Humans are adept at learning regularities in a visual environment, even without explicit cues to structure and in the absence of instruction-this has been termed "visual statistical learning" (VSL). The nature of the representations resulting from VSL are still poorly understood. In five experiments, we examined the specificity of temporal VSL representations. In Experiments 1A, 1B, and 2, we compared recognition rates of triplets and all embedded pairs to chance. Robust learning of all structures was evident, and even pairs of non-adjacent items in a sequentially presented triplet (AC extracted from a triplet composed of ABC) were recognized at above-chance levels. In Experiment 3, we asked whether people could recognize rearranged pairs to examine the flexibility of learned representations. Recognition of all possible orders of target triplets and pairs was significantly higher than chance, and there were no differences between canonical orderings and their corresponding randomized orderings, suggesting that learners were not dependent upon originally experienced stimulus orderings to recognize co-occurrence. Experiment 4 demonstrates the essential role of an interstitial item in VSL representations. By comparing the learning of quadruplet sets (e.g., ABCD) and triplet sets (e.g., ABC), we found learning of AC and BD in ABCD (quadruplet) sets were better than the learning of AC in ABC (triplet) sets. This pattern of results might result from the critical role of interstitial items in statistical learning. In short, our work supports the idea of generalized representation in VSL and provides evidence about how this representation is structured.
Keywords: Associative learning; Generalization; Visual statistical learning.