A Cognitive Computational Approach to Social and Collective Decision-Making

Collective dynamics play a key role in everyday decision-making. Whether social influence promotes the spread of accurate information and ultimately results in adaptive behavior or leads to false information cascades and maladaptive social contagion strongly depends on the cognitive mechanisms underlying social interactions. Here we argue that cognitive modeling, in tandem with experiments that allow collective dynamics to emerge, can mechanistically link cognitive processes at the individual and collective levels. We illustrate the strength of this cognitive computational approach with two highly successful cognitive models that have been applied to interactive group experiments: evidence-accumulation and reinforcement-learning models. We show how these approaches make it possible to simultaneously study (a) how individual cognition drives social systems, (b) how social systems drive individual cognition, and (c) the dynamic feedback processes between the two layers.

When alternative hypotheses shape your beliefs: Context effects in probability judgments

When people are asked to estimate the probability of an event occurring, they sometimes make different subjective probability judgments for different descriptions of the same event. This implies the evidence or support recruited to make these judgments is based on the descriptions of the events (hypotheses) instead of the events themselves, as captured by Tversky and Koehler's (1994) support theory. Support theory, however, assumes each hypothesis elicits a fixed level of support (support invariance). Here, across three studies, we tested this support invariance assumption by asking participants to estimate the probability that an event would occur given a set of relevant statistics. We show that the support recruited about a target hypothesis can depend on the other hypotheses under consideration. Results reveal that for a pair of competing hypotheses, one hypothesis (the target hypothesis) appears more competitive relative to the other when a dud-a hypothesis dominated by the target hypothesis-is present. We also find that the target hypothesis can appear less competitive relative to the other when a resembler-a hypothesis that is similar to the target hypothesis-is present. These context effects invalidate the support invariance assumption in support theory and suggest that a similar process that drives preference construction may also underlie belief construction.

Attention biases preferential choice by enhancing an option’s value

Does attending to an option lead to liking it? Though attention-induced valuation is often hypothesized, evidence for this causal link has remained elusive. We test this hypothesis across 2 studies by manipulating attention during a preferential decision and its perceptual analog. In a free-viewing task, attention impacted choice and eye movement pattern in the preferential decision more than the perceptual analog. Similarly, in a controlled-viewing task, attention had a larger effect on choice in the preferential decision than its perceptual analog. Across these experimental manipulations of attention, choice and eye-tracking data provide converging evidence that attention enhances value, and computational modeling further supports this attention-induced valuation hypothesis. A possible explanation for our results is a normalization mechanism where attention induces a gain modulation on an option's representation at both the sensory and value processing levels. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

I'm wearing a mask, but are they?: Perceptions of self-other differences in COVID-19 health behaviors

As information about COVID-19 safety behavior changed, people had to judge how likely others were to protect themselves through mask-wearing and vaccination seeking. In a large, campus-wide survey, we assessed whether University of Kansas students viewed others' protective behaviors as different from their own, how much students assumed others shared their beliefs and behaviors, and which individual differences were associated with those estimations. Participants in our survey (N = 1, 704; 81.04% white, 64.08% female) estimated how likely they and others were to have worn masks on the University of Kansas campus, have worn masks off-campus, and to seek a vaccine. They also completed measures of political preference, numeracy, and preferences for risk in various contexts. We found that participants estimated that others were less likely to engage in health safety behaviors than themselves, but that their estimations of others were widely shared. While, in general, participants saw themselves as more unique in terms of practicing COVID-19 preventative behaviors, more liberal participants saw themselves as more unique, while those that were more conservative saw their own behavior as more similar to others. At least for masking, this uniqueness was false-estimates of others' health behavior were lower than their actual rates. Understanding this relationship could allow for more accurate norm-setting and normalization of mask-wearing and vaccination.

