Evidence map›Paper›PMID 33343039›Full record

ReviewJournal of mathematical psychology2020

Active inference on discrete state-spaces: A synthesis.

Lancelot Da Costa, Thomas Parr, Noor Sajid, Sebastijan Veselic, Victorita Neacsu, Karl Friston

Abstract readReview
In one paragraph

Review in Journal of mathematical psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
95citing 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

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

  1. Pooled it
  2. Active inference and artificial reasoning.Nature communications · 2026
    Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. The active construction of past episodes.Translational neuroscience · 2026
    Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Review
  17. Gradient-Free De Novo Learning.Entropy (Basel, Switzerland) · 2025
    Article
  18. SPM-30 years and beyond.Cerebral cortex (New York, N.Y. : 1991) · 2025
    Review
  19. Embodied decisions as active inference.PLoS computational biology · 2025
    Article
  20. Deep Hybrid Models: Infer and Plan in a Dynamic World.Entropy (Basel, Switzerland) · 2025
    Article

35 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

6 authors.

Lancelot Da CostaDepartment of Mathematics, Imperial College London, London, SW7 2RH, United Kingdom.
Thomas ParrWellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom.
Noor SajidWellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom.
Sebastijan VeselicWellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom.
Victorita NeacsuWellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom.
Karl FristonWellcome Centre for Human Neuroimaging, University College London, London, WC1N 3AR, United Kingdom.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, its associated process theory has grown to incorporate complex generative models, enabling simulation of a wide range of complex behaviours. Due to successive developments in active inference, it is often difficult to see how its underlying principle relates to process theories and practical implementation. In this paper, we try to bridge this gap by providing a complete mathematical synthesis of active inference on discrete state-space models. This technical summary provides an overview of the theory, derives neuronal dynamics from first principles and relates this dynamics to biological processes. Furthermore, this paper provides a fundamental building block needed to understand active inference for mixed generative models; allowing continuous sensations to inform discrete representations. This paper may be used as follows: to guide research towards outstanding challenges, a practical guide on how to implement active inference to simulate experimental behaviour, or a pointer towards various in-silico neurophysiological responses that may be used to make empirical predictions.

Indexed as

Active inferenceFree energy principleMarkov decision processMathematical reviewProcess theoryVariational Bayesian inference

Identifiers

PMID33343039
PMCPMC7732703

What OpenQuestion holds

Textmetadata
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.