Statistical learning of visual transitions in monkey inferotemporal cortex
Open accessAbstract
One of the most fundamental functions of the brain is to predict upcoming events on the basis of the recent past. A closely related function is to signal when a prediction has been violated. The identity of the brain regions that mediate these functions is not known. We set out to determine whether they are implemented at the level of single neurons in the visual system. We gave monkeys prolonged exposure to pairs of images presented in fixed sequence so that each leading image became a strong predictor for the corresponding trailing image. We then monitored the responses of neurons in the inferotemporal cortex to image sequences that obeyed or violated the transitional rules imposed during training. Inferotemporal neurons exhibited a transitional surprise effect, responding much more strongly to unpredicted transitions than to predicted transitions. Thus, neurons even in the visual system make experience-based predictions and react when they fail.
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Overall, stimuli that are expected evoke a reduced neural response.
“Overall, stimuli that are expected evoke a reduced neural response.This pattern has been observed when stimuli are validly predicted by a preceding, arbitrarily paired, stimulus [65][66][67][68][69], when stimuli comply with a higher-order pattern such as a shape or scene [33,34], or when stimuli are the predictable result of an animal's own actions [70].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [67]
The Results (page unavailable in our copy) show that predicted trailing images evoked weaker responses than unpredicted ones in monkey inferotemporal cortex. The claim generalizes this comparison to expected stimuli overall. The Discussion also says the study cannot determine whether the difference reflects suppression of predicted images or enhancement of unpredicted images. (LLM, conf=0.86)
Experimental — accuracy not yet measuredExpectation 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 [67]
The Results report lower firing rates for predicted than unpredicted images in monkeys, supporting the non-human-primate spike-rate portion of the cited sentence. The Discussion says the study cannot determine whether this difference reflects suppression of predicted responses or enhancement of unpredicted responses, so it does not establish suppression specifically. This citation is attached to the primate portion, not the fMRI, EEG/MEG, or rodent claims. (LLM, conf=0.84)
Experimental — accuracy not yet measuredES effects in macaque IT neurons emerge at early latencies (~150 ms).
“These effects were sensitive to transitional statistics (e.g., the transition from Stimulus A to Stimulus B being more probable than from B to A), 90 , 91 persisted after controlling for RS, 88 , 89 and emerged at early latencies (∼150 ms). 88 , 90 , 91 , 92”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [90]
The Results report that the transitional surprise signal in macaque IT neurons developed at a time statistically indistinguishable from the response to the unpredicted image; the Discussion says it was present from the outset of the visual response. Our copy does not give an onset latency of approximately 150 ms for that effect. The 150–250 ms window in the methods measures an LFP trough, not the effect’s onset. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSome studies have reported that expected stimulus representations are dampened.
“some studies have reported that expected stimulus representations are dampened”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [90]
The Results report weaker neuronal responses and less information about image identity for predicted than for unpredicted images. But the Discussion says the study cannot distinguish suppression of predicted-image responses from enhancement of unpredicted-image responses. It therefore supports a relative difference, not established dampening of expected representations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAnother possibility is that expectations have an anticipatory effect and sensitize representations even before stimulus onset.
“Another possibility is that expectations have an anticipatory effect and sensitize representations even before stimulus onset.Empirical support for such forward-looking perceptual predictions comes from electrophysiological reports of stimulus-specific baseline shifts before stimulus onset [67,97], from time-locked responses to unexpectedly omitted stimuli [69,98], and from predictive preactiv”
From How Do Expectations Shape Perception? (2018) · cites this paper as [67]
The Fig. 3 caption reports prediction-related, image-pair-selective firing that persisted until the trailing image’s response began, supporting an anticipatory effect. But the Results describe changes in responses after image onset, not sensitization of the predicted image’s representation before onset; the Discussion leaves open whether prediction suppresses or enhances responses. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measured
There was a positive relationship between expectation suppression and stimulus preference in the macaque face patch system and macaque inferior temporal cortex.
“and that there was a positive relationship between expectation suppression and stimulus preference in the macaque face patch system [74] and macaque IT [67].”
From How Do Expectations Shape Perception? (2018) · cites this paper as [67]
For the macaque inferior temporal cortex portion attributed to this paper, the Results report a larger difference between predicted and unpredicted responses for more effective images, supporting a positive relationship with stimulus preference. But the Discussion says the authors could not determine whether that difference reflects suppression of predicted images or enhancement of unpredicted ones. The face-patch portion is attached to a different citation. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe neural response to identical sensory input is markedly reduced when the input is predicted, putatively owing to a reduced response of the error units to predictable input, as well as to improved selection of units encoding the relevant causes of the input.
