Statistical Learning of Serial Visual Transitions by Neurons in Monkey Inferotemporal Cortex
Open accessAbstract
If monkeys repeatedly, over the course of weeks, view displays in which two images appear in fixed sequence, then neurons of inferotemporal cortex (ITC) come to exhibit prediction suppression. The response to the trailing image is weaker if it follows the leading image with which it was paired during training than if it follows some other leading image. Prediction suppression is a plausible neural mechanism for statistical learning of visual transitions such as has been demonstrated in behavioral studies of human infants and adults. However, in the human studies, subjects are exposed to continuous sequences in which the same image can be both predicted and predicting and statistical dependency can exist between nonadjacent items. The aim of the present study was to investigate whether prediction suppression in ITC develops under such circumstances. To resolve this issue, we exposed monkeys repeatedly to triplets of images presented in fixed order. The results indicate that prediction suppression can be induced by training not only with pairs of images but also with longer sequences.
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ES 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 [91]
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 [91]
There is conflicting evidence regarding whether IT neurons are sensitive to statistical regularities present in nonadjacent stimuli from a sequence of images or whether these suppression effects are determined by the immediately preceding stimulus.
“There is also conflicting evidence regarding whether IT neurons are sensitive to statistical regularities present in nonadjacent stimuli from a sequence of images 91 or whether these suppression effects are determined by the immediately preceding stimulus. 89 , 96”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [91]
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- Authors
- Meyer, T., Ramachandran, S., & Olson, C. R.
- Affiliations
- Center for the Neural Basis of Cognition (US)
- University of Pittsburgh (US)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2014-07-09
- Volume
- 34
- Issue
- 28
- Pages
- 9332-9337
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC4087210
- OpenAlex
- W1991888778
- Cited by
- 70
- References
- 35
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 20 · 2023: 11 · 2022: 4 · 2021: 1 · 2020: 5 · 2019: 4 · 2018: 3 · 2017: 9 · 2016: 6 · 2015: 4
- FWCI
- 3.50
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Pennsylvania Department of Health (P41RR03631)
- National Institutes of Health (NIH P50, EY08098, MH084053)
- National Institute of Mental Health (P50MH084053, K08MH080329)
- National Eye Institute (R01EY018620, P30EY008098)
- National Center for Research Resources (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 › Face Recognition and Perception
- Keywords
- statistical learning, inferotemporal cortex, monkey visual cortex, image sequences, neural prediction, visual learning
- MeSH terms
- Animals, Cues, Data Interpretation, Statistical, Discrimination Learning, Female, Inhibition, Psychological, Macaca mulatta, Male, Models, Neurological, Neural Inhibition, Pattern Recognition, Visual, Photic Stimulation, Temporal Lobe, Visual Cortex, Models, Statistical
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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