The authors investigated the extent to which touch, vision, and audition mediate the processing of statistical regularities within sequential input. Few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored. The current data reveal not only commonalities but also modality constraints affecting statistical learning across the senses. To be specific, the authors found that the auditory modality displayed a quantitative learning advantage compared with vision and touch. In addition, they discovered qualitative learning biases among the senses: Primarily, audition afforded better learning for the final part of input sequences. These findings are discussed in terms of whether statistical learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones. The world is temporally bounded: Events do not occur all at once but rather are distributed in time. Therefore, it is crucial for organisms to be able to encode and represent temporal order information. One potential method for encoding temporal order is to learn the statistical relationships of elements within sequential input. This process appears to be important in a diverse set of learning situations, including speech segmentation (Saffran, Newport, & Aslin, 1996), learning orthographic regularities of written words (Pacton, Perruchet, Fayol, & Cleeremans, 2001), visual processing (Fiser & Aslin, 2002), visuomotor learning (e.g., serial reaction time tasks; Cleeremans, 1993) and nonlinguistic, auditory

The authors investigated how human adults encode and remember parts of multielement scenes composed of recursively embedded visual shape combinations. The authors found that shape combinations that are parts of larger configurations are less well remembered than shape combinations of the same kind that are not embedded. Combined with basic mechanisms of statistical learning, this embeddedness constraint enables the development of complex new features for acquiring internal representations efficiently without being computationally intractable. The resulting representations also encode parts and wholes by chunking the visual input into components according to the statistical coherence of their constituents. These results suggest that a bootstrapping approach of constrained statistical learning offers a unified framework for investigating the formation of different internal representations in pattern and scene perception.

We investigated the extent to which touch, vision, and audition are similar in the ways they mediate the processing of statistical regularities within sequential input. While previous research has examined statistical/sequential learning in the visual and auditory domains, few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored in such research. Our data reveal commonalities between the ways in which these three modalities afford the learning of sequential information. However, the data also suggest that in terms of sequential learning, audition is superior to the other two senses. We discuss these findings in terms of whether statistical/sequential learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones.

One of the longstanding issues in language research has been the extent to which the mechanisms underlying language acquisition are uniquely human. The primary goal of this article is to introduce the reader to some of the recent developments in comparative language research that have shed new light on this issue. To appreciate the significance of the new developments, we begin with a brief historical overview of language studies that have adopted a comparative approach, and then discuss a subset of the relevant theoretical accounts that seek to explain why humans are the only species capable of acquiring language. We next focus on findings from behavioral studies comparing the performance of human infants and adults with nonhuman primates on tests that tap the perceptual and learning mechanisms that are fundamental to language acquisition. We argue that in cases where the behavioral data appear similar across populations, there is a need to investigate the underlying computational abilities and units of analysis to correctly specify the degree to which the mechanisms are truly shared or are uniquely specified. Dogbert: I once read that given infinite time, a thousand monkeys with typewriters would eventually write the complete works of Shakespeare.

Visual statistical learning allows observers to extract high-level structure from visual scenes (Fiser & Aslin, 2001). Previous work has explored the types of statistical computations afforded but has not addressed to what extent learning results in unbound versus spatially bound representations of element cooccurrences. We explored these two possibilities using an unsupervised learning task with adult participants who observed complex multi-element scenes embedded with consistently paired elements. If learning is mediated by unconstrained associative learning mechanisms, then learning the element pairings may depend only on the co-occurrence of the elements in the scenes, without regard to their specific spatial arrangements. If learning is perceptually constrained, cooccurring elements ought to form perceptual units specific to their observed spatial arrangements. Results showed that participants learned the statistical structure of element cooccurrences in a spatial-specific manner, showing that visual statistical learning is perceptually constrained by spatial grouping principles.

Previous studies of auditory statistical learning have typically presented learners with sequential structural information that is uniformly distributed across the entire exposure corpus. Here we present learners with nonuniform distributions of structural information by altering the organization of trisyllabic nonsense words at midstream. When this structural change was unmarked by low-level acoustic cues, or even when cued by a pitch change, only the first of the two structures was learned. However, both structures were learned when there was an explicit cue to the midstream change or when exposure to the second structure was tripled in duration. These results demonstrate that successful extraction of the structure in an auditory statistical learning task reduces the ability to learn subsequent structures, unless the presence of two structures is marked explicitly or the exposure to the second is quite lengthy. The mechanisms by which learners detect and use changes in distributional information to maintain sensitivity to multiple structures are discussed from both behavioral and computational perspectives.

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