Mathematical modeling of risk-taking in bipolar disorder: Evidence of reduced behavioral consistency, with altered loss aversion specific to those with history of substance use disorder

Bipolar disorder (BD) is associated with excessive pleasure-seeking risk-taking behaviors that often characterize its clinical presentation. However, the mechanisms of risk-taking behavior are not well-understood in BD. Recent data suggest prior substance use disorder (SUD) in BD may represent certain trait-level vulnerabilities for risky behavior. This study examined the mechanisms of risk-taking and the role of SUD in BD via mathematical modeling of behavior on the Balloon Analogue Risk Task (BART). Three groups-18 euthymic BD with prior SUD (BD+), 15 euthymic BD without prior SUD (BD-), and 33 healthy comparisons (HC)-completed the BART. We modeled behavior using 4 competing hierarchical Bayesian models, and model comparison results favored the Exponential-Weight Mean-Variance (EWMV) model, which encompasses and delineates five cognitive components of risk-taking: prior belief, learning rate, risk preference, loss aversion, and behavioral consistency. Both BD groups, regardless of SUD history, showed lower behavioral consistency than HC. BD+ exhibited more pessimistic prior beliefs (relative to BD- and HC) and reduced loss aversion (relative to HC) during risk-taking on the BART. Traditional measures of risk-taking on the BART (adjusted pumps, total points, total pops) detected no group differences. These findings suggest that reduced behavioral consistency is a crucial feature of risky decision-making in BD and that SUD history in BD may signal additional trait vulnerabilities for risky behavior even when mood symptoms and substance use are in remission. This study also underscores the value of using mathematical modeling to understand behavior in research on complex disorders like BD.

Rethinking Rationality

We seek to understand rational decision making and if it exists whether finite (bounded) agents may be able to achieve its principles. This aim has been a singular objective throughout much of human science and philosophy, with early discussions identified since antiquity. More recently, there has been a thriving debate based on differing perspectives on rationality, including adaptive heuristics, Bayesian theory, quantum theory, resource rationality, and probabilistic language of thought. Are these perspectives on rationality mutually exclusive? Are they all needed? Do they undermine an aim to have rational standards in decision situations like politics, medicine, legal proceedings, and others, where there is an expectation and need for decision making as close to "optimal" as possible? This special issue brings together representative contributions from the currently predominant views on rationality, with a view to evaluate progress on these and related questions.

Studies in Ecological Rationality

Ecological rationality represents an alternative to classic frameworks of rationality. Extending on Herbert Simon's concept of bounded rationality, it holds that cognitive processes, including simple heuristics, are not per se rational or irrational, but that their success rests on their degree of fit to relevant environmental structures. The key is therefore to understand how cognitive and environmental structures slot together. In recent years, a growing set of analyses of heuristic-environment systems has deepened the understanding of the human mind and how boundedly rational heuristics can result in successful decision making. This article is concerned with three conceptual challenges in the study of ecological rationality. First, do heuristics also succeed in dynamic contexts involving competitive agents? Second, can the mind adapt to environmental structures via an unsupervised learning process? Third, how can research go beyond mere descriptions of environmental structures to develop theories of the mechanisms that give rise to those structures? In addressing these questions, we illustrate that a successful theory of rationality will focus on the adaptive aspects of the mind and will need to account for three components: the mind's information processing, the environment to which the mind adapts, and the intersection between the environment and the mind.

Temporal oscillations in preference strength provide evidence for an open system model of constructed preference

The decision process is often conceptualized as a constructive process in which a decision maker accumulates information to form preferences about the choice options and ultimately make a response. Here we examine how these constructive processes unfold by tracking dynamic changes in preference strength. Across two experiments, we observed that mean preference strength systematically oscillated over time and found that eliciting a choice early in time strongly affected the pattern of preference oscillation later in time. Preferences following choices oscillated between being stronger than those without prior choice and being weaker than those without choice. To account for these phenomena, we develop an open system dynamic model which merges the dynamics of Markov random walk processes with those of quantum walk processes. This model incorporates two sources of uncertainty: epistemic uncertainty about what preference state a decision maker has at a particular point in time; and ontic uncertainty about what decision or judgment will be observed when a person has some preference state. Representing these two sources of uncertainty allows the model to account for the oscillations in preference as well as the effect of choice on preference formation.

The ecology of competition: A theory of risk-reward environments in adaptive decision making

In many choice environments, risks and rewards-or probabilities and payoffs-seem tightly coupled such that high payoffs only occur with low probabilities. An adaptive mind can exploit this association by, for instance, using a potential reward's size to infer the probability of obtaining it. However, a mind can only adapt to and exploit an environmental structure if it is ecologically reliable, that is if it is frequent and recurrent. We develop the competitive risk-reward ecology theory (CET) that establishes how the ecology of competition can make the association of high rewards with low probabilities ubiquitous. This association occurs because of what is known as the ideal free distribution (IFD) principle. The IFD states that competitors in a landscape of resource patches distribute themselves proportionally to the gross total amount of resources in the patches. CET shows how this principle implies a risk-reward structure: an inverse relationship between probabilities and payoffs. It also identifies boundary conditions for the risk-reward structure, including heterogeneity of resources, computational limits of competitors, and scarcity of resources. Finally, a set of empirical studies (N = 1,255) demonstrate that people's beliefs map onto properties predicted by CET and change as a function of the environment. In sum, grounding people's inferences in CET demonstrates how the behaviors of a boundedly rational mind can be better predicted once accounts of the mind and the environment are fused. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Wise or mad crowds? The cognitive mechanisms underlying information cascades

