All words are not created equal: Expectations about word length guide infant statistical learning

Lew‐Williams, Casey; Saffran, Jenny R.

doi:10.1016/j.cognition.2011.10.007

Cited by 77 publications

(82 citation statements)

References 40 publications

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“…But neither process, alone, is able to account for cue-based statistical learning: the fact that prior experience enables learners to identify phonological regularities and use those regularities to constrain subsequent extraction (e.g., Lew-Williams & Saffran, 2012;Thiessen & Saffran, 2007). By combining the processes of integration and extraction in a single framework, we believe that it is possible to explain cue-based statistical learning.…”

Section: Linking Extraction and Integration Through Attentionmentioning

confidence: 99%

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

Thiessen

Kronstein

Hufnagle

2013

Psychological Bulletin

176

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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Section: Linking Extraction and Integration Through Attentionmentioning

confidence: 99%

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

Thiessen

Kronstein

Hufnagle

2013

Psychological Bulletin

176

193

View full text Add to dashboard Cite

show abstract

“…Recent studies have suggested that the learning of regularities is subject to a primacy effect (Gebhart, Aslin, & Newport, 2009;Jungé, Scholl, & Chun, 2007;Jiang, Swallow, Rosenbaum, & Herzig, 2013;Lew-Williams & Saffran, 2012). For example, in one study (Gebhart et al, 2009) participants were first exposed to one artificial language containing syllables that reliably co-occurred in a specific order.…”

Section: Introductionmentioning

confidence: 99%

The persistence of the attentional bias to regularities in a changing environment

Zhao

2015

Atten Percept Psychophys

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The environment often is stable, but some aspects may change over time. The challenge for the visual system is to discover and flexibly adapt to the changes. We examined how attention is shifted in the presence of changes in the underlying structure of the environment. In six experiments, observers viewed four simultaneous streams of objects while performing a visual search task. In the first half of each experiment, the stream in the structured location contained regularities, the shapes in the random location were randomized, and gray squares appeared in two neutral locations. In the second half, the stream in the structured or the random location may change. In the first half of all experiments, visual search was facilitated in the structured location, suggesting that attention was consistently biased toward regularities. In the second half, this bias persisted in the structured location when no change occurred (Experiment 1), when the regularities were removed (Experiment 2), or when new regularities embedded in the original or novel stimuli emerged in the previously random location (Experiments 3 and 6). However, visual search was numerically but no longer reliably faster in the structured location when the initial regularities were removed and new regularities were introduced in the previously random location (Experiment 4), or when novel random stimuli appeared in the random location (Experiment 5). This suggests that the attentional bias was weakened. Overall, the results demonstrate that the attentional bias to regularities was persistent but also sensitive to changes in the environment.

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“…1 Furthermore, preexposure to units of lengths matching the to-be-segmented units contributes to infants' speech segmentation (Lew-Williams & Saffran, 2012). For adults, learning has been reported for irregularlength units (Tyler & Cutler, 2009), but to our knowledge, no adult study has made direct comparisons of AL learning with units of regular or irregular lengths.…”

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confidence: 95%

Regularity of unit length boosts statistical learning in verbal and nonverbal artificial languages

2012

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Humans have remarkable statistical learning abilities for verbal speech-like materials and for nonverbal music-like materials. Statistical learning has been shown with artificial languages (AL) that consist of the concatenation of nonsense word-like units into a continuous stream. These ALs contain no cues to unit boundaries other than the transitional probabilities between events, which are high within a unit and low between units. Most AL studies have used units of regular lengths. In the present study, the ALs were based on the same statistical structures but differed in unit length regularity (i.e., whether they were made out of units of regular vs. irregular lengths) and in materials (i.e., syllables vs. musical timbres), to allow us to investigate the influence of unit length regularity on domain-general statistical learning. In addition to better performance for verbal than for nonverbal materials, the findings revealed an effect of unit length regularity, with better performance for languages with regular-(vs. irregular-) length units. This unit length regularity effect suggests the influence of dynamic attentional processes (as proposed by the dynamic attending theory; Large & Jones (Psychological Review 106: 119-159, 1999)) on domain-general statistical learning.

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All words are not created equal: Expectations about word length guide infant statistical learning

Cited by 77 publications

References 40 publications

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

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

The persistence of the attentional bias to regularities in a changing environment

Regularity of unit length boosts statistical learning in verbal and nonverbal artificial languages

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