Does Grammar Start Where Statistics Stop?

Seidenberg, Mark S.; MacDonald, Maryellen C.; Saffran, Jenny R.

doi:10.1126/science.1078094

Cited by 100 publications

(73 citation statements)

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“…However, it is important to point out that humans are not unconstrained learners, acquiring any regularity present in their environment. For example, Saffran (2002) demonstrated that there are modality constraints on learning predictive relationships in an artificial language. She exposed adult learners either to a phrase-structure language in which the presence of one item in a phrase predicted the presence of another item (Language P), or to a phrase-structure language in which predictive relationships were absent (Language N).…”

Section: Discussionmentioning

confidence: 99%

The Role of Prior Experience in Language Acquisition

2007

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Learners exposed to an artificial language recognize its abstract structural regularities when instantiated in a novel vocabulary (e.g., Gómez, Gerken, & Schvaneveldt, 2000; Tunney & Altmann, 2001). We asked whether such sensitivity accelerates subsequent learning, and enables acquisition of more complex structure. In Experiment 1, pre‐exposure to a category‐induction language of the form aX bY sped subsequent learning when the language is instantiated in a different vocabulary. In Experiment 2, while naíve learners did not acquire an acX bcY language, in which aX and bY co‐occurrence regularities were separated by a c‐element, prior experience with an aX bY language provided some benefit. In Experiment 3 we replicated this finding with a 24‐hour delay between learning phases, and controlled for prior experience with the aX bY language's prosodic and phonological characteristics. These findings suggest that learners, and the structure they can acquire, change as a function of experience.

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Section: Discussionmentioning

confidence: 99%

The Role of Prior Experience in Language Acquisition

2007

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“…Several proponents of "general associationist mechanisms" (e.g., Perruchet et al, 2004;Seidenberg et al, 2002) have suggested that such mechanisms may account for Peña et al's (2002) and Endress and Bonatti's (2007) results. However, these proposals only stated that the mechanisms of the generalizations may be associationist but not what they actually may be.…”

Section: Four Puzzles Solvedmentioning

confidence: 99%

“…Accordingly, the conclusion that participants analyse the speech streams with qualitatively different mechanisms has met with considerable resistance. For example, it has been suggested that general statistical mechanisms can account for these results without the need to postulate multiple mechanisms (e.g., Perruchet et al, THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 0000, 00 (0) 2004; Seidenberg et al, 2002). By clarifying the mechanisms of the generalizations, we hope to provide answers also to these questions.…”

Section: A Test Case For Statistical and Nonstatistical Operationsmentioning

confidence: 99%

“…However, the mechanisms of the generalizations remained elusive despite substantial research (e.g., Balaguer, Toro, Rodriguez-Fornells, & Bachoud-Lévi, 2007;Mueller, Bahlmann, & Friederici, 2008;Pacton & Perruchet, 2008;Perruchet, Tyler, Galland, & Peereman, 2004;Seidenberg, MacDonald, & Saffran, 2002). A particularly important issue concerns the question of whether these results can be explained by a single, associationist mechanism, or whether one needs to posit different kinds of mechanisms for the generalizations and the statistical computations, respectively.…”

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confidence: 99%

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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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“…Furthermore, available data suggest that the kind of statistical learning that would suffice for the segmentation of speech would also work on the extraction of more complex long-distance regularities (see Seidenberg, MacDonald, & Saffran, 2002). That is, an organism capable of computing the statistical information necessary for the detection of word boundaries should also be capable of extracting grammar-like rules.…”

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confidence: 99%