Distilling human decision-making dynamics: a comparative analysis of low-dimensional architectures

Recent advances in examining biological decision-making behaviors have increasingly favored recurrent neural networks (RNNs) over traditional cognitive models grounded in normative principles such as reinforcement learning. This shift owes to RNN’s superior predictive performance on behavioral data, achieved with minimal manual engineering. To glean insights into biological decision-making through these networks, this approach focuses on identifying a compact set of latent dynamical variables by restricting the size of the recurrent layer's bottleneck. Yet, little is known about the distinctions between these low-dimensional RNN architectures and their practical effectiveness in capturing behavioral patterns of biological decision-making. Our study bridges this knowledge gap by 1) offering a comprehensive comparison of these low-dimensional RNN architectures with standardized terminology; 2) evaluating their predictive accuracy for human decision-making in an explore-exploit task; and 3) delivering these RNN-derived insights that traditional cognitive models overlook. Remarkably, our findings highlight the superiority of low-rank RNNs over alternatives like gated recurrent units or disentangled RNNs in this task setting. More crucially, these low-rank RNNs reveal diverse strategies that individuals employ across different decision-making phases, advancing our understanding of intricate human decision-making dynamics. Our approach offers a powerful framework for discerning individual cognitive nuances.