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Journal ArticleDOI

Connectionist and diffusion models of reaction time.

Roger Ratcliff, +2 more
- 01 Apr 1999 - 
- Vol. 106, Iss: 2, pp 261-300
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TLDR
Two connectionist frameworks, GRAIN and brain-state-in-a-box, and R. Ratcliff's diffusion model were evaluated using data from a signal detection task, showing that the long tradition of reaction-time research and theory is a fertile domain for development and testing of connectionist assumptions about how decisions are generated over time.
Abstract
Two connectionist frameworks, GRAIN (J. L. McClelland, 1993) and brain-state-in-a-box (J. A. Anderson, 1991), and R. Ratcliff's (1978) diffusion model were evaluated using data from a signal detection task. Dependent variables included response probabilities, reaction times for correct and error responses, and shapes of reaction-time distributions. The diffusion model accounted for all aspects of the data, including error reaction times that had previously been a problem for all response-time models. The connectionist models accounted for many aspects of the data adequately, but each failed to a greater or lesser degree in important ways except for one model that was similar to the diffusion model. The findings advance the development of the diffusion model and show that the long tradition of reaction-time research and theory is a fertile domain for development and testing of connectionist assumptions about how decisions are generated over time.

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Citations
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The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks

TL;DR: The diffusion decision model is reviewed to show how it translates behavioral data accuracy, mean response times, and response time distributions into components of cognitive processing, including research in the domains of aging and neurophysiology.
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The time course of perceptual choice: The leaky, competing accumulator model.

TL;DR: The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units that captures choice behavior regardless of the number of alternatives, and explains a complex pattern of visual and contextual priming in visual word identification.
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The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.

TL;DR: In this paper, the authors consider optimal decision making in two-alternative forced-choice (TAFC) tasks and show that all but one can be reduced to the drift diffusion model, implementing the statistically optimal algorithm.
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Decision making, the P3, and the locus coeruleus-norepinephrine system.

TL;DR: The theoretical framework emerging from this research synthesis suggests that the P3 reflects the response of the LC-NE system to the outcome of internal decision-making processes and the consequent effects of noradrenergic potentiation of information processing.
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Modeling Response Times for Two-Choice Decisions

TL;DR: In this article, a diffusion model for two-choice real-time decisions is applied to four psychophysical tasks and the model reveals how stimulus information guides decisions and shows how the information is processed through time to yield sometimes correct and sometimes incorrect decisions.
References
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TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.

An Introduction To Probability Theory And Its Applications

TL;DR: A First Course in Probability (8th ed.) by S. Ross is a lively text that covers the basic ideas of probability theory including those needed in statistics.
Journal ArticleDOI

Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.

TL;DR: The account presented here suggests that memories are first stored via synaptic changes in the hippocampal system, that these changes support reinstatement of recent memories in the neocortex, that neocortical synapses change a little on each reinstatement, and that remote memory is based on accumulated neocorticals changes.
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