by comparison, by which the decision maker learns which of two objects has a higher criterion value, and direct criterion learning, by which the decision maker learns an object's criterion value directly. In three experiments, participants were trained either with learning by comparison or with direct criterion learning and subsequently tested with paired-comparison, classification, and estimation tasks. Experiments 1 and 2 showed that although providing less information, learning by comparison led to better generalization (at test), both when generalizing to new objects and when the task format at test differed from the task format during training. Moreover, learning by comparison enabled participants to provide rather accurate continuous estimates. Computational modeling suggests that the advantage of learning by comparison is due to differences in strategy selection: whereas direct criterion learning fosters the reliance on exemplar processing, learning by comparison fosters cue-based mechanisms. The pattern in decision performance reversed when the task environment was changed from a linear (Experiments 1 and 2) to a nonlinear structure (Experiment 3), where direct criterion learning led to better decisions. Our results demonstrate the critical impact of learning conditions for the subsequent selection of decision strategies and highlight the key role of comparison processes in cognition.