Statistical computations over a speech stream in a rodent

Toro, Juan M.; Trobalón, Josep Batista i

doi:10.3758/bf03193539

Cited by 189 publications

(154 citation statements)

References 45 publications

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“…Second, statistical learning is available across the lifespan from neonates to older adults (e.g. [8,9]), and present in many species other than humans, including primates [10,11] and rats [12]. Third, learners are sensitive to more statistical relations than the sequential conditional relations described in terms of transitional probabilities in the original Saffran et al [1] experiments.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…By now, a number of studies on birds (9)(10)(11)(12)(13)(14)(15)(16), rats (17)(18)(19), and primates (20)(21)(22) have used the artificial grammar paradigm to address animal abilities to learn about grammatical structures. Many of these studies used a discrimination task in which the animal has to learn to distinguish two differently structured string sets which are related to differences in reinforcement.…”

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confidence: 99%

Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

Spierings

Cate

2016

Proc. Natl. Acad. Sci. U.S.A.

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The ability to abstract a regularity that underlies strings of sounds is a core mechanism of the language faculty but might not be specific to language learning or even to humans. It is unclear whether and to what extent nonhuman animals possess the ability to abstract regularities defining the relation among arbitrary auditory items in a string and to generalize this abstraction to strings of acoustically novel items. In this study we tested these abilities in a songbird (zebra finch) and a parrot species (budgerigar). Subjects were trained in a go/no-go design to discriminate between two sets of sound strings arranged in an XYX or an XXY structure. After this discrimination was acquired, each subject was tested with test strings that were structurally identical to the training strings but consisted of either new combinations of known elements or of novel elements belonging to other element categories. Both species learned to discriminate between the two stimulus sets. However, their responses to the test strings were strikingly different. Zebra finches categorized test stimuli with previously heard elements by the ordinal position that these elements occupied in the training strings, independent of string structure. In contrast, the budgerigars categorized both novel combinations of familiar elements as well as strings consisting of novel element types by their underlying structure. They thus abstracted the relation among items in the XYX and XXY structures, an ability similar to that shown by human infants and indicating a level of abstraction comparable to analogical reasoning.artificial grammar learning | rule learning | auditory perception | songbirds | parrots O ne of the critical features of language learning is the ability to abstract the grammatical structure from spoken language. Such abstraction allows humans to learn about regularities in their native language and to generalize these regularities to novel input. This ability is examined in a standardized way in artificial grammar learning experiments, in which humans are exposed to strings of meaningless sounds (e.g., arbitrary speech syllables) organized according to a specific grammatical structure. Several studies have shown that the ability to abstract the underlying structure from such stimuli is present in young infants (1-5) in both the acoustic and the visual domain (6-8). This domain generality and its presence at a very early age have given rise to the notion that this cognitive ability may have preceded language evolution and served as a basis for present-day linguistic complexity. If so, it raises the question to what extent this ability is confined to humans or also can be found in nonhuman animals. In this context, comparative studies on nonhuman animals are needed to reveal the level of abstraction they are able to achieve in artificial grammar learning tasks. This information might provide hypotheses about how and why the more complex human grammatical competences have arisen. The current study addresses whether two bird species, the...

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“…This suggests that infants' abilities to extract information from statistical patterns are more powerful than those of other animals. Additional evidence is provided by the experiments of Toro & Trobalon (2005), who showed that rats were able to segment a speech stream based on syllable co-occurrence frequency (similar to the mutual information explored in Swingley (2005)), but not transition probability alone. The rats also showed no evidence of learning generalizations from non-adjacent dependencies such as those in the Gómez (2002) experiments, or abstract rules as in Marcus et al (1999).…”

Section: Experimental Studies Of Statistical Learning Abilitiesmentioning

confidence: 94%

Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition

Pearl¹,

Goldwater²

2016

Oxford Handbooks Online

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Bayesian models of language acquisition are powerful tools for exploring how linguistic generalizations can be made. Notably, Bayesian models assume children leverage statistical information in sophisticated ways, and so it is important to demonstrate that children’s behavior is consistent with both the assumptions of the Bayesian framework and the predictions of specific models. We first provide a historical overview of behavioral evidence suggesting children utilize available statistical information to make useful generalizations in a variety of tasks. We then discuss the Bayesian modeling framework, including benefits of particular interest to both developmental and theoretical linguists. We conclude with a review of several case studies that demonstrate how Bayesian models can be applied to problems of interest in language acquisition.

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Statistical computations over a speech stream in a rodent

Cited by 189 publications

References 45 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

Budgerigars and zebra finches differ in how they generalize in an artificial grammar learning experiment

Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition

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