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Attention, Uncertainty, and Free-Energy
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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.
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“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 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
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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)
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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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“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
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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]
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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]
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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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Self-citationThe Social Modulation of Pain: Others as Predictive Signals of Salience – a Systematic Review
Krahé, C., Springer, A., Weinman, J. A., & Fotopoulou, A. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00386 · Cited by 245
Attentional Enhancement of Auditory Mismatch Responses: a DCM/MEG Study
Auksztulewicz, R., & Friston, K. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu323 · Cited by 227
Self-citationFocus of attention modulates the heartbeat evoked potential
Petzschner, F. H., Weber, L. A., Wellstein, K. V., Paolini, G., Do, C. T., & Stephan, K. E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.037 · Cited by 248
An integrative tinnitus model based on sensory precision.
Sedley, W., Friston, K. J., Gander, P. E., Kumar, S., & Griffiths, T. D. (2016) · Trends in Neurosciences · doi:10.1016/j.tins.2016.10.004 · Cited by 237
Self-citationThe power of predictions: An emerging paradigm for psychological research
Hutchinson, J. B., & Barrett, L. F. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721419831992 · Cited by 216
Attentional Modulation of Alpha/Beta and Gamma Oscillations Reflect Functionally Distinct Processes
Bauer, M., Stenner, M.-P., Friston, K. J., & Dolan, R. J. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3474-13.2014 · Cited by 224
Self-citationPrediction and memory: A predictive coding account
Barron, H. C., Auksztulewicz, R., & Friston, K. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101821 · Cited by 220
Self-citationNeural signatures of perceptual inference
Sedley, W., Gander, P. E., Kumar, S., Kovach, C. K., Oya, H., Kawasaki, H., Howard, M. A., & Griffiths, T. D. (2016) · eLife · doi:10.7554/elife.11476 · Cited by 194
A predictive coding perspective on autism spectrum disorders
van Boxtel, J. J. A., & Lu, H. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00019 · Cited by 210
Bolis, D., Balsters, J., Wenderoth, N., Becchio, C., & Schilbach, L. (2017) · Psychopathology · doi:10.1159/000484353 · Cited by 204
Virtual reality and consciousness inference in dreaming
Hobson, J. A., Hong, C. C.-H., & Friston, K. J. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01133 · Cited by 197
“Many have assumed that this must be so – and indeed have fruitfully pursued predictive coding in this context (Fletcher and Frith, 2009; Edwards et al., 2012; Adams et al., 2013b; Hohwy, 2013).”
BackgroundSelf-citationThe sense of should: A biologically-based framework for modeling social pressure
Theriault, J. E., Young, L., & Barrett, L. F. (2021) · Physics of Life Reviews · doi:10.1016/j.plrev.2020.01.004 · Cited by 201
Delusions and the role of beliefs in perceptual inference
Schmack, K., Gòmez-Carrillo de Castro, A., Rothkirch, M., Sekutowicz, M., Rössler, H., Haynes, J.-D., Heinz, A., Petrovic, P., & Sterzer, P. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.1778-13.2013 · Cited by 198
The role of prediction in social neuroscience
Brown, E. C., & Brüne, M. (2012) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2012.00147 · Cited by 190
From many to (n)one: Meditation and the plasticity of the predictive mind
Laukkonen, R. E., & Slagter, H. A. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.06.021 · Cited by 187
In the body's eye: the computational anatomy of interoceptive inference
Allen, M., Levy, A., Parr, T., & Friston, K. J. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010490 · Cited by 185
Self-citationSmith, R., Badcock, P., & Friston, K. J. (2021) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13138 · Cited by 174
Prediction in autism spectrum disorder: A systematic review of empirical evidence
Cannon, J., O'Brien, A. M., Bungert, L., & Sinha, P. (2021) · Autism Research · doi:10.1002/aur.2482 · Cited by 168
“…should be central to the discussion: within predictive processing, attention is understood as a mechanism for precision modulation, with top-down attention corresponding to prospective precision modulation based on inferred context [Feldman & Friston, 2010], likely moderated by acetylcholine.”
BackgroundLimanowski, J., & Friston, K. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.00643 · Cited by 154
“This means precision has to be estimated and deployed (at each level of the hierarchy): a process that is equated with attention (Feldman and Friston, 2010).”
