Semantic flow and its relation to controlled semantic retrieval deficits in the narrative production of people with aphasia

Discourse Task Type-Specific Linguistic Characteristics in Anomic Aphasia and Healthy Controls: Evidence From Mandarin-Chinese AphasiaBank

PURPOSE

This study was designed to examine the hypothesis that discourse task types influence language performance in Mandarin Chinese-speaking people and to reveal the discourse task-specific linguistic properties of persons with anomic aphasia compared to neurotypical controls.

METHOD

Language samples from persons with aphasia (n = 31) and age- and education-matched controls (n = 31) across four discourse tasks (sequential-picture description, single-picture description, story narrative, and procedural discourse) were collected from Mandarin AphasiaBank. Task-specific distributions of parts of speech were analyzed using mosaic plots. The main effects of tasks in each group and the between-group differences within each task for several typical linguistic variables were evaluated, including the mean length of utterance, tokens, moving-average type-token ratio, words per minute, propositional density, noun-verb ratio, noun percentage, and verb percentage.

RESULTS

The results revealed an impact of discourse tasks on most language variables in both groups. In the healthy controls, story narratives yielded the highest total words and lowest verb percentage. In the aphasia group, procedural discourse elicited the fewest total words and densest expressions, whereas their single-picture descriptions had the highest noun-verb ratio. For all tasks, the aphasia group performed worse than the control group in the mean length of utterance, tokens, moving-average type-token ratio, and words per minute. For noun-verb ratio, noun percentage, and verb percentage, only one task (i.e., single-picture description) showed significant between-group differences.

CONCLUSION

The selection of discourse tasks should be addressed in assessments and interventions for Mandarin Chinese-speaking individuals with aphasia to obtain more accurate and feasible outcomes.

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.

Bigram semantic distance as an index of continuous semantic flow in natural language: Theory, tools, and applications

Much of our understanding of word meaning has been informed through studies of single words. High-dimensional semantic space models have recently proven instrumental in elucidating connections between words. Here we show how bigram semantic distance can yield novel insights into conceptual cohesion and topic flow when computed over continuous language samples. For example, "Cats drink milk" is comprised of an ordered vector of bigrams (cat-drink, drink-milk). Each of these bigrams has a unique semantic distance. These distances in turn may provide a metric of dispersion or the flow of concepts as language unfolds. We offer an R-package ("semdistflow") that transforms any user-specified language transcript into a vector of ordered bigrams, appending two metrics of semantic distance to each pair. We validated these distance metrics on a continuous stream of simulated verbal fluency data assigning predicted switch markers between alternating semantic clusters (animals, musical instruments, fruit). We then generated bigram distance norms on a large sample of text and demonstrated applications of the technique to a classic work of short fiction, To Build a Fire (London, 1908). In one application, we showed that bigrams spanning sentence boundaries are punctuated by jumps in the semantic distance. We discuss the promise of this technique for characterizing semantic processing in real-world narratives and for bridging findings at the single word level with macroscale discourse analyses. (PsycInfo Database Record (c) 2023 APA, all rights reserved).