Primitive computations in speech processing

Endress, Ansgar D.; Mehler, Jacques

doi:10.1080/17470210902783646

Cited by 46 publications

(76 citation statements)

References 55 publications

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“…Previous literature has shown a distinction between the ability to recall chunks previously heard, and the ability to learn a given pattern as a rule , that is, to be able to use it productively and generalize it to novel contexts (Peña et al. 2002; Endress and Bonatti 2006; Endress and Mehler 2009, among others). In this study we are concerned with learner’s ability to obtain knowledge of non-adjacent dependencies as generalizable rules, and to be sensitive to these rules even when they are instantiated in unfamiliar contexts; this is the type of ability which will serve language acquisition in aiding learners to detect grammatical patterns as generalizable/productive rules of grammar, and not as patterns that occur in familiar contexts.…”

Section: Methodsmentioning

confidence: 98%

“…These studies find that, after familiarization with an aXb language (consisting of nonsense strings such as pel kicey jic ), adult learners show a reliable preference for consistent dependencies over inconsistent ones (where the final element was not predicted by the first; e.g. Endress and Bonatti 2006; Endress and Mehler 2009; Gómez 2002; Newport and Aslin 2004; Onnis et al. 2004; Peña et al.…”

Section: Introductionmentioning

confidence: 99%

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Gleaning Structure from Sound: The Role of Prosodic Contrast in Learning Non-adjacent Dependencies

Grama

Kerkhoff

Wijnen

2016

J Psycholinguist Res

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The ability to detect non-adjacent dependencies (i.e. between a and b in aXb) in spoken input may support the acquisition of morpho-syntactic dependencies (e.g. The princess is kiss ing the frog). Functional morphemes in morpho-syntactic dependencies are often marked by perceptual cues that render them distinct from lexical elements. We use an artificial grammar learning experiment with adults to investigate the role of perceptual cues in non-adjacent dependency learning, by manipulating the perceptual/prosodic properties of the a / b elements in aXb strings and testing participants’ incidental learning of these dependencies. Our results show that non-adjacent dependencies are learned both when the dependent elements are perceptually prominent, and when they are perceptually reduced compared to the intervening material (in the same way that functional words are reduced compared to lexical words), but only if integrated into a natural prosodic contour. This result supports the idea that the prosodic properties of natural languages facilitate non-adjacent dependency learning.

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Section: Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Gleaning Structure from Sound: The Role of Prosodic Contrast in Learning Non-adjacent Dependencies

Grama

Kerkhoff

Wijnen

2016

J Psycholinguist Res

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“…Behaviourally, these two types of statistical learning appear to operate under different constraints (e.g. [26,28,29]), and to mutually interfere with each other such that detecting one kind of structure impairs detection of the other kind [30]. Perhaps related to or emerging from this interference, the time course of the detection of conditional and distributional regularities also differs.…”

Section: Statistical Learning: One Mechanism or Many?mentioning

confidence: 99%

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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“…As an example, consider Endress and Mehler's (2009) demonstration that exposure to a set of words leads to the representation of a "prototype" word that has never been seen. After exposure to words like kobita, lifuta, and kofuno, participants endorse kofuta as familiar, even though they have not previously heard it.…”

Section: Novel Predictions and Next Stepsmentioning

confidence: 99%

“…The compelling nature of illusory words was demonstrated by Endress and Mehler (2009), who familiarized participants with a language containing trisyllabic words that were each generated from a "prototype" word from which they differed by a single syllable (e.g., the prototype kofuta might spawn the words kobita, lifuta, and kofuno). The prototype word was never presented in the language; only the subsidiary words that differed from the prototype were presented to participants.…”

Section: Additional Benefits Of a Synthesized Frameworkmentioning

confidence: 99%

The extraction and integration framework: A two-process account of statistical learning.

Thiessen

Kronstein

Hufnagle

2013

Psychological Bulletin

173

193

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The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other.Keywords: statistical learning, language development, implicit learning, word learning Humans live in a world filled with statistical regularities. Balls thrown into the air typically fall back to earth; nouns such as dog or boy are typically preceded by articles such as a or the. There is no doubt that learners are sensitive to these statistical regularities. One term to describe the ability to detect and use statistical structure is statistical learning. Saffran, Aslin, and Newport (1996) proposed this term to describe infants' ability to identify word boundaries solely from the statistical relation between sounds in the input. It is now widely acknowledged that infants and adults encode the statistical structure of their environment in a variety of tasks, including sequence learning (e.g., Haith, Wentworth, & Canfield, 1993;Stadler, 1992), category boundary detection (e.g., Maye, Werker, & Gerken, 2002), word-object association (Smith & Yu, 2008), cue-category association (Thiessen & Saffran, 2007), and causal learning (Sobel & Kirkham, 2007). Statistical learning likely plays a role in many different aspects of development, but it is thought to play an especially crucial role in language development. The discovery that infants are capable of benefiting from statistical structure in the input led to a reevaluation of the role of learning in language acquisition, ...

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Primitive computations in speech processing

Cited by 46 publications

References 55 publications

Gleaning Structure from Sound: The Role of Prosodic Contrast in Learning Non-adjacent Dependencies

Gleaning Structure from Sound: The Role of Prosodic Contrast in Learning Non-adjacent Dependencies

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

The extraction and integration framework: A two-process account of statistical learning.

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