Evidence map›Paper›PMID 34144895›Full record

ReviewTrends in cognitive sciences2021

The secret life of predictive brains: what's spontaneous activity for?

Giovanni Pezzulo, Marco Zorzi, Maurizio Corbetta

Abstract readReview
In one paragraph

Review in Trends in cognitive sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed, 1 pooled it
–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

82 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
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  6. The hippocampus as a generative model.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026
    Review
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  20. Predictive Coding Light.Nature communications · 2025
    Article

22 more citing papers are in PubMed but not listed here.

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

3 authors.

Giovanni PezzuloInstitute of Cognitive Sciences and Technologies, National Research Council, Roma, Italy. Electronic address: giovanni.pezzulo@istc.cnr.it.
Marco ZorziDepartment of General Psychology and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy; IRCCS San Camillo Hospital, Venice, Italy.
Maurizio CorbettaDepartment of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy; Venetian Institute of Molecular Medicine (VIMM), Fondazione Biomedica, Padova, Italy.

Funding

Stroke, Brain Networks, and BehaviorR01NS095741 · NINDS · WASHINGTON UNIVERSITY · PI CORBETTA, MAURIZIO · 2015 to 2019
$3.0M
NINDS NIH HHS R01 NS095741
6 · The paper itself

Abstract

Brains at rest generate dynamical activity that is highly structured in space and time. We suggest that spontaneous activity, as in rest or dreaming, underlies top-down dynamics of generative models. During active tasks, generative models provide top-down predictive signals for perception, cognition, and action. When the brain is at rest and stimuli are weak or absent, top-down dynamics optimize the generative models for future interactions by maximizing the entropy of explanations and minimizing model complexity. Spontaneous fluctuations of correlated activity within and across brain regions may reflect transitions between 'generic priors' of the generative model: low dimensional latent variables and connectivity patterns of the most common perceptual, motor, cognitive, and interoceptive states. Even at rest, brains are proactive and predictive.

Indexed as

Brain MappingMagnetic Resonance ImagingBrainCognitionHumansgenerative modelspredictive brainsresting statespontaneous activity

Identifiers

PMID34144895
PMCPMC8363551

What OpenQuestion holds

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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.