Literature DB >> 25976728

Language as grist to the mill of cognition.

Alexandros Tillas1.   

Abstract

There is a growing consensus that natural language plays a significant role in our cognitive lives. However, this role of language is not adequately characterised. In this paper, I investigate the relationship between natural language and thinking and argue that thinking operates largely according to associationistic rules. Furthermore, I show that language is neither restricted to interfacing between a 'Language of Thought' and the conscious level, nor is it constitutively involved in thinking. Unlike available alternatives, the suggested view predicts and accommodates a large battery of empirical evidence. Furthermore, it avoids problems that associationistic views traditionally faced, e.g. problems of propositional thinking and compositionality of thought.

Mesh:

Year:  2015        PMID: 25976728     DOI: 10.1007/s10339-015-0656-2

Source DB:  PubMed          Journal:  Cogn Process        ISSN: 1612-4782


  43 in total

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Authors:  Holly Robson; Karen Sage; Matthew A Lambon Ralph
Journal:  Neuropsychologia       Date:  2011-12-08       Impact factor: 3.139

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Journal:  PLoS One       Date:  2010-07-07       Impact factor: 3.240

8.  Infants selectively encode the goal object of an actor's reach.

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Journal:  Cognition       Date:  1998-11

9.  Extracommunicative functions of language: verbal interference causes selective categorization impairments.

Authors:  Gary Lupyan
Journal:  Psychon Bull Rev       Date:  2009-08

10.  Linguistically modulated perception and cognition: the label-feedback hypothesis.

Authors:  Gary Lupyan
Journal:  Front Psychol       Date:  2012-03-08
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  4 in total

1.  On the origins of endogenous thoughts.

Authors:  Alexandros Tillas
Journal:  Cogn Process       Date:  2017-01-13

2.  Exploring self-generated thoughts in a resting state with natural language processing.

Authors:  Hui-Xian Li; Bin Lu; Xiao Chen; Xue-Ying Li; Francisco Xavier Castellanos; Chao-Gan Yan
Journal:  Behav Res Methods       Date:  2021-10-13

3.  Predicting probable Alzheimer's disease using linguistic deficits and biomarkers.

Authors:  Sylvester O Orimaye; Jojo S-M Wong; Karen J Golden; Chee P Wong; Ireneous N Soyiri
Journal:  BMC Bioinformatics       Date:  2017-01-14       Impact factor: 3.169

4.  Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia.

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Journal:  PLoS One       Date:  2018-11-07       Impact factor: 3.240

  4 in total

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