Drift Diffusion Model in the TAFC Paradigm and Bayesian Inference

Somani, Dhanashree Sandeep (2022) Drift Diffusion Model in the TAFC Paradigm and Bayesian Inference. Masters thesis, Indian Institute of Science Education and Research Kolkata.

Text (MS dissertation of Dhanashree Sandeep Somani (17MS087)) 17MS087_Thesis_file.pdf - Submitted Version Restricted to Repository staff only Download (1MB)

Abstract

In this report, we will give a brief introduction to Two-alternative forced choice (TAFC) experiment and study the drift diffusion model for decision making in TAFC tasks. The Drift Diffusion Model is a sequential sampling model used to study and model behavioural data. We present and briefly compare several decisionmaking models and relate them to one that is optimal in the sense that it delivers a decision of specified accuracy in the shortest possible time: the drift diffusion model (DDM). We then use hierarchical Bayesian parameter estimation methods which allows fast and flexible estimation of the the drift-diffusion model. We have performed the Bayesian analysis on simulated aswell as experimental data. We calculate the posterior distributions for the various parameters involved in modeling the reaction time using python and R based packages.

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