Massachusetts Institute of Technology
Primates can richly parse sensory inputs to infer latent information, and adjust their behavior accordingly. It has been hypothesized that such flexible inferences are aided by simulations of internal models of the external world. However, evidence supporting this hypothesis has been based on behavioral models that do not emulate neural computations. Here, we test this hypothesis by directly comparing the behavior of humans and monkeys in a ball interception task to that of recurrent neural network (RNN) models with or without the capacity to “simulate” the underlying latent variables. Humans and monkeys had strikingly similar behavioral patterns suggesting common underlying neural computations. Comparison between primates and a large class of RNNs revealed that only RNNs that were optimized to simulate the position of the ball were able to accurately capture key features of the behavior such as systematic biases in the inference process. These results are consistent with the hypothesis that primates use mental simulation to make flexible inferences. Moreover, our work highlights a general strategy for using model neural systems to test computational hypotheses of higher brain function.