An Emerging Consensus: Younger and Cohen Revisited

Younger, Barbara A.; Hollich, George; Furrer, Stephanie D.

doi:10.1207/s15327078in0502_6

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…However, the ability to detect distributions of features may have a somewhat more different developmental time course than conditional statistical learning (e.g. [24,25]), consistent with suggestions that conditional and distributional statistical learning arise from at least partially independent processes. Indeed, on the surface, conditional and distributional statistical regularities (and the tasks used to measure sensitivity to them) are quite distinct.…”

Section: Statistical Learning: One Mechanism or Many?supporting

confidence: 76%

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

Thiessen¹

2017

Phil. Trans. R. Soc. B

104

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One contribution of 13 to a theme issue 'New frontiers for statistical learning in the cognitive sciences'. . The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting

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Section: Statistical Learning: One Mechanism or Many?supporting

confidence: 76%

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

Thiessen¹

2017

Phil. Trans. R. Soc. B

104

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“…Several computational models focus on the complexity of the stimuli as critical for determining whether infants can recognize the statistical regularity (e.g. Westermann & Mareschal, 2004; see also Younger, Hollich & Furrer, 2004). The stimuli used in the present investigation are complex: They require the infants to integrate motion, sound, and colorful figures together to form a representation of an individual event, and then to integrate those events together in a temporal sequence to form a predictive relation.…”

Section: Discussionmentioning

confidence: 99%

Bayes nets and babies: infants’ developing statistical reasoning abilities and their representation of causal knowledge

Sobel

Kirkham

2007

Developmental Science

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A fundamental assumption of the causal graphical model framework is the Markov assumption, which posits that learners can discriminate between two events that are dependent because of a direct causal relation between them and two events that are independent conditional on the value of another event(s). Sobel and Kirkham (2006) demonstrated that 8-month-old infants registered conditional independence information among a sequence of events; infants responded according to the Markov assumption in such a way that was inconsistent with models that rely on simple calculations of associative strength. The present experiment extends these findings to younger infants, and demonstrates that such responses potentially develop during the second half of the first year of life. These data are discussed in terms of a developmental trajectory between associative mechanisms and causal graphical models as representations of infants' causal and statistical learning.

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“…At test, children retained novel labels at above-chance levels in both conditions, many shared features: t (19 [15,32]. In these studies, 10-month-old infants were sensitive to correlations between configural and perceptual attributes in novel 2D animal stimuli (see also [35]). The current study demonstrates that older children can also generalize labels systematically based on correlations between perceptual features such as geons and colour.…”

Section: Resultsmentioning

confidence: 89%

Testing a Dynamic Neural Field Model of Children's Category Labelling

Twomey¹,

Horst²

2013

Computational Models of Cognitive Processes

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Recently, Dynamic Neural Field models have shed light on the flexible and dynamic processes underlying young children's emergent categorisation and word learning (DNF; e.g., Spencer & Schöner [1]). DNF models are a distinct class of neural network in which perceptual features can be represented topologically and time continuously, complementing existing connectionist models of cognitive development by building category representations that are available for inspection at any given stage in learning. Recent research in infant categorization and word learning has demonstrated that young children's ability to learn and generalise labels for novel object categories is profoundly affected by the perceptual variability of the to-be-learned category. We have captured these data in a DNF model of children's category label learning. Given a known vocabulary, our model exploits mutual exclusivity via simple associative processes to map novel labels to novel categories, and is able to retain and generalize these newlyformed mappings. The model was used to generate the testable prediction that children's generalizations of novel category labels should be contingent on the number and closeness of objects' perceptual neighbours. We present a replication of this prediction, via an empirical study with 30-month-old children. In line with the model, children were only able to generalize novel words to completely novel objects when those objects were central to the just-encountered category, rather than peripheral. This empirical replication demonstrates the predictive validity of DNF models when applied to cognitive development. Further, the data suggest that children's ability to categorise and learn labels is not a conceptually-based, stepwise phenomenon, but rather a graded, emergent process. As such, these data add weight to associative, dynamic systems approaches to understanding language learning, categorisation, and cognition more generally.

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An Emerging Consensus: Younger and Cohen Revisited

Cited by 7 publications

References 17 publications

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

Bayes nets and babies: infants’ developing statistical reasoning abilities and their representation of causal knowledge

Testing a Dynamic Neural Field Model of Children's Category Labelling

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