Recurrent neural network dynamics may not purely reflect cognitive strategies: A commentary on Ji-An et al. (Nature, 2025)

One of the fundamental goals of behavioural science is to explain seemingly complex human and animal behaviour in terms of simple mechanistic principles. Ji-An, Benna, & Mattar1 (henceforth, JBM) recently proposed a novel analytic and interpretative framework toward this goal. By employing tiny recurrent neural networks (RNNs) with a small number of degrees of freedom, they developed a method for extracting the dynamics of learning and decision-making. They demonstrated that tiny RNNs achieve superior predictive performance compared to classical cognitive models such as reinforcement learning (RL) and Bayesian inference models, and that dynamical systems analyses of the RNNs can reveal cognitive dynamics that differ from those assumed in the cognitive models. This framework is highly promising; however, we suggest allocating room for discussion as to whether the dynamics of RNNs purely reflect the cognitive processes at work in the individuals brain. In particular, RNNs may adapt to within-individual state changes (e.g., transitions between engaged and disengaged states) in ways not captured by cognitive models, thereby improving model fit without necessarily representing cognitive strategies per se. This raises the possibility that the dynamics of RNNs may include processes that deviate from, rather than directly reflect, individual cognitive strategies.