The lexical restructuring hypothesis and graph theoretic analyses of networks based on random lexicons

Using network science to examine audio-visual speech perception with a multi-layer graph

To examine visual speech perception (i.e., lip-reading), we created a multi-layer network (the AV-net) that contained: (1) an auditory layer with nodes representing phonological word-forms and edges connecting words that were phonologically related, and (2) a visual layer with nodes representing the viseme representations of words and edges connecting viseme representations that differed by a single viseme (and additional edges to connect related nodes in the two layers). The results of several computer simulations (in which activation diffused across the network to simulate word identification) are reported and compared to the performance of human participants who identified the same words in a condition in which audio and visual information were both presented (Simulation 1), in an audio-only presentation condition (Simulation 2), and a visual-only presentation condition (Simulation 3). Another simulation (Simulation 4) examined the influence of phonological information on visual speech perception by comparing performance in the multi-layer AV-net to a single-layer network that contained only a visual layer with nodes representing the viseme representations of words and edges connecting viseme representations that differed by a single viseme. We also report the results of several analyses of the errors made by human participants in the visual-only presentation condition. The results of our analyses have implications for future research and training of lip-reading, and for the development of automatic lip-reading devices and software for individuals with certain developmental or acquired disorders or for listeners with normal hearing in noisy conditions.

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Using Complex Networks in the Hearing Sciences

In this Point of View, we review a number of recent discoveries from the emerging, interdisciplinary field of Network Science , which uses graph theoretic techniques to understand complex systems. In the network science approach, nodes represent entities in a system, and connections are placed between nodes that are related to each other to form a web-like network . We discuss several studies that demonstrate how the micro-, meso-, and macro-level structure of a network of phonological word-forms influence spoken word recognition in listeners with normal hearing and in listeners with hearing loss. Given the discoveries made possible by this new approach and the influence of several complex network measures on spoken word recognition performance we argue that speech recognition measures-originally developed in the late 1940s and routinely used in clinical audiometry-should be revised to reflect our current understanding of spoken word recognition. We also discuss other ways in which the tools of network science can be used in Speech and Hearing Sciences and Audiology more broadly.

The Relationship Between Speech Perception, Speech Production, and Vocabulary Abilities in Children: Insights From By-Group and Continuous Analyses

PURPOSE

The purpose of this study was to explore the relationship between speech perception, speech production, and vocabulary abilities in children with and without speech sound disorders (SSDs), analyzing the data both by group and continuously.

METHOD

Sixty-one Australian English-speaking children aged 48-69 months participated in this study. Children's speech production abilities ranged along the continuum from SSDs through to typical speech. Their vocabulary abilities ranged along the continuum from typical to above average ("lexically precocious"). Children completed routine speech and language assessments in addition to an experimental Australian English lexical and phonetic judgment task.

RESULTS

When analyzing data by group, there was no significant difference between the speech perception ability of children with SSDs and that of children without SSDs. Children with above-average vocabularies had significantly better speech perception ability than children with average vocabularies. When analyzing data continuously, speech production and vocabulary were both significant positive predictors of variance in speech perception ability, both individually in simple linear regression and when combined in multiple linear regression. There was also a significant positive correlation between perception and production of two of the four target phonemes tested (i.e., /k/ and /ʃ/) among children in the SSD group.

CONCLUSIONS

Results from this study provide further insight into the complex relationship between speech perception, speech production, and vocabulary abilities in children. While there is a clinical and important need for categorical distinctions between SSDs and typically developing speech, findings further highlight the value of investigating speech production and vocabulary abilities continuously and categorically. By capturing the heterogeneity among children's speech production and vocabulary abilities, we can advance our understanding of SSDs in children.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.22229674.

Using Network Science and Psycholinguistic Megastudies to Examine the Dimensions of Phonological Similarity

Network science was used to examine different dimensions of phonological similarity in English. Data from a phonological associate task and an identification of words in noise task were used to create a phonological association network and a misperception network. These networks were compared to a network formed by a computational metric widely used to assess phonological similarity (i.e., one-phoneme metric). The phonological association network and the misperception network were topographically more similar to each other than either were to the one-phoneme metric network, but there were several network features in common between the one-phoneme metric network and the phonological association network. To assess the influence of network structure on processing, we compared the influence of degree (i.e., neighborhood density) from each of the networks on visual and auditory lexical decision reaction times obtained from two psycholinguistic megastudies. The effect of degree differed across network types and tasks. We discuss the use of each approach to determine phonological similarity and a possible direction forward for language research through the use of multiplex networks.

