RT info:eu-repo/semantics/article
T1 Differences of Training Structures on Stimulus Class Formation in Computational Agents
A1 Betancort Montesinos, Moisés
A1 Carrillo, Alexis
A2 Psicología ClínicaPsicobiología y Metodología
K1 Stimulus equivalence
K1 Machine learning
K1 Matching to sample
K1 Artificial neural network
AB Stimulus Equivalence (SE) is a behavioural phenomenon in which organisms respond functionally to stimuli without explicit training. SE provides a framework in the experimental analysis of behaviour to study language, symbolic behaviour, and cognition. It is also a frequently discussed matter in interdisciplinary research, linking behaviour analysis with linguistics and neuroscience. Previous research has attempted to replicate SE with computational agents, mostly based on Artificial Neural Network (ANN) models. The aim of this paper was to analyse the effect of three Training Structures (TSs) on stimulus class formation in a simulation with ANNs as computational agents performing a classification task, in a matching-to-sample procedure. Twelve simulations were carried out as a product of the implementation of four ANN architectures on the three TSs. SE was not achieved, but two agents showed an emergent response on half of the transitivity test pairs on linear sequence TSs and reflexivity on one member of the class. The results suggested that an ANN with a large enough number of units in a hidden layer can perform a limited number of emergent relations within specific experimental conditions: reflexivity on B and transitivity on AC, when pairs AB and BC are trained on a three-member stimulus class and tested in a classification task. Reinforcement learning is proposed as the framework for further simulations.
SN 2414-4088
YR 2023
FD 2023
LK http://riull.ull.es/xmlui/handle/915/39866
UL http://riull.ull.es/xmlui/handle/915/39866
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 30-nov-2024