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{{For|the speech processing technology|Linear predictive coding}}
−In [[neuroscience]], [[psychology]] and [[cognitive science]], '''predictive coding''' (also known as '''predictive processing''') is a theory of [[brain]] function which postulates that the brain is constantly generating and updating a "[[mental model]]" of the environment. According to the theory, such a mental model is used to predict input signals from the [[Sense|senses]] that are then compared with the actual input signals from those senses. Predictive coding is one member of a wider set of theories that follow the [[Bayesian approaches to brain function|Bayesian brain hypothesis]].
+In [[neuroscience]], [[psychology]] and [[cognitive science]], '''predictive coding''' (also known as '''predictive processing''') is a theory of [[brain]] function which postulates that the brain is constantly generating and updating a "[[mental model]]" of the environment. According to the theory, such a mental model is used to predict input signals from the [[sense]]s that are then compared with the actual input signals from those senses. Predictive coding is one member of a wider set of theories that follow the [[Bayesian approaches to brain function|Bayesian brain hypothesis]].
==Origins==
Theoretical ancestors to predictive coding date back as early as 1860 with [[Helmholtz]]'s concept of [[unconscious inference]].<ref>{{Cite web |date=2018-03-20 |title=Helmholtz's Treatise on Physiological Optics - Free |url=http://poseidon.sunyopt.edu/BackusLab/Helmholtz/ |access-date=2022-01-05 |website= |archive-url=https://web.archive.org/web/20180320133752/http://poseidon.sunyopt.edu/BackusLab/Helmholtz/ |archive-date=20 March 2018 |url-status=dead}}</ref> Unconscious inference refers to the idea that the human brain fills in visual information to make sense of a scene. For example, if something is relatively smaller than another object in the visual field, the brain uses that information as a likely cue of depth, such that the perceiver ultimately (and involuntarily) experiences depth. The understanding of perception as the interaction between sensory stimuli (bottom-up) and conceptual knowledge (top-down) continued to be established by [[Jerome Bruner]] who, starting in the 1940s, studied the ways in which needs, motivations and expectations influence perception, research that came to be known as 'New Look' psychology. In 1981, [[James McClelland (psychologist)|McClelland]] and [[David Rumelhart|Rumelhart]] examined the interaction between processing features (lines and contours) which form letters, which in turn form words.<ref>{{cite journal |author1=McClelland, J. L. |author2=Rumelhart, D. E. |name-list-style=amp |title=An interactive activation model of context effects in letter perception: I. An account of basic findings |journal=Psychological Review |date=1981 |volume=88 |issue=5 |pages=375–407 |doi=10.1037/0033-295X.88.5.375}}</ref> While the features suggest the presence of a word, they found that when letters were situated in the context of a word, people were able to identify them faster than when they were situated in a non-word without semantic context. McClelland and Rumelhart's parallel processing model describes perception as the meeting of top-down (conceptual) and bottom-up (sensory) elements.
−In the late 1990s, the idea of top-down and bottom-up processing was translated into a computational model of vision by [[Rajesh Rao|Rao]] and [[Dana H. Ballard|Ballard]].<ref name="rb">{{cite journal | doi=10.1038/4580 | title=Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects | year=1999 | last1=Rao | first1=Rajesh P. N. | last2=Ballard | first2=Dana H. | journal=Nature Neuroscience | volume=2 | issue=1 | pages=79–87 | pmid=10195184 }}</ref> Their paper demonstrated that there could be a [[generative model]] of a scene (top-down processing), which would receive feedback via error signals (how much the visual input varied from the prediction), which would subsequently lead to updating the prediction. The computational model was able to replicate well-established receptive field effects, as well as less understood extra-classical receptive field effects such as [[Hypercomplex cell|end-stopping]].
+In the late 1990s, the idea of top-down and bottom-up processing was translated into a computational model of vision by [[Rajesh Rao|Rao]] and [[Dana H. Ballard|Ballard]].<ref name="rb">{{cite journal | doi=10.1038/4580 | title=Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects | year=1999 | last1=Rao | first1=Rajesh P. N. | last2=Ballard | first2=Dana H. | journal=Nature Neuroscience | volume=2 | issue=1 | pages=79–87 | pmid=10195184 }}</ref> Their paper demonstrated that there could be a [[generative model]] of a scene (top-down processing), which would receive feedback via error signals (how much the visual input varied from the prediction), which would subsequently lead to updating the prediction. The computational model was able to replicate well-established receptive field effects, as well as less understood extra-classical receptive field effects such as [[Hypercomplex cell|end-stopping]].
In 2004,<ref>{{Cite journal |last=Grush |first=Rick |date=2004 |title=The emulation theory of representation: Motor control, imagery, and perception |url=https://www.cambridge.org/core/product/identifier/S0140525X04000093/type/journal_article |journal=Behavioral and Brain Sciences |language=en |volume=27 |issue=3 |pages=377–396 |doi=10.1017/S0140525X04000093 |pmid=15736871 |s2cid=514252 |issn=0140-525X|url-access=subscription }}</ref> Rick Grush proposed a model of neural perceptual processing, the [[emulation theory of representation]], according to which the brain constantly generates predictions based on a generative model (what Grush called an ‘emulator’) and compares that prediction to the actual sensory input. The difference, or ‘sensory residual’, would then be used to update the model so as to produce a more accurate estimate of the perceived domain. On Grush’s account, the top-down and bottom-up signals would be combined in a way sensitive to the expected noise (aka uncertainty) in the bottom-up signal, so that in situations in which the sensory signal was known to be less trustworthy, the top-down prediction would be given greater weight, and vice versa. The emulation framework was also shown to be hierarchical, with modality-specific emulators providing top-down expectations for sensory signals as well as higher-level emulators providing expectations of the distal causes of those signals. Grush applied the theory to [[visual perception]], visual and [[motor imagery]], language, and theory of mind phenomena.
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If, instead, the model accurately predicts driving sensory signals, activity at higher levels cancels out activity at lower levels, and the internal model remains unchanged. Thus, predictive coding inverts the conventional view of perception as a mostly bottom-up process, suggesting that it is largely constrained by prior predictions, where signals from the external world only shape perception to the extent that they are propagated up the cortical hierarchy in the form of prediction error.