Whether getting vaccinated, buying stocks, or crossing streets, people rarely make decisions alone. Rather, multiple people decide sequentially, setting the stage for information cascades whereby early-deciding individuals can influence others' choices. To understand how information cascades through social systems, it is essential to capture the dynamics of the decision-making process. We introduce the social drift-diffusion model to capture these dynamics. We tested our model using a sequential choice task. The model was able to recover the dynamics of the social decision-making process, accurately capturing how individuals integrate personal and social information dynamically over time and when their decisions were timed. Our results show the importance of the interrelationships between accuracy, confidence, and response time in shaping the quality of information cascades. The model reveals the importance of capturing the dynamics of decision processes to understand how information cascades in social systems, paving the way for applications in other social systems.

Comparison of Markov versus quantum dynamical models of human decision making

What kind of dynamic decision process do humans use to make decisions? In this article, two different types of processes are reviewed and compared: Markov and quantum. Markov processes are based on the idea that at any given point in time a decision maker has a definite and specific level of support for available choice alternatives, and the dynamic decision process is represented by a single trajectory that traces out a path across time. When a response is requested, a person's decision or judgment is generated from the current location along the trajectory. By contrast, quantum processes are founded on the idea that a person's state can be represented by a superposition over different degrees of support for available choice options, and that the dynamics of this state form a wave moving across levels of support over time. When a response is requested, a decision or judgment is constructed out of the superposition by "actualizing" a specific degree or range of degrees of support to create a definite state. The purpose of this article is to introduce these two contrasting theories, review empirical studies comparing the two theories, and identify conditions that determine when each theory is more accurate and useful than the other. This article is categorized under: Economics > Individual Decision-Making Psychology > Reasoning and Decision Making Psychology > Theory and Methods.

Competitive retrieval strategy causes multimodal response distributions in multiple-cue judgments

Research on quantitative judgments from multiple cues suggests that judgments are simultaneously influenced by previously abstracted knowledge about cue-criterion relations and memories of past instances (or exemplars). Yet extant judgment theories leave 2 questions unanswered: (a) How are past exemplars and abstracted cue knowledge combined to form a judgment? (b) Are all past exemplars retrieved from memory to form the judgment (integrative retrieval) or is the judgment based on one exemplar (competitive retrieval)? To address these questions we propose and test a new model, CX-COM (combining Cue abstraction with eXemplar memory assuming COMpetitive memory retrieval). In a first step, CX-COM recalls only a single exemplar from memory. In a second step, the initially retrieved judgment is adjusted based on abstracted cue knowledge. Qualitatively, we show that CX-COM naturally captures judgment patterns that have been previously attributed to multiple strategies. Next, we tested CX-COM quantitatively in 2 experiments and found that it accounts well for people's judgment behavior. In the second experiment we additionally tested 2 qualitative predictions of CX-COM: The existence of multimodal response distributions within participants and systematic variability in judgments depending on the distance between similar exemplars in memory. The empirical results confirm CX-COM's assumptions. In sum, the evidence suggests that CX-COM is a viable new model for quantitative judgments and shows the importance of considering judgment variability in addition to average responses in judgment research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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.

The construct-behavior gap and the description-experience gap: Comment on Regenwetter and Robinson (2017)

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).

Mechanisms of deliberation during preferential choice: Perspectives from computational modeling and individual differences

Computational models of decision making typically assume as people deliberate between options they mentally simulate outcomes from each one and integrate valuations of these outcomes to form a preference. In two studies, we investigated this deliberation process using a task where participants make a series of decisions between a certain and an uncertain option, which were shown as dynamic visual samples that represented possible payoffs. We developed and validated a method of reverse correlational analysis for the task that measures how this time-varying signal was used to make a choice. The first study used this method to examine how information processing during deliberation differed from a perceptual analog of the task. We found participants were less sensitive to each sample of information during preferential choice. In a second study, we investigated how these different measures of deliberation were related to impulsivity and drug and alcohol use. We found that while properties of the deliberation process were not related to impulsivity, some aspects of the process may be related to substance use. In particular, alcohol abuse was related to diminished sensitivity to the payoff information and drug use was related to how the initial starting point of evidence accumulation. We synthesized our results with a rank-dependent sequential sampling model which suggests that participants allocated more attentional weight to larger potential payoffs during preferential choice.

