The computational roots of positivity and confirmation biases in reinforcement learning

Humans do not integrate new information objectively: outcomes carrying a positive affective value and evidence confirming one's own prior belief are overweighed. Until recently, theoretical and empirical accounts of the positivity and confirmation biases assumed them to be specific to 'high-level' belief updates. We present evidence against this account. Learning rates in reinforcement learning (RL) tasks, estimated across different contexts and species, generally present the same characteristic asymmetry, suggesting that belief and value updating processes share key computational principles and distortions. This bias generates over-optimistic expectations about the probability of making the right choices and, consequently, generates over-optimistic reward expectations. We discuss the normative and neurobiological roots of these RL biases and their position within the greater picture of behavioral decision-making theories.

Automated summary

Humans tend to integrate new information subjectively, giving more weight to outcomes with positive affective value and evidence that confirms their prior beliefs. Previous studies believed that these biases were specific to "high-level" belief updates, but our findings provide evidence against this claim. The learning rates in reinforcement learning tasks show the same characteristic asymmetry across different contexts and species, indicating that belief and value updating processes share key computational principles and distortions.