−Prediction errors can not only be used for inferring distal causes, but also for [[Unsupervised learning|learning]] them via [[neural plasticity]].<ref name="rb"/> Here the idea is that the [[Neural coding|representations]] learned by cortical neurons reflect the [[Efficient_coding_hypothesis#Natural_images_and_statistics|statistical regularities]] in the sensory data. This idea is also present in many other theories of neural learning, such as [[sparse coding]], with the central difference being that in predictive coding not only the connections to sensory inputs are learned (i.e., the [[receptive field]]), but also top-down predictive connections from higher-level representations. This makes predictive coding similar to some other models of hierarchical learning, such as [[Helmholtz machines]] and [[Deep_belief_network|Deep belief networks]], which however employ different learning algorithms. Thus, the dual use of prediction errors for both inference and learning is one of the defining features of predictive coding.<ref>{{cite journal |last1=Friston |first1=Karl |title=Does predictive coding have a future? |journal=Nature Neuroscience |date=August 2018 |volume=21 |issue=8 |pages=1019–1021 |doi=10.1038/s41593-018-0200-7 |pmid=30038278 |url=https://www.nature.com/articles/s41593-018-0200-7 |language=en |issn=1546-1726|url-access=subscription }}</ref>
+Prediction errors can not only be used for inferring distal causes, but also for [[Unsupervised learning|learning]] them via [[neural plasticity]].<ref name="rb"/> Here the idea is that the [[Neural coding|representations]] learned by cortical neurons reflect the [[Efficient coding hypothesis#Natural images and statistics|statistical regularities]] in the sensory data. This idea is also present in many other theories of neural learning, such as [[sparse coding]], with the central difference being that in predictive coding not only the connections to sensory inputs are learned (i.e., the [[receptive field]]), but also top-down predictive connections from higher-level representations. This makes predictive coding similar to some other models of hierarchical learning, such as [[Helmholtz machines]] and [[Deep belief network]]s, which however employ different learning algorithms. Thus, the dual use of prediction errors for both inference and learning is one of the defining features of predictive coding.<ref>{{cite journal |last1=Friston |first1=Karl |title=Does predictive coding have a future? |journal=Nature Neuroscience |date=August 2018 |volume=21 |issue=8 |pages=1019–1021 |doi=10.1038/s41593-018-0200-7 |pmid=30038278 |url=https://www.nature.com/articles/s41593-018-0200-7 |language=en |issn=1546-1726|url-access=subscription }}</ref>
===Precision weighting===
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=== Dual process theory ===
−The [[dual process theory]] of automatic and conscious cognitive processes lays the groundwork of understanding the operation of the human mind in psychology <ref>{{Cite journal |last1=Shiffrin |first1=Richard M. |last2=Schneider |first2=Walter |date=1977 |title=Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. |url=https://doi.org/10.1037//0033-295x.84.2.127 |journal=Psychological Review |volume=84 |issue=2 |pages=127–190 |doi=10.1037/0033-295x.84.2.127 |issn=0033-295X|url-access=subscription }}</ref><ref>{{Cite journal |last=Sloman |first=Steven A. |date=1996 |title=The empirical case for two systems of reasoning. |url=https://doi.org/10.1037//0033-2909.119.1.3 |journal=Psychological Bulletin |volume=119 |issue=1 |pages=3–22 |doi=10.1037/0033-2909.119.1.3 |bibcode=1996PsycB.119....3S |issn=0033-2909|url-access=subscription }}</ref><ref name=":2">{{Cite journal |last1=Evans |first1=Jonathon St. B.T. |last2=Over |first2=David E. |date=2013-09-13 |title=Rationality and Reasoning |url=https://doi.org/10.4324/9780203027677 |journal=Essays in Cognitive Psychology |doi=10.4324/9780203027677 |isbn=978-1-135-47231-3 }}</ref>. Ideas related to dual process theories can be traced to William James’s work in 1890, which distinguished between habitual processes, based on automatic associations formed through experience, and voluntary processes, involving more effortful, conscious reasoning. It reflects a more hierarchical and conscious reasoning that represents a more willful process. These initial concepts of automatic and voluntary cognitive processes map onto modern dual process theory, conceptualized by many psychologists <ref name=":2" /><ref>{{Cite journal |last1=Shiffrin |first1=Richard M. |last2=Schneider |first2=Walter |date=1977 |title=Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. |url=https://doi.org/10.1037//0033-295x.84.2.127 |journal=Psychological Review |volume=84 |issue=2 |pages=127–190 |doi=10.1037/0033-295x.84.2.127 |issn=0033-295X|url-access=subscription }}</ref>.
+The [[dual process theory]] of automatic and conscious cognitive processes lays the groundwork of understanding the operation of the human mind in psychology.<ref name="doi.org">{{Cite journal |last1=Shiffrin |first1=Richard M. |last2=Schneider |first2=Walter |date=1977 |title=Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. |url=https://doi.org/10.1037//0033-295x.84.2.127 |journal=Psychological Review |volume=84 |issue=2 |pages=127–190 |doi=10.1037/0033-295x.84.2.127 |issn=0033-295X|url-access=subscription }}</ref><ref>{{Cite journal |last=Sloman |first=Steven A. |date=1996 |title=The empirical case for two systems of reasoning. |url=https://doi.org/10.1037//0033-2909.119.1.3 |journal=Psychological Bulletin |volume=119 |issue=1 |pages=3–22 |doi=10.1037/0033-2909.119.1.3 |bibcode=1996PsycB.119....3S |issn=0033-2909|url-access=subscription }}</ref><ref name=":2">{{Cite journal |last1=Evans |first1=Jonathon St. B.T. |last2=Over |first2=David E. |date=2013-09-13 |title=Rationality and Reasoning |url=https://doi.org/10.4324/9780203027677 |journal=Essays in Cognitive Psychology |doi=10.4324/9780203027677 |isbn=978-1-135-47231-3 }}</ref> Ideas related to dual process theories can be traced to William James’s work in 1890, which distinguished between habitual processes, based on automatic associations formed through experience, and voluntary processes, involving more effortful, conscious reasoning. It reflects a more hierarchical and conscious reasoning that represents a more willful process. These initial concepts of automatic and voluntary cognitive processes map onto modern dual process theory, conceptualized by many psychologists
−Some researchers have drawn parallels between dual process theories and predictive coding. In this view, automatic processing (often termed “system 1”) is compared to the initial processing of sensory information, whereas deliberate processing (“system 2”) is compared to the active maintenance of internal representations and the comparison between those representations and sensory input. Specifically, as noted by Jonathan Evans <ref>{{Citation |last=Evans |first=Jonathan St B. T. |title=Dual Process Theory |date=2017-11-09 |work=Dual Process Theory 2.0 |pages=137–155 |url=https://doi.org/10.4324/9781315204550-9 |access-date=2025-12-08 |publisher=Routledge |doi=10.4324/9781315204550-9 |isbn=978-1-315-20455-0|url-access=subscription }}</ref>, system 2 of the dual process theory, which is characterized by a reflective process that allows an individual to override the intuitive process (i.e., initial perception of the input), closely aligns with the computation of the prediction error (i.e., discrepancy between expected value and the actual outcome). Although the process of active maintenance of internal representation, the constant monitoring of the conflict between new experiences and the internal representation, and the shifting of the posterior (i.e., updated belief after combining the prior belief and the actual evidence) is not uniquely differentiated in the dual-process model, the shared mechanism that requires a more deliberate and effortful cognitive process is captured by System 2.
