Deep Learning Improves Parameter Estimation in Reinforcement Learning Models.

ABSTRACT: Cognitive models are widely used in psychology and neuroscience to formulate and test hypotheses about cognitive processes. These processes are characterized by model parameters, which are then used for scientific inference. The reliability of scientific conclusions from cognitive modeling depends critically on the reliability of parameter estimation, yet estimating parameters remains a universal challenge particularly when data are too limited to constrain them. In such cases, multiple sets of parameters may explain the experimental data equally well within the same model, raising the question of which parameters are scientifically meaningful. We refer to this problem as parameter ambiguity. In this paper, we investigate parameter ambiguity in reinforcement learning under two optim

SUBMITTER: Xiong HD 

PROVIDER: S-EPMC12262606 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature