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      Rethinking fast and slow based on a critique of reaction-time reverse inference

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          Abstract

          Do people intuitively favour certain actions over others? In some dual-process research, reaction-time (RT) data have been used to infer that certain choices are intuitive. However, the use of behavioural or biological measures to infer mental function, popularly known as ‘reverse inference', is problematic because it does not take into account other sources of variability in the data, such as discriminability of the choice options. Here we use two example data sets obtained from value-based choice experiments to demonstrate that, after controlling for discriminability (that is, strength-of-preference), there is no evidence that one type of choice is systematically faster than the other. Moreover, using specific variations of a prominent value-based choice experiment, we are able to predictably replicate, eliminate or reverse previously reported correlations between RT and selfishness. Thus, our findings shed crucial light on the use of RT in inferring mental processes and strongly caution against using RT differences as evidence favouring dual-process accounts.

          Abstract

          In cognitive neuroscience, it is common practice to use reaction time data to infer whether decisions are intuitive or deliberate. Here the authors demonstrate that they can replicate, eliminate and reverse previously reported correlations between selfishness and reaction time.

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          Most cited references39

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          Separate neural systems value immediate and delayed monetary rewards.

          When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects' choices, with greater relative fronto-parietal activity when subjects choose longer term options.
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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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              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

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Pub. Group
                2041-1723
                02 July 2015
                2015
                : 6
                : 7455
                Affiliations
                [1 ]Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich , Zürich 8006, Switzerland
                [2 ]Department of Psychology, The Ohio State University , 1827 Neil Avenue, 200E Lazenby Hall, Columbus Ohio 43210, USA
                [3 ]Department of Economics, The Ohio State University , 1945 North High Street, 415 Arps Hall, Columbus, Ohio 43210, USA
                [4 ]Department of Economics, University of Zurich , Zürich 8006, Switzerland
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                ncomms8455
                10.1038/ncomms8455
                4500827
                26135809
                da50449f-af6a-4f62-9a25-d844b1c95ef1
                Copyright © 2015, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 13 January 2015
                : 11 May 2015
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