Statistical learning enables observers to extract regularities from their environment, but what statistical regularity is extracted remains debated. While previous research has mainly focused on conditional probability, recent evidence suggests that observers may instead learn the uniqueness of predictive relationships. In two visual statistical learning experiments, we manipulated the strength and uniqueness of associations between two object stimuli. We contrasted the predictions of three metrics of associative strength, which incorporate the strength and uniqueness of the association differentially. Participants viewed sequences of objects with varying transitional structures and completed an incidental categorization task. Reaction time benefits for expected versus unexpected stimuli were used to gauge learning. Across two experiments, learning benefits were best predicted by the dual factor heuristic (DFH), a heuristic that jointly considers the conditional probabilities of cue given outcome and outcome given cue. This metric predicted learning behavior better than either the conditional probability of outcome given cue, or the normative metric ΔP, which considers the difference in conditional probabilities of outcome given cue, compared to outcome given no cue. These results suggest that visual statistical learning is primarily guided by a heuristic calculation of uniqueness, as formalized by D F H , rather than either simple conditional probability or Δ P .
Nazlı et al. (Fri,) studied this question.