This study examined for the first time the impact of the presence of a phonological neighbour on word recognition when the target word and its neighbour co-occur in a spoken sentence. To do so, we developed a new task, the verb detection task, in which participants were instructed to respond as soon as they detected a verb in a sequence of words, thus allowing us to probe spoken word recognition processes in real time. We found that participants were faster at detecting a verb when it was phonologically related to the preceding noun than when it was phonologically unrelated. This effect was found with both correct sentences (Experiment 1) and with ungrammatical sequences of words (Experiment 2). The effect was also found in Experiment 3 where adjacent phonologically related words were included in the non-verb condition (i.e., word sequences not containing a verb), thus ruling out any strategic influences. These results suggest that activation persists across different words during spoken sentence processing such that processing of a word at position 1 benefits from the sublexical phonology activated during processing of the word at position . We discuss how different models of spoken word recognition might be able (or not) to account for these findings.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1177/17470218231196823 | DOI Listing |
Appl Neuropsychol Adult
January 2025
University Department of Neurology, Sestre Milosrdnice University Hospital Center, Zagreb, Croatia.
Unlabelled: Greater empirical and scientific attention is still put on patients with left brain hemisphere (LBH) damage where language impairments are common and expected. In patients with RBH damage, language assessment is therefore rarely done in the acute phase of stroke recovery.
Purpose: To investigate language impairments in the acute phase of stroke using a Croatian standardized language battery for the first time and compare patients with RBH stroke, LBH stroke and healthy individuals.
J Neurol
January 2025
Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Praha 6, 16000, Prague, Czech Republic.
Background And Objectives: Patients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson's disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD.
Methods: Spontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing.
In the vibrant linguistic landscape of Bengali, spoken by millions in Bangladesh and India, the gap between saintly and common terms is culturally and computationally significant. Recognising this, we introduce BanglaBlend, a pioneering dataset created to capture these stylistic distinctions. BanglaBlend comes with 7350 annotated sentences, 3675 in saintly form and 3675 in common form, covering a crucial need in natural language processing (NLP) resources for Bangla.
View Article and Find Full Text PDFJ Autism Dev Disord
January 2025
School of Foreign Languages and Cultures, Chongqing University, Chongqing, China.
The present study aims to fill the research gap by evaluating published empirical studies and answering the specific research question: Can individuals with autism spectrum disorder (ASD) predict upcoming linguistic information during real-time language comprehension? Following the PRISMA framework, an initial search via PubMed, Web of Science, SCOPUS, and Google Scholar yielded a total of 697 records. After screening the abstract and full text, 10 studies, covering 350 children and adolescents with ASD ranging from 2 to 15 years old, were included for analysis. We found that individuals with ASD may predict the upcoming linguistic information by using verb semantics but not pragmatic prosody during language comprehension.
View Article and Find Full Text PDFFront Psychol
December 2024
Department for General Psychology and Cognitive Neuroscience, Institute of Psychology, Friedrich Schiller University, Jena, Germany.
Introduction: Research has shown that women's vocal characteristics change during the menstrual cycle. Further, evidence suggests that individuals alter their voices depending on the context, such as when speaking to a highly attractive person, or a person with a different social status. The present study aimed at investigating the degree to which women's voices change depending on the vocal characteristics of the interaction partner, and how any such changes are modulated by the woman's current menstrual cycle phase.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!