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Attention, Uncertainty, and Free-Energy
Open accessAbstract
We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and non-attended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.
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“We have proposed that this dependency mediates attention and action selection in hierarchical processing (Feldman and Friston, 2010; Friston et al., 2012)”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Feldman and Friston, 2010
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In PP, stimulus salience and attention are considered emergent properties of this precision‐weighting mechanism. 9 , 28”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [28]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Resistance to devaluation and habitisation also illustrates how, in Active Inference, attention modulates the relative precision of the different levels of the hierarchy (Feldman and Friston, 2010).”
From Active Inference, homeostatic regulation and adaptive behavioural control (2015) · cites this paper as Feldman and Friston, 2010
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“I further hypothesize that the salience network tunes the internal model by predicting which prediction errors to pay attention to [i.e. those errors that are likely to be allostatically relevant and therefore worth the cost of encoding and consolidation; called precision signals (Feldman and Friston, 2010; Clark, 2013a,b; Moran et al., 2013; Shipp et al., 2013)].”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Feldman and Friston, 2010
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“MMN is widely considered to be a robust marker of psychosis15, thought to reflect an attempt to minimize this prediction error and capture its disruption16–19.”
From Deviations in effective connectivity explain different hallucination subtypes in Parkinson's disease psychosis (2026) · cites this paper as [17]
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In the citing paper
“in predictive coding, attention is mediated by increasing the gain of populations encoding prediction error (Spratling, 2008; Feldman and Friston, 2010).”
From Canonical Microcircuits for Predictive Coding (2012) · cites this paper as Feldman and Friston, 2010
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In the citing paper
“posterior beliefs about states of the world can direct attention to sensory features by top-down modulation of sensory precision.”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Feldman and Friston, 2010
The cited source says what the claim attributes to it.
In the citing paper
“the top-down control of sensory precision has been shown to explain several psychophysical and physiological aspects of attention (Feldman and Friston, 2010); thereby providing a formal link between precision and attention.”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Feldman and Friston, 2010
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In the citing paper
“This precision is thought to be encoded by post-synaptic gain of neuronal populations reporting prediction errors – the principal or pyramidal cells of superficial cortical layers (Mumford, 1992; Feldman and Friston, 2010).”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Feldman and Friston, 2010
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“This is the essence of recurrent message passing between hierarchical levels to optimize free energy or suppress prediction error: see Friston and Kiebel (2009b) and Feldman and Friston (2010)”
From The Computational Anatomy of Psychosis (2013) · cites this paper as Feldman and Friston (2010)
The cited source says what the claim attributes to it.
In the citing paper
“From the perspectives of the AIM model and predictive coding, cholinergic modulation may play an important role in modulating and gating prediction errors signals – a role that has been associated with the deployment of attention in waking (Feldman and Friston, 2010) and the neurochemical modulation of hierarchical cortical processing during sleep (Hobson and Friston, 2012).”
From Virtual reality and consciousness inference in dreaming (2014) · cites this paper as Feldman and Friston, 2010
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In the citing paper
“The influence of prior predictions or of PEs during inference is weighted by their respective precision.”
From Aberrant Salience, Information Processing, and Dopaminergic Signaling in People at Clinical High Risk for Psychosis (2020) · cites this paper as [93]
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In the citing paper
“Transposed (in ways we are about to explore) to the neural domain, this makes prediction error into a kind of proxy (Feldman & Friston 2010) for sensory information itself.”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Feldman & Friston 2010
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In the citing paper
“It is in this sense that the residual errors are able, as it is sometimes said (Feldman & Friston 2010) to stand in for the forward fl ow of sensory information itself.”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Feldman & Friston 2010
The cited source says what the claim attributes to it.
In the citing paper
“This represents a subtle but ubiquitous problem for the brain, where the solution rests on modulating the gain or excitability of neuronal populations reporting prediction error [21,40,41].”
From Active interoceptive inference and the emotional brain (2016) · cites this paper as [40]
The cited source says what the claim attributes to it.
