Cue predictiveness and uncertainty determine cue representation during visual statistical learning.

This study investigated how humans process probabilistic-associated information when encountering varying levels of uncertainty during implicit visual statistical learning. A novel probabilistic cueing validation paradigm was developed to probe the representation of cues with high (75% probability), medium (50%), low (25%), or zero levels of predictiveness in response to preceding targets that appeared with high (75%), medium (50%), or low (25%) transitional probabilities (TPs). Experiments 1 and 2 demonstrated a significant negative association between cue probe identification accuracy and cue predictiveness when these cues appeared after high-TP but not medium-TP or low-TP targets, establishing exploration-like cue processing triggered by lower-uncertainty rather than high-uncertainty inputs. Experiment 3 ruled out the confounding factor of probe repetition and extended this finding by demonstrating (1) enhanced representation of low-predictive and zero-predictive but not high-predictive cues across blocks after high-TP targets and (2) enhanced representation of high-predictive but not low-predictive and zero-predictive cues across blocks after low-TP targets for learners who exhibited above-chance awareness of cue-target transition. These results suggest that during implicit statistical learning, input characteristics alter cue-processing mechanisms, such that exploration-like and exploitation-like mechanisms are triggered by lower-uncertainty and higher-uncertainty cue-target sequences, respectively.

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© 2023 Zhang et al.; Published by Cold Spring Harbor Laboratory Press.