Children learn high phonological neighbourhood density words more easily than low phonological neighbourhood density words (Storkel, 2004). However, the strength of this effect relative to alternative predictors of word acquisition is unclear. We addressed this issue using communicative inventory data from 300 British English-speaking children aged 12 to 25 months. Using Bayesian regression, we modelled word understanding and production as a function of: (i) phonological neighbourhood density, (ii) frequency, (iii) length, (iv) babiness, (v) concreteness, (vi) valence, (vii) arousal, and (viii) dominance. Phonological neighbourhood density predicted word production but not word comprehension, and this effect was stronger in younger children.
This study reexamines the claim that difficulty forming memories of words comprising uncommon sound sequences (i.e. low phonological neighbourhood density words) is a determinant of delayed expressive vocabulary development (e.g. Stokes, 2014). Method: We modelled communicative development inventory data from N=442, 18month old children, with expressive lexicon sizes between zero and 517 words (median=84). We fitted a Bayesian regression model in which the production of each communicative inventory word (N=680) by each child was predicted by interactions between that child's expressive lexicon size and the word's (i) phonological neighbourhood density, (ii) frequency in child-directed speech, (iii) length, (iv) babiness, and (v) concreteness. Results: Children with larger expressive lexicons were more likely to produce words comprising uncommon sound sequences than age-matched children with smaller lexicons. However, the magnitude of the interaction between expressive lexicon size and phonological neighbourhood density was modest relative to interactions between expressive lexicon size and word frequency, length, babiness, and concreteness. Conclusion: Emphasis on a difficulty with the memorisation of low neighbourhood density words as a determinant of slow vocabulary growth may be unwarranted, and the current evidence base in this direction is not robust enough to strongly support the development of possible interventions for late talkers (e.g. Stokes, 2014).
Results broadly support the hypothesis that children with DLD have difficulty in forming detailed lexical representations relative to age- though not language-matched peers. However, further work is required to determine the performance profiles of potential subgroups and the impact of manipulating different lexical characteristics, such as the position and degree of nonword error, phonotactic probability, and semantic network size.
Dominant theoretical accounts of developmental language disorder (DLD) commonly invoke working memory capacity limitations. In the current report, we present an alternative view: That working memory in DLD is not under-resourced but overloaded due to operating on speech representations with low discriminability. This account is developed through computational simulations involving deep convolutional neural networks trained on spoken word spectrograms in which information is either retained to mimic typical development or degraded to mimic the auditory processing deficits identified among some children with DLD. We assess not only spoken word recognition accuracy and predictive probability and entropy (i.e., predictive distribution spread), but also use mean-field-theory based manifold analysis to assess; (a) internal speech representation dimensionality and (b) classification capacity, a measure of the networks’ ability to isolate any given internal speech representation that is used as a proxy for attentional control. We show that instantiating a low-level auditory processing deficit results in the formation of internal speech representations with atypically high dimensionality, and that classification capacity is exhausted due to low representation separability. These representation and control deficits underpin not only lower performance accuracy but also greater uncertainty even when making accurate predictions in a simulated spoken word recognition task (i.e., predictive distributions with low maximum probability and high entropy), which replicates the response delays and word finding difficulties often seen in DLD. Overall, these simulations demonstrate a theoretical account of speech representation and processing deficits in DLD in which working memory capacity limitations play no causal role.
Purpose Research in the cognitive and neural sciences has situated predictive processing—the anticipation of upcoming percepts—as a dominant function of the brain. The purpose of this article is to argue that prediction should feature more prominently in explanatory accounts of sentence processing and comprehension deficits in developmental language disorder (DLD). Method We evaluate behavioral and neurophysiological data relevant to the theme of prediction in early typical and atypical language acquisition and processing. Results Poor syntactic awareness—attributable, in part, to an underlying statistical learning deficit—is likely to impede syntax-based predictive processing in children with DLD, conferring deficits in spoken sentence comprehension. Furthermore, there may be a feedback cycle in which poor syntactic awareness impedes children's ability to anticipate upcoming percepts, and this, in turn, makes children unable to improve their syntactic awareness on the basis of prediction error signals. Conclusion This article offers a refocusing of theory on sentence processing and comprehension deficits in DLD, from a difficulty in processing and integrating perceived syntactic features to a difficulty in anticipating what is coming next.
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