Increasing false recognition rates with confirmatory feedback: a phenomenological analysis

During a simulated witness interrogation, participants were encouraged to confabulate an account consistent with false information concerning a videotaped event. The interviewer verbally affirmed some false responses. Previous research has shown that, a week later, participants often recognize confabulated events that were affirmed by the experimenter as being from the video. What is unclear is whether confirmatory feedback encouraged a change in the mental representation of the confabulated events to fit the original event or confirmation might have merely encouraged a change in beliefs about the event. To further understand the mechanisms that underlie the confirmatory feedback effect, participants were asked to judge the phenomenological experience associated with false recognition.     

Environmental support promotes expertise-based mitigation of age differences on pilot communication tasks

The authors investigated whether expertise is more likely to mitigate age declines when experts rely on environmental support in a pilot/Air Traffic Control (ATC) communication task. Pilots and nonpilots listened to ATC messages that described a route through an airspace, while they referred to a chart of the airspace. They read back (repeated) each message and then answered a probe question about the route. In a preliminary study, participants could take notes while listening to the messages and performing the read-back and probe tasks. In Experiment 1, opportunity to take notes was manipulated. Note taking determined when expertise mitigated age differences on the read-back task. With note taking, read-back accuracy declined with age for nonpilots but not for pilots. Without note taking, similar age-related declines occurred for pilots and nonpilots. Benefits of expertise, younger age, and note taking occurred for probe accuracy, but mitigation did not occur. The findings suggest that older adults take advantage of a domain-relevant form of environmental support (note taking) to maintain performance on some complex tasks despite typical age-related declines in cognitive ability.     

Perceptions of Children Who Lie: Influence of Lie Motive and Benefit

KILMARTIN (1994) suggested that masculine traits may result from externalizing defense style and that when unconscious and unwanted feminine traits about the self occur, they are dealt with in an external way, radier man being directiy resolved by the individual. However, this dieory has received little empirical support. Lobel and Winch (1986) measured defense style among male college students; they found mat men who scored higher in masculinity tended to score higher on acting out (finding an object to attack) and splitting (separating affect from content). Lobel and Winch concluded mat use of diese defense styles reflects avoidance of emotional expression. In turn, diese findings have been supported by research that has suggested mat men use avoidance coping strategies more man women do (McCall &; Struthers, 1994).     

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.     

Exemplar models as a mechanism for performing Bayesian inference

Probabilistic models have recently received much attention as accounts of human cognition. However, most research in which probabilistic models have been used has been focused on formulating the abstract problems behind cognitive tasks and their optimal solutions, rather than on mechanisms that could implement these solutions. Exemplar models are a successful class of psychological process models in which an inventory of stored examples is used to solve problems such as identification, categorization, and function learning. We show that exemplar models can be used to perform a sophisticated form of Monte Carlo approximation known as importance sampling and thus provide a way to perform approximate Bayesian inference. Simulations of Bayesian inference in speech perception, generalization along a single dimension, making predictions about everyday events, concept learning, and reconstruction from memory show that exemplar models can often account for human performance with only a few exemplars, for both simple and relatively complex prior distributions. These results suggest that exemplar models provide a possible mechanism for implementing at least some forms of Bayesian inference.     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

Correspondence and mediational properties of the Fishbein model: An application to adolescent alcohol use

The generalizability of the Fishbein model for behavior prediction was extended to a new field behavior, alcohol drinking by adolescents. The model's two components (i.e., attitudes and normative beliefs) varied in their predictive importance depending on the behavior's situational context. Following this field demonstration, two issues related to properties affecting the model's predictive effectiveness were investigated. First, it was found that the predictability of alcohol-drinking intentions (BI) declined when attitudes (A_act) and normative beliefs (NB) did not correspond with BI in terms of alcohol type (beer, liquor, or wine) and drinking situation (home, party, or pub). A second experiment tested the hypothesis that exogenous variables (those other than A_act and NB) would have a direct influence on BI. A comprehensive test was provided within the context of Jessor's multivariate predictive system for adolescent alcohol use. Present data indicate only a marginal increase in explained variance occurred when applying this system, thereby generally indicating the sufficiency of the Fishbein model's two components.