Kavli Affiliate: Marcelo Mattar
| Authors: Hua-Dong Xiong, Li Ji-An, Marcelo G Mattar and Robert C Wilson
| Summary:
Cognitive modeling in psychology and neuroscience provides a formal approach to formulate and test hypotheses of cognitive processes. Such models rely on cognitive parameters that are intended to represent interpretable and identifiable psychological constructs. However, accurately and reliably estimating model parameters remains challenging due to common issues such as limited data, measurement noise, experimental constraints, and model complexity, hindering the interpretability and identifiability of cognitive parameters. Given the recent success of advanced optimization methods in deep learning, we investigate whether a deep learning pipeline that integrates neural networks and modern optimization techniques can improve parameter estimation in reinforcement learning (RL) models. We compare this approach with the Nelder-Mead method (\texttt{fminsearch}), the de facto optimization approach in cognitive modeling, by fitting RL models to \textit{ten} diverse value-based decision-making datasets collected from both humans and animals. Surprisingly, while both approaches achieve comparable predictive performance, they produce distinct parameter estimates, indicating fitting performance alone is insufficient in identifying these parameters. We thus systematically evaluate the reliability of parameters estimated by each approach and find that parameters estimated via the deep learning pipeline consistently demonstrate smaller gaps between training and test performance (better generalizability), increased resistance to parameter perturbations (enhanced robustness), improved recovery of ground-truth parameters in low-data regimes (stronger identifiability), and greater consistency across repeated measurements from the same individuals (better test-retest reliability). Our findings advocate for the deep learning pipeline and systematic evaluation of cognitive parameters to better link these parameters to psychological constructs and neural mechanisms.