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A pipeline for analyzing decision-making processes in a binary choice task

Farnaz Tehranchi
The School of Engineering Design and Innovation
Mr. Amirreza Bagherzadehkhorasani
Penn State University ~ Industrial Engineering

In this study, we propose a roadmap for the analysis of various factors on cognitive models' parameters and utilizing different cognitive models to better understand the human decision making process in a binary choice task. Our experiment of a binary choice task is a biased coin flip game, where users predict the outcome of 150 trials of biased coin flips without knowing the coin's bias. In a previous study, we conducted a factorial ANOVA on the biased coin flip game to identify factors that significantly influence users' decision making strategies, such as Gender, Win rate visibility, and the coin's bias value. In this paper, we employed genetic algorithms to identify cognitive models that fit users' behaviors the best in specific scenarios for each combination of the effective factors. Subsequently, we fitted linear models to examine the relationship between the identified parameters and the influential factors. By analyzing and interpreting the coefficients of these linear models, we aim to gain insights into how these factors affect users' decision making processes and understand human decision making better. Our proposed roadmap serves as a valuable resource for researchers aiming to interpret cognitive model parameters for diverse user behaviors. By providing a systematic approach to investigating the relationships between influential factors and cognitive model parameters, this work provides a deeper understanding of human decision making processes and baselines for future modeling approaches in this domain.



cognitive modeling; ACT-R; decision making; binary choice task
human performance modeling

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Cite this as:

Tehranchi, F., & Bagherzadehkhorasani, A. (2023, July). A pipeline for analyzing decision-making processes in a binary choice task. Abstract published at MathPsych/ICCM/EMPG 2023. Via