Evidence map›Paper›PMID 31298270›Full record

ArticleCerebral cortex (New York, N.Y. : 1991)2020

Prefrontal Computation as Active Inference.

Thomas Parr, Rajeev Vijay Rikhye, Michael M Halassa, Karl J Friston

Abstract read
In one paragraph

Article in Cerebral cortex (New York, N.Y. : 1991), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Review
  2. The myth of the Bayesian brain.European journal of applied physiology · 2025
    Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Flexible intentions: An Active Inference theory.Frontiers in computational neuroscience · 2023
    Article
  8. Geometry of neural computation unifies working memory and planning.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  9. Article
  10. Article
  11. Review
  12. Memory and Markov Blankets.Entropy (Basel, Switzerland) · 2021
    Article
  13. Article
  14. Article
  15. Article
  16. Active inference on discrete state-spaces: A synthesis.Journal of mathematical psychology · 2020
    Review
  17. Article
  18. Article
  19. Article
  20. Inferring What to Do (And What Not to).Entropy (Basel, Switzerland) · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Thomas ParrWellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK.
Rajeev Vijay RikhyeDepartment of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Michael M HalassaDepartment of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Karl J FristonWellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3BG, UK.

Funding

Wellcome Trust 088130/Z/09/Z
6 · The paper itself

Abstract

The prefrontal cortex is vital for a range of cognitive processes, including working memory, attention, and decision-making. Notably, its absence impairs the performance of tasks requiring the maintenance of information through a delay period. In this paper, we formulate a rodent task-which requires maintenance of delay-period activity-as a Markov decision process and treat optimal task performance as an (active) inference problem. We simulate the behavior of a Bayes optimal mouse presented with 1 of 2 cues that instructs the selection of concurrent visual and auditory targets on a trial-by-trial basis. Formulating inference as message passing, we reproduce features of neuronal coupling within and between prefrontal regions engaged by this task. We focus on the micro-circuitry that underwrites delay-period activity and relate it to functional specialization within the prefrontal cortex in primates. Finally, we simulate the electrophysiological correlates of inference and demonstrate the consequences of lesions to each part of our in silico prefrontal cortex. In brief, this formulation suggests that recurrent excitatory connections-which support persistent neuronal activity-encode beliefs about transition probabilities over time. We argue that attentional modulation can be understood as the contextualization of sensory input by these persistent beliefs.

Indexed as

Models, NeurologicalAnimalsAttentionBayes TheoremBehavior, AnimalBrainDecision MakingHumansMarkov ChainsMemory, Short-TermMiceNeural Networks, ComputerNeural PathwaysNeuronsPrefrontal Cortexactive inferenceattentiondecision-makingprefrontal cortexworking memory

Identifiers

PMID31298270
PMCPMC7444741

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.