Predicting cued and oddball visual search performance from fMRI, MEG, and DNN neural representational similarity

Abstract

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.