Visual segregation of moving objects is a considerable computational challenge when the observer moves through space. Recent psychophysical studies suggest that directionally congruent, moving auditory cues can substantially improve parsing object motion in such settings, but the exact brain mechanisms and visual processing stages that mediate these effects are still incompletely known. Here, we utilized multivariate pattern analyses (MVPA) of MRI-informed magnetoencephalography (MEG) source estimates to examine how crossmodal auditory cues facilitate motion detection during the observer's self-motion. During MEG recordings, participants identified a target object that moved either forward or backward within a visual scene that included nine identically textured objects simulating forward observer translation. Auditory motion cues 1) improved the behavioral accuracy of target localization, 2) significantly modulated the MEG source activity in the areas V2 and human middle temporal complex (hMT+), and 3) increased the accuracy at which the target movement direction could be decoded from hMT+ activity using MVPA. The increase of decoding accuracy by auditory cues in hMT+ was significant also when superior temporal activations in or near auditory cortices were regressed out from the hMT+ source activity to control for source estimation biases caused by point spread. Taken together, these results suggest that parsing object motion from self-motion-induced optic flow in the human extrastriate visual cortex can be facilitated by crossmodal influences from auditory system.
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http://dx.doi.org/10.1016/j.brainres.2021.147489 | DOI Listing |
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Center for Hearing Research, Boys Town National Research Hospital, Omaha, Nebraska, USA.
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January 2025
Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA.
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Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ, United States.
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View Article and Find Full Text PDFSci Rep
January 2025
Department of Psychology, Bar-Ilan University, 5290002, Ramat-Gan, Israel.
Large individual differences can be observed in studies reporting spectral TOJ. In the present study, we aimed to explore these individual differences and explain them by employing Warren and Ackroff (1976) framework of direct identification of components and their order (direct ICO) and holistic pattern recognition (HPR). In Experiment 1, results from 177 participants replicated the large variance in participants' performance and suggested three response patterns, validated using the K-Means clustering algorithm.
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