Capturing learning on the fly: an eye-tracking method to quantify prediction errors and updating the prior
The ability to build predictive models of the environment fundamentally drives adaptive behavior. Yet, the real-time dynamics of how these internal models are formed and updated remain poorly understood. Conventional methods often rely on indirect, offline measures or noisy motor responses, limiting insight into the fine-grained computational processes underlying learning. Here, we introduce a generalizable, gaze-based analytical framework that directly tracks the trial-by-trial dynamics of expectation formation and updating. Applying this framework to an unsupervised probabilistic learning task, we categorized anticipatory saccades to dissociate prediction errors arising from environmental stochasticity from those reflecting an inaccurate internal model, and quantified how these predictions were iteratively revised. Learners differentiated between these error types: noise-driven errors were more likely to happen, and triggered less updates than errors reflecting insufficient knowledge of the regularity. At the same time, participants exhibited a strong preference to repeat their previous predictions. This repetition bias was amplified when predictions aligned with the underlying regularity, but was also present for non-aligned responses. Critically, updating depended more strongly on whether a prior belief was consistent with the tasks probabilistic structure than on whether the predicted stimulus matched the actual, presented stimulus. These findings suggest that statistical learning may not strongly be driven by errors; rather, it may rely on conservative updating with relatively low learning rate, or, on a Hebbian, repetition-based process. Our framework thus offers a dual contribution: a broadly applicable tool for quantifying real-time expectations, and evidence for a learning strategy that prioritizes model stability in noisy environments.