Subcortical magnocellular visual system facilities object recognition by processing topological property
Wang, W.; Zhou, T.; Zhuo, Y.; Chen, L.; Huang, Y.
Show abstract
The Magnocellular (M) visual pathway is known as a fast route to convey coarse information and facilitates object recognition by initiating top-down processes. It is unclear what exact properties M pathway conveys to accelerate visual object processing. Previous studies suggest that visual systems are highly sensitive to the perception of topological property (TP), which remains unchanged under various shape changes, and the TP is probably processed through a fast subcortical pathway. Here we hypothesize that a subcortical M system contributes to the fast object recognition by processing TP first. We first demonstrate that the facilitation effect of TP processing on object perception occurs mainly in the M visual system, and then support the subcortical M hypothesis of TP processing by the evidence that the early processing of TP was not affected when cortical function was temporarily damaged by the transcranial magnetic stimulation and when stimuli were biased to M system.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Action-based predictions affect visual perception, neural processing, and pupil size, regardless of temporal predictability 95%
- Representation of Color, Form, and their Conjunctionacross the Human Ventral Visual Pathway 95%
- Alpha Oscillations Encode Bayesian Belief Updating Underlying Attentional Allocation in Dynamic Environments 94%
Similar papers in this journal
Similar papers in this journal
- Top-down control of the left visual field bias in cued visual spatial attention 96%
- Neural mechanisms of sequential dependence in time perception: The impact of prior task and memory processing 94%
- Statistical learning of frequent distractor locations in visual search involves regional signal suppression in early visual cortex 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.