Review:

Authors:
Roger Ratcliff;Gail McKoon
Affiliations:
Department of Psychology, Ohio State University, Columbus, OH 43210, U.S.A.;Department of Psychology, Ohio State University, Columbus, OH 43210, U.S.A.
Venue:
Neural Computation
Year:
2008

Citing 1
Cited 12

A Stochastic version of general recognition theory

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Time-varying perturbations can distinguish among integrate-to-threshold models for perceptual decision making in reaction time tasks

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Optimal decision making on the basis of evidence represented in spike trains

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Inhibition in superior colliculus neurons in a brightness discrimination task?

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Diffusive information accumulation by minimal recurrent neural models of decision making

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Act quickly, decide later: Long-latency visual processing underlies perceptual decisions but not reflexive behavior

Journal of Cognitive Neuroscience
Vigor in the face of fluctuating rates of reward: An experimental examination

Journal of Cognitive Neuroscience
Reinforcement-based decision making in corticostriatal circuits: Mutual constraints by neurocomputational and diffusion models

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Human brain activity predicts individual differences in prior knowledge use during decisions

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Group Decision-Making Models for Sequential Tasks

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The limits of feedforward vision: Recurrent processing promotes robust object recognition when objects are degraded

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Silence is also evidence: interpreting dwell time for recommendation from psychological perspective

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Reaction time for object categorization is predicted by representational distance

Journal of Cognitive Neuroscience

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Abstract

The diffusion decision model allows detailed explanations of behavior in two-choice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral dataaccuracy, mean response times, and response time distributionsinto components of cognitive processing. Three experiments are used to illustrate experimental manipulations of three components: stimulus difficulty affects the quality of information on which a decision is based; instructions emphasizing either speed or accuracy affect the criterial amounts of information that a subject requires before initiating a response; and the relative proportions of the two stimuli affect biases in drift rate and starting point. The experiments also illustrate the strong constraints that ensure the model is empirically testable and potentially falsifiable. The broad range of applications of the model is also reviewed, including research in the domains of aging and neurophysiology.