@inproceedings{mar-liu-2022-cognitive,
title = "From Cognitive to Computational Modeling: {T}ext-based Risky Decision-Making Guided by Fuzzy Trace Theory",
author = "Mar, Jaron and
Liu, Jiamou",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.30",
doi = "10.18653/v1/2022.findings-naacl.30",
pages = "391--409",
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.",
}
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%0 Conference Proceedings
%T From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory
%A Mar, Jaron
%A Liu, Jiamou
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F mar-liu-2022-cognitive
%X 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.
%R 10.18653/v1/2022.findings-naacl.30
%U https://aclanthology.org/2022.findings-naacl.30
%U https://doi.org/10.18653/v1/2022.findings-naacl.30
%P 391-409
Markdown (Informal)
[From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory](https://aclanthology.org/2022.findings-naacl.30) (Mar & Liu, Findings 2022)
ACL