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[Re] The Wisconsin Card Sorting Test: Theoretical analysis and modeling in a neuronal network

Pauline Bock 1 Frédéric Alexandre 1
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : The Wisconsin Card Sorting Test is a psychological test used to evaluate cognitive flexibility capacities in patients with decision-making deficits, to evaluate among other things, some mechanisms dysfunctions of frontal lobe. In the test it is proposed to match several cards, composed of several colored forms, by the 3 different criteria: color, figures or quantity, according to 4 reference cards (Heaton, R.K, (1981))[1]. The subject is told to match the cards, but the rule must be found by trials and errors and might change without notice. The ability of the subject to adapt to the changes is measured in the test. In 1991, S. Dehaene and J.P. Changeux have created a neuronal network model to resolve Wisconsin Card Sorting Test (Dehaene & Changeux, 1991)[2], enhancing 3 cognitive components necessary to pass the test: • the ability to change a followed strategy/rule when an error occurs • the ability to memorise wrong strategies to avoid repeating them • the ability of auto-evaluation, i.e comparing different situations and their outcomes referring to a memory of the past. This model refers to some biological mechanisms and their supposed implementation in the brain circuitry, and is interesting to model some flexible processes used in decisionmaking and executive control. Although this model has been published in 1991, no implementations have been shared. We have implemented this model in Python with some new mechanisms, trying to reach the authorʼs theoretical results.
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Submitted on : Monday, December 9, 2019 - 5:54:33 PM
Last modification on : Monday, April 6, 2020 - 2:35:09 PM
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Pauline Bock, Frédéric Alexandre. [Re] The Wisconsin Card Sorting Test: Theoretical analysis and modeling in a neuronal network. The ReScience journal, GitHub, 2019, 3 (1), pp.5. ⟨hal-02401066⟩

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