+Some researchers have drawn parallels between dual process theories and predictive coding. In this view, automatic processing (often termed “system 1”) is compared to the initial processing of sensory information, whereas deliberate processing (“system 2”) is compared to the active maintenance of internal representations and the comparison between those representations and sensory input. Specifically, as noted by Jonathan Evans,<ref>{{Citation |last=Evans |first=Jonathan St B. T. |chapter=Dual Process Theory |date=2017-11-09 |title=Dual Process Theory 2.0 |pages=137–155 |chapter-url=https://doi.org/10.4324/9781315204550-9 |access-date=2025-12-08 |publisher=Routledge |doi=10.4324/9781315204550-9 |isbn=978-1-315-20455-0|chapter-url-access=subscription }}</ref> system 2 of the dual process theory, which is characterized by a reflective process that allows an individual to override the intuitive process (i.e., initial perception of the input), closely aligns with the computation of the prediction error (i.e., discrepancy between expected value and the actual outcome). Although the process of active maintenance of internal representation, the constant monitoring of the conflict between new experiences and the internal representation, and the shifting of the posterior (i.e., updated belief after combining the prior belief and the actual evidence) is not uniquely differentiated in the dual-process model, the shared mechanism that requires a more deliberate and effortful cognitive process is captured by System 2.
=== Mental representation ===
−Representation in the field of [[cognitive psychology]] and philosophy refers to the mental encoding of external stimuli. Specifically, it is defined as a hypothetical internal cognitive symbol that represents external reality or its abstractions (for more information about representation, see [[Mental representation#Decoding mental representation in cognitive psychology|''Mental Representation'']]). In the field of philosophy, mental representation is treated as a mediator between the real world and the observer (for more information, see [[Philosophy of mind|''Philosophy of Mind'']]). The field of cognitive psychology has attempted to test this concept by using neuroimaging methods, with the most common methods being [[functional magnetic resonance imaging]] (fMRI). This method involves looking at people’s brain activity related to external stimuli (such as showing them a picture)<ref>{{Cite journal |last=Johnson |first=Kenneth O |date=June 2000 |title=Neural Coding |url=https://doi.org/10.1016/s0896-6273(00)81193-9 |journal= Neuron|volume=26 |issue=3 |pages=563–566 |doi=10.1016/s0896-6273(00)81193-9 |pmid=10896153 |issn=0896-6273}}</ref>.
+Representation in the field of [[cognitive psychology]] and philosophy refers to the mental encoding of external stimuli. Specifically, it is defined as a hypothetical internal cognitive symbol that represents external reality or its abstractions (for more information about representation, see [[Mental representation#Decoding mental representation in cognitive psychology|''Mental Representation'']]). In the field of philosophy, mental representation is treated as a mediator between the real world and the observer (for more information, see [[Philosophy of mind|''Philosophy of Mind'']]). The field of cognitive psychology has attempted to test this concept by using neuroimaging methods, with the most common methods being [[functional magnetic resonance imaging]] (fMRI). This method involves looking at people’s brain activity related to external stimuli (such as showing them a picture).<ref>{{Cite journal |last=Johnson |first=Kenneth O |date=June 2000 |title=Neural Coding |url=https://doi.org/10.1016/s0896-6273(00)81193-9 |journal= Neuron|volume=26 |issue=3 |pages=563–566 |doi=10.1016/s0896-6273(00)81193-9 |pmid=10896153 |issn=0896-6273}}</ref>
−In [[computational neuroscience]], active predictive coding models typically include representations of visual stimuli as well as representations of goal-directed behavior. These representations interact to adapt to and learn new concepts<ref>{{Cite journal |last1=Rao |first1=Rajesh P. N. |last2=Gklezakos |first2=Dimitrios C. |last3=Sathish |first3=Vishwas |date=2023-12-12 |title=Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning |url=https://doi.org/10.1162/neco_a_01627 |journal=Neural Computation |volume=36 |issue=1 |pages=1–32 |doi=10.1162/neco_a_01627 |pmid=38052084 |issn=0899-7667}}</ref>. Some work has attempted to link findings in neuroscience and cognitive psychology by examining how prediction errors change over time. Changes in prediction error have been interpreted as evidence that internal representations are constantly adjusted in response to new sensory input, so that they better match events in the external world.
+In [[computational neuroscience]], active predictive coding models typically include representations of visual stimuli as well as representations of goal-directed behavior. These representations interact to adapt to and learn new concepts.<ref>{{Cite journal |last1=Rao |first1=Rajesh P. N. |last2=Gklezakos |first2=Dimitrios C. |last3=Sathish |first3=Vishwas |date=2023-12-12 |title=Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning |url=https://doi.org/10.1162/neco_a_01627 |journal=Neural Computation |volume=36 |issue=1 |pages=1–32 |doi=10.1162/neco_a_01627 |pmid=38052084 |issn=0899-7667}}</ref> Some work has attempted to link findings in neuroscience and cognitive psychology by examining how prediction errors change over time. Changes in prediction error have been interpreted as evidence that internal representations are constantly adjusted in response to new sensory input, so that they better match events in the external world.
