scholarly journals Twelve-Month-Old Infants Benefit From Prior Experience in Statistical Learning

2008 ◽  
Vol 19 (12) ◽  
pp. 1247-1252 ◽  
Author(s):  
Jill Lany ◽  
Rebecca L. Gómez
Keyword(s):  
Statistical Learning ◽  
Verbal Memory ◽  
Prior Experience ◽  
Memory Capacity ◽  
Artificial Language ◽  
Learning Mechanisms ◽  
Language Structure ◽  
Statistical Regularities ◽  
Nonadjacent Dependencies ◽  
Enhanced Learning

A decade of research suggests that infants readily detect patterns in their environment, but it is unclear how such learning changes with experience. We tested how prior experience influences sensitivity to statistical regularities in an artificial language. Although 12-month-old infants learn adjacent relationships between word categories, they do not track nonadjacent relationships until 15 months. We asked whether 12-month-old infants could generalize experience with adjacent dependencies to nonadjacent ones. Infants were familiarized to an artificial language either containing or lacking adjacent dependencies between word categories and were subsequently habituated to novel nonadjacent dependencies. Prior experience with adjacent dependencies resulted in enhanced learning of the nonadjacent dependencies. Female infants showed better discrimination than males did, which is consistent with earlier reported sex differences in verbal memory capacity. The findings suggest that prior experience can bootstrap infants' learning of difficult language structure and that learning mechanisms are powerfully affected by experience.

Statistical learning in infant language development

2007 ◽  
pp. 600-616 ◽  
Author(s):  
Rebecca Gómez
Keyword(s):  
Language Development ◽  
Language Learning ◽  
Statistical Learning ◽  
Artificial Language ◽  
Prior Learning ◽  
Phonological Learning ◽  
Artificial Language Learning ◽  
Learning Capabilities ◽  
Statistical Regularities ◽  
Infant Language

Children learn language over such a short span of time and with such seeming ease, that many have assumed they must master language by means of a language-specific device. Artificial languages provide a useful tool for controlling prior learning and for manipulating specific variables of interest. This approach has resulted in a wealth of findings regarding the learning capabilities of children. Infant artificial language learning has become synonymous with statistical learning because of the emphasis in much of the work on learning statistical regularities. However, not all cases of artificial language learning entail learning statistical structure. For instance, some learning requires generalisation of relational patterns. This article explores statistical learning in language development in infants, phonological learning (discrimination of speech sounds, learning phonotactic regularities, phonological generalization), word segmentation, rudiments of syntax, generalization of sequential word order, category-based abstraction, and bootstrapping from prior learning.

Prior real-world experience influences non-linguistic statistical learning

2020 ◽  
Author(s):  
Stephen Charles Van Hedger ◽  
Ingrid Johnsrude ◽  
Laura Batterink
Keyword(s):  
Statistical Learning ◽  
Real World ◽  
Language Experience ◽  
Pitch Range ◽  
Test Items ◽  
Instrument Measuring ◽  
Statistical Regularities ◽  
Context Free ◽  
Specific Learning

Listeners are adept at extracting regularities from the environment, a process known as statistical learning (SL). SL has been generally assumed to be a form of “context-free” learning that occurs independently of prior knowledge, and SL experiments typically involve exposing participants to presumed novel regularities, such as repeating nonsense words. However, recent work has called this assumption into question, demonstrating that learners’ previous language experience can considerably influence SL performance. In the present experiment, we tested whether previous knowledge also shapes SL in a non-linguistic domain, using a paradigm that involves extracting regularities over tone sequences. Participants learned novel tone sequences, which consisted of pitch intervals not typically found in Western music. For one group of participants, the tone sequences used artificial, computerized instrument sounds. For the other group, the same tone sequences used familiar instrument sounds (piano or violin). Knowledge of the statistical regularities was assessed using both trained sounds (measuring specific learning) and sounds that differed in pitch range and/or instrument (measuring transfer learning). In a follow-up experiment, two additional testing sessions were administered to gauge retention of learning (one day and approximately one-week post-training). Compared to artificial instruments, training on sequences played by familiar instruments resulted in reduced correlations among test items, reflecting more idiosyncratic performance. Across all three testing sessions, learning of novel regularities presented with familiar instruments was worse compared to unfamiliar instruments, suggesting that prior exposure to music produced by familiar instruments interfered with new sequence learning. Overall, these results demonstrate that real-world experience influences SL in a non-linguistic domain, supporting the view that SL involves the continuous updating of existing representations, rather than the establishment of entirely novel ones.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

Investigating implicit statistical learning mechanisms through contextual cueing

2015 ◽  
Vol 19 (9) ◽  
pp. 524-533 ◽  
Author(s):  
Annabelle Goujon ◽  
André Didierjean ◽  
Simon Thorpe
Keyword(s):  
Statistical Learning ◽  
Contextual Cueing ◽  
Learning Mechanisms

scholarly journals Top-down structure influences learning of nonadjacent dependencies in an artificial language.