Universal Features in Phonological Neighbor Networks

Human speech perception involves transforming a countinuous acoustic signal into discrete linguistically meaningful units (phonemes) while simultaneously causing a listener to activate words that are similar to the spoken utterance and to each other. The Neighborhood Activation Model posits that phonological neighbors (two forms [words] that differ by one phoneme) compete significantly for recognition as a spoken word is heard. This definition of phonological similarity can be extended to an entire corpus of forms to produce a phonological neighbor network (PNN). We study PNNs for five languages: English, Spanish, French, Dutch, and German. Consistent with previous work, we find that the PNNs share a consistent set of topological features. Using an approach that generates random lexicons with increasing levels of phonological realism, we show that even random forms with minimal relationship to any real language, combined with only the empirical distribution of language-specific phonological form lengths, are sufficient to produce the topological properties observed in the real language PNNs. The resulting pseudo-PNNs are insensitive to the level of lingustic realism in the random lexicons but quite sensitive to the shape of the form length distribution. We therefore conclude that “universal” features seen across multiple languages are really string universals, not language universals, and arise primarily due to limitations in the kinds of networks generated by the one-step neighbor definition. Taken together, our results indicate that caution is warranted when linking the dynamics of human spoken word recognition to the topological properties of PNNs, and that the investigation of alternative similarity metrics for phonological forms should be a priority.

How children explore the phonological network in child-directed speech: A survival analysis of children's first word productions

We explored how phonological network structure influences the age of words' first appearance in children's (14-50 months) speech, using a large, longitudinal corpus of spontaneous child-caregiver interactions. We represent the caregiver lexicon as a network in which each word is connected to all of its phonological neighbors, and consider both words' local neighborhood density (degree), and also their embeddedness among interconnected neighborhoods (clustering coefficient and coreness). The larger-scale structure reflected in the latter two measures is implicated in current theories of lexical development and processing, but its role in lexical development has not yet been explored. Multilevel discrete-time survival analysis revealed that children are more likely to produce new words whose network properties support lexical access for production: high degree, but low clustering coefficient and coreness. These effects appear to be strongest at earlier ages and largely absent from 30 months on. These results suggest that both a word's local connectivity in the lexicon and its position in the lexicon as a whole influences when it is learned, and they underscore how general lexical processing mechanisms contribute to productive vocabulary development.

Insights into failed lexical retrieval from network science

Previous network analyses of the phonological lexicon (Vitevitch, 2008) observed a web-like structure that exhibited assortative mixing by degree: words with dense phonological neighborhoods tend to have as neighbors words that also have dense phonological neighborhoods, and words with sparse phonological neighborhoods tend to have as neighbors words that also have sparse phonological neighborhoods. Given the role that assortative mixing by degree plays in network resilience, we examined instances of real and simulated lexical retrieval failures in computer simulations, analysis of a slips-of-the-ear corpus, and three psycholinguistic experiments for evidence of this network characteristic in human behavior. The results of the various analyses support the hypothesis that the structure of words in the mental lexicon influences lexical processing. The implications of network science for current models of spoken word recognition, language processing, and cognitive psychology more generally are discussed.

Clustering coefficients of lexical neighborhoods: Does neighborhood structure matter in spoken word recognition?

High neighborhood density reduces the speed and accuracy of spoken word recognition. The two studies reported here investigated whether Clustering Coefficient (CC) - a graph theoretic variable measuring the degree to which a word's neighbors are neighbors of one another, has similar effects on spoken word recognition. In Experiment 1, we found that high CC words were identified less accurately when spectrally degraded than low CC words. In Experiment 2, using a word repetition procedure, we observed longer response latencies for high CC words compared to low CC words. Taken together, the results of both studies indicate that higher CC leads to slower and less accurate spoken word recognition. The results are discussed in terms of activation-plus-competition models of spoken word recognition.