==Neural theory in predictive coding==
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These theories assume that the cortical architecture can be divided into hierarchically stacked levels, which correspond to different cortical regions. Every level is thought to house (at least) two types of neurons: “prediction neurons”, which aim to predict the bottom-up inputs to the current level, and "error neurons", which signal the difference between input and prediction. These neurons are thought to be mainly non-superficial and superficial [[pyramidal neurons]], while [[Interneuron#Cortex|interneurons]] take up different functions.<ref name="bastos"/>
−Within cortical regions, there is evidence that different [[cerebral cortex#Layers | cortical layers]] may facilitate the integration of feedforward and feed-backward projections across hierarchies.<ref name="bastos"/> These cortical layers have therefore been assumed to be central in the computation of predictions and prediction errors, with the basic unit being a [[cortical column]].<ref name="bastos"/><ref name="Bennett2020">{{cite journal |last1=Bennett |first1=Max |title=An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex |journal=Frontiers in Neural Circuits |date=2020 |volume=14 |article-number=40 |doi=10.3389/fncir.2020.00040 |pmid=32848632 |pmc=7416357 |doi-access=free }}</ref> A common view is that<ref name="bastos"/><ref>{{cite journal |last1=Keller |first1=Georg B. |last2=Mrsic-Flogel |first2=Thomas D. |title=Predictive Processing: A Canonical Cortical Computation |journal=Neuron |date=October 2018 |volume=100 |issue=2 |pages=424–435 |doi=10.1016/j.neuron.2018.10.003|pmid=30359606 |pmc=6400266 }}</ref>
+Within cortical regions, there is evidence that different [[cerebral cortex#Layers|cortical layers]] may facilitate the integration of feedforward and feed-backward projections across hierarchies.<ref name="bastos"/> These cortical layers have therefore been assumed to be central in the computation of predictions and prediction errors, with the basic unit being a [[cortical column]].<ref name="bastos"/><ref name="Bennett2020">{{cite journal |last1=Bennett |first1=Max |title=An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex |journal=Frontiers in Neural Circuits |date=2020 |volume=14 |article-number=40 |doi=10.3389/fncir.2020.00040 |pmid=32848632 |pmc=7416357 |doi-access=free }}</ref> A common view is that<ref name="bastos"/><ref>{{cite journal |last1=Keller |first1=Georg B. |last2=Mrsic-Flogel |first2=Thomas D. |title=Predictive Processing: A Canonical Cortical Computation |journal=Neuron |date=October 2018 |volume=100 |issue=2 |pages=424–435 |doi=10.1016/j.neuron.2018.10.003|pmid=30359606 |pmc=6400266 }}</ref>
* ''error neurons'' reside in supragranular layers 2 and 3, since these neurons show sparse activity and tend to respond to unexpected events,
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=== Human development ===
−From a developmental perspective, predictive coding has been examined in relation to the biological maturation of the brain systems involved in sensation and cognition, highlighting how the brain’s capacity to generate and update predictions evolves across early life. Evidence from neonatal studies demonstrates that prediction error mechanisms emerge very early in life: event-related potential recordings (patterns of brain activity observed in relation to specific events) show that even newborns differentiate between expected and unexpected sounds, suggesting the presence of a very basic form of sensory prediction <ref>{{Cite journal |last1=Badcock |first1=Nicholas A. |last2=Preece |first2=Kathryn A. |last3=De Wit |first3=Bianca |last4=Glenn |first4=Katharine |last5=Fieder |first5=Nora |last6=Thie |first6=Johnson |last7=McArthur |first7=Genevieve |title=Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children |journal=PeerJ |date=2015 |volume=3 |pages=e907 |doi=10.7717/peerj.907 |doi-access=free }}</ref><ref>{{Cite journal |last1=Cheour |first1=Marie |last2=H.T. Leppänen |first2=Paavo |last3=Kraus |first3=Nina |date=January 2000 |title=Mismatch negativity (MMN) as a tool for investigating auditory discrimination and sensory memory in infants and children |url=https://doi.org/10.1016/s1388-2457(99)00191-1 |journal=Clinical Neurophysiology |volume=111 |issue=1 |pages=4–16 |doi=10.1016/s1388-2457(99)00191-1 |pmid=10656505 |issn=1388-2457|url-access=subscription }}</ref><ref>{{Cite journal |last=Paavilainen |first=Petri |date=May 2013 |title=The mismatch-negativity (MMN) component of the auditory event-related potential to violations of abstract regularities: A review |url=https://doi.org/10.1016/j.ijpsycho.2013.03.015 |journal=International Journal of Psychophysiology |volume=88 |issue=2 |pages=109–123 |doi=10.1016/j.ijpsycho.2013.03.015 |pmid=23542165 |issn=0167-8760|url-access=subscription }}</ref>. As children grow, these predictive capacities become more sophisticated as their brains mature and they gain experience (see more detailed information in [[Development of the nervous system|''Development of the nervous system'']]). Research across later human developmental stages indicates that the development of abilities to direct and control attention and the inferential reasoning process occur together, as repeated interactions with the environment strengthen the brain’s internal models of sensory regularities <ref>{{Cite journal |last1=Rapaport |first1=Hannah |last2=Seymour |first2=Robert A |last3=Benikos |first3=Nicholas |last4=He |first4=Wei |last5=Pellicano |first5=Elizabeth |last6=Brock |first6=Jon |last7=Sowman |first7=Paul F |date=2023-03-16 |title=Investigating predictive coding in younger and older children using MEG and a multi-feature auditory oddball paradigm |url=https://doi.org/10.1093/cercor/bhad054 |journal=Cerebral Cortex |volume=33 |issue=12 |pages=7489–7499 |doi=10.1093/cercor/bhad054 |pmid=36928162 |pmc=10267635 |issn=1047-3211}}</ref><ref>{{cite journal | last1=Ficco | first1=Linda | last2=Mancuso | first2=Lorenzo | last3=Manuello | first3=Jordi | last4=Teneggi | first4=Alessia | last5=Liloia | first5=Donato | last6=Duca | first6=Sergio | last7=Costa | first7=Tommaso | last8=Kovacs | first8=Gyula Zoltán | last9=Cauda | first9=Franco | title=Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network | journal=Scientific Reports | date=2021 | volume=11 | issue=1 | article-number=16258 | doi=10.1038/s41598-021-95603-5 | pmid=34376727 | pmc=8355157 | bibcode=2021NatSR..1116258F }}</ref><ref>{{Cite journal |last1=Thillay |first1=Alix |last2=Roux |first2=Sylvie |last3=Gissot |first3=Valérie |last4=Carteau-Martin |first4=Isabelle |last5=Knight |first5=Robert T. |last6=Bonnet-Brilhault |first6=Frédérique |last7=Bidet-Caulet |first7=Aurélie |date=2015-09-24 |title=Sustained attention and prediction: distinct brain maturation trajectories during adolescence |journal=Frontiers in Human Neuroscience |volume=9 |page=519 |doi=10.3389/fnhum.2015.00519 |pmid=26483653 |pmc=4586321 |doi-access=free |issn=1662-5161}}</ref>(for more information on the sophistication and specialization of neural connections across developmental stages, see [[Synaptic pruning]]). This developmental trajectory has been described as a shift from mainly reactive sensory processing in infancy toward proactive, model-based perception in childhood. As networks of connected brain regions mature, they support top-down modulation, in which prior knowledge shapes how sensory information is processed, and precision weighting, which refers to how strongly prior expectations versus new sensory input are taken into account (see ''Precision Weighting'' section of this Wikipedia page). Altogether, this line of work has been interpreted as suggesting that predictive coding may contribute to the development of efficient perception, attention, and learning across childhood, providing a computational framework for understanding how experience shapes the developing brain.
