A mechanistic perspective on canonical neural computation

Autores
Wajnerman Paz, Abel
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Although it has been argued that mechanistic explanation is compatible with abstraction (i.e., that there are abstract mechanistic models), there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations (CNCs) must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation (as opposed to a purely mathematical or non-causal interpretation). Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways.
Fil: Wajnerman Paz, Abel. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Abstraction
Causal Explanation
Cognitive Neuroscience
Integration
Mechanism
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/53338

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network_name_str CONICET Digital (CONICET)
spelling A mechanistic perspective on canonical neural computationWajnerman Paz, AbelAbstractionCausal ExplanationCognitive NeuroscienceIntegrationMechanismhttps://purl.org/becyt/ford/6.3https://purl.org/becyt/ford/6Although it has been argued that mechanistic explanation is compatible with abstraction (i.e., that there are abstract mechanistic models), there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations (CNCs) must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation (as opposed to a purely mathematical or non-causal interpretation). Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways.Fil: Wajnerman Paz, Abel. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaTaylor & Francis2017-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/53338Wajnerman Paz, Abel; A mechanistic perspective on canonical neural computation; Taylor & Francis; Philosophical Psychology; 30; 3; 4-2017; 209-2300951-50891465-394XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1080/09515089.2016.1271117info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/09515089.2016.1271117info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:48:14Zoai:ri.conicet.gov.ar:11336/53338instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:48:14.51CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A mechanistic perspective on canonical neural computation
title A mechanistic perspective on canonical neural computation
spellingShingle A mechanistic perspective on canonical neural computation
Wajnerman Paz, Abel
Abstraction
Causal Explanation
Cognitive Neuroscience
Integration
Mechanism
title_short A mechanistic perspective on canonical neural computation
title_full A mechanistic perspective on canonical neural computation
title_fullStr A mechanistic perspective on canonical neural computation
title_full_unstemmed A mechanistic perspective on canonical neural computation
title_sort A mechanistic perspective on canonical neural computation
dc.creator.none.fl_str_mv Wajnerman Paz, Abel
author Wajnerman Paz, Abel
author_facet Wajnerman Paz, Abel
author_role author
dc.subject.none.fl_str_mv Abstraction
Causal Explanation
Cognitive Neuroscience
Integration
Mechanism
topic Abstraction
Causal Explanation
Cognitive Neuroscience
Integration
Mechanism
purl_subject.fl_str_mv https://purl.org/becyt/ford/6.3
https://purl.org/becyt/ford/6
dc.description.none.fl_txt_mv Although it has been argued that mechanistic explanation is compatible with abstraction (i.e., that there are abstract mechanistic models), there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations (CNCs) must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation (as opposed to a purely mathematical or non-causal interpretation). Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways.
Fil: Wajnerman Paz, Abel. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Although it has been argued that mechanistic explanation is compatible with abstraction (i.e., that there are abstract mechanistic models), there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations (CNCs) must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation (as opposed to a purely mathematical or non-causal interpretation). Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways.
publishDate 2017
dc.date.none.fl_str_mv 2017-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/53338
Wajnerman Paz, Abel; A mechanistic perspective on canonical neural computation; Taylor & Francis; Philosophical Psychology; 30; 3; 4-2017; 209-230
0951-5089
1465-394X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/53338
identifier_str_mv Wajnerman Paz, Abel; A mechanistic perspective on canonical neural computation; Taylor & Francis; Philosophical Psychology; 30; 3; 4-2017; 209-230
0951-5089
1465-394X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1080/09515089.2016.1271117
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/09515089.2016.1271117
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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