Expectation and Surprise Determine Neural Population Responses in the Ventral Visual Stream
Open accessAbstract
Visual cortex is traditionally viewed as a hierarchy of neural feature detectors, with neural population responses being driven by bottom-up stimulus features. Conversely, “predictive coding” models propose that each stage of the visual hierarchy harbors two computationally distinct classes of processing unit: representational units that encode the conditional probability of a stimulus and provide predictions to the next lower level; and error units that encode the mismatch between predictions and bottom-up evidence, and forward prediction error to the next higher level. Predictive coding therefore suggests that neural population responses in category-selective visual regions, like the fusiform face area (FFA), reflect a summation of activity related to prediction (“face expectation”) and prediction error (“face surprise”), rather than a homogenous feature detection response. We tested the rival hypotheses of the feature detection and predictive coding models by collecting functional magnetic resonance imaging data from the FFA while independently varying both stimulus features (faces vs houses) and subjects' perceptual expectations regarding those features (low vs medium vs high face expectation). The effects of stimulus and expectation factors interacted, whereby FFA activity elicited by face and house stimuli was indistinguishable under high face expectation and maximally differentiated under low face expectation. Using computational modeling, we show that these data can be explained by predictive coding but not by feature detection models, even when the latter are augmented with attentional mechanisms. Thus, population responses in the ventral visual stream appear to be determined by feature expectation and surprise rather than by stimulus features per se.
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The full pattern of effects in Egner et al. was best explained by a PP model in which BOLD responses were dominated by strong prediction-driven face-selective expectation unit activity and weak face-selective error unit activity on both trial types.
“The full pattern of effects was best explained by a PP model in which BOLD responses were dominated by strong prediction‐driven, face‐selective expectation unit activity and weak face‐selective error unit activity on both trial types (since houses would evoke minimal error signals from face‐selective cells).”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [76]
The Results (Modeling data) report the opposite weighting: face-surprise (error) activity contributed about twice as strongly to FFA BOLD responses as face-expectation activity. Materials and Methods says the model assigned zero face-selective error response to house trials, rather than weak error activity on both trial types. (LLM, conf=0.94)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredEgner et al. observed FFA responses to faces and houses were statistically indistinguishable when those stimuli were expected.
“The authors also made the surprising observation that, despite its well‐established role in face processing, 86 FFA responses to faces and houses were statistically indistinguishable when those stimuli were expected.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [76]
The Results (fMRI data; page unavailable) report that FFA responses to faces and houses were statistically indistinguishable under high face expectation (t(15) = 0.4, p > 0.1). The qualifier matters: faces were expected in that condition, but houses were less likely, so the paper does not show that the responses were indistinguishable when both stimuli were expected. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA number of human studies have demonstrated that magnitude of stimulus-evoked responses is inversely proportional to degree to which stimulus was expected.
“and a number of human studies have demonstrated that the magnitude of stimulus‐evoked responses is inversely proportional to the degree to which the stimulus was expected 75 , 76 , 83 , 84”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [76]
In Results > Modeling data, the paper says that greater probability of a face was associated with less FFA activity. But Results > fMRI data reports no overall main effect of expectation: the linear finding concerns the difference between face- and house-evoked responses, not a general inverse relationship between response magnitude and how expected any stimulus was. The source therefore supports the claim for a specific stimulus and brain region, but not its unqualified form. (LLM, conf=0.85)
Experimental — accuracy not yet measuredMany studies have reported that stimulus-evoked responses are inversely proportional to degree to which stimulus was expected, although this effect is not universally observed.
“Indeed, as mentioned above, many studies have reported that stimulus‐evoked responses are inversely proportional to the degree to which the stimulus was expected, 47 , 75 , 76 , 83 , 84 , 85 , 98 although this effect is not universally observed. 79 , 82”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [76]
The Results (fMRI data; page unavailable) show that face-versus-house response differences in the FFA increased linearly as face expectation decreased. But the paper reports no main effect of expectation on FFA responses, so it does not establish that stimulus-evoked responses generally decrease in proportion to how expected a stimulus is. The sentence’s caveat about effects not being universally observed is attached to other citations. (LLM, conf=0.84)
Experimental — accuracy not yet measuredEgner et al. show that neurons in the fusiform face area respond every bit as strongly to non-face house stimuli under high expectation of faces as they do to face stimuli.
