Statistical learning is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. Recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal, however, modality and stimulus specificity. An important question is, therefore, how and why a hypothesized domain-general learning mechanism systematically produces such effects. We offer a theoretical framework according to which statistical learning is not a unitary mechanism, but a set of domain-general computational principles, that operate in different modalities and therefore are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.

The notion that the form of a word bears an arbitrary relation to its meaning accounts only partly for the attested relations between form and meaning in the languages of the world. Recent research suggests a more textured view of vocabulary structure, in which arbitrariness is complemented by iconicity (aspects of form resemble aspects of meaning) and systematicity (statistical regularities in forms predict function). Experimental evidence suggests these form-to-meaning correspondences serve different functions in language processing, development, and communication: systematicity facilitates category learning by means of phonological cues, iconicity facilitates word learning and communication by means of perceptuomotor analogies, and arbitrariness facilitates meaning individuation through distinctive forms. Processes of cultural evolution help to explain how these competing motivations shape vocabulary structure.

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the brain deal successfully with the continual deluge of linguistic input? We argue that, to deal with this "Now-or-Never" bottleneck, the brain must compress and recode linguistic input as rapidly as possible. This observation has strong implications for the nature of language processing: (1) the language system must "eagerly" recode and compress linguistic input; (2) as the bottleneck recurs at each new representational level, the language system must build a multilevel linguistic representation; and (3) the language system must deploy all available information predictively to ensure that local linguistic ambiguities are dealt with "Right-First-Time"; once the original input is lost, there is no way for the language system to recover. This is "Chunk-and-Pass" processing. Similarly, language learning must also occur in the here and now, which implies that language acquisition is learning to process, rather than inducing, a grammar. Moreover, this perspective provides a cognitive foundation for grammaticalization and other aspects of language change. Chunk-and-Pass processing also helps explain a variety of core properties of language, including its multilevel representational structure and duality of patterning. This approach promises to create a direct relationship between psycholinguistics and linguistic theory. More generally, we outline a framework within which to integrate often disconnected inquiries into language processing, language acquisition, and language change and evolution.

It is a long established convention that the relationship between sounds and meanings of words is essentially arbitrary—typically the sound of a word gives no hint of its meaning. However, there are numerous reported instances of systematic sound–meaning mappings in language, and this systematicity has been claimed to be important for early language development. In a large-scale corpus analysis of English, we show that sound–meaning mappings are more systematic than would be expected by chance. Furthermore, this systematicity is more pronounced for words involved in the early stages of language acquisition and reduces in later vocabulary development. We propose that the vocabulary is structured to enable systematicity in early language learning to promote language acquisition, while also incorporating arbitrariness for later language in order to facilitate communicative expressivity and efficiency.

It is widely assumed that one of the fundamental properties of spoken language is the arbitrary relation between sound and meaning. Some exceptions in the form of nonarbitrary associations have been documented in linguistics, cognitive science, and anthropology, but these studies only involved small subsets of the 6,000+ languages spoken in the world today. By analyzing word lists covering nearly two-thirds of the world's languages, we demonstrate that a considerable proportion of 100 basic vocabulary items carry strong associations with specific kinds of human speech sounds, occurring persistently across continents and linguistic lineages (linguistic families or isolates). Prominently among these relations, we find property words ("small" and i, "full" and p or b) and body part terms ("tongue" and l, "nose" and n). The areal and historical distribution of these associations suggests that they often emerge independently rather than being inherited or borrowed. Our results therefore have important implications for the language sciences, given that nonarbitrary associations have been proposed to play a critical role in the emergence of cross-modal mappings, the acquisition of language, and the evolution of our species' unique communication system. linguistics | cognitive sciences | language evolution | iconicity | sound symbolism

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Statistical learning (SL) is involved in a wide range of basic and higher-order cognitive functions and is taken to be an important building block of virtually all current theories of information processing. In the last two decades, a large and continuously growing research community has therefore focused on the ability to extract embedded patterns of regularity in time and space. This work has mostly focused on transitional probabilities, in vision, audition, by newborns, children, adults, in normal developing and clinical populations. Here we appraise this research approach, we critically assess what it has achieved, what it has not, and why it is so. We then center on present SL research to examine whether it has adopted novel perspectives. These discussions lead us to outline possible blueprints for a novel research agenda.

Implicit learning and statistical learning are two contemporary approaches to the long-standing question in psychology and cognitive science of how organisms pick up on patterned regularities in their environment. Although both approaches focus on the learner's ability to use distributional properties to discover patterns in the input, the relevant research has largely been published in separate literatures and with surprisingly little cross-pollination between them. This has resulted in apparently opposing perspectives on the computations involved in learning, pitting chunk-based learning against probabilistic learning. In this paper, I trace the nearly century-long historical pedigree of the two approaches to learning and argue for their integration under the heading of "implicit statistical learning." Building on basic insights from the memory literature, I sketch a framework for statistically based chunking that aims to provide a unified basis for understanding implicit statistical learning.