From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory

Jaron Mar, Jiamou Liu


Abstract
Understanding, modelling and predicting human risky decision-making is challenging due to intrinsic individual differences and irrationality. Fuzzy trace theory (FTT) is a powerful paradigm that explains human decision-making by incorporating gists, i.e., fuzzy representations of information which capture only its quintessential meaning. Inspired by Broniatowski and Reyna’s FTT cognitive model, we propose a computational framework which combines the effects of the underlying semantics and sentiments on text-based decision-making. In particular, we introduce Category-2-Vector to learn categorical gists and categorical sentiments, and demonstrate how our computational model can be optimised to predict risky decision-making in groups and individuals.
Anthology ID:
2022.findings-naacl.30
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
391–409
Language:
URL:
https://aclanthology.org/2022.findings-naacl.30
DOI:
10.18653/v1/2022.findings-naacl.30
Bibkey:
Cite (ACL):
Jaron Mar and Jiamou Liu. 2022. From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 391–409, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory (Mar & Liu, Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-naacl.30.pdf
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