Subha Nawer Pushpita
@pushpita1729.bsky.social
Cognitive Computational Neuroscientist in Training
Neuro + ML PhD @ CMU'29 | BS in Math and in CS @ MIT' 23 & MEng' 24
Neuro + ML PhD @ CMU'29 | BS in Math and in CS @ MIT' 23 & MEng' 24
9/n
The coexistence of these systems points to a cortical architecture that flexibly reweights sensory inputs while maintaining balanced multimodal representations, supporting robust comprehension of complex natural events.
The coexistence of these systems points to a cortical architecture that flexibly reweights sensory inputs while maintaining balanced multimodal representations, supporting robust comprehension of complex natural events.
November 7, 2025 at 4:16 PM
9/n
The coexistence of these systems points to a cortical architecture that flexibly reweights sensory inputs while maintaining balanced multimodal representations, supporting robust comprehension of complex natural events.
The coexistence of these systems points to a cortical architecture that flexibly reweights sensory inputs while maintaining balanced multimodal representations, supporting robust comprehension of complex natural events.
8/n
Notably, our multimodal transformer independently rediscovered the brain’s modality organization, assigning higher visual weight to occipital regions, higher auditory weight to temporal areas, variable modality weight to switching areas, and balanced weight to jointly predicted areas.
Notably, our multimodal transformer independently rediscovered the brain’s modality organization, assigning higher visual weight to occipital regions, higher auditory weight to temporal areas, variable modality weight to switching areas, and balanced weight to jointly predicted areas.
November 7, 2025 at 4:15 PM
8/n
Notably, our multimodal transformer independently rediscovered the brain’s modality organization, assigning higher visual weight to occipital regions, higher auditory weight to temporal areas, variable modality weight to switching areas, and balanced weight to jointly predicted areas.
Notably, our multimodal transformer independently rediscovered the brain’s modality organization, assigning higher visual weight to occipital regions, higher auditory weight to temporal areas, variable modality weight to switching areas, and balanced weight to jointly predicted areas.
7/n
The circular belts of regions that alternate their dominant sensory modality over time(shades of red below) , and the axis of regions along the occipito-temporal lobe that jointly track both modalities(shades of blue below), together form a bow-and-arrow-like pattern.
The circular belts of regions that alternate their dominant sensory modality over time(shades of red below) , and the axis of regions along the occipito-temporal lobe that jointly track both modalities(shades of blue below), together form a bow-and-arrow-like pattern.
November 7, 2025 at 4:13 PM
7/n
The circular belts of regions that alternate their dominant sensory modality over time(shades of red below) , and the axis of regions along the occipito-temporal lobe that jointly track both modalities(shades of blue below), together form a bow-and-arrow-like pattern.
The circular belts of regions that alternate their dominant sensory modality over time(shades of red below) , and the axis of regions along the occipito-temporal lobe that jointly track both modalities(shades of blue below), together form a bow-and-arrow-like pattern.
6/n
PIP reveals an axis of regions that are jointly predicted more often than other cortical regions - they extend from the lateral occipital cortex to the temporal cortex
PIP reveals an axis of regions that are jointly predicted more often than other cortical regions - they extend from the lateral occipital cortex to the temporal cortex
November 7, 2025 at 4:10 PM
6/n
PIP reveals an axis of regions that are jointly predicted more often than other cortical regions - they extend from the lateral occipital cortex to the temporal cortex
PIP reveals an axis of regions that are jointly predicted more often than other cortical regions - they extend from the lateral occipital cortex to the temporal cortex
5/n
Analogously, the Performance Indication Paradigm (PIP) captures periods of statistically reliable performance by both modalities. This allows us to identify regions that are well predicted by both modalities for a substantial number of TRs.
Analogously, the Performance Indication Paradigm (PIP) captures periods of statistically reliable performance by both modalities. This allows us to identify regions that are well predicted by both modalities for a substantial number of TRs.
November 7, 2025 at 4:09 PM
5/n
Analogously, the Performance Indication Paradigm (PIP) captures periods of statistically reliable performance by both modalities. This allows us to identify regions that are well predicted by both modalities for a substantial number of TRs.
Analogously, the Performance Indication Paradigm (PIP) captures periods of statistically reliable performance by both modalities. This allows us to identify regions that are well predicted by both modalities for a substantial number of TRs.
4/n
DIP reveals a pair of “bows” of modality switching - one posterior bow encircling category-selective visual cortex and another anterior bow spanning dorso-lateral frontal areas (purple to pink in the plot are switching regions)
DIP reveals a pair of “bows” of modality switching - one posterior bow encircling category-selective visual cortex and another anterior bow spanning dorso-lateral frontal areas (purple to pink in the plot are switching regions)
November 7, 2025 at 4:08 PM
4/n
DIP reveals a pair of “bows” of modality switching - one posterior bow encircling category-selective visual cortex and another anterior bow spanning dorso-lateral frontal areas (purple to pink in the plot are switching regions)
DIP reveals a pair of “bows” of modality switching - one posterior bow encircling category-selective visual cortex and another anterior bow spanning dorso-lateral frontal areas (purple to pink in the plot are switching regions)
3/n
Using the Dominance Indication Paradigm (DIP), we capture periods of sustained dominance of one modality’s prediction performance over the other for an ROI. This allows us to identify regions that both switch modalities they encode and do so for a substantial number of TRs.
Using the Dominance Indication Paradigm (DIP), we capture periods of sustained dominance of one modality’s prediction performance over the other for an ROI. This allows us to identify regions that both switch modalities they encode and do so for a substantial number of TRs.
November 7, 2025 at 4:07 PM
3/n
Using the Dominance Indication Paradigm (DIP), we capture periods of sustained dominance of one modality’s prediction performance over the other for an ROI. This allows us to identify regions that both switch modalities they encode and do so for a substantial number of TRs.
Using the Dominance Indication Paradigm (DIP), we capture periods of sustained dominance of one modality’s prediction performance over the other for an ROI. This allows us to identify regions that both switch modalities they encode and do so for a substantial number of TRs.
2/n
Using large-scale fMRI data collected while participants watched movies, we developed two computational approaches relying on prediction performance to analyze temporal dynamics of sensory processing across cortical regions
Using large-scale fMRI data collected while participants watched movies, we developed two computational approaches relying on prediction performance to analyze temporal dynamics of sensory processing across cortical regions
November 7, 2025 at 4:06 PM
2/n
Using large-scale fMRI data collected while participants watched movies, we developed two computational approaches relying on prediction performance to analyze temporal dynamics of sensory processing across cortical regions
Using large-scale fMRI data collected while participants watched movies, we developed two computational approaches relying on prediction performance to analyze temporal dynamics of sensory processing across cortical regions