Forstmann et al. 2015 - Sequential Sampling Models in Cognitive Neuroscience - Advantages, Applicati

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Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions B.U. Forstmann, 1 R. Ratcliff, 2 and E.-J. Wagenmakers 3 1 Amsterdam Brain and Cognition Center, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; email: [email protected] 2 Department of Psychology, Ohio State University, Columbus, Ohio 43210 3 Department of Methodology, University of Amsterdam, 1018 WV Amsterdam, The Netherlands Annu. Rev. Psychol. 2016. 67:641–66 First published online as a Review in Advance on September 17, 2015 The Annual Review of Psychology is online at psych.annualreviews.org This article’s doi: 10.1146/annurev-psych-122414-033645 Copyright c 2016 by Annual Reviews. All rights reserved Keywords diffusion decision model, information accumulation, decision making, response time, speed-accuracy trade-off, drift rate Abstract Sequential sampling models assume that people make speeded decisions by gradually accumulating noisy information until a threshold of evidence is reached. In cognitive science, one such model—the diffusion decision model—is now regularly used to decompose task performance into underly- ing processes such as the quality of information processing, response caution, and a priori bias. In the cognitive neurosciences, the diffusion decision model has recently been adopted as a quantitative tool to study the neural basis of decision making under time pressure. We present a selective overview of several recent applications and extensions of the diffusion decision model in the cognitive neurosciences. 641 Click here to view this article's online features: • Download figures as PPT slides • Navigate linked references • Download citations • Explore related articles • Search keywords ANNUAL REVIEWS Further Annu. Rev. Psychol. 2016.67:641-666. Downloaded from Access provided by Carnegie Mellon University on 03/31/16. For personal use only.
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Speed-accuracy trade-off: the universal finding that response time can be shortened at the expense of a higher error rate Contents INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642 SEQUENTIAL SAMPLING MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643 Interdisciplinary History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 Current Standard Form: The Diffusion Decision Model . . . . . . . . . . . . . . . . . . . . . . . . . . 645 ADVANTAGES OF THE DIFFUSION DECISION MODEL . . . . . . . . . . . . . . . . . . . . 647 Advantages for the Analysis of Choice Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647 Advantages for Cognitive Neuroscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649 THE DIFFUSION DECISION MODEL IN COGNITIVE NEUROSCIENCE: APPLICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 650 Application in Low-Level Cognitive Neuroscience: Neural Firing Rates and Single-Cell Recordings in Monkeys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 650 Application in High-Level Cognitive Neuroscience: Measuring Human Brain Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 THE DIFFUSION DECISION MODEL IN COGNITIVE NEUROSCIENCE: EXTENSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 Extension to Multi-Alternative Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 Extension to Confidence Judgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 Extension to Value-Based Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 Extension to Changes of Mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 Extension to Dynamic Thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659 CONCLUDING COMMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659 INTRODUCTION Every day, people make thousands of small decisions. Many of these decisions are trivial (e.g., what pair of socks to wear or what TV series to watch), many are to some degree automatic (e.g.,

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