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A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes

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A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes. / Tovar, Angel E.; Westermann, Gert.
In: Frontiers in Psychology, Vol. 8, 1848, 18.10.2017.

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Tovar AE, Westermann G. A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes. Frontiers in Psychology. 2017 Oct 18;8:1848. doi: 10.3389/fpsyg.2017.01848

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Bibtex

@article{4339a37651e84a488e0eafc26fd15299,
title = "A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes",
abstract = "A stimulus class can be composed of perceptually different but functionally equivalent stimuli. The relations between the stimuli that are grouped in a class can be learned or derived fromother stimulus relations. If stimulus A is equivalent to B, and B is equivalent to C, then the equivalence between A and C can be derived without explicit training. In this work we propose, with a neurocomputational model, a basic learning mechanism for the formation of equivalence. We also describe how the relatedness between the membersof an equivalence class is developed for both trained and derived stimulus relations.Three classic studies on stimulus equivalence are simulated covering typical and atypical populations as well as nodal distance effects. This model shows a mechanism by which certain stimulus associations are selectively strengthened even when they are not co-presented in the environment. This model links the field of equivalence classes to accounts of Hebbian learning and categorization, and points to the pertinence of modeling stimulus equivalence to explore the effect of variations in training protocols.",
keywords = "Equivalence classes, Transitive relations, neurocomputational model, Hebbian Learning, Categorization",
author = "Tovar, {Angel E.} and Gert Westermann",
year = "2017",
month = oct,
day = "18",
doi = "10.3389/fpsyg.2017.01848",
language = "English",
volume = "8",
journal = "Frontiers in Psychology",
issn = "1664-1078",
publisher = "Frontiers Media S.A.",

}

RIS

TY - JOUR

T1 - A Neurocomputational Approach to Trained and Transitive Relations in Equivalence Classes

AU - Tovar, Angel E.

AU - Westermann, Gert

PY - 2017/10/18

Y1 - 2017/10/18

N2 - A stimulus class can be composed of perceptually different but functionally equivalent stimuli. The relations between the stimuli that are grouped in a class can be learned or derived fromother stimulus relations. If stimulus A is equivalent to B, and B is equivalent to C, then the equivalence between A and C can be derived without explicit training. In this work we propose, with a neurocomputational model, a basic learning mechanism for the formation of equivalence. We also describe how the relatedness between the membersof an equivalence class is developed for both trained and derived stimulus relations.Three classic studies on stimulus equivalence are simulated covering typical and atypical populations as well as nodal distance effects. This model shows a mechanism by which certain stimulus associations are selectively strengthened even when they are not co-presented in the environment. This model links the field of equivalence classes to accounts of Hebbian learning and categorization, and points to the pertinence of modeling stimulus equivalence to explore the effect of variations in training protocols.

AB - A stimulus class can be composed of perceptually different but functionally equivalent stimuli. The relations between the stimuli that are grouped in a class can be learned or derived fromother stimulus relations. If stimulus A is equivalent to B, and B is equivalent to C, then the equivalence between A and C can be derived without explicit training. In this work we propose, with a neurocomputational model, a basic learning mechanism for the formation of equivalence. We also describe how the relatedness between the membersof an equivalence class is developed for both trained and derived stimulus relations.Three classic studies on stimulus equivalence are simulated covering typical and atypical populations as well as nodal distance effects. This model shows a mechanism by which certain stimulus associations are selectively strengthened even when they are not co-presented in the environment. This model links the field of equivalence classes to accounts of Hebbian learning and categorization, and points to the pertinence of modeling stimulus equivalence to explore the effect of variations in training protocols.

KW - Equivalence classes

KW - Transitive relations

KW - neurocomputational model

KW - Hebbian Learning

KW - Categorization

U2 - 10.3389/fpsyg.2017.01848

DO - 10.3389/fpsyg.2017.01848

M3 - Journal article

VL - 8

JO - Frontiers in Psychology

JF - Frontiers in Psychology

SN - 1664-1078

M1 - 1848

ER -