Nature NeuroscienceNeurosciencePeer-reviewed journal article
Triple dissociation of attention and decision computations across prefrontal cortex
Laurence Tudor HuntiD, W. M. Nishantha MalalasekeraiD, Archy O. de BerkeriD, Bruno MirandaiD, Simon Francis FarmeriD, Timothy E.J. BehrensiD, Steven Wayne KennerleyiD
Abstract
Anatomical, neuroimaging and lesion studies indicate that prefrontal cortex (PFC) can be subdivided into different subregions supporting distinct aspects of decision making. However, explanations of neuronal computations within these subregions varies widely across studies. An integrated and mechanistic account of PFC function therefore remains elusive. Resolving these debates demands a rich dataset that directly contrasts neuronal activity across multiple PFC subregions within a single paradigm, whilst experimentally controlling factors such as the order, duration and frequency in which choice options are attended and compared. Here, we contrast neuronal population responses between macaque orbitofrontal (OFC), anterior cingulate (ACC) and dorsolateral prefrontal cortices (DLPFC) during sequential value-guided information search and choice. From the first fixation of choice-related stimuli, a strong triple dissociation of information encoding emerges in parallel across these PFC subregions. As further information is gathered, population responses in OFC reflect an attention-guided value comparison process. Meanwhile, parallel signals in ACC reflect belief updating in light of new evidence, integration of that evidence to a decision bound, and an emerging action plan for which option should be chosen. Our findings demonstrate the co-existence of multiple, distributed decision-related computations across PFC subregions during value-guided choice. They provide a synthesis of several competing accounts of PFC function.
- organism: non-human primate
- assay: electronic health records
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 222
- FWCI
- 14.72
- Field percentile
- top 1%
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