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.

Time pressure changes how people explore and respond to uncertainty.

How does time pressure influence exploration and decision-making? We investigated this question with several four-armed bandit tasks manipulating (within subjects) expected reward, uncertainty, and time pressure (limited vs. unlimited). With limited time, people have less opportunity to perform costly computations, thus shifting the cost-benefit balance of different exploration strategies. Through behavioral, reinforcement learning (RL), reaction time (RT), and evidence accumulation analyses, we show that time pressure changes how people explore and respond to uncertainty. Specifically, participants reduced their uncertainty-directed exploration under time pressure, were less value-directed, and repeated choices more often. Since our analyses relate uncertainty to slower responses and dampened evidence accumulation (i.e., drift rates), this demonstrates a resource-rational shift towards simpler, lower-cost strategies under time pressure. These results shed light on how people adapt their exploration and decision-making strategies to externally imposed cognitive constraints.

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.

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.

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.

When money talks: Judging risk and coercion in high-paying clinical trials.

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.

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).

Markov versus quantum dynamic models of belief change during evidence monitoring.

Two different dynamic models for belief change during evidence monitoring were evaluated: Markov and quantum. They were empirically tested with an experiment in which participants monitored evidence for an initial period of time, made a probability rating, then monitored more evidence, before making a second rating. The models were qualitatively tested by manipulating the time intervals in a manner that provided a test for interference effects of the first rating on the second. The Markov model predicted no interference, whereas the quantum model predicted interference. More importantly, a quantitative comparison of the two models was also carried out using a generalization criterion method: the parameters were fit to data from one set of time intervals, and then these same parameters were used to predict data from another set of time intervals. The results indicated that some features of both Markov and quantum models are needed to accurately account for the results.

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.

Preference accumulation as a process model of desirability ratings.

In desirability rating tasks, decision makers evaluate objects on a continuous response scale. Despite their prominence, full process models of these rating tasks have not been developed. We investigated whether a preference accumulation process, a process often used to model discrete choice, might explain ratings as well. According to our model, attributes from each option are sampled and evaluated stochastically. The evaluations are integrated over time, forming a preference. Preferences for options compete with each other, and accumulated preferences can decay. The model makes precise predictions regarding the statistical distribution of desirability ratings, as well as their dependence on deliberation time and on context. We test and confirm these predictions in two experimental studies. Additionally, quantitative model fits indicate that participants are better described by our proposed model, relative to a model without dynamism, competition, or stochastic attribute sampling. Our results show that the descriptive power of models of preference accumulation extends beyond discrete choice, and that the assumptions of this framework accurately characterize the core cognitive processes at play in the construction of preference and the evaluation of objects.

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.

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).