Computation of Conditional Probability Statistics by 8-Month-Old Infants

Aslin, Richard Ν.; Saffran, Jenny R.; Newport, Elissa L.

doi:10.1111/1467-9280.00063

Cited by 977 publications

(920 citation statements)

References 25 publications

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“…(We use the term "word" just to designate statistically coherent syllable sequences, but do not imply any prosodic or syntactic properties that real words may have.) In line with much research showing that learners can use co-occurrence statistics among syllables such as transitional probabilities (TPs) 1 to extract words from fluent speech (e.g., Aslin et al, 1998;Saffran, 2001;Saffran et al, 1996), also participants in Peña et al's (2002) experiments could use the statistical dependency between the first and the last syllables of words to segment the stream. However, they could not generalize this dependency to new items.…”

Section: A Test Case For Statistical and Nonstatistical Operationsmentioning

confidence: 74%

Primitive computations in speech processing

Endress

Mehler

2009

Quarterly Journal of Experimental Psychology

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Previous research suggests that artificial-language learners exposed to quasi-continuous speech can learn that the first and the last syllables of words have to belong to distinct classes (e.g., Endress & Bonatti, 2007;Peña, Bonatti, Nespor, & Mehler, 2002). The mechanisms of these generalizations, however, are debated. Here we show that participants learn such generalizations only when the crucial syllables are in edge positions (i.e., the first and the last), but not when they are in medial positions (i.e., the second and the fourth in pentasyllabic items). In contrast to the generalizations, participants readily perform statistical analyses also in word middles. In analogy to sequential memory, we suggest that participants extract the generalizations using a simple but specific mechanism that encodes the positions of syllables that occur in edges. Simultaneously, they use another mechanism to track the syllable distribution in the speech streams. In contrast to previous accounts, this model explains why the generalizations are faster than the statistical computations, require additional cues, and break down under different conditions, and why they can be performed at all. We also show that that similar edge-based mechanisms may explain many results in artificial-grammar learning and also various linguistic observations. Permanent repository link

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Section: A Test Case For Statistical and Nonstatistical Operationsmentioning

confidence: 74%

Primitive computations in speech processing

Endress

Mehler

2009

Quarterly Journal of Experimental Psychology

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“…At their best, artificial language studies can be highly informative about the fundamental mechanisms of learning that are continuous across development and even across species. Work on statistical segmentation and grouping has exemplified this description (Saffran et al, 1996b(Saffran et al, , 1996aAslin, Saffran, & Newport, 1998;Hauser, Newport, & Aslin, 2001;Kirkham, Slemmer, & Johnson, 2002;Fiser & Aslin, 2002). Although it is not always clear how this work connects with particular tasks in language acquisition, the identification and characterization of basic learning mechanisms is in itself an important task.…”

Section: Discussionmentioning

confidence: 99%

Overcoming Memory Limitations in Rule Learning

Frank¹,

Gibson

2011

Language Learning and Development

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Adults, infants, and other species are able to learn and generalize abstract patterns from sequentially-presented stimuli. Rule learning of this type may be involved in children's acquisition of linguistic structure, but the nature of the mechanisms underlying these abilities is unknown. While inferences regarding the capabilities of these mechanisms are commonly made based on the pattern of successes and failures in simple artificial-language rule-learning tasks, failures may be driven by memory limitations rather than intrinsic limitations on the kinds of computations that learners can perform. Here we show that alleviating memory constraints on adult learners through concurrent visual presentation of stimuli allowed them to succeed in learning regularities in three difficult artificial rule-learning experiments where participants had previously failed to learn via sequential auditory presentation. These results suggest that memory constraints, rather than intrinsic limitations on learning, may be a parsimonious explanation for many previously reported failures. We argue that future work should attempt to characterize the role of memory constraints in natural and artificial language learning.

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“…The benefits of using artificial languages as a means of obtaining precise control over the input to learning is now well established (Aslin, Saffran & Newport, 1998;Braine, 1963;Hudson Kam & Newport, 2005;Gerken, 2006;Gomez, 2002;Mintz, 2002;Moeser & Bregman, 1972;Morgan & Newport, 1981;Morgan, Meier & Newport, 1987;Wonnacott & Newport, 2005). In addition, there is emerging evidence that artificial languages exhibit many of the same signature results in processing as those obtained with natural language stimuli (e.g., Magnuson, Tanenhaus, Aslin & Dahan, 2003).…”

Section: The Current Workmentioning

confidence: 99%

Acquiring and processing verb argument structure: Distributional learning in a miniature language

Wonnacott

Newport

Tanenhaus

2008

Cognitive Psychology

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Adult knowledge of a language involves correctly balancing lexically-based and more languagegeneral patterns. For example, verb-argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems (Baker 1979), with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur (Pinker, 1989). Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing (Trueswell et al 1993;Mitchell 1987) and on the role of semantics in this process (Hare et al 2003). The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input-statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings.

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Computation of Conditional Probability Statistics by 8-Month-Old Infants

Cited by 977 publications

References 25 publications

Primitive computations in speech processing

Primitive computations in speech processing

Overcoming Memory Limitations in Rule Learning

Acquiring and processing verb argument structure: Distributional learning in a miniature language

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