Predicting human decision-making across task conditions via individuality transfer

Predicting an individual's behavior in one task condition based on their behavior 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 behavior in a 'source' task condition via an encoder network, captures an individual's 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 individual's behavior 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.