Comparison of single-word and adjective-noun phrase production using event-related brain potentials

Production of nouns and adjectives of children with cochlear implants and of children with typical hearing

This analytical cross-sectional study aimed to investigate the production of nouns and adjectives in 62 children between the ages 5 and 7, with 31 children having Cochlear Implants (CIs) and 31 children having Typical Hearing (TH). The study compaired their performance in a picture naming test of nouns and adjectives. Poisson regression models were fitted to compare the responses of both groups of children, and intra-subject differences between responses to the noun and adjective naming tasks were also analyzed. The results showed that both groups of children produced the same number of non-responses of nouns and of adjectives and a higher number of correct productions of nouns than of adjectives. However, children with CIs produced more errors when naming adjectives than when naming nouns, while this difference is not observed in children with TH. The comparative analysis between both groups of children indicates that children with CIs produced a higher proportion of non-responses when naming nouns, but the same proportion as children with TH when naming adjectives. Children with CIs also produced fewer correct nouns and adjectives and more errors than children with TH. Vocabulary expansion and repair of production errors in children with CIs should be targeted by speech-language pathologists in intervention programs.

How children learn to understand language meanings: a neural model of adult-child multimodal interactions in real-time

This article describes a biological neural network model that can be used to explain how children learn to understand language meanings about the perceptual and affective events that they consciously experience. This kind of learning often occurs when a child interacts with an adult teacher to learn language meanings about events that they experience together. Multiple types of self-organizing brain processes are involved in learning language meanings, including processes that control conscious visual perception, joint attention, object learning and conscious recognition, cognitive working memory, cognitive planning, emotion, cognitive-emotional interactions, volition, and goal-oriented actions. The article shows how all of these brain processes interact to enable the learning of language meanings to occur. The article also contrasts these human capabilities with AI models such as ChatGPT. The current model is called the ChatSOME model, where SOME abbreviates Self-Organizing MEaning.

Noun-phrase production as a window to language selection: An ERP study

Characterising the time course of non-native language production is critical in understanding the mechanisms behind successful communication. Yet, little is known about the modulating role of cross-linguistic influence (CLI) on the temporal unfolding of non-native production and the locus of target language selection. In this study, we explored CLI effects on non-native noun phrase production with behavioural and neural methods. We were particularly interested in the modulation of the P300 as an index for inhibitory control, and the N400 as an index for co-activation and CLI. German late learners of Spanish overtly named pictures while their EEG was monitored. Our results indicate traceable CLI effects at the behavioural and neural level in both early and late production stages. This suggests that speakers faced competition between the target and non-target language until advanced production stages. Our findings add important behavioural and neural evidence to the underpinnings of non-native production processes, in particular for late learners.

Behavioral and Brain Responses Highlight the Role of Usage in the Preparation of Multiword Utterances for Production

Usage-based theories assume that all aspects of language processing are shaped by the distributional properties of the language. The frequency not only of words but also of larger chunks plays a major role in language processing. These theories predict that the frequency of phrases influences the time needed to prepare these phrases for production and their acoustic duration. By contrast, dominant psycholinguistic models of utterance production predict no such effects. In these models, the system keeps track of the frequency of individual words but not of co-occurrences. This study investigates the extent to which the frequency of phrases impacts naming latencies and acoustic duration with a balanced design, where the same words are recombined to build high- and low-frequency phrases. The brain signal of participants is recorded so as to obtain information on the electrophysiological bases and functional locus of frequency effects. Forty-seven participants named pictures using high- and low-frequency adjective-noun phrases. Naming latencies were shorter for high-frequency than low-frequency phrases. There was no evidence that phrase frequency impacted acoustic duration. The electrophysiological signal differed between high- and low-frequency phrases in time windows that do not overlap with conceptualization or articulation processes. These findings suggest that phrase frequency influences the preparation of phrases for production, irrespective of the lexical properties of the constituents, and that this effect originates at least partly when speakers access and encode linguistic representations. Moreover, this study provides information on how the brain signal recorded during the preparation of utterances changes with the frequency of word combinations.

