Effects of Statistical Learning on Stimulus Detection Speed Reflect Attention Rather Than Representational Changes.

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.