Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -