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Jenny R. Saffran - One of the best experts on this subject based on the ideXlab platform.

  • Constraints on Statistical Learning Across Species
    Trends in cognitive sciences, 2017
    Co-Authors: Chiara Santolin, Jenny R. Saffran
    Abstract:

    Both human and nonhuman organisms are sensitive to Statistical regularities in sensory inputs that support functions including communication, visual processing, and sequence Learning. One of the issues faced by comparative research in this field is the lack of a comprehensive theory to explain the relevance of Statistical Learning across distinct ecological niches. In the current review we interpret cross-species research on Statistical Learning based on the perceptual and cognitive mechanisms that characterize the human and nonhuman models under investigation. Considering Statistical Learning as an essential part of the cognitive architecture of an animal will help to uncover the potential ecological functions of this powerful Learning process.

  • Infant Statistical Learning
    Annual review of psychology, 2017
    Co-Authors: Jenny R. Saffran, Natasha Z. Kirkham
    Abstract:

    Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the Statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant Statistical Learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent Learning. In this selective review, we summarize findings that show that Statistical Learning is both a broad and flexible mechanism (supporting Learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of Learning). We suggest that Statistical Learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing Learning environments, and differential developmental trajectories.

  • Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials.
    Cognitive science, 2016
    Co-Authors: Christine E Potter, Tianlin Wang, Jenny R. Saffran
    Abstract:

    Recent research has begun to explore individual differences in Statistical Learning, and how those differences may be related to other cognitive abilities, particularly their effects on language Learning. In this research, we explored a different type of relationship between language Learning and Statistical Learning: the possibility that Learning a new language may also influence Statistical Learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 months apart. At each time point, participants performed two different Statistical Learning tasks: an artificial tonal language Statistical Learning task and a visual Statistical Learning task. Only the Mandarin-Learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on Statistical Learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli.

  • Statistical Learning and language acquisition.
    Wiley interdisciplinary reviews. Cognitive science, 2010
    Co-Authors: Alexa R. Romberg, Jenny R. Saffran
    Abstract:

    Human learners, including infants, are highly sensitive to structure in their environment. Statistical Learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use Statistical Learning mechanisms to acquire their native language. There have been many demonstrations showing infants' ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how Statistical Learning contributes to language acquisition. Current research is extending the initial findings of infants' sensitivity to basic Statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying Statistical language Learning in context: within language, within the infant learner, and within the environment as a whole. WIREs Cogn Sci 2010 1 906-914 This article is categorized under: Linguistics > Language Acquisition Psychology > Language.

Laura J Batterink - One of the best experts on this subject based on the ideXlab platform.

  • understanding the neural bases of implicit and Statistical Learning
    Topics in Cognitive Science, 2019
    Co-Authors: Laura J Batterink, Ken A Paller, Paul J Reber
    Abstract:

    Both implicit Learning and Statistical Learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of "implicit Statistical Learning." However, by comparing the neural processes that give rise to implicit versus Statistical Learning, we may determine the extent to which these two Learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit Learning and Statistical Learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that Learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining Learning according to experimental paradigm, with "implicit Learning" reserved as a specific term to denote Learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and Statistical Learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities.

Gabor Lugosi - One of the best experts on this subject based on the ideXlab platform.

  • introduction to Statistical Learning theory
    Lecture Notes in Computer Science, 2004
    Co-Authors: Olivier Bousquet, Stephane Boucheron, Gabor Lugosi
    Abstract:

    The goal of Statistical Learning theory is to study, in a Statistical framework, the properties of Learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.

Tibshirani Rober - One of the best experts on this subject based on the ideXlab platform.

  • An Introduction to Statistical Learning with Applications in R
    Current medicinal chemistry, 2000
    Co-Authors: James Gareth, Witten Daniela, Hastie Trevor, Tibshirani Rober
    Abstract:

    An Introduction to Statistical Learning provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these Statistical Learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source Statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine Learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge Statistical Learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Erik D Thiessen - One of the best experts on this subject based on the ideXlab platform.

  • Individual Differences in Statistical Learning: Conceptual and Measurement Issues
    Collabra, 2016
    Co-Authors: Lucy C Erickson, Erik D Thiessen, Michael P. Kaschak, Cassie Stutts Berry
    Abstract:

    The ability to adapt to Statistical structure (often referred to as “Statistical Learning”) has been proposed to play a major role in the acquisition and use of natural languages. Several recent studies have explored the relationship between individual differences in Statistical Learning and language outcomes. These studies have produced mixed results, with some studies finding a significant relationship between Statistical Learning and language outcomes, and others finding weak or null results. Furthermore, the few studies that have used multiple measures of Statistical Learning have reported that they are not correlated (e.g., [1]). The current study assesses the reliability of various measures of auditory Statistical segmentation, and their consistency over time. That is, do the generally low correlations observed between measures of Statistical Learning stem from task demands, the psychometric properties of the measures, or the fact that Statistical Learning may be a highly fragmented construct? Our results confirm previous reports that individual measures of Statistical Learning tend not to correlate with each other, and suggest that the somewhat weak reliability of the measures may be an important factor in the low correlations. Our data also suggest that aggregating performance across tasks may be an avenue for improving the reliability of the measures.

  • Statistical Learning of language theory validity and predictions of a Statistical Learning account of language acquisition
    Developmental Review, 2015
    Co-Authors: Lucy C Erickson, Erik D Thiessen
    Abstract:

    Abstract Considerable research indicates that learners are sensitive to probabilistic structure in laboratory studies of artificial language Learning. However, the artificial and simplified nature of the stimuli used in the pioneering work on the acquisition of Statistical regularities has raised doubts about the scalability of such Learning to the complexity of natural language input. In this review, we explore a central prediction of Statistical Learning accounts of language acquisition – that sensitivity to Statistical structure should be linked to real language processes – via an examination of: (1) recent studies that have increased the ecological validity of the stimuli; (2) studies that suggest Statistical segmentation produces representations that share properties with real words; (3) correlations between individual variability in Statistical Learning ability and individual variability in language outcomes; and (4) atypicalities in Statistical Learning in clinical populations characterized by language delays or deficits.

  • CogSci - Is Statistical Learning trainable
    Cognitive Science, 2015
    Co-Authors: Luca Onnis, Matthew Lou-magnuson, Hongoak Yun, Erik D Thiessen
    Abstract:

    Statistical Learning (SL) is the ability to implicitly extract regularities in the environment, and likely supports various higher-order behaviors, from language to music and vision. While specific patterns experience are likely to influence SL outcomes, this ability is tacitly conceptualized as a fixed construct, and few studies to date have investigated how experience may shape Statistical Learning. We report one experiment that directly tested whether SL can be modulated by previous experience. We used a prepost treatment design allowing us to pinpoint what specific aspects of “previous experience” matter for SL. The results show that performance on an artificial grammar Learning task at post-test depends on whether the grammar to be learned at post-test matches the underlying grammar structures learned during treatment. Our study is the first to adopt a pre-post test design to directly modulate the effects of Learning on Learning itself.