How race affects evidence accumulation during the decision to shoot

The biasing role of stereotypes is a central theme in social cognition research. For example, to understand the role of race in police officers' decisions to shoot, participants have been shown images of Black and White males and instructed to shoot only if the target is holding a gun. Findings show that Black targets are shot more frequently and more quickly than Whites. The decision to shoot has typically been modeled and understood as a signal detection process in which a sample of information is compared against a criterion, with the criterion set for Black targets being lower. We take a different approach, modeling the decision to shoot as a dynamic process in which evidence is accumulated over time until a threshold is reached. The model accounts for both the choice and response time data for both correct and incorrect decisions using a single set of parameters. Across four studies, this dynamic perspective revealed that the target's race did not create an initial bias to shoot Black targets. Instead, race impacted the rate of evidence accumulation with evidence accumulating faster to shoot for Black targets. Some participants also tended to be more cautious with Black targets, setting higher decision thresholds. Besides providing a more cohesive and richer account of the decision to shoot or not, the dynamic model suggests interventions that may address the use of race information in decisions to shoot and a means to measure their effectiveness.

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.

Advancing Research on Cognitive Processes in Social and Personality Psychology

We provide a primer on a hierarchical extension of the drift diffusion model (DDM). This formal model of decisions is frequently used in the cognitive sciences but infrequently used in social and personality research. Recent advances in model estimation have overcome issues that previously made the hierarchical DDM impractical to implement. Using examples from two paradigms, the first-person shooter task and the flash gambling task, we demonstrate that the hierarchical DDM can provide novel insights into cognitive processes underlying decisions. Finally, we compare the DDM to dual-process models of decision-making. We hope this primer will provide researchers a new tool for investigating psychological processes.

Dynamics of postdecisional processing of confidence

Most cognitive theories assume that confidence and choice happen simultaneously and are based on the same information. The 3 studies presented in this article instead show that confidence judgments can arise, at least in part, from a postdecisional evidence accumulation process. As a result of this process, increasing the time between making a choice and confidence judgment improves confidence resolution. This finding contradicts the notion that confidence judgments are biased by decision makers seeking confirmatory evidence. Further analysis reveals that the improved resolution is due to a reduction in confidence in incorrect responses, while confidence in correct responses remains relatively constant. These results are modeled with a sequential sampling process that allows evidence accumulation to continue after a choice is made and maps the amount of accumulated evidence onto a confidence rating. The cognitive modeling analysis reveals that the rate of evidence accumulation following a choice does slow relative to the rate preceding choice. The analysis also shows that the asymmetry between confidence in correct and incorrect choices is compatible with state-dependent decay in the accumulated evidence: Evidence consistent with the current state results in a deceleration of accumulated evidence and consequently evidence appears to have a decreasing impact on observed confidence. In contrast, evidence inconsistent with the current state results in an acceleration of accumulated evidence toward the opposite direction and consequently evidence appears to have an increasing impact on confidence. Taken together, this process-level understanding of confidence suggests a simple strategy for improving confidence accuracy: take a bit more time to make confidence judgments.

Ecologically rational choice and the structure of the environment

In life, risk is reward and vice versa. Unfortunately, the big rewards people desire are relatively unlikely to occur. This relationship between risk and reward or probabilities and payoffs seems obvious to the financial community and to laypeople alike. Yet theories of decision making have largely ignored it. We conducted an ecological analysis of life's gambles, ranging from the domains of roulette and life insurance to scientific publications and artificial insemination. Across all domains, payoffs and probabilities proved intimately tied, with payoff magnitudes signaling their probabilities. In some cases, the constraints of the market result in these two core elements of choice being related via a power function; in other cases, other factors such as social norms appear to produce the inverse relationship between risks and rewards. We offer evidence that decision makers exploit this relationship in the form of a heuristic--the risk-reward heuristic--to infer the probability of a payoff during decisions under uncertainty. We demonstrate how the heuristic can help explain observed ambiguity aversion. We further show how this ecological relationship can inform other aspects of decision making, particularly the approach of using monetary lotteries to study choice under risk and uncertainty. Taken together, these findings suggest that theories of decision making need to model not only the decision process but also the environment to which the process is adapted.

