Variability and detection of invariant structure

Gómez, Rebecca L.

doi:10.1037/e537102012-224

Cited by 238 publications

(592 citation statements)

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“…However, when methodological drawbacks were controlled for, the pattern of results was different and fairly consistent with the action of ubiquitous mechanisms of associative memory and learning. The current attempts to explore the learning of nonadjacent dependencies within a statistical approach (Gomez, 2002;Newport & Aslin, 2004) and to simulate its key aspects with connectionist models (Christiansen & Chater, 1999) appear to be much more promising avenues of research than the rule-based framework suggested by Peña et al (2002). 4 Newport and Aslin (2004) failed to obtain evidence of learning between nonadjacent syllables when the speech flow was not segmented.…”

Section: Resultsmentioning

confidence: 99%

“…If associative mechanisms turn out to be able to learn from nonadjacent dependencies, this will undoubtedly increase their potential participation in language acquisition. The Peña, Bonatti, Nespor, and Mehler (2002) Study In light of these potential implications, a few studies have recently investigated the acquisition of remote dependencies (Cleeremans, 1993, Chapter 3;Gomez, 2002;Newport & Aslin, 2004;Santelmann & Jusczyk, 1998). The present article focuses on a study published in a recent issue of Science (Peña et al, 2002).…”

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confidence: 99%

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Learning Nonadjacent Dependencies: No Need for Algebraic-Like Computations.

Perruchet¹,

Tyler²,

Galland³

et al. 2004

Journal of Experimental Psychology: General

114

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Is it possible to learn the relation between 2 nonadjacent events? M. Peña, L. L. Bonatti, M. Nespor, and J. Mehler (2002) claimed this to be possible, but only in conditions suggesting the involvement of algebraic-like computations. The present article reports simulation studies and experimental data showing that the observations on which Peña et al. grounded their reasoning were flawed by deep methodological inadequacies. When the invalid data are set aside, the available evidence fits exactly with the predictions of a theory relying on ubiquitous associative mechanisms. Because nonadjacent dependencies are frequent in natural language, this reappraisal has far-reaching implications for the current debate on the need for rule-based computations in human adaptation to complex structures.The idea that most higher cognitive activities, especially language comprehension and production, are based on abstract, rulebased operations on symbolic contents is one of the cornerstones of the mainstream tradition in cognitive psychology. However, over the last 2 decades or so, an alternative view has gained increasing influence. This alternative conception is rooted in the traditional associative view of mind, but its recent upsurge is essentially related to the growth of connectionist modeling (e.g., McClelland & Rumelhart, 1986). Indeed, connectionist studies have shown that certain activities that were once thought of as straightforward evidence for rule-based computations can be simulated by models that rely only on associative mechanisms. This issue has crystallized around two broad conceptions of the mind, often thought of today as an opposition between those who advocate the need for assuming algebraic-like computations (e.g., Marcus, Vijayan, Rao, & Vishton, 1999;Pinker, 1997) and the proponents of statistical/distributional approaches (e.g., Seidenberg & MacDonald, 1999). The English past tense has been a focus for this debate from its outset, and the number of papers pertaining to this issue over the last few years (e.g., Pinker & Ullman, 2002;Ramscar, 2002) suggests that it is still unsettled despite a considerable amount of research effort.A newcomer in this lively debate is the learning of nonadjacent (or remote) dependencies. The major part of the traditional literature on associative learning has dealt with relations between adjacent events. This is true both for the domain of animal conditioning and for studies on paired-associate learning in humans. In both cases, the items to be associated are displayed in close temporal or spatial proximity. The same is true for the more recent studies on implicit learning (e.g. Stadler & Frensch, 1998). Looking at a standard flowchart of a finite-state grammar commonly used in artificial grammar studies is sufficient to show that relations are built between contiguous elements. 1 Several studies have shown that those adjacent relations were far more relevant for linguistic structure than researchers had claimed in the past. For instance, Redington, Chater, and Finch (199...

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Learning Nonadjacent Dependencies: No Need for Algebraic-Like Computations.

Perruchet¹,

Tyler²,

Galland³

et al. 2004

Journal of Experimental Psychology: General

114

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show abstract

“…For instance, the variability of the intervening event (X) may draw participants' attention toward the repeated elements (A and C) as a result of their relative frequencies, hence favoring the discovery of their relations, as observed by Gómez (2002) 2 and Onnis et al (2003). The factors revealed in the studies by Creel et al (2004) and Newport and Aslin (2004;see also Onnis et al, 2005) originally accounted for by reference to the Gestalt law of similarity, may also be encompassed in a view centered on attentional processing.…”

Section: A Unitary View Of Adjacent and Nonadjacentmentioning

confidence: 98%

An attention-based associative account of adjacent and nonadjacent dependency learning.

Pacton¹,

Perruchet²

2008

Journal of Experimental Psychology: Learning, Memory, and Cogni

118

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In 5 experiments, results showed that when participants are faced with materials embedding relations between both adjacent and nonadjacent elements, they learn exclusively the type of relations they had to actively process in order to meet the task demands, irrespective of the spatial contiguity of the paired elements. These results are consonant with current theories positing that attention is a necessary condition for learning. More important, the results provide support for a more radical conception, in which the joint attentional processing of 2 events is also a sufficient condition for learning the relation between them. The well-documented effect of contiguity could be a by-product of the fact that attention generally focuses on contiguous events. This reappraisal considerably extends the scope of approaches based on associative or statistical processes.

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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%

“…Although most artificial language experiments have relied on grammaticality judgments (Braine 1990;Gomez, 2002;Moeser & Bregman, 1972;Morgan et al, 1987;Morgan & Newport, 1981;, some studies have also included tests of production and online comprehension (production: Hudson- Kam & Newport, 2005;Wonnacott & Newport, 2005;online comprehension: Magnuson et al, 2003). Including all three measures in the same experiments will allow us to compare different modes of learning.…”

Section: The Current Workmentioning

confidence: 99%

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

Wonnacott

Newport

Tanenhaus

2008

Cognitive Psychology

193

218

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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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Variability and detection of invariant structure

Cited by 238 publications

References 0 publications

Learning Nonadjacent Dependencies: No Need for Algebraic-Like Computations.

Learning Nonadjacent Dependencies: No Need for Algebraic-Like Computations.

An attention-based associative account of adjacent and nonadjacent dependency learning.

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

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