How listeners weight a wide variety of information to interpret ambiguities in the speech signal is a question of interest in speech perception, particularly when understanding how listeners process speech in the context of phrases or sentences. Dominant views of cue use for language comprehension posit that listeners integrate multiple sources of information to interpret ambiguities in the speech signal. Here, we study how semantic context, sentence rate, and vowel length all influence identification of word-final stops. We find that while at the group level all sources of information appear to influence how listeners interpret ambiguities in speech, at the level of the individual listener, we observe systematic differences in cue reliance, such that some individual listeners favor certain cues (e.g., speech rate and vowel length) to the exclusion of others (e.g., semantic context). While listeners exhibit a range of cue preferences, across participants we find a negative relationship between individuals' weighting of semantic and acoustic-phonetic (sentence rate, vowel length) cues. Additionally, we find that these weightings are stable within individuals over a period of 1 month. Taken as a whole, these findings suggest that theories of cue integration and speech processing may fail to capture the rich individual differences that exist between listeners, which could arise due to mechanistic differences between individuals in speech perception.
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http://dx.doi.org/10.3758/s13414-024-02889-4 | DOI Listing |
Bioengineering (Basel)
December 2024
Software College, Northeastern University, Shenyang 110819, China.
Cervical cancer is one of the most prevalent cancers among women, posing a significant threat to their health. Early screening can detect cervical precancerous lesions in a timely manner, thereby enabling the prevention or treatment of the disease. The use of pathological image analysis technology to automatically interpret cells in pathological slices is a hot topic in digital medicine research, as it can reduce the substantial effort required from pathologists to identify cells and can improve diagnostic efficiency and accuracy.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Institute of Computer Science, University of Rostock, 18051, Rostock, Germany.
Background: Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous fashion.
Results: We introduce the novel Joint Embedding Learning-classifier for improved Interpretability (JELI).
Mediterr J Hematol Infect Dis
January 2025
Hematology, Department of Translational and Precision Medicine, Sapienza University of Rome, Italy.
Background: Clonal mature B-cell lymphoproliferative disorders (B-LPDs) are a heterogeneous group of neoplasia characterized by the proliferation of mature B lymphocytes in the peripheral blood, bone marrow and/or lymphoid tissues. B-LPDs classification into different subtypes and their diagnosis is based on a multiparametric approach. However, accurate diagnosis may be challenging, especially in cases of ambiguous interpretation.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Université Paris Cité, Université Sorbonne Paris Nord, INSERM, INRAE, Centre for Research in Epidemiology and StatisticS (CRESS), Paris, France.
While machine learning (ML)-based solutions-often referred to as artificial intelligence (AI) solutions-have demonstrated comparable or superior performance to human experts across various healthcare applications, their vulnerability to perturbations and stability to variations due to new environments-essentially, their robustness-remains ambiguous and often overlooked. In this review, we aimed to identify the types of robustness addressed in the literature for ML models in healthcare. A total of 274 eligible records were retrieved from PubMed, Web of Science, IEEE Xplore, and additional sources.
View Article and Find Full Text PDFJ Contin Educ Health Prof
January 2025
Dr. Fernandez: Associate Professor, Department of Family Medicine and Emergency Medicine, Faculty of Medicine Université de Montréal, Centre-ville Montréal, Québec, Canada.
Introduction: Health care providers (HCPs) use reflection to intervene in complex, ambiguous clinical situations. Yet, there is scant evidence about the circumstances when HCPs use reflection and how they perceive reflection within their continuing professional development. We selected a narrative inquiry approach to study how HCPs perceive reflection's role in learning in four health professions.
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