Evidence map›Paper›PMID 23416066›Full record

ReviewNeuroscience and biobehavioral reviews2014

The free-energy self: a predictive coding account of self-recognition.

Matthew A J Apps, Manos Tsakiris

Abstract readReview
In one paragraph

Review in Neuroscience and biobehavioral reviews, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
176citing 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

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

  1. Neuroscience of Virtual Reality: From Virtual Exposure to Embodied Medicine.Cyberpsychology, behavior and social networking · 2019
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116 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

2 authors.

Matthew A J AppsLaboratory of Action and Body, Department of Psychology, Royal Holloway, University of London, UK. Electronic address: m.apps@rhul.ac.uk.
Manos TsakirisLaboratory of Action and Body, Department of Psychology, Royal Holloway, University of London, UK. Electronic address: manos.tsakiris@rhul.ac.uk.

Funding

European Research Council 262853
6 · The paper itself

Abstract

Recognising and representing one's self as distinct from others is a fundamental component of self-awareness. However, current theories of self-recognition are not embedded within global theories of cortical function and therefore fail to provide a compelling explanation of how the self is processed. We present a theoretical account of the neural and computational basis of self-recognition that is embedded within the free-energy account of cortical function. In this account one's body is processed in a Bayesian manner as the most likely to be "me". Such probabilistic representation arises through the integration of information from hierarchically organised unimodal systems in higher-level multimodal areas. This information takes the form of bottom-up "surprise" signals from unimodal sensory systems that are explained away by top-down processes that minimise the level of surprise across the brain. We present evidence that this theoretical perspective may account for the findings of psychological and neuroimaging investigations into self-recognition and particularly evidence that representations of the self are malleable, rather than fixed as previous accounts of self-recognition might suggest.

Indexed as

Models, NeurologicalSelf ConceptAnimalsBayes TheoremBrainHumansIllusionsPerceptionRecognition, PsychologyBayesianBody ownershipEnfacementFace recognitionFree energyPrediction errorPredictive codingRubber hand illusionSelf-awarenessSelf-recognitionVoice recognition

Identifiers

PMID23416066
PMCPMC3848896

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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.