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

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 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.

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.

scholarly journals Neurophysiological coding of statistical and deterministic rule information

2020 ◽  
Author(s):  
Ádám Takács ◽  
Andrea Kóbor ◽  
Zsófia Kardos ◽  
Karolina Janacsek ◽  
Kata Horváth ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Visual Information ◽  
Time Window ◽  
Learning Mechanisms ◽  
Decomposition Approach ◽  
Rule Based ◽  
Parallel Learning ◽  
Time Sensitivity ◽  
Types Of Information ◽  
The Right

AbstractHumans are capable of acquiring multiple types of information presented in the same visual information stream. It has been suggested that at least two parallel learning processes are important during learning of sequential patterns – statistical learning and rule-based learning. Yet, the neurophysiological underpinnings of these parallel learning mechanisms in visual sequences are not fully understood. To differentiate between the simultaneous mechanisms at the single trial level, we apply a temporal EEG signal decomposition approach together with sLORETA source localization method to delineate whether distinct statistical and rule-based learning codes can be distinguished in EEG data and can be related to distinct functional neuroanatomical structures. We demonstrate that concomitant but distinct aspects of information coded in the N2 time window play a role in these mechanisms: mismatch detection and response control underlie statistical learning and rule-based learning, respectively, albeit with different levels of time-sensitivity. Moreover, the effects of the two learning mechanisms in the different temporally decomposed clusters of neural activity also differed from each other in neural sources. Importantly, the right inferior frontal cortex (BA44) was specifically implicated in statistical learning, confirming its role in the acquisition of transitional probabilities. In contrast, rule-based learning was associated with the prefrontal gyrus (BA6). The results show how simultaneous learning mechanisms operate at the neurophysiological level and are orchestrated by distinct prefrontal cortical areas. The current findings deepen our understanding on the mechanisms how humans are capable of learning multiple types of information from the same stimulus stream in a parallel fashion.

scholarly journals Verbal Working Memory, Long-Term Knowledge, and Statistical Learning

2020 ◽  
Vol 29 (4) ◽  
pp. 340-345
Author(s):  
Satoru Saito ◽  
Masataka Nakayama ◽  
Yuki Tanida
Keyword(s):  
Working Memory ◽  
Statistical Learning ◽  
Language Processing ◽  
Memory Performance ◽  
Serial Order ◽  
Verbal Working Memory ◽  
Learning Mechanisms ◽  
Learning Techniques ◽  
Statistical Learning Techniques

Evidence supporting the idea that serial-order verbal working memory is underpinned by long-term knowledge has accumulated over more than half a century. Recent studies using natural-language statistics, artificial statistical-learning techniques, and the Hebb repetition paradigm have revealed multiple types of long-term knowledge underlying serial-order verbal working memory performance. These include (a) element-to-element association knowledge, which slowly accumulates through extensive exposure to an exemplar; (b) position–element knowledge, which is acquired through several encounters with an exemplar; and (c) whole-sequence knowledge, which is captured by the Hebb repetition paradigm and acquired rapidly with a few repetitions. Arguably, the first two are a basis for fluent and efficient language usage, and the third is a basis for vocabulary learning. Thus, statistical-learning mechanisms (and possibly episodic-learning mechanisms) may form the foundation of language acquisition and language processing, which characterize linguistic long-term knowledge for verbal working memory.

scholarly journals The neurobiology of uncertainty: implications for statistical learning

2017 ◽  
Vol 372 (1711) ◽  
pp. 20160048 ◽  
Author(s):  
Uri Hasson
Keyword(s):  
Statistical Learning ◽  
Historical Development ◽  
Visual Stimuli ◽  
Learning Mechanisms ◽  
Cognitive Sciences ◽  
Input Uncertainty ◽  
Environmental Features ◽  
Online Prediction ◽  
Neurobiological Research ◽  
The Brain

The capacity for assessing the degree of uncertainty in the environment relies on estimating statistics of temporally unfolding inputs. This, in turn, allows calibration of predictive and bottom-up processing, and signalling changes in temporally unfolding environmental features. In the last decade, several studies have examined how the brain codes for and responds to input uncertainty. Initial neurobiological experiments implicated frontoparietal and hippocampal systems, based largely on paradigms that manipulated distributional features of visual stimuli. However, later work in the auditory domain pointed to different systems, whose activation profiles have interesting implications for computational and neurobiological models of statistical learning (SL). This review begins by briefly recapping the historical development of ideas pertaining to the sensitivity to uncertainty in temporally unfolding inputs. It then discusses several issues at the interface of studies of uncertainty and SL. Following, it presents several current treatments of the neurobiology of uncertainty and reviews recent findings that point to principles that serve as important constraints on future neurobiological theories of uncertainty, and relatedly, SL. This review suggests it may be useful to establish closer links between neurobiological research on uncertainty and SL, considering particularly mechanisms sensitive to local and global structure in inputs, the degree of input uncertainty, the complexity of the system generating the input, learning mechanisms that operate on different temporal scales and the use of learnt information for online prediction. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.

scholarly journals Nonverbal action interpretation guides novel word disambiguation in 12-month-olds

2020 ◽  
Author(s):  
Barbara Pomiechowska ◽  
Gergely Csibra
Keyword(s):  
Statistical Learning ◽  
Communicative Action ◽  
The Novel ◽  
Learning Mechanisms ◽  
New Words ◽  
Young Infants ◽  
Pragmatic Information ◽  
Distractor Object ◽  
Instrumental Action ◽  
Novel Words

Whether young infants can exploit socio-pragmatic information to interpret new words is a matter of debate. Based on findings and theories from the action interpretation literature, we hypothesized that 12-month-olds should distinguish communicative object-directed actions expressing reference from instrumental object-directed actions indicative of one’s goals, and selectively use the former to identify referents of novel linguistic expressions. This hypothesis was tested across four eye-tracking experiments. Infants watched pairs of unfamiliar objects, one of which was first targeted by either a communicative action (e.g., pointing) or an instrumental action (e.g., grasping) and then labeled with a novel word. As predicted, infants fast-mapped the novel words onto the targeted objects after pointing (Experiments 1 and 4) but not after grasping (Experiment 2) unless the grasping action was preceded by an ostensive signal (Experiment 3). Moreover, whenever infants mapped a novel word onto the object indicated by a communicative action, they tended to map a different novel word onto the distractor object, displaying a mutual exclusivity effect. This reliance on nonverbal action interpretation in the disambiguation of novel words indicates that socio-pragmatic inferences about reference likely supplement associative and statistical learning mechanisms from the outset of word learning.

scholarly journals Implicit statistical learning in naturalistic and instructed morphosyntactic attainment: An aptitude-treatment interaction design

2021 ◽  
Author(s):  
Cylcia Bolibaugh ◽  
Pauline Foster
Keyword(s):  
Implicit Learning ◽  
Statistical Learning ◽  
Interaction Design ◽  
Short Term Memory ◽  
Age Of Onset ◽  
Learning Mechanisms ◽  
Short Term ◽  
Learning Abilities ◽  
Term Memory ◽  
Wide Range

We investigated the potential influence of implicit learning mechanisms on L2 morphosyntactic attainment by examining the relationship between age of onset (AoO), two cognitive abilities hypothesised to underlie implicit learning (phonological short-term memory and implicit statistical learning), and performance on an auditory grammatically judgement test (GJT). Participants were 71 Polish - English long term bilinguals with a wide range of AoO (1–35), who differed in their context of learning and use (immersed vs instructed). In immersed learners, we observed a growing dissociation between performance on grammatical and ungrammatical sentences as AoO was delayed. This effect was attenuated in those with better phonological short-term memory and statistical learning abilities, and is consistent with a decline in the ability to learn from implicit negative evidence. In instructed learners, GJT performance was subject to additive effects of AoO and grammaticality, and was not associated with either cognitive predictor, suggesting that implicit learning mechanisms were not involved.

scholarly journals On the limits of statistical learning: Intertrial contextual cueing is confined to temporally close contingencies

2018 ◽  
Vol 80 (6) ◽  
pp. 1420-1435 ◽  
Author(s):  
Cyril Thomas ◽  
André Didierjean ◽  
François Maquestiaux ◽  
Annabelle Goujon
Keyword(s):  
Statistical Learning ◽  
Contextual Cueing

scholarly journals Parents fine-tune their speech to children’s vocabulary knowledge

2020 ◽  
Author(s):  
Ashley Leung ◽  
Alex Tunkel ◽  
Daniel Yurovsky
Keyword(s):  
Young Children ◽  
Language Development ◽  
Statistical Learning ◽  
Vocabulary Knowledge ◽  
Learning Mechanisms ◽  
Referring Expressions ◽  
Target Animal ◽  
Language Knowledge ◽  
Fine Tune ◽  
Parent Child

Young children learn language at an incredible rate. While children come prepared with powerful statistical learning mechanisms, the statistics they encounter are also prepared for them: Children learn from caregivers motivated to communicate with them. How precisely do parents tune their speech to their children's individual language knowledge? To answer this question, we asked parent-child pairs (n=41) to play a reference game in which the parent's goal was to guide their child to select a target animal from a set of three. Parents fine-tuned their referring expressions to their children's knowledge at the lexical level, producing more informative references for animals they thought their children did not know. Further, parents learned about their children's knowledge over the course of the game, and tuned their referring expressions accordingly. Child-directed speech may thus support children's learning not because it is uniformly simplified, but because it is tuned to individual children's language development.

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