Studies of predictive coding in a developmental context often involve using repetition suppression (a reduction in a specific pattern of brain activity observed when someone is exposed to the same stimuli repeatedly), as it is commonly treated as a measure of reduced prediction error. In other words, diminished prediction error would indicate that the participant has been updating their mental representation (i.e., expectation) to be closer to the presented stimuli. Therefore, examination of the development of repetition suppression has been treated as a proxy for the development of predictive inference and mental representation. The application of predictive coding in human development is not without its limitations. For instance, most research studies testing predictive coding through neural measures (e.g., [[Event-related potential|event-related brain potentials]]) require responses from the participant, which is not possible for infants. Furthermore, developmental changes in the anatomy and network of the brain make the interpretations of prediction error more complex, which warrants caution in the interpretations of the current literature.<ref>{{Cite journal |last1=Philippsen |first1=Anja |last2=Tsuji |first2=Sho |last3=Nagai |first3=Yukie |date=2022-06-20 |title=Simulating Developmental and Individual Differences of Drawing Behavior in Children Using a Predictive Coding Model |journal=Frontiers in Neurorobotics |language=English |volume=16 |article-number=856184 |doi=10.3389/fnbot.2022.856184 |pmid=35795004 |doi-access=free |issn=1662-5218|pmc=9251405 }}</ref>
==== Neurodevelopmental disorders ====
−Differences in predictive coding processes have been proposed to play a role in neurodevelopmental disorders, such as [[autism spectrum disorders]] and [[Attention-deficit hyperactivity disorder (ADHD)|Attention-Deficit Hyperactivity Disorder (ADHD)]]. Given the role of predictive coding in guiding the perception of the environment as well as further interaction with the environment, some authors have suggested that differences in attention and cognitive processes related to predictive coding could serve as potential biomarkers, or biological correlates, for understanding neurodevelopmental disorders <ref>{{Cite journal |last1=Marais |first1=Anne-Lise |last2=Roche-Labarbe |first2=Nadege |date=April 2025 |title=Predictive coding and attention in developmental cognitive neuroscience and perspectives for neurodevelopmental disorders |url=https://doi.org/10.1016/j.dcn.2025.101519 |journal=Developmental Cognitive Neuroscience |volume=72 |article-number=101519 |doi=10.1016/j.dcn.2025.101519 |pmid=39864185 |pmc=11795830 |issn=1878-9293}}</ref>.
+Differences in predictive coding processes have been proposed to play a role in neurodevelopmental disorders, such as [[autism spectrum disorders]] and [[Attention-deficit hyperactivity disorder (ADHD)|Attention-Deficit Hyperactivity Disorder (ADHD)]]. Given the role of predictive coding in guiding the perception of the environment as well as further interaction with the environment, some authors have suggested that differences in attention and cognitive processes related to predictive coding could serve as potential biomarkers, or biological correlates, for understanding neurodevelopmental disorders.<ref>{{Cite journal |last1=Marais |first1=Anne-Lise |last2=Roche-Labarbe |first2=Nadege |date=April 2025 |title=Predictive coding and attention in developmental cognitive neuroscience and perspectives for neurodevelopmental disorders |url=https://doi.org/10.1016/j.dcn.2025.101519 |journal=Developmental Cognitive Neuroscience |volume=72 |article-number=101519 |doi=10.1016/j.dcn.2025.101519 |pmid=39864185 |pmc=11795830 |issn=1878-9293}}</ref>
−Under typical development, the predictive coding framework suggests that perception and interpretation of the perceived information rely on higher-order cognitive processes that minimize prediction error by continuously adjusting expectations to match incoming sensory input. According to predictive coding accounts, individuals with neurodevelopmental disorders, such as [[Autism|autism spectrum disorder (ASD)]] and [[Attention deficit hyperactivity disorder|ADHD]], may show imbalances in how much weight is given to prior expectations versus incoming sensory evidence—a phenomenon sometimes referred to as ''precision weighting dysfunction''<ref>{{Cite journal |last1=Gonzalez-Gadea |first1=Maria Luz |last2=Chennu |first2=Srivas |last3=Bekinschtein |first3=Tristan A. |last4=Rattazzi |first4=Alexia |last5=Beraudi |first5=Ana |last6=Tripicchio |first6=Paula |last7=Moyano |first7=Beatriz |last8=Soffita |first8=Yamila |last9=Steinberg |first9=Laura |last10=Adolfi |first10=Federico |last11=Sigman |first11=Mariano |last12=Marino |first12=Julian |last13=Manes |first13=Facundo |last14=Ibanez |first14=Agustin |date=November 2015 |title=Predictive coding in autism spectrum disorder and attention deficit hyperactivity disorder |url=https://doi.org/10.1152/jn.00543.2015 |journal=Journal of Neurophysiology |volume=114 |issue=5 |pages=2625–2636 |doi=10.1152/jn.00543.2015 |pmid=26311184 |pmc=4643094 |issn=0022-3077}}</ref>. For example, in autism, research studies suggest that prior beliefs may be underweighted, leading to an overreliance on moment-to-moment sensory input and difficulties filtering out irrelevant stimuli, which manifests as sensory hypersensitivity and reduced ability to account for context when processing the stimulus<ref>{{Cite journal |last1=Cannon |first1=Jonathan |last2=O'Brien |first2=Amanda M. |last3=Bungert |first3=Lindsay |last4=Sinha |first4=Pawan |date=2021-02-11 |title=Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence |url=https://doi.org/10.1002/aur.2482 |journal=Autism Research |volume=14 |issue=4 |pages=604–630 |doi=10.1002/aur.2482 |pmid=33570249 |pmc=8043993 |issn=1939-3792}}</ref>. Across disorders, such differences have been proposed to lead to less accurate internal representations, which may impair the brain’s ability to form accurate predictions about social cues, rewards, and environments. As a result, predictive coding abnormalities have been proposed as a possible cognitive model that could help link diverse symptom profiles in neurodevelopmental conditions to underlying differences in hierarchical information processing and learning.
+Under typical development, the predictive coding framework suggests that perception and interpretation of the perceived information rely on higher-order cognitive processes that minimize prediction error by continuously adjusting expectations to match incoming sensory input. According to predictive coding accounts, individuals with neurodevelopmental disorders, such as [[Autism|autism spectrum disorder (ASD)]] and [[Attention deficit hyperactivity disorder|ADHD]], may show imbalances in how much weight is given to prior expectations versus incoming sensory evidence—a phenomenon sometimes referred to as ''precision weighting dysfunction''
=== Psychopathology ===
Altered predictive coding in psychological disorders has received wide attention, likely in an attempt to explain how symptoms of psychological disorders occur. Below are the descriptions of the current research looking at how problems with predictive coding may contribute to different psychological disorders.