How Do We Segment Text? Two-Stage Chunking Operation in Reading

Chunking in language comprehension is a process that segments continuous linguistic input into smaller chunks that are in the reader's mental lexicon. Effective chunking during reading facilitates disambiguation and enhances efficiency for comprehension. However, the chunking mechanisms remain elusive, especially in reading, given that information arrives simultaneously yet the written systems may not have explicit cues for labeling boundaries such as Chinese. What are the mechanisms of chunking that mediates the reading of the text that contains hierarchical information? We investigated this question by manipulating the lexical status of the chunks at distinct levels in four-character Chinese strings, including the two-character local chunk and four-character global chunk. Male and female human participants were asked to make lexical decisions on these strings in a behavioral experiment, followed by a passive reading task when their electroencephalography (EEG) was recorded. The behavioral results showed that the lexical decision time of lexicalized two-character local chunks was influenced by the lexical status of the four-character global chunk, but not vice versa, which indicated the processing of global chunks possessed priority over the local chunks. The EEG results revealed that familiar lexical chunks were detected simultaneously at both levels and further processed in a different temporal order, the onset of lexical access for the global chunks was earlier than that of local chunks. These consistent results suggest a two-stage operation for chunking in reading, the simultaneous detection of familiar lexical chunks at multiple levels around 100 ms followed by recognition of chunks with global precedence.

Anterior and Posterior Left Inferior Frontal Gyrus Contribute to the Implementation of Grammatical Determiners During Language Production

The left inferior frontal gyrus (IFG) is a key region for language comprehension and production. Previous studies point to a preferential involvement of left anterior IFG (aIFG) in lexical and semantic processes, while the posterior IFG (pIFG) has been implicated in supporting syntactic and phonological processes. Here we used focal neuronavigated transcranial magnetic stimulation (TMS) to probe the functional involvement of left IFG in lexical and grammatical processing at the sentence level. We applied 10 Hz TMS effective or sham bursts to left aIFG and pIFG, while healthy volunteers performed an adjective-noun production task contrasting grammatical and lexical determiners. For each trial, we measured the time from the stimulus onset to the moment of articulation (response time) and the time from articulation onset to the end of articulation (duration). Focal TMS of IFG generally delayed response times. The TMS-induced delay in response times was relatively stronger for the grammatical condition compared to the lexical condition, when TMS targeted aIFG. Articulation of the determiner was generally shorter in trials presenting grammatical determiners relative to lexical determiners. The shorter articulation time for grammar determiners was facilitated by effective TMS to pIFG. Together, the effects of TMS on task performance provide novel evidence for a joint involvement of anterior and posterior parts of left IFG in implementing grammatical determiners during language production, suggesting an involvement of aIFG in the initiation and pIFG in the production of grammatically appropriate verbal responses at the sentence level.

Group-Level Multivariate Analysis in EasyEEG Toolbox: Examining the Temporal Dynamics Using Topographic Responses

Electroencephalography (EEG) provides high temporal resolution cognitive information from non-invasive recordings. However, one of the common practices–using a subset of sensors in ERP analysis is hard to provide a holistic and precise dynamic results. Selecting or grouping subsets of sensors may also be subject to selection bias, multiple comparison, and further complicated by individual differences in the group-level analysis. More importantly, changes in neural generators and variations in response magnitude from the same neural sources are difficult to separate, which limit the capacity of testing different aspects of cognitive hypotheses. We introduce EasyEEG, a toolbox that includes several multivariate analysis methods to directly test cognitive hypotheses based on topographic responses that include data from all sensors. These multivariate methods can investigate effects in the dimensions of response magnitude and topographic patterns separately using data in the sensor space, therefore enable assessing neural response dynamics. The concise workflow and the modular design provide user-friendly and programmer-friendly features. Users of all levels can benefit from the open-sourced, free EasyEEG to obtain a straightforward solution for efficient processing of EEG data and a complete pipeline from raw data to final results for publication.