Bias or Optimality? Disentangling Bayesian Inference and Learning Biases in Human Decision-Making
This work addresses a fundamental issue in cognitive science by clarifying whether observed biases in human behavior are artifacts of modeling or reflect true cognitive limitations.
The study tackled the problem of distinguishing between cognitive biases and optimal Bayesian inference in human decision-making, showing that both confirmation bias and unbiased decreasing learning rates produce identical behavioral signatures in a two-armed Bernoulli bandit task.
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.