Abstract
The ability to continuously make inferences about another person's latent states from their behavior is integral to how people behave in social situations, yet is lacking from most artificial intelligence (AI) systems. The present study tests the capacity of cognitive models to assess latent motives in real time by evaluating different deep neural networks trained to infer a human player's intent during a continuous control task. These networks were trained by (a) directly using observable information or (b) selecting important features by estimating the parameters of a generative model of movement behavior inspired by approach-avoidance theory. Comparisons of classifier accuracy suggest that latent model parameters predict a participant's intent at a level exceeding human performance. Furthermore, classifier performance was best when model-based inferences were combined with summary statistics about behavior, yielding faster and more stable network training compared to networks that had no manual feature extraction. Equipping AI with cognitive models is a promising avenue for developing explainable, accurate, and trustworthy systems.