Objectives: The present study tested the effects of background speech and nonspeech noise on 5-year-old children's working memory span.
Design: Five-year-old typically developing children (range = 58.6 to 67.6 months; n = 94) completed a modified version of the Missing Scan Task, a missing-item working memory task, in quiet and in the presence of two types of background noise: male two-talker speech and speech-shaped noise. The two types of background noise had similar spectral composition and overall intensity characteristics but differed in whether they contained verbal content. In Experiments 1 and 2, children's memory span (i.e., the largest set size of items children successfully recalled) was subjected to analyses of variance designed to look for an effect of listening condition (within-subjects factor: quiet, background noise) and an effect of background noise type (between-subjects factor: two-talker speech, speech-shaped noise).
Results: In Experiment 1, children's memory span declined in the presence of two-talker speech but not in the presence of speech-shaped noise. This result was replicated in Experiment 2 after accounting for a potential effect of proactive interference due to repeated administration of the Missing Scan Task.
Conclusions: Background speech, but not speech-shaped noise, disrupted working memory span in 5-year-old children. These results support the idea that background speech engages domain-general cognitive processes used during the recall of known objects in a way that speech-shaped noise does not.
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http://dx.doi.org/10.1097/AUD.0000000000000636 | DOI Listing |
J Neurol
January 2025
Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Background: Longitudinal qualitative data on what matters to people with Parkinson's disease are lacking and needed to guide patient-centered clinical care and development of outcome measures.
Objective: To evaluate change over time in symptoms, impacts, and relevance of digital measures to monitor disease progression in early Parkinson's.
Methods: In-depth, online symptom mapping interviews were conducted with 33 people with early Parkinson's at baseline and 1 year later to evaluate (A) symptoms, (B) impacts, and (C) relevance of digital measures to monitor personally relevant symptoms.
J Neurol
January 2025
Western Institute of Neuroscience, Western University, London, Canada.
Background: Repeat neurological assessment is standard in cases of severe acute brain injury. However, conventional measures rely on overt behavior. Unfortunately, behavioral responses may be difficult or impossible for some patients.
View Article and Find Full Text PDFJ 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.
J Neurol
January 2025
Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028, Lisbon, Portugal.
Background: Drooling, defined as the unintentional loss of saliva from the anterior oral cavity, remains poorly understood in terms of the underlying clinical factors in people with Parkinson's disease (PwP). This study aims to clarify these factors by analyzing predictors and secondarily the correlates with the severity of drooling in PwP.
Methods: We conducted a cross-sectional study involving 42 PwP with drooling and 59 without drooling.
Ophthalmologie
January 2025
Augenklinik Sulzbach, Knappschaftsklinikum Saar, An der Klinik 10, 66280, Sulzbach/Saar, Deutschland.
Background: The increasing bureaucratic burden in everyday clinical practice impairs doctor-patient communication (DPC). Effective use of digital technologies, such as automated semantic speech recognition (ASR) with automated extraction of diagnostically relevant information can provide a solution.
Objective: The aim was to determine the extent to which ASR in conjunction with semantic information extraction for automated documentation of the doctor-patient dialogue (ADAPI) can be integrated into everyday clinical practice using the IVI routine as an example and whether patient care can be improved through process optimization.
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