A common finding in time psychophysics is that temporal acuity is much better for auditory than for visual stimuli. The present study aimed to examine modality-specific differences in duration discrimination within the conceptual framework of the Distinct Timing Hypothesis. This theoretical account proposes that durations in the lower milliseconds range are processed automatically while longer durations are processed by a cognitive mechanism. A sample of 46 participants performed two auditory and visual duration discrimination tasks with extremely brief (50-ms standard duration) and longer (1000-ms standard duration) intervals. Better discrimination performance for auditory compared to visual intervals could be established for extremely brief and longer intervals. However, when performance on duration discrimination of longer intervals in the 1-s range was controlled for modality-specific input from the sensory-automatic timing mechanism, the visual-auditory difference disappeared completely as indicated by virtually identical Weber fractions for both sensory modalities. These findings support the idea of a sensory-automatic mechanism underlying the observed visual-auditory differences in duration discrimination of extremely brief intervals in the millisecond range and longer intervals in the 1-s range. Our data are consistent with the notion of a gradual transition from a purely modality-specific, sensory-automatic to a more cognitive, amodal timing mechanism. Within this transition zone, both mechanisms appear to operate simultaneously but the influence of the sensory-automatic timing mechanism is expected to continuously decrease with increasing interval duration.
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http://dx.doi.org/10.3389/fpsyg.2015.01626 | DOI Listing |
Discov Med
January 2025
Department of Urology, The Affiliated Hefei Hospital of Anhui Medical University (The Second People's Hospital of Hefei), 230011 Hefei, Anhui, China.
Background: Diabetes mellitus is a common metabolic disorder, and diabetic erectile dysfunction (DMED) is one of its common complications. The differentiation of the types of erectile dysfunction (ED) is fundamental to treatment, yet there is a lack of simple and efficacious tools for this purpose in clinical practice. In this study, we endeavor to predict ED types using commonly available clinical data from diabetic patients, aiming to develop and assess a risk prediction model for organic erectile dysfunction in individuals with type 2 diabetes.
View Article and Find Full Text PDFSci Rep
January 2025
The Department of Cellular and Integrative Physiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
Fragile X syndrome (FXS) is a neurodevelopmental disorder oftentimes associated with abnormal social behaviors and altered sensory responsiveness. It is hypothesized that the inappropriate filtering of sensory stimuli, including olfaction, can lead to aberrant social behavior in FXS. However, previous studies investigating olfaction in animal models of FXS have shown inconsistent results.
View Article and Find Full Text PDFJ Vasc Interv Radiol
January 2025
Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA; Department of Biomedical Engineering, The George Washington University, Washington, DC 20052, USA. Electronic address:
Purpose: To determine the technical feasibility of discriminating discontiguous from contiguous ablation zones between a pair of microwave ablation (MWA) applicators using broadband microwave transmission signal measurements in an in vivo porcine liver model.
Methods: Dual applicator 2.45GHz MWA was performed using one directional and one omnidirectional applicator, spaced 3cm apart, under imaging guidance.
Brain Topogr
January 2025
Aging and Neuroscience Laboratory (LABEN), Federal University of Paraíba, João Pessoa, PB, Brazil.
Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke. Thus, the aim of the study was to identify biomarkers to discriminate stroke patients from healthy individuals based on EEG-MS and clinical features using a machine learning approach. Fifty-four participants (27 stroke patients and 27 healthy age and sex-matched controls) were recruited.
View Article and Find Full Text PDFSurg Endosc
January 2025
Department of Gastroenterology, Lanzhou University Second Hospital, Lanzhou, Gansu Province, China.
Background: Endoscopic diagnosis of early gastric cancer (EGC) is a challenge. It is not clear whether deep convolutional neural network (DCNN) model could improve the endoscopists' diagnostic performance.
Methods: We established a DCNN-assisted system and found that accuracy of diagnosis is higher than endoscopists.
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