Individuality transfer: Predicting human decision-making across task conditions

Abstract

Predicting an individuals behaviour in one task condition based on their behaviour in a different condition is a key challenge in modeling individual decision-making tendencies. We propose a novel framework that addresses this challenge by leveraging neural networks and introducing a concept we term the "individual latent representation." This representation, extracted from behaviour in a "source" task condition via an encoder network, captures an individuals unique decision-making tendencies. A decoder network then utilizes this representation to generate the weights of a task-specific neural network (a "task solver"), which predicts the individuals behaviour in a "target" task condition. We demonstrate the effectiveness of our approach in two distinct decision-making tasks: a value-guided task and a perceptual task. Our framework offers a robust and generalizable approach for parameterizing individual variability, providing a promising pathway toward computational modeling at the individual level--replicating individuals in silico.