Learning signatures of decision making from many individuals playing the same game

TL;DR

This paper introduces a predictive framework that learns multi-scale representations of individual decision-making styles from large-scale behavioral data, enabling accurate future choice prediction and revealing individual differences.

Contribution

The method uniquely separates behavioral representations into multiple temporal scales and combines a multi-scale convolutional network with latent prediction tasks for comprehensive analysis.

Findings

Successfully predicts future choices in a 3-armed bandit task

Learns rich, multi-scale behavioral embeddings

Identifies signatures of individual decision-making differences

Abstract

Human behavior is incredibly complex and the factors that drive decision making--from instinct, to strategy, to biases between individuals--often vary over multiple timescales. In this paper, we design a predictive framework that learns representations to encode an individual's 'behavioral style', i.e. long-term behavioral trends, while simultaneously predicting future actions and choices. The model explicitly separates representations into three latent spaces: the recent past space, the short-term space, and the long-term space where we hope to capture individual differences. To simultaneously extract both global and local variables from complex human behavior, our method combines a multi-scale temporal convolutional network with latent prediction tasks, where we encourage embeddings across the entire sequence, as well as subsets of the sequence, to be mapped to similar points in the latent space. We develop and apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task, and analyze what our model's resulting embeddings reveal about the human decision making process. In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.