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      The speed-accuracy tradeoff: history, physiology, methodology, and behavior

      review-article
      Frontiers in Neuroscience
      Frontiers Media S.A.
      speed-accuracy tradeoff, decision-making

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          Abstract

          There are few behavioral effects as ubiquitous as the speed-accuracy tradeoff (SAT). From insects to rodents to primates, the tendency for decision speed to covary with decision accuracy seems an inescapable property of choice behavior. Recently, the SAT has received renewed interest, as neuroscience approaches begin to uncover its neural underpinnings and computational models are compelled to incorporate it as a necessary benchmark. The present work provides a comprehensive overview of SAT. First, I trace its history as a tractable behavioral phenomenon and the role it has played in shaping mathematical descriptions of the decision process. Second, I present a “users guide” of SAT methodology, including a critical review of common experimental manipulations and analysis techniques and a treatment of the typical behavioral patterns that emerge when SAT is manipulated directly. Finally, I review applications of this methodology in several domains.

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          Most cited references164

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          The time course of perceptual choice: the leaky, competing accumulator model.

          The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units. Special cases of the model match a classical diffusion process, but leakage and competition work together to address several challenges to existing diffusion, random walk, and accumulator models. The model accounts for data from choice tasks using both time-controlled (e.g., response signal) and standard reaction time paradigms and its adequacy compares favorably with other approaches. A new paradigm that controls the time of arrival of information supporting different choice alternatives provides further support. The model captures choice behavior regardless of the number of alternatives, accounting for the log-linear relation between reaction time and number of alternatives (Hick's law) and explains a complex pattern of visual and contextual priming in visual word identification.
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            The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective

            We provide an "executive-attention" framework for organizing the cognitive neuroscience research on the constructs of working-memory capacity (WMC), general fluid intelligence, and prefrontal cortex (PFC) function. Rather than provide a novel theory of PFC function, we synthesize a wealth of single-cell, brain-imaging, and neuropsychological research through the lens of our theory of normal individual differences in WMC and attention control (Engle, Kane, & Tuholski, 1999; Engle, Tuholski, Laughlin, & Conway, 1999). Our critical review confirms the prevalent view that dorsolateral PFC circuitry is critical to executive-attention functions. Moreover, although the dorsolateral PFC is but one critical structure in a network of anterior and posterior "attention control" areas, it does have a unique executive-attention role in actively maintaining access to stimulus representations and goals in interference-rich contexts. Our review suggests the utility of an executive-attention framework for guiding future research on both PFC function and cognitive control.
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              Visual fixations and the computation and comparison of value in simple choice.

              Most organisms facing a choice between multiple stimuli will look repeatedly at them, presumably implementing a comparison process between the items' values. Little is known about the nature of the comparison process in value-based decision-making or about the role of visual fixations in this process. We created a computational model of value-based binary choice in which fixations guide the comparison process and tested it on humans using eye-tracking. We found that the model can quantitatively explain complex relationships between fixation patterns and choices, as well as several fixation-driven decision biases.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                11 June 2014
                2014
                : 8
                : 150
                Affiliations
                Department of Psychology, Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Vanderbilt University Nashville, TN, USA
                Author notes

                Edited by: Patrick Simen, Oberlin College, USA

                Reviewed by: Milica Mormann, University of Miami, USA; Long Ding, University of Pennsylvania, USA

                *Correspondence: Richard P. Heitz, Department of Psychology, Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Vanderbilt University, 301 Wilson Hall, 111 21st Ave. South, TN 37240-781, Nashville, USA e-mail: richard.p.heitz@ 123456vanderbilt.edu

                This article was submitted to Decision Neuroscience, a section of the journal Frontiers in Neuroscience.

                Article
                10.3389/fnins.2014.00150
                4052662
                24478622
                e14602ba-469c-4f3f-bb1b-51933af2220c
                Copyright © 2014 Heitz.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 February 2014
                : 23 May 2014
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 211, Pages: 19, Words: 17618
                Categories
                Neuroscience
                Review Article

                Neurosciences
                speed-accuracy tradeoff,decision-making
                Neurosciences
                speed-accuracy tradeoff, decision-making

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