Learning asymmetry or perseveration? A critical re-evaluation and solution to a pervasive confound

A central challenge in cognitive science is to distinguish between multiple processes that can result in similar behaviours. In reinforcement learning (RL), one prominent example concerns two potential drivers of choice repetition: (i) a confirmation-bias learning asymmetry, in which agents learn more from outcomes that confirm their choices, and (ii) 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 behavioural markers designed to reveal asymmetric learning. 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 novel statistical test that distinguishes genuine learning asymmetries from spurious effects. Applying this test to a large dataset spanning ten published experiments, we find that some previously reported confirmation biases are fragile, albeit others remain robust even at a meta-analytic level. Finally, we propose a new task design that can yield a more valid qualitative signature of confirmation bias. We suggest 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.