Individuality transfer: Predicting human decision-making across tasks

Predicting an individual’s behaviour in one task based on their behaviour in a different task 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 “individuality index.” This index, extracted from behaviour in a “source” task via an encoder network, captures an individual’s unique decision-making tendencies. A decoder network then utilizes this index to generate the weights of a task-specific neural network (a “task solver”), which predicts the individual’s behaviour in a “target” task. 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 individuality, providing a promising pathway toward computational modeling at the individual level—that is, replicating individuals in silico.