Ecologically enriched quantitative examination of the metalevel MDP in decision making

In daily life, humans selectively search for information about options to make decisions. The metalevel MDP framework, proposed to understand this information search process, has so far been evaluated only for its predictive performance regarding group differences in summary measures under unrealistic scenarios. This study aimed to examine whether the metalevel MDP can account for the sequential patterns of information search in more realistic decision-making situations. We conducted a chatbot-based experiment that enables diverse information search actions. We then cast the existing greedy metalevel MDP (GML-MDP) into a probabilistic form to estimate participant-level latent parameters and assess its fit to sequential action data. The experimental results showed that the model partially explains the information search process, and we discussed potential directions for model improvement.