“Egner et al. (2010) show that neurons in the fusiform face area (FFA) respond every bit as strongly to non-face (in this experiment, house) stimuli under high expectation of faces as they do to face-stimuli.”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Egner et al. 2010
The Results (fMRI data) report that FFA responses to faces and houses were statistically indistinguishable under high face expectation, and the Discussion describes identical FFA population responses in that condition. The study measured fMRI BOLD activity, not responses of individual neurons, so the claim's wording overstates what was observed. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Expectation suppression is observed in non-invasive fMRI BOLD and EEG/MEG signals, and in spike rates in non-human primates and rodents.
“Moreover, expectation suppression is observed not only in non-invasive fMRI blood oxygen level-dependent (BOLD) [65,72,73] and electroencephalography (EEG)/magnetoencephalography (MEG) signals [68,69,77], but also in spike rates in non-human primates [66,67,74,76] and rodents [70,75,78].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [73]
Egner et al. observed effects consistent with ES in FFA when examining BOLD responses to expected or unexpected face and house stimuli.
“Egner et al. 76 examined BOLD responses to face and house stimuli that were either expected or unexpected, and observed effects consistent with ES in FFA.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [76]
Only under conditions of low face expectation was FFA response maximally different for face and house probes, suggesting that FFA responses appear to be determined by feature expectation and surprise rather than by stimulus features per se.
“Only under conditions of low face expectation was FFA response maximally different for the face and house probes, suggesting that ' [FFA] responses appear to be determined by feature expectation and surprise rather than by stimulus features per se ' (Egner et al. 2010, p. 16601).”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Egner et al. 2010
FFA activity displayed an interaction of stimulus feature and expectation factors, where differentiation between FFA responses to face and house stimuli decreased linearly with increasing face expectation, with face and house evoked signals indistinguishable under high face expectation.
“FFA activity displayed an interaction of stimulus feature and expectation factors, where the differentiation between FFA responses to face and house stimuli decreased linearly with increasing levels of face expectation, with face and house evoked signals being indistinguishable under high face expectation. (Egner et al. 2010, p. 16607)”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Egner et al. 2010
The empirical bet is that the encoding of predictions and prediction errors may be a ubiquitous feature of cognition in the brain rather than a curiosity of reward learning or motor planning.
“The empirical bet is thus, as Egner and colleagues recently put it, that ' the encoding of predictions (based on internal forward models) and prediction errors may be a ubiquitous feature of cognition in the brain … rather than a curiosity of reward learning … or motor planning ' (Egner et al. 2010, p. 16607).”
From Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) · cites this paper as Egner et al. 2010
Studies using orthogonal designs or multivoxel pattern analysis have been able to identify activity related to error-related and probabilistic processes.
“studies using carefully considered designs in which error-related and probabilistic information are orthogonal (Egner et al., 2010), or using advanced analysis techniques such as multivoxel pattern analysis, have been able to identify activity related to these two distinct processes (de Gardelle et al., in press)”
From The free-energy self: a predictive coding account of self-recognition (2014) · cites this paper as Egner et al., 2010
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- Authors
- Egner, T., Monti, J. M., & Summerfield, C.
- Affiliations
- Duke University (US)
- University of Illinois Urbana-Champaign (US)
- University of Oxford (GB)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2010-12-08
- Volume
- 30
- Issue
- 49
- Pages
- 16601-16608
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3975573
- OpenAlex
- W2134178808
- Cited by
- 350
- References
- 37
- Citations by year
- 2026: 12 · 2025: 16 · 2024: 25 · 2023: 34 · 2022: 21 · 2021: 19 · 2020: 22 · 2019: 25 · 2018: 23 · 2017: 23 · 2016: 31 · 2015: 23 · 2014: 21 · 2013: 19 · 2012: 22
- FWCI
- 10.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- Funders
- Northwestern University
- Wellcome Trust (092646)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- predictive coding, fusiform face area, ventral stream, neural population responses, prediction error, perceptual expectations, face perception
- MeSH terms
- Adult, Analysis of Variance, Brain Mapping, Computer Simulation, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Oxygen, Photic Stimulation, Predictive Value of Tests, Reaction Time, Sensory Receptor Cells, Visual Cortex, Visual Pathways, Visual Perception, Signal Detection, Psychological, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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