Making assessments while taking repeated risks: a pattern of multiple response pathways

Beyond simply a decision process, repeated risky decisions also require a number of cognitive processes including learning, search and exploration, and attention. In this article, we examine how multiple response pathways develop over repeated risky decisions. Using the Balloon Analogue Risk Task (BART) as a case study, we show that 2 different response pathways emerge over the course of the task. The assessment pathway is a slower, more controlled pathway where participants deliberate over taking a risk. The 2nd pathway is a faster, more automatic process where no deliberation occurs. Results imply the slower assessment pathway is taken as choice conflict increases and that the faster automatic response is a learned response. Based on these results, we modify an existing formal cognitive model of decision making during the BART to account for these dual response pathways. The slower more deliberative response process is modeled with a sequential sampling process where evidence is accumulated to a threshold, while the other response is given automatically. We show that adolescents with conduct disorder and substance use disorder symptoms not only evaluate risks differently during the BART but also differ in the rate at which they develop the more automatic response. More broadly, our results suggest cognitive models of judgment decision making need to transition from treating observed decisions as the result of a single response pathway to the result of multiple response pathways that change and develop over time.

Comparability effects in probability judgments

Psychological theories of subjective probability judgments assume that accumulated evidence (support) mediates the relation between the description of a to-be-judged event (hypothesis) and the judgment. To scale a probability from the support for a hypothesis, these psychological theories make a strong independence assumption. This assumption is stated in the form of the product rule, in which the support garnered for a particular hypothesis is independent of the support for the alternative hypothesis. In the study reported here, I asked participants to judge the likelihood of a bicyclist winning a simulated race. Results showed that the independence assumption was systematically violated. Observed judgments suggest that when a probability judgment is made, the comparability or similarity of the hypotheses on one dimension increases the weight that judges allocate to differences on the other dimensions. These results speak against the simple scalability-processing assumption of support theory, and they illustrate the need for a theory of judgment processes that describes how the similarity between hypotheses shapes judgment.

Neural correlates of evidence accumulation in a perceptual decision task

Sequential sampling models provide a useful framework for understanding human decision making. A key component of these models is an evidence accumulation process in which information is accrued over time to a threshold, at which point a choice is made. Previous neurophysiological studies on perceptual decision making have suggested accumulation occurs only in sensorimotor areas involved in making the action for the choice. Here we investigated the neural correlates of evidence accumulation in the human brain using functional magnetic resonance imaging (fMRI) while manipulating the quality of sensory evidence, the response modality, and the foreknowledge of the response modality. We trained subjects to perform a random dot motion direction discrimination task by either moving their eyes or pressing buttons to make their responses. In addition, they were cued about the response modality either in advance of the stimulus or after a delay. We isolated fMRI responses for perceptual decisions in both independently defined sensorimotor areas and task-defined nonsensorimotor areas. We found neural signatures of evidence accumulation, a higher fMRI response on low coherence trials than high coherence trials, primarily in saccade-related sensorimotor areas (frontal eye field and intraparietal sulcus) and nonsensorimotor areas in anterior insula and inferior frontal sulcus. Critically, such neural signatures did not depend on response modality or foreknowledge. These results help establish human brain areas involved in evidence accumulation and suggest that the neural mechanism for evidence accumulation is not specific to effectors. Instead, the neural system might accumulate evidence for particular stimulus features relevant to a perceptual task.

Two-stage dynamic signal detection: a theory of choice, decision time, and confidence

The 3 most often-used performance measures in the cognitive and decision sciences are choice, response or decision time, and confidence. We develop a random walk/diffusion theory-2-stage dynamic signal detection (2DSD) theory-that accounts for all 3 measures using a common underlying process. The model uses a drift diffusion process to account for choice and decision time. To estimate confidence, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The model explains all known interrelationships between the 3 indices of performance. Furthermore, the model also accounts for the distributions of each variable in both a perceptual and general knowledge task. The dynamic nature of the model also reveals the moderating effects of time pressure on the accuracy of choice and confidence. Finally, the model specifies the optimal solution for giving the fastest choice and confidence rating for a given level of choice and confidence accuracy. Judges are found to act in a manner consistent with the optimal solution when making confidence judgments.

