Frontiers in NeuroscienceNeurosciencePeer-reviewed journal article
Decoding neural responses to motion-in-depth using EEG
Marc M. HimmelbergiD, Federico Gabriele SegalaiD, Ryan T. MaloneyiD, Julie M. HarrisiD, Alex R. WadeiD
Abstract
Two stereoscopic cues that underlie the perception of motion-in-depth (MID) are changes in retinal disparity over time (CD) and interocular velocity differences (IOVD). These cues have independent spatiotemporal sensitivity profiles, depend upon different low-level stimulus properties, and are potentially processed along separate cortical pathways. Here, we ask whether these MID cues code for different motion directions: do they give rise to discriminable patterns of neural signals, and is there evidence for their convergence onto a single motion-in-depth pathway? To answer this, we use a decoding algorithm to test whether, and when, patterns of electroencephalogram (EEG) signals measured from across the full scalp, generated in response to CD- and IOVD-isolating stimuli moving towards or away in depth can be distinguished. We find that both MID cue type and 3D-motion direction can be decoded at different points in the EEG timecourse and that direction decoding cannot be accounted for by static disparity information. Remarkably, we find evidence for late processing convergence: IOVD motion direction can be decoded relatively late in the timecourse based on a decoder trained on CD stimuli, and vice versa. We conclude that early CD and IOVD direction decoding performance is dependent upon fundamentally different low-level stimulus features, but that later stages of decoding performance may be driven by a central, shared pathway that is agnostic to these features. Overall, these data are the first to show that neural responses to CD and IOVD cues that move towards and away in depth can be decoded from EEG signals, and that different aspects of MID-cues contribute to decoding performance at different points along the EEG timecourse.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 11
- FWCI
- 0.27
- Field percentile
- top 46%
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