Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Language, Cognition and Neuroscience on 20/05/2022, available online: http://www.tandfonline.com/10.1080/23273798.2022.2069278
Accepted author manuscript, 19.1 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Understanding the role of linguistic distributional knowledge in cognition
AU - Wingfield, Cai
AU - Connell, Louise
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Language, Cognition and Neuroscience on 20/05/2022, available online: http://www.tandfonline.com/10.1080/23273798.2022.2069278
PY - 2022/10/31
Y1 - 2022/10/31
N2 - The distributional pattern of words in language forms the basis of linguistic distributional knowledge and contributes to conceptual processing, yet many questions remain regarding its role in cognition. We propose that corpus-based linguistic distributional models can represent a cognitively plausible approach to understanding linguistic distributional knowledge when assumed to represent an essential component of semantics, when trained on corpora representative of human language experience, and when they capture the diverse distributional relations that are useful to cognition. Using an extensive set of cognitive tasks that vary in the complexity of conceptual processing required, we systematically evaluate a wide range of model families, corpora, and parameters, and demonstrate that there is no one-size-fits-all approach for how linguistic distributional knowledge is used across cognition. Rather, linguistic distributional knowledge is a rich source of information about the world that can be accessed flexibly according to the conceptual complexity of the task at hand.
AB - The distributional pattern of words in language forms the basis of linguistic distributional knowledge and contributes to conceptual processing, yet many questions remain regarding its role in cognition. We propose that corpus-based linguistic distributional models can represent a cognitively plausible approach to understanding linguistic distributional knowledge when assumed to represent an essential component of semantics, when trained on corpora representative of human language experience, and when they capture the diverse distributional relations that are useful to cognition. Using an extensive set of cognitive tasks that vary in the complexity of conceptual processing required, we systematically evaluate a wide range of model families, corpora, and parameters, and demonstrate that there is no one-size-fits-all approach for how linguistic distributional knowledge is used across cognition. Rather, linguistic distributional knowledge is a rich source of information about the world that can be accessed flexibly according to the conceptual complexity of the task at hand.
KW - Conceptual processing
KW - linguistic distributional knowledge
KW - distributional semantics
KW - computational modelling
U2 - 10.31234/osf.io/hpm4z
DO - 10.31234/osf.io/hpm4z
M3 - Journal article
VL - 37
SP - 1220
EP - 1270
JO - Language, Cognition and Neuroscience
JF - Language, Cognition and Neuroscience
SN - 2327-3798
IS - 10
ER -