2017 ◽  
Vol 146 (12) ◽  
pp. 1738-1748 ◽  
Author(s):  
Felix Hao Wang ◽  
Jason D. Zevin ◽  
Toben H. Mintz
Keyword(s):  
Artificial Language ◽  
Top Down ◽  
Nonadjacent Dependencies

Relational problems are not fully solved by a temporal sequence of statistical learning episodes

1997 ◽  
Vol 20 (1) ◽  
pp. 82-82 ◽  
Author(s):  
A. Vinter ◽  
P. Perruchet
Keyword(s):  
Implicit Learning ◽  
Statistical Learning ◽  
Temporal Sequence ◽  
The Other ◽  
Learning Mechanisms ◽  
Learning Research ◽  
The One

Clark & Thornton's conception finds an echo in implicit learning research, which shows that subjects may perform adaptively in complex structured situations through the use of simple statistical learning mechanisms. However, the authors fail to draw a distinction between, on the one hand, subjects' representations which emerge from type-1 learning mechanisms, and, on the other, their knowledge of the genuine abstract “recoding function” which defines a type-2 problem.

scholarly journals Auditory statistical learning in children: Novel insights from an online measure

2018 ◽  
Vol 40 (2) ◽  
pp. 279-302 ◽  
Author(s):  
IMME LAMMERTINK ◽  
MEREL VAN WITTELOOSTUIJN ◽  
PAUL BOERSMA ◽  
FRANK WIJNEN ◽  
JUDITH RISPENS
Keyword(s):  
Response Times ◽  
Learning Trajectory ◽  
Online Measurement ◽  
Artificial Language ◽  
Future Studies ◽  
Grammaticality Judgment ◽  
Grammaticality Judgments ◽  
Syntax Acquisition ◽  
Nonadjacent Dependencies ◽  
Child Friendly

AbstractNonadjacent dependency learning is thought to be a fundamental skill for syntax acquisition and often assessed via an offline grammaticality judgment measure. Asking judgments of children is problematic, and an offline task is suboptimal as it reflects only the outcome of the learning process, disregarding information on the learning trajectory. Therefore, and following up on recent methodological advancements in the online measurement of nonadjacent dependency learning in adults, the current study investigates if the recording of response times can be used to establish nonadjacent dependency learning in children. Forty-six children (mean age: 7.3 years) participated in a child-friendly adaptation of a nonadjacent dependency learning experiment (López-Barroso, Cucurell, Rodríguez-Fornells, & de Diego-Balaguer, 2016). They were exposed to an artificial language containing items with and without nonadjacent dependencies while their response times (online measure) were measured. After exposure, grammaticality judgments (offline measure) were collected. The results show that children are sensitive to nonadjacent dependencies, when using the online measure (the results of our offline measure did not provide evidence of learning). We therefore conclude that future studies can use online response time measures (perhaps in addition to the offline grammaticality judgments) to further investigate nonadjacent dependency learning in children.

scholarly journals Reading as Statistical Learning

2018 ◽  
Vol 49 (3S) ◽  
pp. 634-643 ◽  
Author(s):  
Joanne Arciuli
Keyword(s):  
Implicit Learning ◽  
Statistical Learning ◽  
Literacy Instruction ◽  
Word Reading ◽  
Implicit Methods ◽  
New Techniques ◽  
Linguistic Research ◽  
Statistical Regularities ◽  
Basic And Applied Research ◽  
Deep Orthography

Purpose The purpose of this tutorial is to explain how learning to read can be thought of as learning statistical regularities and to demonstrate why this is relevant for theory, modeling, and practice. This tutorial also shows how triangulation of methods and cross-linguistic research can be used to gain insight. Method The impossibility of conveying explicitly all of the regularities that children need to acquire in a deep orthography, such as English, can be demonstrated by examining lesser-known probabilistic orthographic cues to lexical stress. Detection of these kinds of cues likely occurs via a type of implicit learning known as statistical learning (SL). The first part of the tutorial focuses on these points. Next, studies exploring how individual differences in the capacity for SL relate to variability in word reading accuracy in the general population are discussed. A brief overview of research linking impaired SL and dyslexia is also provided. The final part of the tutorial focuses on how we might supplement explicit literacy instruction with implicit learning methods and emphasizes the value of testing the efficacy of new techniques in the classroom. The basic and applied research reviewed here includes corpus analyses, behavioral testing, computational modeling, and classroom-based research. Although some of these methods are not commonly used in clinical research, the depth and breadth of this body of work provide a compelling case for why reading can be thought of as SL and how this view can inform practice. Conclusion Implicit methods that draw on the principles of SL can supplement the much-needed explicit instruction that helps children learn to read. This synergy of methods has the potential to spark innovative practices in literacy instruction and remediation provided by educators and clinicians to support typical learners and those with developmental disabilities.

scholarly journals Learning Without Trying: The Clinical Relevance of Statistical Learning

2018 ◽  
Vol 49 (3S) ◽  
pp. 710-722 ◽  
Author(s):  
Elena Plante ◽  
Rebecca L. Gómez
Keyword(s):  
Statistical Learning ◽  
Language Impairment ◽  
Basic Research ◽  
Learning Mechanisms ◽  
Applied Learning ◽  
Perceived Effort ◽  
The Past ◽  
Powerful Learning ◽  
Outcomes For Children ◽  
Learning Principles

Purpose Statistical learning research seeks to identify the means by which learners, with little perceived effort, acquire the complexities of language. In the past 50 years, numerous studies have uncovered powerful learning mechanisms that allow for learning within minutes of exposure to novel language input. Method We consider the value of information from statistical learning studies that show potential for making treatment of language disorders faster and more effective. Results Available studies include experimental research that demonstrates the conditions under which rapid learning is possible, research showing that these findings apply to individuals with disorders, and translational work that has applied learning principles in treatment and educational contexts. In addition, recent research on memory formation has implications for treatment of language deficits. Conclusion The statistical learning literature offers principles for learning that can improve clinical outcomes for children with language impairment. There is potential for further applications of this basic research that is yet unexplored.

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