scholarly journals What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

2021 ◽  
Vol 11 (12) ◽  
pp. 1628
Author(s):  
Michael S. Vitevitch ◽  
Gavin J. D. Mullin
Keyword(s):  
Word Recognition ◽  
Network Model ◽  
Language Processing ◽  
Network Science ◽  
Spoken Word Recognition ◽  
Cognitive Networks ◽  
Spoken Word ◽  
Cognitive Network ◽  
Behavioral Studies ◽  
Trace Model

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.

Children With Cochlear Implants Use Semantic Prediction to Facilitate Spoken Word Recognition

2021 ◽  
pp. 1-14
Author(s):  
Christina Blomquist ◽  
Rochelle S. Newman ◽  
Yi Ting Huang ◽  
Jan Edwards
Keyword(s):  
Word Recognition ◽  
Target Word ◽  
Cochlear Implants ◽  
Language Processing ◽  
Spoken Word Recognition ◽  
Spoken Language ◽  
Spoken Word ◽  
Semantic Cues ◽  
Matched Group ◽  
Spoken Language Processing

Purpose Children with cochlear implants (CIs) are more likely to struggle with spoken language than their age-matched peers with normal hearing (NH), and new language processing literature suggests that these challenges may be linked to delays in spoken word recognition. The purpose of this study was to investigate whether children with CIs use language knowledge via semantic prediction to facilitate recognition of upcoming words and help compensate for uncertainties in the acoustic signal. Method Five- to 10-year-old children with CIs heard sentences with an informative verb ( draws ) or a neutral verb ( gets ) preceding a target word ( picture ). The target referent was presented on a screen, along with a phonologically similar competitor ( pickle ). Children's eye gaze was recorded to quantify efficiency of access of the target word and suppression of phonological competition. Performance was compared to both an age-matched group and vocabulary-matched group of children with NH. Results Children with CIs, like their peers with NH, demonstrated use of informative verbs to look more quickly to the target word and look less to the phonological competitor. However, children with CIs demonstrated less efficient use of semantic cues relative to their peers with NH, even when matched for vocabulary ability. Conclusions Children with CIs use semantic prediction to facilitate spoken word recognition but do so to a lesser extent than children with NH. Children with CIs experience challenges in predictive spoken language processing above and beyond limitations from delayed vocabulary development. Children with CIs with better vocabulary ability demonstrate more efficient use of lexical-semantic cues. Clinical interventions focusing on building knowledge of words and their associations may support efficiency of spoken language processing for children with CIs. Supplemental Material https://doi.org/10.23641/asha.14417627

scholarly journals Monolingual and bilingual children's processing of coarticulation cues during spoken word recognition

2020 ◽  
Vol 47 (6) ◽  
pp. 1189-1206
Author(s):  
Félix DESMEULES-TRUDEL ◽  
Charlotte MOORE ◽  
Tania S. ZAMUNER
Keyword(s):  
Word Recognition ◽  
Eye Tracking ◽  
Old English ◽  
Language Processing ◽  
Spoken Word Recognition ◽  
Spoken Word ◽  
Bilingual Children ◽  
Phonetic Cues ◽  
Vowel Nasalization ◽  
English Monolingual

AbstractBilingual children cope with a significant amount of phonetic variability when processing speech, and must learn to weigh phonetic cues differently depending on the cues’ respective roles in their two languages. For example, vowel nasalization is coarticulatory and contrastive in French, but coarticulatory-only in English. In this study, we extended an investigation of the processing of coarticulation in two- to three-year-old English monolingual children (Zamuner, Moore & Desmeules-Trudel, 2016) to a group of four- to six-year-old English monolingual children and age-matched English–French bilingual children. Using eye tracking, we found that older monolingual children and age-matched bilingual children showed more sensitivity to coarticulation cues than the younger children. Moreover, when comparing the older monolinguals and bilinguals, we found no statistical differences between the two groups. These results offer support for the specification of coarticulation in word representations, and indicate that, in some cases, bilingual children possess language processing skills similar to monolinguals.

scholarly journals Does signal reduction imply predictive coding in models of spoken word recognition?

2021 ◽  
Author(s):  
Sahil Luthra ◽  
Monica Y. C. Li ◽  
Heejo You ◽  
Christian Brodbeck ◽  
James S. Magnuson
Keyword(s):  
Language Processing ◽  
Cognitive Neuroscience ◽  
Spoken Word Recognition ◽  
Predictive Coding ◽  
Spoken Word ◽  
Predictive Processing ◽  
Neural Signal ◽  
Lexical Activation ◽  
Trace Model ◽  
Interactive Activation

AbstractPervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. Formally, predictive coding is a computational mechanism where only deviations from top-down expectations are passed between levels of representation. In many cognitive neuroscience studies, a reduction of signal for expected inputs is taken as being diagnostic of predictive coding. In the present work, we show that despite not explicitly implementing prediction, the TRACE model of speech perception exhibits this putative hallmark of predictive coding, with reductions in total lexical activation, total lexical feedback, and total phoneme activation when the input conforms to expectations. These findings may indicate that interactive activation is functionally equivalent or approximant to predictive coding or that caution is warranted in interpreting neural signal reduction as diagnostic of predictive coding.

scholarly journals Contra assertions, feedback improves word recognition

2021 ◽  
Author(s):  
James Magnuson ◽  
Samantha Grubb ◽  
Anne Marie Crinnion ◽  
Sahil Luthra ◽  
Phoebe Gaston
Keyword(s):  
Word Recognition ◽  
Speech Processing ◽  
Recognition Performance ◽  
Spoken Word Recognition ◽  
Spoken Word ◽  
Feedback Signal ◽  
Activation Model ◽  
Trace Model ◽  
Interactive Activation ◽  
Joint Probabilities

Norris and Cutler (in press) revisit their arguments that (lexical-to-sublexical) feedback cannot improve word recognition performance, based on the assumption that feedback must boost signal and noise equally. They also argue that demonstrations that feedback improves performance (Magnuson, Mirman, Luthra, Strauss, & Harris, 2018) in the TRACE model of spoken word recognition (McClelland & Elman, 1986) were artifacts of converting activations to response probabilities. We first evaluate their claim that feedback in an interactive activation model must boost noise and signal equally. This is not true in a fully interactive activation model such as TRACE, where the feedback signal does not simply mirror the feedforward signal; it is instead shaped by joint probabilities over lexical patterns, and the dynamics of lateral inhibition. Thus, even under high levels of noise, lexical feedback will selectively boost signal more than noise. We demonstrate that feedback promotes faster word recognition and preserves accuracy under noise whether one uses raw activations or response probabilities. We then document that lexical feedback selectively boosts signal (i.e., lexically-coherent series of phonemes) more than noise by tracking sublexical (phoneme) activations under noise with and without feedback. Thus, feedback in a model like TRACE does improve word recognition, exactly by selective reinforcement of lexically-coherent signal. We conclude that whether lexical feedback is integral to human speech processing is an empirical question, and briefly review a growing body of work at behavioral and neural levels that is consistent with feedback and inconsistent with autonomous (non-feedback) architectures.

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