Biology subjects

Thorat, S.

Publications and source records attributed to Thorat, S..

3 recordsLinked to original sources

bioRxiv2023

Predicting cued and oddball visual search performance from neural representational similarity

Capacity limitations in visual tasks can be observed when the number of task-related objects increases. An influential idea is that such capacity limitations are determined by competition at the neural level: two objects that are encoded by shared neural populations interfere more in behavior (e.g., visual search) than two objects encoded by separate neural populations. However, the neural representational similarity of objects varies across brain regions and across time, raising the question of where and when competition determines task performance. Furthermore, it is unclear whether the association between neural representational similarity and task performance is common or unique across tasks. Here, we used neural representational similarity derived from fMRI, MEG, and deep neural networks (DNN) to predict performance on two visual search tasks involving the same objects and requiring the same responses but differing in instructions: cued visual search and oddball visual search. Separate groups of human participants (both sexes) viewed the individual objects in neuroimaging experiments to establish the neural representational similarity between those objects. Results showed that performance on both search tasks could be predicted by neural representational similarity throughout the visual system (fMRI), from 80 msec after onset (MEG), and in all DNN layers. Stepwise regression analysis, however, revealed task-specific associations, with unique variability in oddball search performance predicted by early/posterior neural similarity, and unique variability in cued search task performance predicted by late/anterior neural similarity. These results reveal that capacity limitations in superficially similar visual search tasks may reflect competition at different stages of visual processing. Significance StatementVisual search for target objects is slowed down by the presence of distractors, but not all distractors are equally distracting - the more similar a distractor is to the target, the more it slows down search. Here, we used fMRI, MEG, and a deep neural network to reveal where, when, and how neural similarity between targets and distractors predicts visual search performance across two search tasks: oddball visual search (locating the different-looking object) and cued visual search (locating the cued object). Results also revealed brain regions, time points, and feature levels that predicted task-unique performance. These results provide a neural basis for similarity theories of visual search and show that this neural basis differs across visual search tasks.

neuroscience↗

bioRxiv2022

Statistical learning of distractor co-occurrences facilitates visual search

Visual search is facilitated by knowledge of the relationship between the target and the distractors, including both where the target is likely to be amongst the distractors and how it differs from the distractors. Whether the statistical structure amongst distractors themselves, unrelated to target properties, facilitates search is less well understood. Here, we assessed the benefit of distractor structure using novel shapes whose relationship to each other was learned implicitly during visual search. Participants searched for target items in arrays of shapes that comprised either four pairs of co-occurring distractor shapes (structured scenes) or eight distractor shapes randomly partitioned into four pairs on each trial (unstructured scenes). Across five online experiments (N=1140), we found that after a period of search training, participants were more efficient when searching for targets in structured than unstructured scenes. This structure-benefit emerged independently of whether the position of the shapes within each pair was fixed or variable, and despite participants having no explicit knowledge of the structured pairs they had seen. These results show that implicitly learned co-occurrence statistics between distractor shapes increases search efficiency. Increased efficiency in the rejection of regularly co-occurring distractors may contribute to the efficiency of visual search in natural scenes, where such regularities are abundant.

neuroscience↗

bioRxiv2021

Body shape as a visual feature: evidence from spatially-global attentional modulation in human visual cortex

Feature-based attention modulates visual processing beyond the focus of spatial attention. Previous work has reported such spatially-global effects for low-level features such as color and orientation, as well as for faces. Here, using fMRI, we provide evidence for spatially-global attentional modulation for human bodies. Participants were cued to search for one of six object categories in two vertically-aligned images. Two additional, horizontally-aligned, images were simultaneously presented but were never task-relevant across three experimental sessions. Analyses time-locked to the objects presented in these task-irrelevant images revealed that responses evoked by body silhouettes were modulated by the participants top-down attentional set, becoming more body-selective when participants searched for bodies in the task-relevant images. These effects were observed both in univariate analyses of the body-selective cortex and in multivariate analyses of the object-selective visual cortex. Additional analyses showed that this modulation reflected response gain rather than a bias induced by the cues, and that it reflected enhancement of body responses rather than suppression of non-body responses. These findings provide evidence for a spatially-global attention mechanism for body shapes, supporting the rapid and parallel detection of conspecifics in our environment.

neuroscience↗