Higashi, H.

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. Notably, this prediction does not require behavioural data from the target task for that individual. We demonstrate the effectiveness of our approach in two distinct decision-making tasks: a value-guided task and a perceptual task. Our framework provides a robust and generalizable method for parameterizing individuality, offering new insights into individual differences in decision-making.