# Deciding for others diminishes model-based decision-making but depends on individual prosociality

## TL;DR

People use less effortful decision-making when choosing for others, and this depends on how prosocial they are.

## Contribution

The study identifies computational mechanisms linking prosociality to model-based decision-making differences between self and others.

## Key findings

- Deciding for others slows model-free learning and reduces model-based strategy use.
- Non-decision time partially explains the self-other difference in model-based decisions.
- Prosocial individuals show smaller self-other discrepancies in model-based decision-making.

## Abstract

Acting successfully in dynamic environments requires learning supported by two systems that differ in computational demand: a fast, model-free system that repeats rewarded actions, and a more effortful model-based system that uses a mental model of the task structure to guide flexible, goal-directed decisions. A key open question is whether people engage effortful model-based strategies to the same extent when deciding for themselves versus others, and which computations underpin self-other differences. Using a two-step task with reinforcement learning drift-diffusion modelling in 92 adults, we found that deciding for others slowed down model-free learning and reduced reliance on model-based strategies, with the latter partially mediated by differences in non-decision time. Moreover, individual differences in social value orientation predicted the self-other discrepancy in model-based decision-making, with more prosocial individuals showing smaller gaps. Together, these findings identify the computational mechanisms underpinning prosocial model-based decision-making and demonstrate how individual differences modulate this computation.