Differences of Training Structures on Stimulus Class Formation in Computational Agents
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 inte...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Multimodal Technologies and Interaction |
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| Online Access: | https://www.mdpi.com/2414-4088/7/4/39 |
| _version_ | 1827744175781576704 |
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| author | Alexis Carrillo Moisés Betancort |
| author_facet | Alexis Carrillo Moisés Betancort |
| author_sort | Alexis Carrillo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:40:49Z |
| format | Article |
| id | doaj.art-e3e33cf4cbc24d0ab215297d8c373a32 |
| institution | Directory Open Access Journal |
| issn | 2414-4088 |
| language | English |
| last_indexed | 2024-03-11T04:40:49Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Multimodal Technologies and Interaction |
| spelling | doaj.art-e3e33cf4cbc24d0ab215297d8c373a322023-11-17T20:42:23ZengMDPI AGMultimodal Technologies and Interaction2414-40882023-04-01743910.3390/mti7040039Differences of Training Structures on Stimulus Class Formation in Computational AgentsAlexis Carrillo0Moisés Betancort1Departamento de Psicología Clínica, Psicobiología y Metodología, Campus de Guajara, Universidad de La Laguna, Apartado 456, 38200 San Cristóbal de La Laguna, SpainDepartamento de Psicología Clínica, Psicobiología y Metodología, Campus de Guajara, Universidad de La Laguna, Apartado 456, 38200 San Cristóbal de La Laguna, SpainStimulus 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.https://www.mdpi.com/2414-4088/7/4/39stimulus equivalencemachine learningmatching to sampleartificial neural network |
| spellingShingle | Alexis Carrillo Moisés Betancort Differences of Training Structures on Stimulus Class Formation in Computational Agents Multimodal Technologies and Interaction stimulus equivalence machine learning matching to sample artificial neural network |
| title | Differences of Training Structures on Stimulus Class Formation in Computational Agents |
| title_full | Differences of Training Structures on Stimulus Class Formation in Computational Agents |
| title_fullStr | Differences of Training Structures on Stimulus Class Formation in Computational Agents |
| title_full_unstemmed | Differences of Training Structures on Stimulus Class Formation in Computational Agents |
| title_short | Differences of Training Structures on Stimulus Class Formation in Computational Agents |
| title_sort | differences of training structures on stimulus class formation in computational agents |
| topic | stimulus equivalence machine learning matching to sample artificial neural network |
| url | https://www.mdpi.com/2414-4088/7/4/39 |
| work_keys_str_mv | AT alexiscarrillo differencesoftrainingstructuresonstimulusclassformationincomputationalagents AT moisesbetancort differencesoftrainingstructuresonstimulusclassformationincomputationalagents |