−[[Psychotic disorders|Psychotic Disorders]]. Psychotic disorders are characterized by symptoms of hallucination (seeing, hearing, feeling, smelling, or tasting something that is not actually there) and delusion (a strongly held false belief that persists despite clear conflicting evidence). In applications of predictive coding, a mismatch between priors and prediction errors may explain these psychotic symptoms<ref>{{Cite journal |last1=Sterzer |first1=Philipp |last2=Adams |first2=Rick A. |last3=Fletcher |first3=Paul |last4=Frith |first4=Chris |last5=Lawrie |first5=Stephen M. |last6=Muckli |first6=Lars |last7=Petrovic |first7=Predrag |last8=Uhlhaas |first8=Peter |last9=Voss |first9=Martin |last10=Corlett |first10=Philip R. |date=November 2018 |title=The Predictive Coding Account of Psychosis |url=https://doi.org/10.1016/j.biopsych.2018.05.015 |journal=Biological Psychiatry |volume=84 |issue=9 |pages=634–643 |doi=10.1016/j.biopsych.2018.05.015 |pmid=30007575 |pmc=6169400 |issn=0006-3223}}</ref><ref>{{Cite journal |last1=Corlett |first1=P. R. |last2=Frith |first2=C. D. |last3=Fletcher |first3=P. C. |date=2009-05-28 |title=From drugs to deprivation: a Bayesian framework for understanding models of psychosis |url=https://doi.org/10.1007/s00213-009-1561-0 |journal=Psychopharmacology |volume=206 |issue=4 |pages=515–530 |doi=10.1007/s00213-009-1561-0 |pmid=19475401 |pmc=2755113 |issn=0033-3158}}</ref>. There are three ways in which impaired predictive coding might contribute to these symptoms: 1) overweighing of sensory prediction errors, 2) weakened top-down priors, and 3) disrupted hierarchical communication between frontal and sensory regions<ref>{{Cite journal |last1=Keller |first1=Georg B. |last2=Sterzer |first2=Philipp |date=2024-08-08 |title=Predictive Processing: A Circuit Approach to Psychosis |url=https://doi.org/10.1146/annurev-neuro-100223-121214 |journal=Annual Review of Neuroscience |volume=47 |issue=1 |pages=85–101 |doi=10.1146/annurev-neuro-100223-121214 |pmid=38424472 |issn=0147-006X|doi-access=free }}</ref>. However, research disentangling different contributors to prediction error is limited.
+[[Psychotic disorders|Psychotic Disorders]]. Psychotic disorders are characterized by symptoms of hallucination (seeing, hearing, feeling, smelling, or tasting something that is not actually there) and delusion (a strongly held false belief that persists despite clear conflicting evidence). In applications of predictive coding, a mismatch between priors and prediction errors may explain these psychotic symptoms.<ref>{{Cite journal |last1=Sterzer |first1=Philipp |last2=Adams |first2=Rick A. |last3=Fletcher |first3=Paul |last4=Frith |first4=Chris |last5=Lawrie |first5=Stephen M. |last6=Muckli |first6=Lars |last7=Petrovic |first7=Predrag |last8=Uhlhaas |first8=Peter |last9=Voss |first9=Martin |last10=Corlett |first10=Philip R. |date=November 2018 |title=The Predictive Coding Account of Psychosis |url=https://doi.org/10.1016/j.biopsych.2018.05.015 |journal=Biological Psychiatry |volume=84 |issue=9 |pages=634–643 |doi=10.1016/j.biopsych.2018.05.015 |pmid=30007575 |pmc=6169400 |issn=0006-3223}}</ref><ref>{{Cite journal |last1=Corlett |first1=P. R. |last2=Frith |first2=C. D. |last3=Fletcher |first3=P. C. |date=2009-05-28 |title=From drugs to deprivation: a Bayesian framework for understanding models of psychosis |url=https://doi.org/10.1007/s00213-009-1561-0 |journal=Psychopharmacology |volume=206 |issue=4 |pages=515–530 |doi=10.1007/s00213-009-1561-0 |pmid=19475401 |pmc=2755113 |issn=0033-3158}}</ref> There are three ways in which impaired predictive coding might contribute to these symptoms: 1) overweighing of sensory prediction errors, 2) weakened top-down priors, and 3) disrupted hierarchical communication between frontal and sensory regions
−Unlike typical conditions, where perception depends on balancing prior expectations (top-down predictions) with sensory evidence (bottom-up input), weighted by their precision, or estimated reliability, some studies show evidence that precision weighting may be dysregulated in people with psychosis, which leads to either underweighted priors or overweighted sensory prediction errors<ref name=":3">{{Cite journal |last1=Haarsma |first1=J. |last2=Fletcher |first2=P. C. |last3=Griffin |first3=J. D. |last4=Taverne |first4=H. J. |last5=Ziauddeen |first5=H. |last6=Spencer |first6=T. J. |last7=Miller |first7=C. |last8=Katthagen |first8=T. |last9=Goodyer |first9=I. |last10=Diederen |first10=K. M. J. |last11=Murray |first11=G. K. |date=2020-06-24 |title=Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis |url=https://doi.org/10.1038/s41380-020-0803-8 |journal=Molecular Psychiatry |volume=26 |issue=9 |pages=5320–5333 |doi=10.1038/s41380-020-0803-8 |pmid=32576965 |issn=1359-4184|pmc=8589669 }}</ref>. Under this account, internal noise may be misinterpreted as meaningful sensory data, which could contribute to hallucinations, and spurious associations may contribute to delusional beliefs that are resistant to updating. Neurophysiological evidence supports this imbalance: individuals with schizophrenia show reduced mismatch negativity (MMN) and impaired prediction-error signaling in frontotemporal circuits of the brain, indicating failures to suppress or appropriately update sensory predictions<ref>{{Cite journal |last1=Keller |first1=Georg B. |last2=Sterzer |first2=Philipp |date=2024-08-08 |title=Predictive Processing: A Circuit Approach to Psychosis |url=https://doi.org/10.1146/annurev-neuro-100223-121214 |journal=Annual Review of Neuroscience |volume=47 |issue=1 |pages=85–101 |doi=10.1146/annurev-neuro-100223-121214 |pmid=38424472 |issn=0147-006X|doi-access=free }}</ref><ref>{{Cite journal |last1=Erickson |first1=Molly A. |last2=Ruffle |first2=Abigail |last3=Gold |first3=James M. |date=June 2016 |title=A Meta-Analysis of Mismatch Negativity in Schizophrenia: From Clinical Risk to Disease Specificity and Progression |url=https://doi.org/10.1016/j.biopsych.2015.08.025 |journal=Biological Psychiatry |volume=79 |issue=12 |pages=980–987 |doi=10.1016/j.biopsych.2015.08.025 |pmid=26444073 |pmc=4775447 |issn=0006-3223}}</ref>.
+Unlike typical conditions, where perception depends on balancing prior expectations (top-down predictions) with sensory evidence (bottom-up input), weighted by their precision, or estimated reliability, some studies show evidence that precision weighting may be dysregulated in people with psychosis, which leads to either underweighted priors or overweighted sensory prediction errors
−At higher cognitive levels, some researchers link predictive coding accounts to the concept of aberrant salience, which refers to the attribution of undue importance to stimuli that would typically be considered irrelevant. This mechanism aligns with dopaminergic dysfunction, as [[dopamine]] is hypothesized to encode the precision of prediction errors; hyperdopaminergic states amplify noisy error signals, fueling delusional inferences and unstable perception. Together, these findings have been interpreted as consistent with the idea that psychosis may involve a breakdown in hierarchical predictive coding, in which disturbances in both low-level sensory prediction and high-level belief formation interact to produce characteristic symptoms<ref name=":3" />.
