Biology subjects

Pramod, R.

Publications and source records attributed to Pramod, R..

2 recordsLinked to original sources

bioRxiv2022

Visual homogeneity computations in the brain enable solving generic visual tasks

Most visual tasks involve looking for specific object features. But we also often perform property-based tasks where we look for specific property in an image, such as finding an odd item, deciding if two items are same, or if an object has symmetry. How do we solve such tasks? These tasks do not fit into standard models of decision making because their underlying feature space and decision process is unclear. Using well-known principles governing multiple object representations, we show that displays with repeating elements can be distinguished from heterogeneous displays using a property we define as visual homogeneity. In behavior, visual homogeneity predicted response times on visual search, same-different and symmetry tasks. Brain imaging during visual search and symmetry tasks revealed that visual homogeneity was localized to a region in the object-selective cortex. Thus, property-based visual tasks are solved in a localized region in the brain by computing visual homogeneity. SIGNIFICANCE STATEMENTMost visual tasks involve looking for specific features, like finding a face in a crowd. But we also often look for a particular image property - such as finding an odd item, deciding if two items are same, or judging if an object is symmetric. How does our brain solve these disparate tasks? Here, we show that these tasks can all be solved using a simple computation over object representations in higher visual cortex, which we define as visual homogeneity.

neuroscience↗

bioRxiv2021

Invariant Representation of Physical Stability in the Human Brain

Successful engagement with the world requires the ability to predict what will happen next. Here we investigate how the brain makes the most basic prediction about the physical world: whether the situation in front of us is stable, and hence likely to stay the same, or unstable, and hence likely to change in the immediate future. Specifically, we ask if judgements of stability can be supported by the kinds of representations that have proven to be highly effective at visual object recognition in both machines and brains, or instead if the ability to determine the physical stability of natural scenes may require generative algorithms that simulate the physics of the world. To find out, we measured responses in both convolutional neural networks (CNNs) and the brain (using fMRI) to natural images of physically stable versus unstable scenarios. We find no evidence for generalizable representations of physical stability in either standard CNNs trained on visual object and scene classification (ImageNet), or in the human ventral visual pathway, which has long been implicated in the same process. However, in fronto-parietal regions previously implicated in intuitive physical reasoning we find both scenario-invariant representations of physical stability, and higher univariate responses to unstable than stable scenes. These results demonstrate abstract representations of physical stability in the dorsal but not ventral pathway, consistent with the hypothesis that the computations underlying stability entail not just pattern classification but forward physical simulation.

neuroscience↗