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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The dependency of precisions on expected hidden causes and states mediates attention and action selection in hierarchical processing.
“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
The Methods section states that precisions depend on expected hidden causes and states and proposes that this dependency mediates attentional processing in a hierarchy. The paper does not establish that the same dependency mediates action selection; its discussion of attentional selection is not evidence for action selection. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredIn PP, stimulus salience and attention are considered emergent properties of precision-weighting mechanism.
“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]
The Discussion (‘Attention, gain and learning’; page unavailable) links attention to inference about precision and changes in synaptic gain. But the Introduction (‘Attention, biased competition and the Posner paradigm’; page unavailable) describes stimulus salience as a property of the stimulus that can attract attention; it does not establish salience itself as an emergent property of precision weighting. (LLM, conf=0.85)
Experimental — accuracy not yet measuredResistance to devaluation and habitisation illustrates how, in Active Inference, attention modulates the relative precision of the different levels of the hierarchy.
“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
The Methods section describes attention as modulating the precision of prediction errors through synaptic gain in a cortical hierarchy. The paper examines perceptual attention in the Posner paradigm; the passages do not connect this mechanism to resistance to devaluation or habitisation. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe salience network tunes the internal model by predicting which prediction errors to pay attention to.
“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
The Introduction and Methods sections describe attention as precision-dependent weighting of prediction errors, with expected states modulating the gain of prediction-error units. They do not identify the salience network as performing this function or connect precision to allostatic relevance and the cost of encoding or consolidation. The citing sentence frames the salience-network account as the citing author's hypothesis, while this source supports its underlying precision-weighting mechanism. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMMN is widely considered a robust marker of psychosis, thought to reflect an attempt to minimize prediction error and capture its disruption.
“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]
In Results > Simulating the posner paradigm (page unavailable), the paper models some event-related potentials as signals of prediction error, supporting the general proposed link. The passages concern P1, N1, and P3 in a spatial-cueing task; they do not establish that MMN specifically reflects prediction-error minimization or captures its disruption. The psychosis-marker assertion has a separate citation in the citing sentence and is not assessed against this source. (LLM, conf=0.75)
Experimental — accuracy not yet measured
In predictive coding, attention is mediated by increasing the gain of populations encoding prediction error.
“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
Posterior beliefs about states of the world can direct attention to sensory features by top-down modulation of sensory precision.
“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
Top-down control of sensory precision explains several psychophysical and physiological aspects of attention, providing a formal link between precision and attention.
“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
Precision is encoded by post-synaptic gain of neuronal populations reporting prediction errors - the principal or pyramidal cells of superficial cortical layers.
“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
Recurrent message passing between hierarchical levels optimizes free energy or suppresses prediction error.
“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)
Cholinergic modulation may play an important role in modulating and gating prediction error signals, a role associated with attention in waking and neurochemical modulation of hierarchical cortical processing during sleep.
“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
The influence of prior predictions or of prediction errors during inference is weighted by their respective precision.
“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]
Transposed to the neural domain, prediction error becomes a kind of proxy for sensory information itself.
“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
Residual errors are able to stand in for the forward flow of sensory information itself.
“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 solution rests on modulating the gain or excitability of neuronal populations reporting prediction error.
“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]
According to some more recent formulations of predictive coding, the propagated error is weighted by the sensory reliability or precision.
“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]
When sensory precision is low, prediction errors are downweighted and observers rely more on predictions; conversely, when it is high, prediction errors are up-weighted and observers rely more strongly on their input.
“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]
Precision has to be estimated and deployed at each level of the hierarchy, a process equated with attention.
“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
We have to infer the deployment of precision, which in a hierarchical setting starts to look like attention.
“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
Precision is encoded by the post-synaptic gain of neurons that report prediction errors, and sensory processing channels that convey precise information are selectively enabled by an increase in their precision.
“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
Reducing sensory precision from the top down explains psychophysical and physiological features of attention.
“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]
Reducing sensory precision from the top down offers a hierarchically and biologically plausible explanation for the attentional field that Rosenberg et al. modeled.
“providing a hierarchically and biologically plausible account of the”
From A more precise look at context in autism (2015) · cites this paper as [4]
Under predictive coding, uncertainty or relative confidence in sensory information can be encoded by the gain of neuronal populations that pass information from one hierarchical cortical level to the next.
“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]
This gain control corresponds to a Bayes-optimal encoding of precision in terms of the excitability of neuronal populations reporting prediction errors.
“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]
This form of attention is distinct from simply boosting sensory precision as in simulations of the Posner paradigm or biased competition.
“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]
The dynamic and context-sensitive control of precision has been associated with attentional gain control in sensory processing.
“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]
Synchronous gain control is closely related to notions of communication through coherence and may reflect an important mechanism for precision engineering in attention.
“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]
Our findings accord with a theoretical Bayesian scheme in which uncertainty, or precision, is an inherent property of the neural code.
