Statistical Learning Signals in Macaque Inferior Temporal Cortex
Open accessAbstract
Humans are sensitive to statistical regularities in their visual environment, but the nature of the underlying neural statistical learning signals still remains to be clarified. As in human behavioral and neuroimaging studies of statistical learning, we exposed rhesus monkeys to a continuous stream of images, presented without interstimulus interval or reward association. The stimulus set consisted of 3 groups of 5 images each (quintets). The stimulus order within each quintet was fixed, but the quintets were presented repeatedly in a random order without interruption. Thus, only transitional probabilities defined quintets of images. Postexposure recordings in inferior temporal (IT) cortex showed an enhanced response to stimuli that violated the exposed sequence. This enhancement was found only for stimuli that were not predicted by the just preceding stimulus, reflecting a temporally adjacent stimulus relationship, and was sensitive to stimulus order. By comparing IT responses with sequences with and without statistical regularities, we observed a short latency, transient response suppression for stimuli of the sequence with regularities, in addition to a later sustained response enhancement to stimuli that violated the sequence with regularities. These findings constrain models of mechanisms underlying neural responses in predictable temporal sequences, such as predictive coding.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ES effects in macaque IT neurons persist after controlling for RS.
“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 [89]
A series of single- and multiunit recording studies have reported evidence of ES in macaque IT neurons.
“A series of single‐ and multiunit recording studies have also reported evidence of ES in macaque IT neurons. 85 , 88 , 89 , 90 , 91 , 92”
From Evaluating the neurophysiological evidence for predictive processing as a model of perception (2020) · cites this paper as [89]
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 [89]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Statistical learning by 8-month-old infants
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996) · Science · doi:10.1126/science.274.5294.1926 · Cited by 5,659 · see citing works
Rao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · see citing works
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578 · see citing works
Using confidence intervals in within-subject designs
Loftus, G. R., & Masson, M. E. J. (1994) · Psychonomic Bulletin & Review · doi:10.3758/bf03210951 · Cited by 2,448 · see citing works
Inferotemporal cortex and object vision
Tanaka, K. (1996) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.19.030196.000545 · Cited by 1,402 · see citing works
The Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal
Logothetis, N. K. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-10-03963.2003 · Cited by 1,024 · see citing works
Rossion, B., & Pourtois, G. (2004) · Perception · doi:10.1068/p5117 · Cited by 978 · see citing works
Variability and Detection of Invariant Structure
Gómez, R. (2002) · Psychology Science · doi:10.1111/1467-9280.00476 · Cited by 898 · see citing works
Expectation in perceptual decision making: neural and computational mechanisms
Summerfield, C., & de Lange, F. P. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3838 · Cited by 832 · see citing works
Kobatake, E., & Tanaka, K. (1994) · Journal of Neurophysiology · doi:10.1152/jn.1994.71.3.856 · Cited by 807 · see citing works
Unsupervised Statistical Learning of Higher-Order Spatial Structures from Visual Scenes
Fiser, J., & Aslin, R. Ν. (2001) · Psychological Science · doi:10.1111/1467-9280.00392 · Cited by 806 · see citing works
Neural organization for the long-term memory of paired associates
Sakai, K., & Miyashita, Y. (1991) · Nature · doi:10.1038/354152a0 · Cited by 743 · see citing works
Neuronal correlate of visual associative long-term memory in the primate temporal cortex
Miyashita, Y. (1988) · Nature · doi:10.1038/335817a0 · Cited by 741 · see citing works
Learning at a distance I. Statistical learning of non-adjacent dependencies.
Newport, E., & Aslin, R. (2004) · Cognitive Psychology · doi:10.1016/s0010-0285(03)00128-2 · Cited by 735 · see citing works
A Neural Mechanism for Working and Recognition Memory in Inferior Temporal Cortex
Miller, E. K., Li, L., & Desimone, R. (1991) · Science · doi:10.1126/science.1962197 · Cited by 706 · see citing works
Statistical learning of higher-order temporal structure from visual shape sequences.
Fiser, J., & Aslin, R. (2002) · Journal of Experimental Psychology. Learning, Memory and Cognition · doi:10.1037//0278-7393.28.3.458 · Cited by 641 · see citing works
Turk-Browne, N. B., Scholl, B. J., Chun, M. M., & Johnson, M. K. (2009) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21131 · Cited by 523 · see citing works
Statistical learning of higher-order temporal structure from visual shape sequences.
