Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making

TL;DR

This paper investigates whether human decision biases in a bandit task are genuine or artifacts of learning models, showing that Bayesian inference can produce similar behaviors as biased models and proposing methods to distinguish them.

Contribution

It demonstrates that standard Bayesian inference can mimic biases in learning models and introduces experimental protocols to differentiate true biases from model artifacts.

Findings

Bayesian inference can produce behaviors similar to biases in standard models.

Decreasing learning rates can mimic confirmation and positivity biases.

Proposed protocols to distinguish genuine biases from model artifacts.

Abstract

Recent studies claim that human behavior in a two-armed Bernoulli bandit (TABB) task is described by positivity and confirmation biases, implying that humans do not integrate new information objectively. However, we find that even if the agent updates its belief via objective Bayesian inference, fitting the standard Q-learning model with asymmetric learning rates still recovers both biases. Bayesian inference cast as an effective Q-learning algorithm has symmetric, though decreasing, learning rates. We explain this by analyzing the stochastic dynamics of these learning systems using master equations. We find that both confirmation bias and unbiased but decreasing learning rates yield the same behavioral signatures. Finally, we propose experimental protocols to disentangle true cognitive biases from artifacts of decreasing learning rates.