Evidence map›Paper›PMID 42757072›Full record

ArticleiScience2026

Neural subspace reorganization reflects value-based decision-making.

Huidi Li, Nikolaos Chrysanthidis, Scott L Brincat, Jonas Rose, Earl K Miller

Abstract read
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Huidi LiThe Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Nikolaos ChrysanthidisThe Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Scott L BrincatThe Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Jonas RoseThe Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Earl K MillerThe Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Funding

Interhemispheric coordination and transfer of visual informationR01EY033430 · NEI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI MILLER, EARL K · 2022 to 2025
$1.5M
NEI NIH HHS R01 EY033430
6 · The paper itself

Abstract

Value-based decision-making requires maintaining and comparing multiple options to guide behavior. While the neural circuits supporting these decisions are increasingly understood, how decisions reshape option representations remains unclear. We recorded from lateral prefrontal cortex (LPFC) while non-human primates held two sequentially presented option-value pairs in working memory and selected the higher value option. We found that, once a decision could be made, representations of the chosen and unchosen options rotated into orthogonal neural subspaces, while the chosen representation expanded. Before the decision, representations were organized by presentation order. After the decision, the chosen option, regardless of presentation order, was transformed into a common subspace aligned with the prescribed action. These findings suggest that value-based decisions reorganize LPFC population activity to selectively represent the chosen option, enabling efficient downstream readout for action selection.

Indexed as

neural population analysisprefrontal cortexsubspace codingvalue-based decision makingworking memory

Identifiers

PMID42757072
PMCPMC13584006

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.