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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Dynamic noise estimation: A generalized method for modeling noise fluctuations in decision-making.

Jing-Jing Li¹, Chengchun Shi², Lexin Li^1,3

¹Helen Wills Neuroscience Institute, University of California, Berkeley, 175 Li Ka Shing Center, Berkeley, 94720, CA, United States.

Biorxiv : the Preprint Server for Biology

|February 8, 2024

Summary

This study introduces a dynamic noise estimation method for computational cognitive models. It improves decision-making analysis by accounting for changing noise levels, outperforming static methods.

Keywords:

attention cognitive modeling decision noise decision-making hidden Markov model lapses reinforcement learning task engagement

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Area of Science:

Computational cognitive science
Neuroscience
Behavioral economics

Background:

Computational cognitive modeling is crucial for understanding decision-making in humans and animals.
Existing models often assume constant noise levels, which may not reflect real-world behavior where noise can fluctuate.
This limitation can hinder accurate parameter estimation and model fit.

Conclusions:

Dynamic noise estimation offers a more accurate and robust approach to computational modeling of decision-making.
The method is computationally inexpensive and minimally increases model complexity.
This approach is expected to enhance modeling across various decision-making paradigms by better capturing behavioral variability.