The researchers exposed people to paired visual shapes and then measured how quickly they detected targets. Their careful design separated the role of a target’s predictability from the influence of the element that came before it. The results showed that reaction time advantages align with which objects have acted as reliable predictors, not with whether a target was itself predictable across learning. That pattern points toward attention being steered by knowledge of what usually comes next, rather than to strengthened perceptual representations of the predicted items.
These findings matter because they change how we think about learning from patterns in everyday life. If statistical learning mainly tunes attention, then tasks that depend on noticing relevant signals—classroom learning, navigating crowded spaces, or designing inclusive interfaces—may benefit from shaping what cues people attend to. To learn how this attention-based mechanism unfolds and what it means for building environments that support growth and inclusion, read the full article.
Abstract
Statistical learning (SL) is believed to enable humans to assimilate a range of statistical structures, and thus plays a role in many cognitive functions. There is also a growing interest in how SL interacts with basic cognitive processes, including perception and attention. Here, we ask how the extent to which stimuli predict, and are predicted by, other elements in a continuous stream affects their perception (i.e., encoding and representation) and the attention they attract. In two experiments, participants were first exposed to a stream of structured pairs of visual shapes (e.g., AB and CD). Then, they completed a target detection task to test if stimulus detection speed is influenced by a target’s predictability during exposure (indexing representation) or by whether the shape preceding the target reliably predicted elements in the input (indexing attention). In Experiment 1 (N = 86), Reaction Times (RTs) were faster for second elements from structured pairs (i.e., cued elements) than first elements (i.e., cue elements), even when they appeared in new configurations (e.g., AD and CB). However, in Experiment 2 (N = 89), which orthogonally manipulated the target and the preceding shape properties, RTs were influenced solely by whether the preceding element was a cue for other elements, but not by the target’s predictability. Thus, in contrast to previous studies using the same paradigm, our results do not provide evidence for an effect of SL on representation. Instead, our findings highlight how attention is guided by knowledge of statistical regularities, pointing to SL as a system that helps minimize uncertainty in structured environments.
