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 discusse

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

Evaluating cognitive sequential risk-taking models: Manipulations of the stochastic process

This dissertation evaluates, refines, and extends to a new paradigm, a set of stochastic models that describe the cognitive processes of individuals while they complete multiple trials of the Balloon Analogue Risk Task (BART; Lejuez et al., 2002). Wallsten, Pleskac, and Lejuez (2004) designed the models using prospect theory and a Bayesian learning process to better understand why the BART correlates so well with self-reported risky behaviors. The models differed in terms of the individuals' beliefs of the task's probabilistic structure and when option evaluations occur. The models revealed that although respondents use a Bayesian learning process to understand the task, they misunderstand the BART's stochastic process as stationary. Results also indicated that individuals' attitudes toward outcomes are, in part, a source of the BART's success. From these conclusions a new task was developed that allows manipulations of both the actual stochastic structure and the individuals' level of knowledge regarding the structure. Participants (N = 71) completed four different conditions of the task. Fitting the various cognitive models to each individual's data revealed that only a subset of the models correctly distinguished between the stochastic processes underlying the different conditions. Incorporating prospect theory's weighting function and a trial-dependent bias component into the models accounted for performance differences between conditions. Of the assorted model parameters, only prospect theory's value function correlated with external self-reported risky drug use. The results also showed that the learning component of the original BART may cloud its association to risky behaviors. Implications in terms of gambling tasks and the cognitive models will be discussed

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