Publications by authors named "Peter Cassey"

Article Synopsis
  • Most data analyses in psychology utilize models, including cognitive models that interpret variables into psychological constructs, with response time models focusing on factors like processing ease, caution, and bias.
  • In a study with 17 research teams analyzing the same 14 data sets, teams operated blindly to determine manipulated aspects of behavior in a two-alternative forced choice task, leading to similar conclusions across various models and methods, despite the impact of modeler’s choices on inferences.
  • The findings suggest that simpler cognitive models are as effective as complex ones for analyzing response time data in standard experiments, while also highlighting circumstances where more complicated approaches might be necessary and the potential pitfalls of interpreting model results.
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Automaticity allows us to perform tasks in a fast, efficient, and effortless manner after sufficient practice. Theories of automaticity propose that across practice processing transitions from being controlled by working memory to being controlled by long-term memory retrieval. Recent event-related potential (ERP) studies have sought to test this prediction, however, these experiments did not use the canonical paradigms used to study automaticity.

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Theory development in both psychology and neuroscience can benefit by consideration of both behavioral and neural data sets. However, the development of appropriate methods for linking these data sets is a difficult statistical and conceptual problem. Over the past decades, different linking approaches have been employed in the study of perceptual decision-making, beginning with rudimentary linking of the data sets at a qualitative, structural level, culminating in sophisticated statistical approaches with quantitative links.

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Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we asked people for prior and posterior inferences about the probability that 1 of 2 coins would generate certain outcomes.

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Speed-accuracy tradeoff (SAT) is an adaptive process balancing urgency and caution when making decisions. Computational cognitive theories, known as "evidence accumulation models", have explained SATs via a manipulation of the amount of evidence necessary to trigger response selection. New light has been shed on these processes by single-cell recordings from monkeys who were adjusting their SAT settings.

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