Biology subjects

O'Connell, T. P.

Publications and source records attributed to O'Connell, T. P..

3 recordsLinked to original sources

bioRxiv2026

Predictive coding video models capture dorsal parietal representations and human judgments for surfaces defined by motion

Interacting with the world relies on knowing its three-dimensional structure, yet vision receives a stream of two-dimensional images. Recovering 3D geometry from dynamic input is therefore foundational to vision, but lacks a computational account. Here we identify one, using artificial neural networks to predict human behavior and neuronal responses from the two major visual pathways in macaques. Using videos of camouflaged objects whose shape is revealed through motion, we found both pathways carry information for motion-defined surfaces, while neurons in parietal cortex most closely track what humans perceive. Only predictive coding video models--trained to fill in missing content in natural videos--reproduced both neuronal responses and human behavior. Predictive coding thus emerges as a principle that yields a brain-like geometric world model of the physical world.

neuroscience↗

bioRxiv2020

Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain

The extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model are more widely distributed across the brain than previously acknowledged. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.

neuroscience↗

bioRxiv2017

Predicting eye movements from deep neural network activity decoded from fMRI responses to natural scenes

Computational models of selective spatial attention can reliably predict eye movements to complex images. However, researchers lack a simple way to measure covert representations of spatial attention in the brain and their link to overt eye movement behavior, especially in response to natural scenes. Here, we predict eye movement patterns from spatial priority maps reconstructed from brain activity measured with functional magnetic resonance imaging (fMRI). First, we define a computational spatial attention model using a deep convolutional neural network (CNN) pre-trained for scene categorization. Next, we decode CNN unit activity from fMRI activity and reconstruct spatial priority maps by applying our computational spatial attention model to decoded CNN activity. Finally, we predict eye movements in a subsequent behavioral experiment within and between individuals using reconstructed spatial priority maps. These results demonstrate that features represented in CNN unit activity can guide spatial attention and eye movements, providing a crucial link between CNN models, brain activity, and behavior.

neuroscience↗