The multi-component nature of statistical learning

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

<p class="first" id="d313425e123">The central argument presented in this paper is that statistical learning (SL) is an ability comprised of multiple components that operate largely implicitly. Components relating to the stimulus encoding, retention and abstraction required for SL may include, but are not limited to, certain types of attention, processing speed and memory. It is likely that individuals vary in terms of the efficiency of these underlying components, and in patterns of connectivity among these components, and that SL tasks differ from one another in how they draw on certain underlying components more than others. This theoretical framework is of value because it can assist in gaining a clearer understanding of how SL is linked with individual differences in complex mental activities such as language processing. Variability in language processing across individuals is of central concern to researchers interested in child development, including those interested in neurodevelopmental disorders where language can be affected such as autism spectrum disorders (ASD). This paper discusses the link between SL and individual differences in language processing in the context of age-related changes in SL during infancy and childhood, and whether SL is affected in ASD. Viewing SL as a multi-component ability may help to explain divergent findings from previous empirical research in these areas and guide the design of future studies. </p><p id="d313425e125">This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. </p>

Related collections

Most cited references 81

Record: found
Abstract: found
Article: not found

Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.

Giulio Tononi, Chiara Cirelli (2014)

Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity. Copyright © 2014 Elsevier Inc. All rights reserved.

0 comments Cited 729 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

An Investigation of Language Impairment in Autism: Implications for Genetic Subgroups.

Margaret M Kjelgaard, Helen Tager-Flusberg (2001)

Autism involves primary impairments in both language and communication, yet in recent years the main focus of research has been on the communicative deficits that define the population. The study reported in this paper investigated language functioning in a group of 89 children diagnosed with autism using the ADI-R, and meeting DSM-IV criteria. The children, who were between 4- and 14- years-old were administered a battery of standardized language tests tapping phonological, lexical, and higher-order language abilities. The main findings were that among the children with autism there was significant heterogeneity in their language skills, although across all the children, articulation skills were spared. Different subgroups of children with autism were identified on the basis on their performance on the language measures. Some children with autism have normal language skills; for other children, their language skills are significantly below age expectations. The profile of performance across the standardized measures for the language-impaired children with autism was similar to the profile that defines the disorder specific language impairment (or SLI). The implications of this language impaired subgroup in autism for understanding the genetics and definition of both autism and SLI are discussed.

0 comments Cited 200 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Visual statistical learning in infancy: evidence for a domain general learning mechanism.

Natasha Z. Kirkham, Jonathan Slemmer, Scott Johnson (2002)

The rapidity with which infants come to understand language and events in their surroundings has prompted speculation concerning innate knowledge structures that guide language acquisition and object knowledge. Recently, however, evidence has emerged that by 8 months, infants can extract statistical patterns in auditory input that are based on transitional probabilities defining the sequencing of the input's components (Science 274 (1996) 1926). This finding suggests powerful learning mechanisms that are functional in infancy, and raises questions about the domain generality of such mechanisms. We habituated 2-, 5-, and 8-month-old infants to sequences of discrete visual stimuli whose ordering followed a statistically predictable pattern. The infants subsequently viewed the familiar pattern alternating with a novel sequence of identical stimulus components, and exhibited significantly greater interest in the novel sequence at all ages. These results provide support for the likelihood of domain general statistical learning in infancy, and imply that mechanisms designed to detect structure inherent in the environment may play an important role in cognitive development.