A computational theory for the learning of equivalence relations
Equivalence relations are logical entities that emerge concurrently with the development of language capabilities.In this work we propose a computational model that learns to build equivalence relations by learning simple conditional rules. The model includes visual areas, dopaminergic and noradrene...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2011-10-01
|
| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00113/full |
| _version_ | 1818467006344593408 |
|---|---|
| author | Sergio E Lew Silvano B Zanutto Silvano B Zanutto |
| author_facet | Sergio E Lew Silvano B Zanutto Silvano B Zanutto |
| author_sort | Sergio E Lew |
| collection | DOAJ |
| description | Equivalence relations are logical entities that emerge concurrently with the development of language capabilities.In this work we propose a computational model that learns to build equivalence relations by learning simple conditional rules. The model includes visual areas, dopaminergic and noradrenergic structures as well as prefrontal and motor areas, each of them modeled as a group of continuous valued units that simulate clusters of real neurons. In the model, lateral interaction between neurons of visual structures and top-down modulation of prefrontal/premotor structures over the activity of neurons in visual structures are necessary conditions for learning the paradigm. In terms of the number of neurons and their interaction, we show that a minimal structural complexity is required for learning equivalence relations among conditioned stimuli. Paradoxically, the emergence of the equivalence relation drives a reduction in the number of neurons needed to maintain those previously specific stimulus-response learned rules, allowing an efficient use of neuronal resources. |
| first_indexed | 2024-04-13T21:03:56Z |
| format | Article |
| id | doaj.art-9fbcbc29f9124dbdadde38df485d5e2d |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-04-13T21:03:56Z |
| publishDate | 2011-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-9fbcbc29f9124dbdadde38df485d5e2d2022-12-22T02:30:04ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612011-10-01510.3389/fnhum.2011.001131869A computational theory for the learning of equivalence relationsSergio E Lew0Silvano B Zanutto1Silvano B Zanutto2Universidad de Buenos AiresCONICETUniversidad de Buenos AiresEquivalence relations are logical entities that emerge concurrently with the development of language capabilities.In this work we propose a computational model that learns to build equivalence relations by learning simple conditional rules. The model includes visual areas, dopaminergic and noradrenergic structures as well as prefrontal and motor areas, each of them modeled as a group of continuous valued units that simulate clusters of real neurons. In the model, lateral interaction between neurons of visual structures and top-down modulation of prefrontal/premotor structures over the activity of neurons in visual structures are necessary conditions for learning the paradigm. In terms of the number of neurons and their interaction, we show that a minimal structural complexity is required for learning equivalence relations among conditioned stimuli. Paradoxically, the emergence of the equivalence relation drives a reduction in the number of neurons needed to maintain those previously specific stimulus-response learned rules, allowing an efficient use of neuronal resources.http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00113/fullLanguageNeural NetworkEquivalence relations |
| spellingShingle | Sergio E Lew Silvano B Zanutto Silvano B Zanutto A computational theory for the learning of equivalence relations Frontiers in Human Neuroscience Language Neural Network Equivalence relations |
| title | A computational theory for the learning of equivalence relations |
| title_full | A computational theory for the learning of equivalence relations |
| title_fullStr | A computational theory for the learning of equivalence relations |
| title_full_unstemmed | A computational theory for the learning of equivalence relations |
| title_short | A computational theory for the learning of equivalence relations |
| title_sort | computational theory for the learning of equivalence relations |
| topic | Language Neural Network Equivalence relations |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2011.00113/full |
| work_keys_str_mv | AT sergioelew acomputationaltheoryforthelearningofequivalencerelations AT silvanobzanutto acomputationaltheoryforthelearningofequivalencerelations AT silvanobzanutto acomputationaltheoryforthelearningofequivalencerelations AT sergioelew computationaltheoryforthelearningofequivalencerelations AT silvanobzanutto computationaltheoryforthelearningofequivalencerelations AT silvanobzanutto computationaltheoryforthelearningofequivalencerelations |