Recurrent Processes Emulate a Cascade of Hierarchical Decisions - Laura Gwilliams, Jean-Remi King
Mounting evidence suggests that perception depends on a largely-feedforward brain network. However, the discrepancy between (i) the latency of the corresponding feed forward responses (150-200 ms) and (ii) the time it takes human subjects to recognize brief images (often>500 ms) suggests that recurrent neuronal activity is critical to visual processing. Here, we use magneto-encephalography to localize, track and decode the feedforward and recurrent responses elicited by brief presentations of variably-ambiguous letters and digits. We first confirm that these stimuli trigger, within the first 200 ms, a feedforward response in the ventral and dorsal cortical pathways. The subsequent activity is distributed across temporal, parietal and prefrontal cortices and leads to a slow and incremental cascade of representations culminating in action-specific motor signals. We introduce an analytical framework to show that these brain responses are best accounted for by a hierarchy of recurrent neural assemblies. An accumulation of computational delays across specific processing stages explains subjects’ reaction times. Finally, the slow convergence of neural representations towards perceptual categories is quickly followed by all-or-none motor decision signals.Together, these results show how recurrent processes generate, over extended time periods, a cascade of hierarchical decisions that ultimately predicts subjects’ perceptual reports.
Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway - Yalda Mohsenzadeh, Sheng Qin, Radoslaw M Cichy, and Dimitrios Pantazis
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.