Radial frequency (RF) motion trajectories are visual stimuli that consist of a difference of Gaussians moving along a closed trajectory defined by a sinusoidal variation of the radius relative to a circular path. In the current study, multivoxel fMRI analyses demonstrated that spatial patterns of activity in visual regions V2, V3, and MT can predict RF motion trajectory shape regardless of whether an observer can behaviorally identify the shape or not. This result suggests that processing in these regions is concerned with local properties of the trajectories and not directly linked with a conscious percept of global trajectory shape. Whole-brain analyses show that RF motion trajectories also evoke premotor and posterior parietal cortical activity that may be a neural correlate of shape recognizability. Further, comparisons with activity evoked by static versions of the RF shapes reveal cue-invariant processing in regions of the posterior parietal and occipitotemporal cortices. Interestingly, the RF motion trajectories evoke patterns of dorsal visual stream cortical activity typical of visually guided movement preparation or action observation, suggesting that these stimuli may be processed as potential motor actions rather than as purely visual experiences.
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http://dx.doi.org/10.1167/14.1.11 | DOI Listing |
Magn Reson Med
January 2025
Department of Radiology, Stanford University, Stanford, California, USA.
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Phys Med Biol
January 2025
Tianjin University, Centre for advanced Mechanisms and Robotics, Tianjin University, 135 Yaguan Road, Jinnan District, Tianjin, China., Tianjin, 300072, CHINA.
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View Article and Find Full Text PDFJ Neurol
January 2025
Centre for Vestibular Neurology (CVeN), Department of Brain Sciences, Charing Cross Hospital, Imperial College London, London, W6 8RF, UK.
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View Article and Find Full Text PDFWearable Technol
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Department of Kinesiology, Iowa State University, Ames, IA, USA.
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View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
The State Key Laboratory of Digital Medical Engineering, Jiangsu Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Object handover is a fundamental task for collaborative robots, particularly service robots. In in-home assistance scenarios, individuals often face constraints due to their posture and declining physical functions, necessitating high demands on robots for flexible real-time control and intuitive interactions. During robot-to-human handovers, individuals are limited to making perceptual judgements based on the appearance of the object and the consistent behaviour of the robot.
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