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Inspirational anchors: minimal computational models in cognitive science.

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Title: Inspirational anchors: minimal computational models in cognitive science.
Authors: Schonbein, Whit1 (AUTHOR) 71whit@gmail.com
Source: Journal of Experimental & Theoretical Artificial Intelligence. Sep2012, Vol. 24 Issue 3, p385-400. 16p.
Subjects: Cognitive science, Model-integrated computing, Simulation methods & models, Empirical research, Inference (Logic), Economic models
Abstract: In model-based science, a minimal computational model (MCM) is a computational model developed without the guidance of significant empirical evidence about the mechanism being modelled. Despite their historical and contemporary prominence in cognitive science, MCMs face serious challenges: (1) because of the lack of empirical grounding, it is hard to see how we are justified in making inferences from a model to its target and (2) if they say nothing about their targets, it seems that their utility is limited to the articulation of mere logical possibilities. In this article, I scrutinise this challenge by surveying and rejecting some alternative accounts of the epistemological role of MCMs. I argue that these models are best viewed as fulcra upon which additional research is leveraged. In support, I draw connections between cognitive and economic modelling, and sketch how a prominent account of the latter can be extended to cover the former. [ABSTRACT FROM PUBLISHER]
Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Inspirational anchors: minimal computational models in cognitive science.
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  Data: <searchLink fieldCode="DE" term="%22Cognitive+science%22">Cognitive science</searchLink><br /><searchLink fieldCode="DE" term="%22Model-integrated+computing%22">Model-integrated computing</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Inference+%28Logic%29%22">Inference (Logic)</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+models%22">Economic models</searchLink>
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  Data: In model-based science, a minimal computational model (MCM) is a computational model developed without the guidance of significant empirical evidence about the mechanism being modelled. Despite their historical and contemporary prominence in cognitive science, MCMs face serious challenges: (1) because of the lack of empirical grounding, it is hard to see how we are justified in making inferences from a model to its target and (2) if they say nothing about their targets, it seems that their utility is limited to the articulation of mere logical possibilities. In this article, I scrutinise this challenge by surveying and rejecting some alternative accounts of the epistemological role of MCMs. I argue that these models are best viewed as fulcra upon which additional research is leveraged. In support, I draw connections between cognitive and economic modelling, and sketch how a prominent account of the latter can be extended to cover the former. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/0952813X.2012.693666
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      – SubjectFull: Simulation methods & models
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