“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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Whatever next? Predictive brains, situated agents, and the future of cognitive science
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Canonical Microcircuits for Predictive Coding
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“…suppression of responses to predictable stimuli can coexist with (top-down) attentional enhancement of evoked processing (Wyart et al., 2012): in predictive coding, attention is mediated by increasing the gain of populations encoding prediction error (Spratling, 2008; Feldman and Friston, 2010).”
InfluentialSelf-citationThe brain basis of emotion: a meta-analytic review
Lindquist, K. A., Wager, T. D., Kober, H., Bliss-Moreau, E., & Barrett, L. F. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x11000446 · Cited by 2,320 · see citing works
“Feldman and Friston (2010) argue that attention may be the process of optimising the post-synaptic Commentary/Lindquist et al.: The brain basis of emotion 166 BEHAVIORAL AND BRAIN SCIENCES (2012) 35:3 https://doi.org/10.1017/S0140525X11001750 Downloaded from https://www.cambridge.org/core.”
InfluentialInteroceptive predictions in the brain
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The theory of constructed emotion: an active inference account of interoception and categorization
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“This hypothesis is variously called predictive coding, active inference, or belief propagation (e.g. Rao and Ballard, 1999; Friston, 2010; Seth et al. , 2012; Clark, 2013a,b; Hohwy, 2013; Seth, 2013; Barrett and Simmons, 2015; Chanes and Barrett, 2016; Deneve and Jardri, 2016).”
How Do Expectations Shape Perception?
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Active interoceptive inference and the emotional brain
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Self-citationThe theory of constructed emotion: an active inference account of interoception and categorization
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“…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)].20 Specifically, I hypothesize that precision signals optimize…”
Expectation in perceptual decision making: neural and computational mechanisms
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The Computational Anatomy of Psychosis
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“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).”
BackgroundInfluentialSelf-citationPredictions not commands: Active inference in the motor system
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Self-citationA Bayesian account of ‘hysteria’
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Self-citationLess Is More: Expectation Sharpens Representations in the Primary Visual Cortex
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An aberrant precision account of autism
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Self-citationActive Inference, homeostatic regulation and adaptive behavioural control
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“in Active Inference, attention modulates the relative precision of the different levels of the hierarchy (Feldman and Friston, 2010).”
InfluentialSelf-citationAction understanding and active inference
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Self-citationAn active inference theory of allostasis and interoception in depression
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Hallucinations and strong priors
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Computational psychiatry: the brain as a phantastic organ
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Symptoms and the body: taking the inferential leap
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How prediction errors shape perception, attention, and motivation
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The hierarchical basis of neurovisceral integration
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Confidence in value-based choice
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Active inference, sensory attenuation and illusions
Brown, H., Adams, R. A., Parees, I., Edwards, M., & Friston, K. (2013) · Cognitive Processing · doi:10.1007/s10339-013-0571-3 · Cited by 488 · see citing works
Self-citationPerceptions as Hypotheses: Saccades as Experiments
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Self-citationGreat Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?
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Attention reverses the effect of prediction in silencing sensory signals
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Cerebral hierarchies: predictive processing, precision and the pulvinar
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Self-citationSelective Activation of the Deep Layers of the Human Primary Visual Cortex by Top-Down Feedback
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A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions
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Attention and conscious perception in the hypothesis testing brain
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Autism, oxytocin and interoception
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Self-citationDopamine, affordance and active inference
Friston, K. J., Shiner, T., FitzGerald, T., Galea, J. M., Adams, R., Brown, H., Dolan, R. J., Moran, R., Stephan, K. E., & Bestmann, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002327 · Cited by 381 · see citing works
Self-citationTemporal coding organized by coupled alpha and gamma oscillations prioritize visual processing
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From cognitivism to autopoiesis: towards a computational framework for the embodied mind
Allen, M., & Friston, K. J. (2018) · Synthese · doi:10.1007/s11229-016-1288-5 · Cited by 386 · see citing works
Self-citationA free energy principle for biological systems
Karl, F. (2012) · Entropy · doi:10.3390/e14112100 · Cited by 377 · see citing works
Evaluating the neurophysiological evidence for predictive processing as a model of perception
Walsh, K. S., McGovern, D. P., Clark, A., & O'Connell, R. G. (2020) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14321 · Cited by 333 · see citing works
Seth, A. K. (2014) · Cognitive Neuroscience · doi:10.1080/17588928.2013.877880 · Cited by 346 · see citing works
Neural elements for predictive coding
Shipp, S. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01792 · Cited by 340 · see citing works
Friston, K., & Frith, C. (2015) · Consciousness and Cognition · doi:10.1016/j.concog.2014.12.003 · Cited by 359 · see citing works
Self-citationRepetition suppression and its contextual determinants in predictive coding
Auksztulewicz, R., & Friston, K. (2016) · Cortex · doi:10.1016/j.cortex.2015.11.024 · Cited by 307 · see citing works
Self-citationEmotion words, emotion concepts, and emotional development in children: A constructionist hypothesis
Hoemann, K., Xu, F., & Barrett, L. F. (2019) · Developmental Psychology · doi:10.1037/dev0000686 · Cited by 354 · see citing works
Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns
Barascud, N., Pearce, M. T., Griffiths, T. D., Friston, K. J., & Chait, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508523113 · Cited by 296 · see citing works
Self-citationBeing a Beast Machine: The Somatic Basis of Selfhood
Seth, A. K., & Tsakiris, M. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.08.008 · Cited by 357 · see citing works
“The optimisation of precision-weighting, through changes in precision-related priors (precision expectations) is frequently associated with attention [31].”
