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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/53338
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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 |
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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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Taylor & Francis |
publisher.none.fl_str_mv |
Taylor & Francis |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.13397 |