Out-of-synchrony experiences can easily recalibrate one's subjective simultaneity point in the direction of the experienced asynchrony. Although temporal adjustment of multiple audiovisual stimuli has been recently demonstrated to be spatially specific, perceptual grouping processes that organize separate audiovisual stimuli into distinctive "objects" may play a more important role in forming the basis for subsequent multiple temporal recalibrations. We investigated whether apparent physical differences between audiovisual pairs that make them distinct from each other can independently drive multiple concurrent temporal recalibrations regardless of spatial overlap. Experiment 1 verified that reducing the physical difference between two audiovisual pairs diminishes the multiple temporal recalibrations by exposing observers to two utterances with opposing temporal relationships spoken by one single speaker rather than two distinct speakers at the same location. Experiment 2 found that increasing the physical difference between two stimuli pairs can promote multiple temporal recalibrations by complicating their non-temporal dimensions (e.g., disks composed of two rather than one attribute and tones generated by multiplying two frequencies); however, these recalibration aftereffects were subtle. Experiment 3 further revealed that making the two audiovisual pairs differ in temporal structures (one transient and one gradual) was sufficient to drive concurrent temporal recalibration. These results confirm that the more audiovisual pairs physically differ, especially in temporal profile, the more likely multiple temporal perception adjustments will be content-constrained regardless of spatial overlap. These results indicate that multiple temporal recalibrations are based secondarily on the outcome of perceptual grouping processes.
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http://dx.doi.org/10.3758/s13414-015-0856-y | DOI Listing |
Conscious Cogn
December 2024
Department of Business and Marketing, Faculty of Commerce, Kyushu Sangyo University, 3-1 Matsukadai 2-Chome, Higashi-ku, Fukuoka 813-8503, Japan. Electronic address:
Biosensors (Basel)
December 2024
Optoelectronics and Measurement Techniques Research Unit, University of Oulu, 90570 Oulu, Finland.
There is an ongoing search for a reliable and continuous method of noninvasive blood pressure (BP) tracking. In this study, we investigate the feasibility of utilizing seismocardiogram (SCG) signals, i.e.
View Article and Find Full Text PDFMed Image Anal
February 2025
Department of Computer and Data Science and Department of Biomedical Engineering, Case Western Reserve University, USA.
CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal-Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously.
View Article and Find Full Text PDFCross-modal temporal recalibration guarantees stable temporal perception across ever-changing environments. Yet, the mechanisms of cross-modal temporal recalibration remain unknown. Here, we conducted an experiment to measure how participants' temporal perception was affected by exposure to audiovisual stimuli with consistent temporal delays.
View Article and Find Full Text PDFmedRxiv
September 2024
Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA.
Importance: Declining mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children at risk for incurring neurologic morbidity would facilitate heightened surveillance that could lead to timelier clinical detection, earlier interventions, and preserved neurodevelopmental trajectory.
Objective: Develop machine-learning models for identifying acquired neurologic morbidity while hospitalized with critical illness and assess correlation with contemporary serum-based, brain injury-derived biomarkers.
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