Bayesian approaches to autism: Towards volatility, action, and behavior.
Palmer, C. J., Lawson, R. P., & Hohwy, J. (2017) · Psychological Bulletin · doi:10.1037/bul0000097 · Cited by 307 · see citing works
Hierarchical active inference: A theory of motivated control
Pezzulo, G., Rigoli, F., & Friston, K. J. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.01.009 · Cited by 334 · see citing works
Self-citationExpectation and Attention in Hierarchical Auditory Prediction
Chennu, S., Noreika, V., Gueorguiev, D., Blenkmann, A., Kochen, S., Ibanez, A., Owen, A. M., & Bekinschtein, T. A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0114-13.2013 · Cited by 299 · see citing works
Computational psychiatry: towards a mathematically informed understanding of mental illness
Adams, R. A., Huys, Q. J. M., & Roiser, J. P. (2015) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2015-310737 · Cited by 285 · see citing works
Free Energy, Precision and Learning: The Role of Cholinergic Neuromodulation
Moran, R. J., Campo, P., Symmonds, M., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.4255-12.2013 · Cited by 312 · see citing works
Self-citation‘Bodily precision’: A predictive coding account of individual differences in interoceptive accuracy
Ainley, V., Apps, M. A. J., Fotopoulou, A., & Tsakiris, M. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0003 · Cited by 285 · see citing works
A tutorial on the free-energy framework for modelling perception and learning
Bogacz, R. (2017) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2015.11.003 · Cited by 283 · see citing works
Uncertainty, epistemics and active inference
Parr, T., & Friston, K. J. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0376 · Cited by 252 · see citing works
Self-citationActive inference, communication and hermeneutics
Friston, K. J., & Frith, C. D. (2015) · Cortex · doi:10.1016/j.cortex.2015.03.025 · Cited by 266 · see citing works
“The dynamic and contextsensitive control of precision has been associated with attentional gain control in sensory processing (Feldman & Friston, 2010; Jiang, Summerfield, & Egner, 2013) and has been discussed in terms of affordance in active inference and action selection (Cisek, 2007; Frank,…”
BackgroundSelf-citationReflections on agranular architecture: predictive coding in the motor cortex
Shipp, S., Adams, R. A., & Friston, K. J. (2013) · Trends in Neurosciences · doi:10.1016/j.tins.2013.09.004 · Cited by 239 · see citing works
Self-citationActive inference and agency: optimal control without cost functions
Friston, K., Samothrakis, S., & Montague, R. (2012) · Biological Cybernetics · doi:10.1007/s00422-012-0512-8 · Cited by 241 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Smith, R., Badcock, P., & Friston, K. J. (2021) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13138 · Cited by 174 · see citing works
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 · see citing works
“…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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationCan predictive coding explain repetition suppression?
Grotheer, M., & Kovács, G. (2016) · Cortex · doi:10.1016/j.cortex.2015.11.027 · Cited by 108 · see citing works
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 · see citing works
Deep active inference as variational policy gradients
Millidge, B. (2020) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2020.102348 · Cited by 115 · see citing works
Clark, A. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00270 · Cited by 108 · see citing works
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 · see citing works
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 · see citing works
Self-citationActive 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 · see citing works
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 · see citing works
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 · see citing works
Palmer, C. J., Seth, A. K., & Hohwy, J. (2015) · Consciousness and Cognition · doi:10.1016/j.concog.2015.04.007 · Cited by 101 · see citing works
The Faces of Predictive Coding
Brodski, A., Paasch, G.-F., Helbling, S., & Wibral, M. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1529-14.2015 · Cited by 99 · see citing works
Network activity underlying the illusory self‐attribution of a dummy arm
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SanMiguel, I., Saupe, K., & Schröger, E. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00407 · Cited by 89 · see citing works
Predictive Coding in Sensory Cortex
Kok, P., & de Lange, F. P. (2015) · An Introduction to Model-Based Cognitive Neuroscience · doi:10.1007/978-1-4939-2236-9_11 · Cited by 98 · see citing works
- 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
- 163
- 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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