Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway
Abstract
Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions.
Data availability
All data generated or analysed during this study to support the main findings are included in the manuscript and supporting files. Source data files have been provided for Figures 2, 3 and 5.
Article and author information
Author details
Funding
McGovern Institute (Neurotechnology Program)
- Dimitrios Pantazis
Emmy Noether Award (CI241/1-1)
- Radoslaw M Cichy
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The study was approved by the Institutional Review Board of the Massachusetts Institute of Technology and followed the principles of the Declaration of Helsinki. All subjects signed an informed consent form and were compensated for their participation.
Copyright
© 2018, Mohsenzadeh et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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