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

This work is licensed under a Creative Commons Attribution 4.0 International License.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

Markov versus quantum dynamic models of belief change during evidence monitoring

This work is licensed under a Creative Commons Attribution 4.0 International License.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

Wise or mad crowds? The cognitive mechanisms underlying information cascades

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.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.German Research Foundation, grant number: KU 3369/1-1Germany’s Excellence Strategy—EXC 2002/1 “Science of Intelligence”—project number 39052313

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

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.Peer Reviewe

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 four 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

A Dynamic and Stochastic Theory of Choice, Response Time, and Confidence

The three most basic performance measures used in cognitive research are choice, response time, and confidence. We present a diffusion model that accounts for all three using a common underlying process. The model uses a standard drift diffusion process to account for choice and decision time. To make a confidence judgment, 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 fully specified model is shown to account qualitatively for the most important interrelationships between all three response variables found in past research