Purpose: Two new treatments, 1 based on sentence to picture matching (SPM) and the other on object manipulation (OM), that train participants on the thematic roles of sentences using pictures or by manipulating objects were piloted.
Method: Using a single-subject multiple-baseline design, sentence comprehension was trained on the affected sentence type in 1 task-related protocol in 15 participants with aphasia. The 2 tasks were SPM and OM; the treatment stimuli were object relatives, object clefts, passives, and unaccusatives, as well as two control structures-object relatives with a complex noun phrase (NP) and active sentences with three NPs.
Results: The criteria for efficacious treatment was an increase in the level of performance from the pretreatment probes to the posttreatment probes for the treated structure such that accuracy rose from at or below chance to above chance and either (a) accuracy rose by 33% or (b) the effect size was 2.6. Based on these criteria, the success rate for training the target structure was 2/6 participants in the SPM condition and 4/7 participants in the OM condition.
Conclusion: The outcome of this study illustrates the utility of this theoretically motivated and efficacious treatment for sentence comprehension deficits in individuals with aphasia.
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http://dx.doi.org/10.1044/1058-0360(2012/11-0106) | DOI Listing |
PeerJ
January 2025
School of Psychology, Liaoning Normal University, Dalian, Liaoning Province, China.
Objective: This study aimed to examine the impact of social presence on Chinese reading comprehension and associated neural responses.
Methods: Participants tasked with reading Chinese sentences either alone or in the presence of others and subsequently assessing the accuracy of the sentences' meanings. Concurrently, we recorded the participants' electrical brain responses to critical word processing.
Data Brief
February 2025
ADA University, Baku, Azerbaijan.
Advancements in sign language processing technology hinge on the availability of extensive, reliable datasets, comprehensive instructions, and adherence to ethical guidelines. To facilitate progress in gesture recognition and translation systems and to support the Azerbaijani sign language community we present the Azerbaijani Sign Language Dataset (AzSLD). This comprehensive dataset was collected from a diverse group of sign language users, encompassing a range of linguistic parameters.
View Article and Find Full Text PDFData Brief
February 2025
Tashkent institute of textile and light industry, 5, Shoxdjaxon str., Tashkent city 100100, Uzbekistan.
In this study, the authors presented a dataset for named entity recognition in the Uzbek language. The dataset consists of 2000 sentences and 25,865 words, and the sources were legal documents and hand-crafted sentences annotated using the BIOES scheme. The study is complemented by the fact that the authors demonstrated the applications of the created dataset by training a language model using the CNN + LSTM architecture, which achieves high accuracy in NER tasks, with an F1 score of 90.
View Article and Find Full Text PDFJ Neurosci
January 2025
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742
When we listen to speech, our brain's neurophysiological responses "track" its acoustic features, but it is less well understood how these auditory responses are enhanced by linguistic content. Here, we recorded magnetoencephalography (MEG) responses while subjects of both sexes listened to four types of continuous-speech-like passages: speech-envelope modulated noise, English-like non-words, scrambled words, and a narrative passage. Temporal response function (TRF) analysis provides strong neural evidence for the emergent features of speech processing in cortex, from acoustics to higher-level linguistics, as incremental steps in neural speech processing.
View Article and Find Full Text PDFDev Sci
March 2025
Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria.
Newborns are able to neurally discriminate between speech and nonspeech right after birth. To date it remains unknown whether this early speech discrimination and the underlying neural language network is associated with later language development. Preterm-born children are an interesting cohort to investigate this relationship, as previous studies have shown that preterm-born neonates exhibit alterations of speech processing and have a greater risk of later language deficits.
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