Back

In the brain of the beholder: whole brain dynamics shape the perception during ambiguous motion

Pizzuti, A.; Acero Pousa, I.; Gulban, O. F.; Peters, J.; Goebel, R. W.

2025-02-06 neuroscience
10.1101/2025.02.05.636650 bioRxiv
Show abstract

Visual perception is typically based on a one-to-one mapping between stimuli and conscious experiences. However, under bistable conditions, identical sensory inputs can elicit alternating perceptions, requiring the brain to resolve ambiguity. The mechanisms underlying transitions between distinct perceptual states and their sustained maintenance remain poorly understood. In this ultra-high-field (7T) fMRI study, we investigated the neural dynamics of perception using a bistable motion stimulus (ambiguous motion quartet) that evoked endogenous alternations between horizontal and vertical motion, compared to a control condition (physical motion quartet) with unambiguous sensory input. Consistent with previous findings, the human motion complex (hMT+) played a central role in processing both physical and ambiguous motion conditions. By dissociating neural activity during perceptual transitions from sustained perceptions, we found that hMT+ mostly interacts dynamically with area 46 in the frontal cortex and PF/PFm within the inferior parietal lobe during transitions and with subregions of the superior parietal lobe during sustained perceptions. Beyond local activity, computational modeling revealed an increase in hierarchical organization across cortical networks during the ambiguous condition. In particular, the same frontal and parietal regions exhibited ascension within the functional hierarchy, likely reflecting their specific role in coordinating computations for resolving ambiguity.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.