Differentiating word learning processes may yield new insights – a commentary on Stoel-Gammon's ‘Relationships between lexical and phonological development in young children’

Storkel, Holly L.

doi:10.1017/s0305000910000486

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…This does not mean that the frequency with which segments occur in words (i.e., phonotactic probability) does not affect processing, or more specifically word-learning; indeed work by Storkel and Lee (2011) suggests that it does. Rather we believe that the influence of phonotactic probability may be due to another level of representation (i.e., something smaller than words, like biphones or phones, which are representations that were not included in the present model), or a different process related to word-learning (i.e., a process that signals the cognitive system that the input is not known and should be learned as described in Storkel (2011); the word-learning problems exhibited by children with functional phonological delays as reported in Storkel (2004) is also consistent with the hypothesis that lexical and sub-lexical representations are involved in word-learning).…”

Section: Resultsmentioning

confidence: 99%

Examining the Acquisition of Phonological Word Forms with Computational Experiments

Vitevitch

Storkel

2012

Lang Speech

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It has been hypothesized that known words in the lexicon strengthen newly formed representations of novel words, resulting in words with dense neighborhoods being learned more quickly than words with sparse neighborhoods. Tests of this hypothesis in a connectionist network showed that words with dense neighborhoods were learned better than words with sparse neighborhoods when the network was exposed to the words all at once (Experiment 1), or gradually over time, like human word-learners (Experiment 2). This pattern was also observed despite variation in the availability of processing resources in the networks (Experiment 3). A learning advantage for words with sparse neighborhoods was observed only when the network was initially exposed to words with sparse neighborhoods and exposed to dense neighborhoods later in training (Experiment 4). The benefits of computational experiments for increasing our understanding of language processes and for the treatment of language processing disorders are discussed.

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Section: Resultsmentioning

confidence: 99%

Examining the Acquisition of Phonological Word Forms with Computational Experiments

Vitevitch

Storkel

2012

Lang Speech

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“…From Storkel’s perspective (e.g. Storkel, 2011), it is important to differentiate these word learning processes because phonotactic probability and neighborhood density may interact differently with each process. It is not clear that differentiating these processes could resolve the discriminant earlier and later phonotactic probability research findings, but it points to the necessity of carefully describing what we measure in word learning and raises the interesting possibility of testing this kind of word learning model experimentally.…”

Section: Word Form Representationsmentioning

confidence: 99%

“…Because word learning was studied across four days with a post-task assessment on the fifth day, this permitted us to evaluate whether word form (phonotactic probability) and semantic characteristics (object familiarity) appeared to differentially influence the processes of ‘triggering’ (Storkel, 2011) and ‘configuration’ (Samuel & Leach, 2007) in children. If low phonotactic probability is important for triggering we would expect to see a low phonotactic probability advantage on the first of four word learning days when the words would stand out as a mismatch to other known words.…”

Section: Purposementioning

confidence: 99%

Word Learning by Preschoolers With SLI: Effect of Phonotactic Probability and Object Familiarity

Gray

Brinkley

Svetina

2012

J Speech Lang Hear Res

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Purpose This study investigated whether previous findings of a low phonotactic probability/unfamiliar object word learning advantage in preschoolers could be replicated, whether this advantage would be apparent at different ‘stages’ of word learning, and whether findings would differ for preschoolers with specific language impairment (SLI) and typical development (TD). Method One hundred fourteen children participated: 40 with SLI, 39 with TD matched for age and gender, and 35 with TD matched for expressive vocabulary and gender. Comprehension and production were assessed during word learning and at post-test for words that varied in phonotactic probability and object familiarity. Results Across groups, comprehension performance increased significantly from days 1–3, but there was no significant word/object type effect. Production performance increased significantly for days 1–4 for all groups and there was a clear low phonotactic probability/unfamiliar object advantage during word learning, but not at post-test. Conclusions Results help to establish that preschoolers with TD and SLI show a low phonotactic probability/unfamiliar object production advantage during word learning that is not restricted to the first few exposures to words, but continues over time. This study illustrates how the interaction of phonological characteristics in nascent and extant words can affect word learning.

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“… 2 Interestingly, such an “oddball” strategy may be advantageous for triggering the acquisition of novel phonological word-forms in typically developing individuals (Storkel, 2011). That is, novel words that are phonologically less similar to other known words can be more easily identified as a novel word to which resources should be allocated in order to acquire it.…”

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confidence: 99%

Using network science in the language sciences and clinic

Vitevitch

Castro

2014

International Journal of Speech-Language Pathology

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A number of variables—word frequency, word length—have long been known to influence language processing. We briefly review the effects in speech perception and production of two more recently examined variables: phonotactic probability and neighborhood density. We then describe a new approach to study language, network science, which is an interdisciplinary field drawing from mathematics, computer science, physics, and other disciplines. In this approach, nodes represent individual entities in a system (i.e., phonological word-forms in the lexicon), links between nodes represent relationships between nodes (i.e., phonological neighbors), and various measures enable researchers to assess the micro-level (i.e., the individual word), the macro-level (i.e., characteristics about the whole system), and the meso-level (i.e., how an individual fits into smaller sub-groups in the larger system). Although research on individual lexical characteristics such as word-frequency has increased our understanding of language processing, these measures only assess the “micro-level.” Using network science, researchers can examine words at various levels in the system, and how each word relates to the many other words stored in the lexicon. Several new findings using the network science approach are summarized to illustrate how this approach can be used to advance basic research as well as clinical practice.

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Differentiating word learning processes may yield new insights – a commentary on Stoel-Gammon's ‘Relationships between lexical and phonological development in young children’

Cited by 7 publications

References 16 publications

Examining the Acquisition of Phonological Word Forms with Computational Experiments

Examining the Acquisition of Phonological Word Forms with Computational Experiments

Word Learning by Preschoolers With SLI: Effect of Phonotactic Probability and Object Familiarity

Using network science in the language sciences and clinic

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