“Second, the neural response to identical sensory input is markedly reduced when the input is predicted [67,74], putatively owing to a reduced response of the error units to predictable input, as well as to improved selection of units encoding the relevant causes of the input.”
From How Do Expectations Shape Perception? (2018) · cites this paper as [67]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredNeurons in monkey inferotemporal cortex respond significantly less to a predicted sequence of natural images than to an unpredicted sequence.
“Furthermore, neurons in monkey inferotemporal cortex respond significantly less to a predicted sequence of natural images, compared to an unpredicted sequence (Meyer and Olson, 2011).”
From Canonical Microcircuits for Predictive Coding (2012) · cites this paper as Meyer and Olson, 2011
ES effects in macaque IT neurons are sensitive to transitional statistics.
“These effects were sensitive to transitional statistics (e.g., the transition from Stimulus A to Stimulus B being more probable than from B to A), 90 , 91 persisted after controlling for RS, 88 , 89 and emerged at early latencies (∼150 ms). 88 , 90 , 91 , 92”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [90]
Expectations can arise from arbitrary stimulus pairings, predictive cues, or higher order regularities.
“Similarly, expectations can arise from arbitrary stimulus pairings, 90 predictive cues, or higher order regularities.”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [90]
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Kumar, S., Kaposvari, P., & Vogels, R. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01135 · Cited by 75 · see citing works
Repetition Probability Effects Depend on Prior Experiences
Grotheer, M., & Kovács, G. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.5326-13.2014 · Cited by 83 · see citing works
Kaliukhovich, D. A., & Vogels, R. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2154-14.2014 · Cited by 69 · see citing works
Statistical Learning of Serial Visual Transitions by Neurons in Monkey Inferotemporal Cortex
Meyer, T., Ramachandran, S., & Olson, C. R. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1215-14.2014 · Cited by 70 · see citing works
Self-citationThe relationship between stimulus repetitions and fulfilled expectations
Grotheer, M., & Kovács, G. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2014.12.017 · Cited by 63 · see citing works
Face Repetition Probability Does Not Affect Repetition Suppression in Macaque Inferotemporal Cortex
Vinken, K., Op de Beeck, H. P., & Vogels, R. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0462-18.2018 · Cited by 49 · see citing works
Expectation Suppression in Early Visual Cortex Depends on Task Set
St. John-Saaltink, E., Utzerath, C., Kok, P., Lau, H. C., & de Lange, F. P. (2015) · PLOS ONE · doi:10.1371/journal.pone.0131172 · Cited by 43 · see citing works
Ramachandran, S., Meyer, T., & Olson, C. R. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00091.2015 · Cited by 38 · see citing works
Self-citationGrounding predictive coding models in empirical neuroscience research
Egner, T., & Summerfield, C. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1200218x · Cited by 36 · see citing works
Pajani, A., Kouider, S., Roux, P., & de Gardelle, V. (2017) · Scientific Reports · doi:10.1038/s41598-017-00243-3 · Cited by 25 · see citing works
Repetition suppression to objects is modulated by stimulus-specific expectations
Utzerath, C., St. John-Saaltink, E., Buitelaar, J., & de Lange, F. P. (2017) · Scientific Reports · doi:10.1038/s41598-017-09374-z · Cited by 28 · see citing works
Rethinking Predictive Processing
Furutachi, S., & Hofer, S. B. (2026) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-102124-031410 · Cited by 3 · see citing works
- Authors
- Meyer, T., & Olson, C. R.
- Affiliations
- Center for the Neural Basis of Cognition (US)
- University of Pittsburgh (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-11-14
- Volume
- 108
- Issue
- 48
- Pages
- 19401-19406
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3228439
- OpenAlex
- W2125240230
- Cited by
- 325
- References
- 67
- Citations by year
- 2026: 14 · 2025: 44 · 2024: 39 · 2023: 45 · 2022: 13 · 2021: 19 · 2020: 20 · 2019: 14 · 2018: 22 · 2017: 23 · 2016: 16 · 2015: 16 · 2014: 18 · 2013: 13 · 2012: 9
- FWCI
- 6.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Pennsylvania Department of Health (P41RR03631)
- National Institutes of Health (EY08098, P41RR03631, MH45156)
- National Institute of Mental Health (P50 MH-45156, P50MH045156)
- National Eye Institute (P30-EY-08098, R01EY018620, P30EY008098)
- National Center for Research Resources (P41-RR-03631, P41RR003631)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- statistical learning, inferotemporal cortex, prediction, prediction violation, surprise, single neuron responses, image sequences, visual prediction, monkey visual system
- MeSH terms
- Analysis of Variance, Animals, Discrimination Learning, Female, Macaca, Male, Pattern Recognition, Visual, Photic Stimulation, Temporal Lobe, Functional Neuroimaging
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