# Cognitive models facilitate real-time inference of latent motives

## TL;DR

This study shows that AI can better understand human intent in real time by using cognitive models inspired by psychological theories.

## Contribution

The novel approach combines cognitive models with deep learning to infer human intent more accurately than human observers.

## Key findings

- Latent model parameters predict human intent better than human performance.
- Combining model-based inferences with behavior statistics improves AI training speed and stability.
- Cognitive models enhance AI's ability to make explainable and trustworthy decisions.

## 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.