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      The Attentional Drift-Diffusion Model Extends to Simple Purchasing Decisions

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          Abstract

          How do we make simple purchasing decisions (e.g., whether or not to buy a product at a given price)? Previous work has shown that the attentional drift-diffusion model (aDDM) can provide accurate quantitative descriptions of the psychometric data for binary and trinary value-based choices, and of how the choice process is guided by visual attention. Here we extend the aDDM to the case of purchasing decisions, and test it using an eye-tracking experiment. We find that the model also provides a reasonably accurate quantitative description of the relationship between choice, reaction time, and visual fixations using parameters that are very similar to those that best fit the previous data. The only critical difference is that the choice biases induced by the fixations are about half as big in purchasing decisions as in binary choices. This suggests that a similar computational process is used to make binary choices, trinary choices, and simple purchasing decisions.

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          Most cited references 43

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          The neural basis of decision making.

          The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.
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            The diffusion decision model: theory and data for two-choice decision tasks.

            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 data-accuracy, mean response times, and response time distributions-into 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.
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              A framework for studying the neurobiology of value-based decision making.

              Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.
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                Author and article information

                Journal
                Front Psychol
                Front Psychol
                Front. Psychology
                Frontiers in Psychology
                Frontiers Research Foundation
                1664-1078
                13 June 2012
                2012
                : 3
                Affiliations
                1simpleDivision of the Humanities and Social Sciences, California Institute of Technology Pasadena, CA, USA
                2simpleDepartment of Economics, University of Zurich Zurich, Switzerland
                3simpleComputational and Neural Systems, California Institute of Technology Pasadena, CA, USA
                Author notes

                Edited by: Konstantinos Tsetsos, Oxford University, UK

                Reviewed by: Adele Diederich, Jacobs University Bremen, Germany; Andreas Glöckner, Max Planck Institute for Research on Collective Goods, Germany

                *Correspondence: Antonio Rangel, Division of the Humanities and Social Sciences, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA. e-mail: rangel@ 123456hss.caltech.edu

                This article was submitted to Frontiers in Cognitive Science, a specialty of Frontiers in Psychology.

                Article
                10.3389/fpsyg.2012.00193
                3374478
                22707945
                Copyright © 2012 Krajbich, Lu, Camerer and Rangel.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

                Counts
                Figures: 11, Tables: 0, Equations: 2, References: 52, Pages: 18, Words: 11552
                Categories
                Psychology
                Original Research

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