From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory
Jaron Mar, Jiamou Liu · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
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 decisionmaking 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 decisionmaking.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.