Neural coding of visual objects rapidly reconfigures to reflect subtrial shifts in attentional focus

Lydia Barnes*, Erin Goddard, Alexandra Woolgar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
31 Downloads (Pure)


Every day, we respond to the dynamic world around us by choosing actions to meet our goals. Flexible neural populations are thought to support this process by adapting to prioritize task-relevant information, driving coding in specialized brain regions toward stimuli and actions that are currently most important. Accordingly, human fMRI shows that activity patterns in frontoparietal cortex contain more information about visual features when they are task-relevant. However, if this preferential coding drives momentary focus, for example, to solve each part of a task in turn, it must reconfigure more quickly than we can observe with fMRI. Here, we used multivariate pattern analysis of magnetoencephalography data to test for rapid reconfiguration of stimulus information when a new feature becomes relevant within a trial. Participants saw two displays on each trial. They attended to the shape of a first target then the color of a second, or vice versa, and reported the attended features at a choice display. We found evidence of preferential coding for the relevant features in both trial phases, even as participants shifted attention mid-trial, commensurate with fast subtrial reconfiguration. However, we only found this pattern of results when the stimulus displays contained multiple objects and not in a simpler task with the same structure. The data suggest that adaptive coding in humans can operate on a fast, subtrial timescale, suitable for supporting periods of momentary focus when complex tasks are broken down into simpler ones, but may not always do so.

Original languageEnglish
Pages (from-to)806-822
Number of pages17
JournalJournal of cognitive neuroscience
Issue number5
Publication statusPublished - May 2022

Bibliographical note

Copyright Massachusetts Institute of Technology 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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