Prediction of 4-year college student performance using cognitive and noncognitive predictors and the impact on demographic status of admitted students

This study was conducted to determine the validity of noncognitive and cognitive predictors of the performance of college students at the end of their 4th year in college. Results indicate that the primary predictors of cumulative college grade point average (GPA) were Scholastic Assessment Test/American College Testing Assessment (SAT/ACT) scores and high school GPA (HSGPA) though biographical data and situational judgment measures added incrementally to this prediction. SAT/ACT scores and HSGPA were collected and used in various ways by participating institutions in the admissions process while situational judgment measures and biodata were collected for research purposes only during the first few weeks of the participating students' freshman year. Alternative outcomes such as a self-report of performance on a range of student performance dimensions and a measure of organizational citizenship behavior, as well as class absenteeism, were best predicted by noncognitive measures. The racial composition of a student body selected with only cognitive measures or both cognitive and noncognitive measures under various levels of selectivity as well as the performance of students admitted under these scenarios is also reported. The authors concluded that both the biodata and situational judgment measures could be useful supplements to cognitive indexes of student potential in college admissions.

Similar Processes Despite Divergent Behavior in Two Commonly Used Measures of Risky Decision Making

Performance on complex decision-making tasks may depend on a multitude of processes. Two such tasks, the Iowa Gambling Task (IGT) and Balloon Analog Risk Task (BART), are of particular interest because they are associated with real world risky behavior, including illegal drug use. We used cognitive models to disentangle underlying processes in both tasks. Whereas behavioral measures from the IGT and BART were uncorrelated, cognitive models revealed two reliable cross-task associations. Results suggest that the tasks similarly measure loss aversion and decision-consistency processes, but not necessarily the same learning process. Additionally, substance-using individuals (and especially stimulant users) performed worse on the IGT than healthy controls did, and this pattern could be explained by reduced decision consistency.

Development of an automatic response mode to improve the clinical utility of sequential risk-taking tasks

Sequential risk-taking tasks, especially the Balloon Analogue Risk Task (BART), have proven powerful and useful methods in studying and identifying real-world risk takers. A natural index in these tasks is the average number of risks the participant takes in a trial (e.g., pumps on the balloons), but this is difficult to estimate because some trials terminate early because of the consequences of those risks (e.g., when the desired number of balloon pumps exceeds the explosion point). The standard corrective strategy is to use an adjusted score that ignores such event-terminated trials. Although previous data supports the utility of this adjusted score, the authors show formally that it is biased. Therefore, the authors developed an automatic response procedure, in which respondents state at the beginning of each trial how many risks they wish to take and then observe the sequence of events unfold. A study comparing this new automatic and the original manual BART shows that the automatic procedure yields unbiased statistics whereas maintaining the BART's predictive validity of substance use. The authors also found that providing respondents with the expected-value-maximizing strategy and complete trial-by-trial feedback increased the number of risks they were willing to take during the BART. The authors interpret these results in terms of the potential utility of the automatic version including shorter administration time, unbiased behavioral measures, and minimizing motor involvement, which is important in neuroscientific investigations or with clinical populations with motor limitations.

A signal detection analysis of the recognition heuristic

The recognition heuristic uses a recognition decision to make an inference about an unknown variable in the world. Theories of recognition memory typically use a signal detection framework to predict this binary recognition decision. In this article, I integrate the recognition heuristic with signal detection theory to formally investigate how judges use their recognition memory to make inferences. The analysis reveals that false alarms and misses systematically influence the performance of the recognition heuristic. Furthermore, judges should adjust their recognition response criterion according to their experience with the environment to exploit the structure of information in it. Finally, the less-is-more effect is found to depend on the distribution of cue knowledge and judges' sensitivity to the difference between experienced and novel items. Theoretical implications of this bridge between the recognition heuristic and models of recognition memory are discussed.

Modeling behavior in a clinically diagnostic sequential risk-taking task

This article models the cognitive processes underlying learning and sequential choice in a risk-taking task for the purposes of understanding how they occur in this moderately complex environment and how behavior in it relates to self-reported real-world risk taking. The best stochastic model assumes that participants incorrectly treat outcome probabilities as stationary, update probabilities in a Bayesian fashion, evaluate choice policies prior to rather than during responding, and maintain constant response sensitivity. The model parameter associated with subjective value of gains correlates well with external risk taking. Both the overall approach, which can be expanded as the basic paradigm is varied, and the specific results provide direction for theories of risky choice and for understanding risk taking as a public health problem.