Anticipation of teammates and opponents is a critical factor in many sports played in interactive environments. Deceptive actions are used in sports such as basketball to counteract anticipation of an opponent. In this study, we investigated the effects of shot deception on the players' anticipation behaviour in basketball. Thirty one basketball players (15 expert, 16 novice) watched life-sized videos of basketball players performing real shots or shot fakes aimed at the basket. Four different shot outcomes were presented in the video stimuli: a head fake, a ball fake, a high shot fake, and a genuine shot. The videos were temporally occluded at three different time points (-160 ms, -80 ms, 0 ms to ball release) during a shooting motion. The participants had to perform a basketball-related response action to either shots or shot fakes. Response accuracy, response time, and decision confidence were recorded along with gaze behaviour. Anticipation accuracy was reduced at later occlusion points for fake shooting actions. For expert athletes, this effect occurred at later occlusion points compared to novices. The gaze analysis of successful and unsuccessful shot anticipations revealed more gaze fixations towards the hip and legs in successful anticipations, whereas more fixations towards the ball and the head were found in shots unsuccessfully anticipated. It is proposed that hip and leg regions may contain causal information concerning the vertical trajectory of the shooter and identifying this information may be important for perceiving genuine and deceptive shots in basketball.
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http://dx.doi.org/10.1016/j.humov.2022.102975 | DOI Listing |
J Diabetes Sci Technol
January 2025
Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: Diet interventions often have poor adherence due to burdensome food logging. Approaches using photographs assessed by artificial intelligence (AI) may make food logging easier, if they are adequately accurate.
Method: We used OpenAI's GPT-4o model with one-shot prompts and no fine-tuning to assess energy, fat, protein, carbohydrate, fiber, and salt through photographs of 22 meals, comparing assessments to weighed food records for each meal and to assessments of dieticians.
Magn Reson Med
January 2025
National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland, USA.
Purpose: Two-shot γ-aminobutyric acid (GABA) difference editing techniques have been used widely to detect the GABA H4 resonance at 3.01 ppm. Here, we introduce a single-shot method for detecting the full GABA H2 resonance signal, which avoids contamination from the coedited M macromolecules.
View Article and Find Full Text PDFNat Comput Sci
January 2025
Key Lab of Fabrication Technologies for Integrated Circuits and Key Laboratory of Microelectronic Devices and Integrated Technology, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China.
The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Observational Health Data Science and Informatics, New York, NY, USA.
Using administrative claims and electronic health records for observational studies is common but challenging due to data limitations. Researchers rely on phenotype algorithms, requiring labor-intensive chart reviews for validation. This study investigates whether case adjudication using the previously introduced Knowledge-Enhanced Electronic Profile Review (KEEPER) system with large language models (LLMs) is feasible and could serve as a viable alternative to manual chart review.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Computer Vision and Image Processing Lab., UofL, Louisville, KY, 40292, USA.
Purpose: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings.
Methods: The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e.
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