In the citing paper
“According to some more recent formulations of predictive coding [111,112], the propagated error is weighted by the sensory reliability or precision.”
From How Do Expectations Shape Perception? (2018) · cites this paper as [111]
The cited source says what the claim attributes to it.
In the citing paper
“When sensory precision is low, prediction errors are downweighted and observers will rely more on predictions.Conversely, when it is high, prediction errors are up-weighted and observers will rely more strongly on their input.”
From How Do Expectations Shape Perception? (2018) · cites this paper as [111]
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In the citing paper
“This means precision has to be estimated and deployed (at each level of the hierarchy): a process that is equated with attention”
From 'Seeing the Dark': Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference (2018) · cites this paper as Feldman and Friston, 2010
The cited source says what the claim attributes to it.
In the citing paper
“From the perspective of predictive coding, this means that we also have to infer the deployment of precision which, in a hierarchical setting, starts to look like attention”
From 'Seeing the Dark': Grounding Phenomenal Transparency and Opacity in Precision Estimation for Active Inference (2018) · cites this paper as Feldman and Friston, 2010
The cited source says what the claim attributes to it.
In the citing paper
“Precision is thought to be encoded by the post-synaptic gain of neurons that report prediction errors and has been used to explain both the psychophysical and electrophysiological correlates of attention, so that sensory processing channels that convey precise information are selectively enabled by an increase in their precision (Friston, 2008, Feldman and Friston, 2010, Bastos et al., 2012).”
From The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding (2014) · cites this paper as Feldman and Friston, 2010
In the citing paper
“Top-down attenuation of sensory precision accounts for psychophysical and physiological aspects of attention (4)”
From A more precise look at context in autism (2015) · cites this paper as [4]
In the citing paper
“providing a hierarchically and biologically plausible account of the”
From A more precise look at context in autism (2015) · cites this paper as [4]
In the citing paper
“It transpires that—under predictive coding—this uncertainty or relative confidence in sensory (and extrasensory) information can be succinctly encoded by the gain of certain neuronal populations that pass information from one hierarchal cortical level to the next [2,3].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [2]
In the citing paper
“This gain control corresponds to a (Bayes-optimal) encoding of precision in terms of the excitability of neuronal populations reporting prediction errors [2,19].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [2]
In the citing paper
“However, this form of attention is distinct from simply boosting sensory precision (the precision of sensory prediction errors) as in simulations of the Posner paradigm or biased competition [2].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [2]
In the citing paper
“The dynamic and context-sensitive control of precision has been associated with attentional gain control in sensory processing [2,31] and has been discussed in terms of affordance and action selection [32–34].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [2]
In the citing paper
“Furthermore, it is closely related to notions of communication through coherence (see below) and may reflect an important mechanism for precision engineering in attention [2].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [2]
In the citing paper
“Our findings also accord with a theoretical Bayesian scheme in which uncertainty, or precision, is an inherent property of the neural code29-31.”
From Confidence in value-based choice (2013) · cites this paper as [29]
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Metadata
- Authors
- Feldman, H., & Friston, K. J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Corresponding author
- Friston, Karl J.
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2010
- Volume
- 4
- Pages
- 215-215
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC3001758
- OpenAlex
- W2096962395
- Semantic Scholar
- 3b08af33a7646a88f393f384c24d95627dfe9bfd
- Cited by
- 1,365
- References
- 162
- Citations by year
- 2026: 128 · 2025: 118 · 2024: 104 · 2023: 98 · 2022: 98 · 2021: 121 · 2020: 115 · 2019: 122 · 2018: 97 · 2017: 70 · 2016: 68 · 2015: 73 · 2014: 58 · 2013: 50 · 2012: 38
- FWCI
- 18.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (088130)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- free energy principle, Bayesian inference, uncertainty, predictive coding, evidence accumulation, biased competition, Posner paradigm, attentional capture, speed-accuracy trade-off, attentional gating, hierarchical perception, neural simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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