+At higher cognitive levels, some researchers link predictive coding accounts to the concept of aberrant salience, which refers to the attribution of undue importance to stimuli that would typically be considered irrelevant. This mechanism aligns with dopaminergic dysfunction, as [[dopamine]] is hypothesized to encode the precision of prediction errors; hyperdopaminergic states amplify noisy error signals, fueling delusional inferences and unstable perception. Together, these findings have been interpreted as consistent with the idea that psychosis may involve a breakdown in hierarchical predictive coding, in which disturbances in both low-level sensory prediction and high-level belief formation interact to produce characteristic symptoms.<ref name=":3" />
−[[Eating disorder|Eating Disorders]]. The predictive coding framework has been applied to the study of eating disorders. In this approach, some theorists propose that disordered eating behaviors may partly arise from differences in interoception, the perception of internal bodily signals. Studies of interoception in the eating disorder field have focused on gastrointestinal [[interoception]], which is defined as the process by which the nervous system detects and integrates signals originating from the gastrointestinal system. Specifically, recent studies started focusing on the relationships between different facets of gastrointestinal interoception profiles and various disordered eating behaviors (e.g., [[binge eating]], [[restrictive eating]]), warranting the utility of a predictive coding framework in furthering the understanding of the mechanisms that drive disordered eating behaviors <ref>{{Cite journal |last1=Poovey |first1=Kendall |last2=Rancourt |first2=Diana |date=May 2024 |title=Visceral sensitivity, hunger responsiveness, and satiety responsiveness: Associations between facets of gastrointestinal interoception and disordered eating profiles in an undergraduate sample |url=https://doi.org/10.1016/j.appet.2024.107252 |journal=Appetite |volume=196 |article-number=107252 |doi=10.1016/j.appet.2024.107252 |pmid=38355050 |issn=0195-6663|url-access=subscription }}</ref><ref>{{Cite journal |last1=Khalsa |first1=Sahib S. |last2=Berner |first2=Laura A. |last3=Anderson |first3=Lisa M. |date=January 2022 |title=Gastrointestinal Interoception in Eating Disorders: Charting a New Path |url=https://doi.org/10.1007/s11920-022-01318-3 |journal=Current Psychiatry Reports |volume=24 |issue=1 |pages=47–60 |doi=10.1007/s11920-022-01318-3 |pmid=35061138 |pmc=8898253 |issn=1523-3812}}</ref><ref>{{Cite journal |last1=Jeune |first1=Shanté C. |last2=Graziano |first2=Paulo |last3=Campa |first3=Adriana |last4=Coccia |first4=Catherine C. |date=2024-03-05 |title=Interoception and self-regulation of eating behaviors and weight status in college students |url=https://doi.org/10.1080/07448481.2024.2319200 |journal=Journal of American College Health |volume=73 |issue=6 |pages=2605–2616 |doi=10.1080/07448481.2024.2319200 |pmid=38442359 |issn=0744-8481|url-access=subscription }}</ref><ref>{{Cite journal |last=Herbert |first=Beate M. |date=October 2020 |title=Interoception and Its Role for Eating, Obesity, and Eating Disorders |url=https://doi.org/10.1027/2512-8442/a000062 |journal=European Journal of Health Psychology |volume=27 |issue=4 |pages=188–205 |doi=10.1027/2512-8442/a000062 |issn=2512-8442|url-access=subscription }}</ref>.
+[[Eating disorder|Eating Disorders]]. The predictive coding framework has been applied to the study of eating disorders. In this approach, some theorists propose that disordered eating behaviors may partly arise from differences in interoception, the perception of internal bodily signals. Studies of interoception in the eating disorder field have focused on gastrointestinal [[interoception]], which is defined as the process by which the nervous system detects and integrates signals originating from the gastrointestinal system. Specifically, recent studies started focusing on the relationships between different facets of gastrointestinal interoception profiles and various disordered eating behaviors (e.g., [[binge eating]], [[restrictive eating]]), warranting the utility of a predictive coding framework in furthering the understanding of the mechanisms that drive disordered eating behaviors
===Computer science===
Line 95Line 95
===Challenges===
−One of the biggest challenges to test predictive coding has been the imprecision of exactly how prediction error minimization works.<ref name="kt">{{cite journal | doi=10.3389/fncom.2015.00111 | doi-access=free | title=Is predictive coding theory articulated enough to be testable? | year=2015 | last1=Kogo | first1=Naoki | last2=Trengove | first2=Chris | journal=Frontiers in Computational Neuroscience | volume=9 | page=111 | pmid=26441621 | pmc=4561670 }}</ref> In some studies, the increase in [[Blood-oxygen-level dependent imaging|BOLD]] signal has been interpreted as error signal while in others it indicates changes in the input representation.<ref name="kt"/> A crucial question that needs to be addressed is what exactly constitutes error signal and how it is computed at each level of information processing.<ref name="bastos">{{cite journal | doi=10.1016/j.neuron.2012.10.038 | title=Canonical Microcircuits for Predictive Coding | year=2012 | last1=Bastos | first1=Andre M. | last2=Usrey | first2=W. Martin | last3=Adams | first3=Rick A. | last4=Mangun | first4=George R. | last5=Fries | first5=Pascal | last6=Friston | first6=Karl J. | journal=Neuron | volume=76 | issue=4 | pages=695–711 | pmid=23177956 | pmc=3777738 }}</ref> Another challenge that has been posed is predictive coding's computational tractability. According to Kwisthout and van Rooij, the subcomputation in each level of the predictive coding framework potentially hides a computationally intractable problem, which amounts to “intractable hurdles” that computational modelers have yet to overcome.<ref>{{cite journal | doi=10.1007/s42113-019-00032-3 | title=Computational Resource Demands of a Predictive Bayesian Brain | year=2020 | last1=Kwisthout | first1=Johan | last2=Van Rooij | first2=Iris | journal=Computational Brain & Behavior | volume=3 | issue=2 | pages=174–188 | s2cid=196045530 | hdl=2066/218854 | hdl-access=free }}</ref>
