Metameric

2018 ◽  
Vol 13 (3) ◽  
pp. 333-353
Author(s):  
Stéphan Tulkens ◽  
Dominiek Sandra ◽  
Walter Daelemans
Keyword(s):  
User Interfaces ◽  
Word Reading ◽  
Spoken Word Recognition ◽  
Spoken Word ◽  
Activation Model ◽  
Single Model ◽  
Psychological Model ◽  
Depth Analysis ◽  
Specific Factors ◽  
Interactive Activation

Abstract An oft-cited shortcoming of Interactive Activation as a psychological model of word reading is that it lacks the ability to simultaneously represent words of different lengths. We present an implementation of the Interactive Activation model, which we call Metameric, that can simulate words of different lengths, and show that there is nothing inherent to Interactive Activation which prevents it from simultaneously representing multiple word lengths. We provide an in-depth analysis of which specific factors need to be present, and show that the inclusion of three specific adjustments, all of which have been published in various models before, lead to an Interactive Activation model which is fully capable of representing words of different lengths. Finally, we show that our implementation is fully capable of representing all words between 2 and 11 letters in length from the English Lexicon Project (31, 416 words) in a single model. Our implementation is completely open source, heavily optimized, and includes both command line and graphical user interfaces, but is also agnostic to specific input data or problems. It can therefore be used to simulate a myriad of other models, e.g., models of spoken word recognition. The implementation can be accessed at www.github.com/clips/metameric.

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.

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 Computational modelling of spoken-word recognition processes

2010 ◽  
Vol 18 (1) ◽  
pp. 136-164 ◽  
Author(s):  
Odette Scharenborg ◽  
Lou Boves
Keyword(s):  
Word Recognition ◽  
Word Processing ◽  
Goodness Of Fit ◽  
Computational Models ◽  
Spoken Word Recognition ◽  
Computational Modelling ◽  
Spoken Word ◽  
Recognition Process ◽  
Depth Analysis ◽  
Cognitive Plausibility

Computational modelling has proven to be a valuable approach in developing theories of spoken-word processing. In this paper, we focus on a particular class of theories in which it is assumed that the spoken-word recognition process consists of two consecutive stages, with an ‘abstract’ discrete symbolic representation at the interface between the stages. In evaluating computational models, it is important to bring in independent arguments for the cognitive plausibility of the algorithms that are selected to compute the processes in a theory. This paper discusses the relation between behavioural studies, theories, and computational models of spoken-word recognition. We explain how computational models can be assessed in terms of the goodness of fit with the behavioural data and the cognitive plausibility of the algorithms. An in-depth analysis of several models provides insights into how computational modelling has led to improved theories and to a better understanding of the human spoken-word recognition process.

Children's spoken word recognition and contributions to phonological awareness and nonword repetition: A 1-year follow-up

2009 ◽  
Vol 30 (1) ◽  
pp. 101-121 ◽  
Author(s):  
JAMIE L. METSALA ◽  
DESPINA STAVRINOS ◽  
AMANDA C. WALLEY
Keyword(s):  
Word Recognition ◽  
Phonological Awareness ◽  
Word Reading ◽  
Spoken Word Recognition ◽  
Spoken Word ◽  
Time Span ◽  
Nonword Repetition ◽  
Older Children ◽  
Word Repetition

ABSTRACTThis study examined effects of lexical factors on children's spoken word recognition across a 1-year time span, and contributions to phonological awareness and nonword repetition. Across the year, children identified words based on less input on a speech-gating task. For word repetition, older children improved for the most familiar words. There was a competition effect for the word repetition task, but this effect was present only for the most familiar words on the gating task. Recognition for words from sparse neighborhoods predicted phonological awareness 1 year later, and children poorer at recognizing these words in Year 1 scored lower on word reading in Year 2. Spoken word recognition also accounted for unique variance in nonword repetition across the 1-year time span. Findings are discussed in terms of understanding the effects of vocabulary growth on spoken word recognition, phonological awareness, and nonword repetition.

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