Distinguishing learning asymmetry from perseveration in behavioral models

Abstract A central challenge in cognitive science is to distinguish between multiple processes that can result in similar behaviors. In reinforcement learning, one prominent example concerns two potential drivers of choice repetition: a learning asymmetry, in which agents learn differently from outcomes depending on their valence and/or whether they confirm choice, and choice perseveration, an outcome-independent tendency to repeat past choices. Evidence for asymmetric learning has typically relied on computational models that control for perseveration, or specific behavioral markers. Here, we show both these approaches have critical flaws and can spuriously detect learning asymmetries even in perseverative, symmetric-learning agents. To address this, we introduce a statistical test that distinguishes genuine learning asymmetries from spurious effects. Applying this test to a large dataset spanning ten published experiments across four studies, we find that some previously reported confirmation biases are fragile, albeit others remain robust even at an across-study level. Finally, we propose a task design that can yield a more valid qualitative signature of confirmation bias. Our approach provides a reliable framework for disentangling processes underlying choice repetition, while providing tools for the wider research community that can minimize potential spurious effects arising from process mimicry and biased parameter estimation.