From Cognitive to Computational Modeling: Text-based Risky Decision-Making Guided by Fuzzy Trace Theory
This work addresses the problem of predicting risky decision-making from text for researchers in psychology and computational modeling, representing an incremental advancement by applying an existing cognitive theory to a computational context.
The authors tackled the challenge of modeling human risky decision-making by proposing a computational framework based on Fuzzy Trace Theory, which incorporates semantic and sentiment analysis to predict decisions in groups and individuals, achieving optimized prediction capabilities.
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.