Neural coding for instruction-based task sets in human frontoparietal and visual cortex

Paul S. Muhle-Karbe, John Duncan, W. De Baene, Daniel Mitchell, Marcel Brass

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Task preparation has traditionally been thought to rely upon persistent memory representations of instructions that permit their execution after delays. Accumulating evidence suggests, however, that accurate retention of task knowledge can be insufficient for successful performance. Here, we hypothesized that instructed facts would be organized into a task set, a temporary coding scheme that proactively tunes sensorimotor pathways based on instructions to enable highly efficient “reflex-like” performance. We devised a paradigm requiring either implementation or memorization of novel stimulus-response mapping instructions, and used multivoxel pattern analysis of neuroimaging data to compare neural coding of instructions during the pre-target phase. Although participants could retain instructions under both demands, we observed striking differences in their representation. To-be-memorized instructions could only be decoded from mid-occipital and posterior parietal cortices, consistent with previous work on visual short-term memory storage. By contrast, to-be- implemented instructions could also be decoded from frontoparietal “multiple- demand” regions, and dedicated visual areas, implicated in processing instructed stimuli. Neural specificity in the latter correlated with performance speed only when instructions were prepared, likely reflecting the pre-activation of instructed decision circuits. Together, these data illuminate how the brain proactively optimizes performance, and help dissociate neural mechanisms supporting task control and short- term memory storage.
Original languageEnglish
Pages (from-to)1891-1905
JournalCerebral Cortex
Volume27
Issue number3
DOIs
Publication statusPublished - 2017

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Visual Cortex
Short-Term Memory
Neuroimaging

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Muhle-Karbe, Paul S. ; Duncan, John ; De Baene, W. ; Mitchell, Daniel ; Brass, Marcel. / Neural coding for instruction-based task sets in human frontoparietal and visual cortex. In: Cerebral Cortex. 2017 ; Vol. 27, No. 3. pp. 1891-1905.
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abstract = "Task preparation has traditionally been thought to rely upon persistent memory representations of instructions that permit their execution after delays. Accumulating evidence suggests, however, that accurate retention of task knowledge can be insufficient for successful performance. Here, we hypothesized that instructed facts would be organized into a task set, a temporary coding scheme that proactively tunes sensorimotor pathways based on instructions to enable highly efficient “reflex-like” performance. We devised a paradigm requiring either implementation or memorization of novel stimulus-response mapping instructions, and used multivoxel pattern analysis of neuroimaging data to compare neural coding of instructions during the pre-target phase. Although participants could retain instructions under both demands, we observed striking differences in their representation. To-be-memorized instructions could only be decoded from mid-occipital and posterior parietal cortices, consistent with previous work on visual short-term memory storage. By contrast, to-be- implemented instructions could also be decoded from frontoparietal “multiple- demand” regions, and dedicated visual areas, implicated in processing instructed stimuli. Neural specificity in the latter correlated with performance speed only when instructions were prepared, likely reflecting the pre-activation of instructed decision circuits. Together, these data illuminate how the brain proactively optimizes performance, and help dissociate neural mechanisms supporting task control and short- term memory storage.",
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Neural coding for instruction-based task sets in human frontoparietal and visual cortex. / Muhle-Karbe, Paul S.; Duncan, John; De Baene, W.; Mitchell, Daniel; Brass, Marcel.

In: Cerebral Cortex, Vol. 27, No. 3, 2017, p. 1891-1905.

Research output: Contribution to journalArticleScientificpeer-review

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