Explaining decision biases through context-dependent repetition

Humans are prone to decision biases, which make behavior seemingly irrational. An important cause for decision biases is that the context in which decisions are made can later influence which choices humans prefer in new situations. Current computational models often require extensive environmental knowledge to explain these biases. Here, we tested the hypothesis that decision biases are mainly driven by a tendency to repeat context-specific actions. We implemented a series of nine value-based decision experiments on n = 351 male and female participants and reanalyzed six previously published datasets (n=350 participants). We found that higher within-context repetition of an option was associated with biased choices including higher subjective valuation and lower uncertainty for repeated actions. Next, we used a computational model based on two basic principles, learning by reward and decision repetition and tested it on all datasets. Our results show that the combination of these two basic principles is sufficient to explain biased choices. We demonstrate via hierarchical Bayesian model comparison that our model outperforms all alternative models. These results provide novel insights into human choice behavior and offer a solution to difficulties in current explanations and models.