Recent studies of visual statistical learning (VSL) have demonstrated that statistical regularities in sequences of visual stimuli can be automatically extracted, even without intent or awareness. Despite much work on this topic, however, several fundamental questions remain about the nature of VSL. In particular, previous experiments have not explored the underlying units over which VSL operates. In a sequence of colored shapes, for example, does VSL operate over each feature dimension independently, or over multidimensional objects in which color and shape are bound together? The studies reported here demonstrate that VSL can be both object-based and feature-based, in systematic ways based on how different feature dimensions covary. For example, when each shape covaried perfectly with a particular color, VSL was object-based: Observers expressed robust VSL for colored-shape sub-sequences at test but failed when the test items consisted of monochromatic shapes or color patches. When shape and color pairs were partially decoupled during learning, however, VSL operated over features: Observers expressed robust VSL when the feature dimensions were tested separately. These results suggest that VSL is object-based, but that sensitivity to feature correlations in multidimensional sequences (possibly another form of VSL) may in turn help define what counts as an object.

What mechanisms support the ability of human infants, adults, and other primates to identify words from fluent speech using distributional regularities? In order to better characterize this ability, we collected data from adults in an artificial language segmentation task similar to Saffran, Newport, and Aslin (1996) in which the length of sentences was systematically varied between groups of participants. We then compared the fit of a variety of computational models— including simple statistical models of transitional probability and mutual information, a clustering model based on mutual information by Swingley (2005), PARSER (Perruchet & Vintner, 1998), and a Bayesian model. We found that while all models were able to successfully complete the task, fit to the human data varied considerably, with the Bayesian model achieving the highest correlation with our results.

ABSTRACT—Sequences of speech sounds play a central role in human cognitive life, and the principles that govern such sequences are crucial in determining the syntax and semantics of natural languages. Infants are capable of extracting both simple transitional probabilities and simple algebraic rules from sequences of speech, as demonstrated by studies using ABB grammars (la ta ta, gai mu mu, etc.). Here, we report a striking finding: Infants are better able to extract rules from sequences of nonspeech— such as sequences of musical tones, animal sounds, or varying timbres—if they first hear those rules instantiated in sequences of speech. A hallmark of human language is its abstract character; learners do not simply memorize particular sentences, but rather learn generalizable rules that govern the sequencing of linguistic elements, both familiar and unfamiliar. Proceeding from recent observations that infants are able to extract transitional probabilities from both speech sequences (Saffran, Aslin, & Newport, 1996) and nonspeech sequences (e.g., musical tones: Saffran,

Human infants and adults are able to segment coherent sequences from unsegmented strings of auditory stimuli after only a short exposure, an ability thought to be linked to early language acquisition. Although some research has hypothesized that learners succeed in these tasks by computing transitional probabilities between syllables, current experimental results do not differentiate between a range of models of different computations that learners could perform. We created a set of stimuli that was consistent with two different lexicons—one consisting of two-syllable words and one of three-syllable words—but where transition probabilities would not lead learners to segment sentences consistently according to either lexicon. Participants ’ responses formed a distribution over possible segmentations that included consistent segmentations into both two- and three-syllable words, suggesting that learners do not use pure transitional probabilities to segment but instead impose a bias towards parsimony on the lexicons they learn.

Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming. Theoretical accounts of AGL are reviewed, together with relevant human experimental and neuroscience data. The author concludes that satisfactory understanding of AGL requires (a) an understanding of implicit knowledge as knowledge that is not consciously activated at the time of a cognitive operation; this could be because the corresponding representations are impoverished or they cannot be concurrently supported in working memory with other representations or operations, and (b) adopting a frequency-independent view of rule knowledge and contrasting rule knowledge with specific similarity and associative learning (co-occurrence) knowledge.

Purpose: In this study, the authors examined (a) whether children with specific language impairment (SLI) can implicitly compute the probabilities of adjacent sound sequences, (b) if this ability is related to degree of exposure, (c) if it is domain specific or domain general and, (d) if it is related to vocabulary. Method: Children with SLI and normal language controls (ages 6;5–14;4 [years;months]) listened to 21 min of a language in which transitional probabilities within words were higher than those between words. In a second study, children with SLI and Age– Nonverbal IQ matched controls (8;0–10;11) listened to the same language for 42 min and to a second 42 min “tone ” language containing the identical statistical structure as the “speech ” language. Results: After 21 min, the SLI group’s performance was at chance, whereas performance for the control group was significantly greater than chance and significantly correlated with receptive and expressive vocabulary knowledge. In the 42-minute speech condition, the SLI group’s performance was significantly greater than chance and correlated with receptive vocabulary but was no different from chance in the analogous 42-minute tone condition. Performance for the control group was again significantly greater than chance in 42-minute speech and tone conditions. Conclusions: These findings suggest that poor implicit learning may underlie aspects of the language impairments in SLI. KEY WORDS: specific language impairment, implicit learning, statistical learning, child language development, child language disorders

Motivated by evidence that image source statistics predict the response properties of several visual perception aspects, we provide an empirical analysis of the relation between chroma statistics and human judgments of tonality. To accomplish this, a statistical analysis method based on chroma feature covariance is proposed. It makes use of a large collection of western music to build a tonal profile. The obtained profile is compared to alternative tonal profiles proposed in the literature, either cognitively, perceptually, or theoretically inspired. The high degree of correlation we find between the covariance-based tonal profile proposed here and several ones proposed in the literature (reaching values higher than 0.9) is interpreted as evidence that human-derived profiles faithfully reflect the statistics of the musical input listeners have been exposed to. Furthermore, we show that very short time scales allow us to correctly predict these profiles, which brings us to discuss the role that local-scale implicit learning plays in building mental representations of tonality. 1

Statistical learning, the process by which people learn patterns of information from their environment that they can apply to new situations, is central to the development of many higher order cognitive skills. Despite a growing research literature, little is still known about how statistical learning operates across perceptual categories. To investigate this issue we assessed college students on their ability to learn a multisensory artificial grammar containing both auditory and visual elements and both within-categorical and crosscategorical associations. The results of Experiment 1 showed that participants were sensitive to grammatically correct test items and ungrammatical test items that contained withincategorical grammatical violations, but were not sensitive to items that contained cross-categorical violations across sensory modalities. Experiment 2 showed that participants were not sensitive to items that contained cross-categorical violations within the same sensory modality. Our findings suggest that multisensory integration across perceptual categories does not occur easily during statistical learning.

•Number of studies have found that human infants and adults are able to extract the visual statistical relationships between elements during passive observation of a large number of similar scenes (Fiser & Aslin 2001, 2002a,b). •The process of learning the co-occurrences between objects has been linked to higher order representations in the visual cortex (Logothetis & Sheinberg 1996). •Learning the statistical structures enables humans to recognize complex objects and scenes. •To date, visual statistical learning has been investigated with simple 2D stimuli rather than realistic scenes. Less-Is-More principle: 2D condition Methods: Stimuli

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.