Quickly recovering comprehensive individual mental representations of facial affect

The categorization of complex real-world stimuli, such as facial expressions, appears to vary greatly between people. This raises a crucial methodological challenge: how is it possible to elicit the mental representation of a complex category for a specific individual? Comprehensive category-elicitation methods such as Markov Chain Monte Carlo with People (MCMCP) work well across populations, but converge too slowly to be usable with individual participants. Here, we address the problem of slow convergence with a new method: combining MCMCP with an adapted Variational Auto-Encoder (VAE) acting as a “gatekeeper”. We tested this approach in a new experiment (N=90) on facial affect comparing MCMCP with the “gatekeeper” (MCMCPG) against baseline MCMCP and other variants. MCMCPG converged substantially faster than the other methods, in about 10 minutes for our task, with showing more representative recovered faces than its competitors. Further analyses captured participants’ substantial individual differences in a categorization task at an individual level. And, uniquely, the resulting model generalized these individual differences to real-world faces outside of our training set. Our study demonstrates the potential of MCMCPG for investigating generalizable human representations of complex stimuli at the individual level and illustrates the power of integrating Artificial Intelligence into psychological experiments.