BackgroundSelf-citationLoss of sensory attenuation in patients with functional (psychogenic) movement disorders
Pareés, I., Brown, H., Nuruki, A., Adams, R. A., Davare, M., Bhatia, K. P., Friston, K., & Edwards, M. J. (2014) · Brain · doi:10.1093/brain/awu237 · Cited by 155
Self-citationPredictive processing as a systematic basis for identifying the neural correlates of consciousness
Hohwy, J., & Seth, A. (2020) · Philosophy and the Mind Sciences · doi:10.33735/phimisci.2020.ii.64 · Cited by 170
Active Inference, Attention, and Motor Preparation
Brown, H., Friston, K., & Bestmann, S. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00218 · Cited by 165
Self-citationOn hyperpriors and hypopriors: comment on Pellicano and Burr
Friston, K. J., Lawson, R., & Frith, C. D. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.11.003 · Cited by 151
Self-citationCanales-Johnson, A., Silva, C., Huepe, D., Rivera-Rei, Á., Noreika, V., Garcia, M. D. C., Silva, W., Ciraolo, C., Vaucheret, E., Sedeño, L., et al. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv076 · Cited by 146
A nice surprise? Predictive processing and the active pursuit of novelty
Clark, A. (2018) · Phenomenology and the Cognitive Sciences · doi:10.1007/s11097-017-9525-z · Cited by 156
Spatial attention, precision, and Bayesian inference: a study of saccadic response speed
Vossel, S., Mathys, C., Daunizeau, J., Bauer, M., Driver, J., Friston, K. J., & Stephan, K. E. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs418 · Cited by 146
Self-citationAttention Sharpens the Distinction between Expected and Unexpected Percepts in the Visual Brain
Jiang, J., Summerfield, C., & Egner, T. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3308-13.2013 · Cited by 133
Hallucinations as Top-Down Effects on Perception
Powers, A. R., Kelley, M., & Corlett, P. R. (2016) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.04.003 · Cited by 138
A social Bayesian brain: how social knowledge can shape visual perception
Otten, M., Seth, A. K., & Pinto, Y. (2017) · Brain and Cognition · doi:10.1016/j.bandc.2016.05.002 · Cited by 124
Model averaging, optimal inference, and habit formation
FitzGerald, T. H. B., Dolan, R. J., & Friston, K. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00457 · Cited by 121
Self-citationA mathematical model of embodied consciousness.
Rudrauf, D., Bennequin, D., Granic, I., Landini, G., Friston, K., & Williford, K. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.05.032 · Cited by 118
Self-citationCan predictive coding explain repetition suppression?
Grotheer, M., & Kovács, G. (2016) · Cortex · doi:10.1016/j.cortex.2015.11.027 · Cited by 108
Chennu, S., Noreika, V., Gueorguiev, D., Shtyrov, Y., Bekinschtein, T. A., & Henson, R. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1125-16.2016 · Cited by 107
The functional role of cardiac activity in perception and action
Skora, L. I., Livermore, J. J. A., & Roelofs, K. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104655 · Cited by 125
Deep active inference as variational policy gradients
Millidge, B. (2020) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2020.102348 · Cited by 115
Clark, A. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00270 · Cited by 108
Howes, O. D., Hird, E. J., Adams, R. A., Corlett, P. R., & McGuire, P. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.03.012 · Cited by 110
Autonomic and brain responses associated with empathy deficits in autism spectrum disorder
Gu, X., Eilam‐Stock, T., Zhou, T., Anagnostou, E., Kolevzon, A., Soorya, L., Hof, P. R., Friston, K. J., & Fan, J. (2015) · Human Brain Mapping · doi:10.1002/hbm.22840 · Cited by 110
Self-citationRespiratory rhythms of the predictive mind.
Allen, M., Varga, S., & Heck, D. H. (2023) · Psychological Review · doi:10.1037/rev0000391 · Cited by 114
Active inference as a theory of sentient behavior
Pezzulo, G., Parr, T., & Friston, K. (2024) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108741 · Cited by 109
Self-citationPredictive Coding: a Theoretical and Experimental Review
Millidge, B., Seth, A., & Buckley, C. L. (2021) · arXiv · doi:10.48550/arxiv.2107.12979 · Cited by 106
Enhanced deviant responses in patterned relative to random sound sequences
Southwell, R., & Chait, M. (2018) · Cortex · doi:10.1016/j.cortex.2018.08.032 · Cited by 80
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
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