More accurate behavioral predictions with hybrid Bayesian-Transformer models

Bayesian cognitive models incorporate prior knowledge explicitly but often impose restrictive assumptions, whereas neural networks are flexible but make such knowledge difficult to specify. Brenden Lake presents Behaviorally-Informed Meta-Learning, which distils Bayesian priors into Transformer networks before fine-tuning them on behavioural observations. This combination can model behaviour with little experimental data. Initial studies of human concept learning obtain better fits by capturing heuristics and biases that simple Bayesian models miss. The talk also examines the challenge of interpreting these hybrid models and understanding which learned mechanisms account for their predictions.