Cortical transformation of stimulus space in order to linearize a linearly inseparable task

Meng Huan Wu, David Kleinschmidt, Lauren Emberson, Donias Doko, Shimon Edelman, Robert Jacobs, Rajeev Raizada

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The human brain is able to learn difficult categorization tasks, even ones that have linearly inseparable boundaries; however, it is currently unknown how it achieves this computational feat. We investigated this by training participants on an animal categorization task with a linearly inseparable prototype structure in a morph shape space. Participants underwent fMRI scans before and after 4 days of behavioral training. Widespread representational changes were found throughout the brain, including an untangling of the categories’ neural patterns that made them more linearly separable after behavioral training. These neural changes were task dependent, as they were only observed while participants were performing the categorization task, not during passive viewing. Moreover, they were found to occur in frontal and parietal areas, rather than ventral temporal cortices, suggesting that they reflected attentional and decisional reweighting, rather than changes in object recognition templates. These results illustrate how the brain can flexibly transform neural representational space to solve computationally challenging tasks.

Original languageEnglish (US)
Pages (from-to)2342-2355
Number of pages14
JournalJournal of cognitive neuroscience
Issue number12
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience


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