+One of the biggest challenges to test predictive coding has been the imprecision of exactly how prediction error minimization works.<ref name="kt">{{cite journal | doi=10.3389/fncom.2015.00111 | doi-access=free | title=Is predictive coding theory articulated enough to be testable? | year=2015 | last1=Kogo | first1=Naoki | last2=Trengove | first2=Chris | journal=Frontiers in Computational Neuroscience | volume=9 | page=111 | pmid=26441621 | pmc=4561670 }}</ref> In some studies, the increase in [[Blood-oxygen-level dependent imaging|BOLD]] signal has been interpreted as error signal while in others it indicates changes in the input representation.<ref name="kt"/> A crucial question that needs to be addressed is what exactly constitutes error signal and how it is computed at each level of information processing.<ref name="bastos">{{cite journal | doi=10.1016/j.neuron.2012.10.038 | title=Canonical Microcircuits for Predictive Coding | year=2012 | last1=Bastos | first1=Andre M. | last2=Usrey | first2=W. Martin | last3=Adams | first3=Rick A. | last4=Mangun | first4=George R. | last5=Fries | first5=Pascal | last6=Friston | first6=Karl J. | journal=Neuron | volume=76 | issue=4 | pages=695–711 | pmid=23177956 | pmc=3777738 }}</ref> Another challenge that has been posed is predictive coding's computational tractability. According to Kwisthout and van Rooij, the subcomputation in each level of the predictive coding framework potentially hides a computationally intractable problem, which amounts to “intractable hurdles” that computational modelers have yet to overcome.<ref>{{cite journal | doi=10.1007/s42113-019-00032-3 | title=Computational Resource Demands of a Predictive Bayesian Brain | year=2020 | last1=Kwisthout | first1=Johan | last2=Van Rooij | first2=Iris | journal=Computational Brain & Behavior | volume=3 | issue=2 | pages=174–188 | s2cid=196045530 | hdl=2066/218854 | hdl-access=free }}</ref>
−Future research could focus on clarifying the neurophysiological mechanism and computational model of predictive coding.{{according to|date=January 2021}}
+Future research could focus on clarifying the neurophysiological mechanism and computational model of predictive coding.{{according to whom|date=January 2021}}
== Studies of predictive coding ==
=== [[Electroencephalography|EEG/ERP]] ===
−[[Electroencephalography|Electroencephalography (EEG)]] and [[Event-related potential|event-related potential (ERP)]] research have been widely used to investigate predictive coding in humans. Within this framework, ERP components are often interpreted as neural markers of prediction-error signaling, generated when sensory input differs from what is expected. For example, the [[Mismatch negativity|mismatch negativity (MMN)]], elicited by unexpected sounds, reflects automatic detection of prediction violations and adapts with learning and attention. Components related to performance monitoring, such as the [[error-related negativity]] (ERN/Ne) and error positivity, have been associated with the discrepancy between the internal representation of the correct response versus actual response <ref>{{Cite journal |last1=Falkenstein |first1=Michael |last2=Hoormann |first2=Jörg |last3=Christ |first3=Stefan |last4=Hohnsbein |first4=Joachim |date=January 2000 |title=ERP components on reaction errors and their functional significance: a tutorial |url=https://doi.org/10.1016/s0301-0511(99)00031-9 |journal=Biological Psychology |volume=51 |issue=2–3 |pages=87–107 |doi=10.1016/s0301-0511(99)00031-9 |pmid=10686361 |issn=0301-0511|url-access=subscription }}</ref><ref>{{Cite journal |last1=Orr |first1=Joseph M. |last2=Carrasco |first2=Melisa |date=2011-04-20 |title=The Role of the Error Positivity in the Conscious Perception of Errors |url=https://doi.org/10.1523/jneurosci.0279-11.2011 |journal=The Journal of Neuroscience |volume=31 |issue=16 |pages=5891–5892 |doi=10.1523/jneurosci.0279-11.2011 |pmid=21508213 |issn=0270-6474|pmc=6632949 }}</ref><ref>{{Cite journal |last1=Gehring |first1=William J. |last2=Goss |first2=Brian |last3=Coles |first3=Michael G. H. |last4=Meyer |first4=David E. |last5=Donchin |first5=Emanuel |date=March 2018 |title=The Error-Related Negativity |url=https://doi.org/10.1177/1745691617715310 |journal=Perspectives on Psychological Science |volume=13 |issue=2 |pages=200–204 |doi=10.1177/1745691617715310 |pmid=29592655 |issn=1745-6916|url-access=subscription }}</ref><ref>{{Cite journal |last1=Ullsperger |first1=Markus |last2=Harsay |first2=Helga A. |last3=Wessel |first3=Jan R. |last4=Ridderinkhof |first4=K. Richard |date=2010-05-29 |title=Conscious perception of errors and its relation to the anterior insula |url=https://doi.org/10.1007/s00429-010-0261-1 |journal=Brain Structure and Function |volume=214 |issue=5–6 |pages=629–643 |doi=10.1007/s00429-010-0261-1 |pmid=20512371 |issn=1863-2653|doi-access=free }}</ref>. Later components such as the [[P300 (neuroscience)|P300]] and feedback-related negativity (FRN) have been linked to higher-order updating of cognitive or reward models. These findings have been interpreted as consistent with predictive coding models in which processing is organized hierarchically, from early perceptual mismatches to more abstract belief revisions.
−Despite these insights, linking ERP components uniquely to prediction errors remains challenging. ERPs represent aggregate neural activity from overlapping sources, and their amplitudes are influenced by multiple cognitive processes such as attention, novelty, and salience of the stimuli, practice effects, and habituation to the stimuli. For instance, P300 amplitude often reflects general updating or arousal. EEG and ERP paradigms have provided important evidence for predictive processing, although alternative explanations remain. Nonetheless, careful experimental design and model-based analyses are required to distinguish between genuine prediction-error signals from broader cognitive or perceptual influences.<ref>{{Citation |last1=Hawkins |first1=Guy E. |title=Cognitive Models as a Tool to Link Decision Behavior with EEG Signals |date=2023-11-20 |work=An Introduction to Model-Based Cognitive Neuroscience |pages=241–263 |url=https://doi.org/10.1007/978-3-031-45271-0_10 |access-date=2025-12-08 |place=Cham |publisher=Springer International Publishing |isbn=978-3-031-45270-3 |last2=Cavanagh |first2=James F. |last3=Brown |first3=Scott D. |last4=Steyvers |first4=Mark |doi=10.1007/978-3-031-45271-0_10 |url-access=subscription }}</ref>
+Despite these insights, linking ERP components uniquely to prediction errors remains challenging. ERPs represent aggregate neural activity from overlapping sources, and their amplitudes are influenced by multiple cognitive processes such as attention, novelty, and salience of the stimuli, practice effects, and habituation to the stimuli. For instance, P300 amplitude often reflects general updating or arousal. EEG and ERP paradigms have provided important evidence for predictive processing, although alternative explanations remain. Nonetheless, careful experimental design and model-based analyses are required to distinguish between genuine prediction-error signals from broader cognitive or perceptual influences.<ref>{{Citation |last1=Hawkins |first1=Guy E. |chapter=Cognitive Models as a Tool to Link Decision Behavior with EEG Signals |date=2023-11-20 |title=An Introduction to Model-Based Cognitive Neuroscience |pages=241–263 |chapter-url=https://doi.org/10.1007/978-3-031-45271-0_10 |access-date=2025-12-08 |place=Cham |publisher=Springer International Publishing |isbn=978-3-031-45270-3 |last2=Cavanagh |first2=James F. |last3=Brown |first3=Scott D. |last4=Steyvers |first4=Mark |doi=10.1007/978-3-031-45271-0_10 |chapter-url-access=subscription }}</ref>
== See also ==
