Time-varying decision boundaries: insights from optimality analysis

The most widely used account of decision-making proposes that people choose between alternatives by accumulating evidence in favor of each alternative until this evidence reaches a decision boundary. It is frequently assumed that this decision boundary stays constant during a decision, depending on the evidence collected but not on time. Recent experimental and theoretical work has challenged this assumption, showing that constant decision boundaries are, in some circumstances, sub-optimal. We introduce a theoretical model that facilitates identification of the optimal decision boundaries under a wide range of conditions. Time-varying optimal decision boundaries for our model are a result only of uncertainty over the difficulty of each trial and do not require decision deadlines or costs associated with collecting evidence, as assumed by previous authors. Furthermore, the shape of optimal decision boundaries depends on the difficulties of different decisions. When some trials are very difficult, optimal boundaries decrease with time, but for tasks that only include a mixture of easy and medium difficulty trials, the optimal boundaries increase or stay constant. We also show how this simple model can be extended to more complex decision-making tasks such as when people have unequal priors or when they can choose to opt out of decisions. The theoretical model presented here provides an important framework to understand how, why, and whether decision boundaries should change over time in experiments on decision-making.     

Fluctuating confidence: the dynamic consequences of true/false affirmatives and denials on how a listener appraises their personal past

The present study examined the mnemonic consequences of true/false denials and affirmatives on how a listener appraises their personal past. To this end, participants (listeners) rated the extent to which they were confident certain events occurred during their childhood. They rated these events both before and after a confederate (speaker) denied or affirmed the occurrence of four different childhood events each, for a total of eight “rehearsed” events. For each set (denials and affirmatives) of events, half were true and half were false. In turn, this created four types of events (two each): true denials, true affirmatives, false denials, and false affirmatives. Additionally, half of the participants were told that the speaker was provided independent information about the veracity of the event’s occurrence (“expert” condition). Overall, listeners were less confident in the occurrence of false denial events, but more so when they believed the speaker to be more knowledgeable of the listeners memories, more confident in false affirmative events and, counter intuitively, more confident in the occurrence of true denial events. These results underscore the importance of a nuanced approach to the mnemonic consequences of true and false denials and affirmations in the course of social interactions.     

Empowerment,Leadership, and Sustainability in a Faith-Based Partnership to Improve Health

Community-based participatory research is a noted approach for improving community health and reducing health disparities. Community partnerships can serve as a catalyst for change in public health efforts. This article will apply empowerment theory and sustainability principles to an existing faith-based partnership. BRANCH Out is a partnership among 13 African American churches, the City of Milwaukee Health Department—Community Nutrition, and the Medical College of Wisconsin. The partnership goal was to change inaccurate perceptions, knowledge and negative attitudes, and behaviors about chronic disease and promote healthy youth leadership. Faith-based empowerment can occur at the individual, organizational, and community level. BRANCH Out demonstrates how partnerships can be sustained in multiple ways. The partnership also highlights the unique contributions of churches to community health outcomes.     

Do Police Officers' Beliefs About Emotional Witnesses Influence the Questions They Ask?

High emotional arousal associated with witnessing a crime promotes memory error. Police are trained to use open‐ended questioning (i.e., Cognitive Interview) to guard against contaminating fragile witness memory, but do they follow this protocol? We investigated whether officers' belief about arousal's impact on crime scene memory influenced their questioning procedures. Officers read crime scenarios describing the witness/victim as either emotionally distraught or calm, and then they chose among open‐ended and close‐ended question options for witness interviews. Results showed that emotionally aroused witnesses were asked more closed questions by officers who believed arousal did not hurt memory, while officers who believed arousal negatively impacted memory accuracy asked more open‐ended questions. This relationship was not influenced by police experience. Results suggest that regardless of training and empirical findings, beliefs about the arousal–memory relationship may guide the questioning technique that officers employ, potentially contaminating already vulnerable witness memory. Copyright © 2015 John Wiley & Sons, Ltd.     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.