Fiser, J., & Aslin, R. Ν. (2002) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/0278-7393.28.3.458 · Cited by 505 · see citing works
Dehaene, S., Meyniel, F., Wacongne, C., Wang, L., & Pallier, C. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.019 · Cited by 478 · see citing works
Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity
Sirotin, Y. B., & Das, A. (2009) · Nature · doi:10.1038/nature07664 · Cited by 437 · see citing works
Implicit Perceptual Anticipation Triggered by Statistical Learning
Turk‐Browne, N. B., Scholl, B. J., Johnson, M. K., & Chun, M. M. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0858-10.2010 · Cited by 360 · see citing works
Sawamura, H., Orban, G. A., & Vogels, R. (2006) · Neuron · doi:10.1016/j.neuron.2005.11.028 · Cited by 331 · see citing works
Self-citationHow the hippocampus preserves order: the role of prediction and context
Davachi, L., & DuBrow, S. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.12.004 · Cited by 328 · see citing works
Todorovic, A., & de Lange, F. P. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.2227-12.2012 · Cited by 326 · see citing works
Statistical learning of visual transitions in monkey inferotemporal cortex
Meyer, T., & Olson, C. R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1112895108 · Cited by 325 · see citing works
Different Dynamics of Performance and Brain Activation in the Time Course of Perceptual Learning
Yotsumoto, Y., Watanabe, T., & Sasaki, Y. (2008) · Neuron · doi:10.1016/j.neuron.2008.02.034 · Cited by 320 · see citing works
The Necessity of the Medial Temporal Lobe for Statistical Learning
Schapiro, A. C., Gregory, E., Landau, B., McCloskey, M. E., & Turk‐Browne, N. B. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00578 · Cited by 315 · see citing works
Impact of learning on representation of parts and wholes in monkey inferotemporal cortex
Baker, C. I., Behrmann, M., & Olson, C. R. (2002) · Nature Neuroscience · doi:10.1038/nn960 · Cited by 293 · see citing works
Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey
Maier, A., Wilke, M., Aura, C. J., Zhu, C., Ye, F. Q., & Leopold, D. A. (2008) · Nature Neuroscience · doi:10.1038/nn.2173 · Cited by 291 · see citing works
Functional Significance of Long-Term Potentiation for Sequence Learning and Prediction
Abbott, L. F., & Blum, K. I. (1996) · Cerebral Cortex · doi:10.1093/cercor/6.3.406 · Cited by 271 · see citing works
Denys, K., Vanduffel, W., Fize, D., Nelissen, K., Peuskens, H., Essen, D. C. V., & Orban, G. A. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.3569-03.2004 · Cited by 266 · see citing works
Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex
Li, N., & DiCarlo, J. J. (2008) · Science · doi:10.1126/science.1160028 · Cited by 263 · see citing works
Experience-dependent sharpening of visual shape selectivity in inferior temporal cortex.
Freedman, D. J., Riesenhuber, M., Poggio, T., & Miller, E. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhj100 · Cited by 241 · see citing works
Learned spatiotemporal sequence recognition and prediction in primary visual cortex
Gavornik, J. P., & Bear, M. F. (2014) · Nature Neuroscience · doi:10.1038/nn.3683 · Cited by 227 · see citing works
Responses of Macaque Perirhinal Neurons during and after Visual Stimulus Association Learning
Erickson, C. A., & Desimone, R. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-23-10404.1999 · Cited by 221 · see citing works
Representation of Numerical and Sequential Patterns in Macaque and Human Brains
Wang, L., Uhrig, L., Jarraya, B., & Dehaene, S. (2015) · Current Biology · doi:10.1016/j.cub.2015.06.035 · Cited by 166 · see citing works
Woloszyn, L., & Sheinberg, D. L. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.032 · Cited by 149 · see citing works
Orthographic Processing in Baboons ( Papio papio )
(2012) · doi:10.1126/science.1218152 · Cited by 144 · see citing works
Li, N., & DiCarlo, J. J. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.029 · Cited by 130 · see citing works
Repetition Suppression in Monkey Inferotemporal Cortex: Relation to Behavioral Priming
McMahon, D. B. T., & Olson, C. R. (2007) · Journal of Neurophysiology · doi:10.1152/jn.01042.2006 · Cited by 126 · see citing works
Statistical Learning Across Development: Flexible Yet Constrained
Krogh, L., Vlach, H. A., & Johnson, S. P. (2013) · Front. Psychology · doi:10.3389/fpsyg.2012.00598 · Cited by 113 · see citing works
Complex temporal sequence learning based on short-term memory
Wang, D., & Arbib, M. A. (1990) · Proceedings of the IEEE · doi:10.1109/5.58329 · Cited by 106 · see citing works
Auditory Artificial Grammar Learning in Macaque and Marmoset Monkeys
Wilson, B., Slater, H., Kikuchi, Y., Milne, A. E., Marslen‐Wilson, W. D., Smith, K., & Petkov, C. I. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2414-13.2013 · Cited by 105 · see citing works
The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components
Cardoso, M. M. B., Sirotin, Y. B., Lima, B., Glushenkova, E., & Das, A. (2012) · Nature Neuroscience · doi:10.1038/nn.3170 · Cited by 99 · 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
Kaliukhovich, D. A., & Vogels, R. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2154-14.2014 · Cited by 69 · see citing works
Self-citationStatistical learning and its consequences.
Turk-Browne, N. (2012) · Nebraska Symposium on Motivation. Nebraska Symposium on Motivation · doi:10.1007/978-1-4614-4794-8_6 · Cited by 63 · see citing works
Action at a distance: dependency sensitivity in a New World primate
Ravignani, A., Sonnweber, R. S., Stobbe, N., et al. (2013) · Biology Letters · doi:10.1098/rsbl.2013.0852 · Cited by 62 · see citing works
Wilson, B., Smith, K., & Petkov, C. I. (2015) · European Journal of Neuroscience · doi:10.1111/ejn.12834 · Cited by 57 · see citing works
Configurational encoding of complex visual forms by single neurons of monkey temporal cortex
Miyashita, Y., Date, A., & Okuno, H. (1993) · Neuropsychologia · doi:10.1016/0028-3932(93)90036-y · Cited by 57 · see citing works
Baene, W. D., Premereur, E., & Vogels, R. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00741.2006 · Cited by 56 · see citing works
Self-citationLi, N., & DiCarlo, J. J. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3786-11.2012 · Cited by 49 · 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
The Influence of Attention, Learning, and Motivation on Visual Search
Dodd, M. D., & Flowers, J. H. (2012) · Nebraska Symposium on Motivation/The Nebraska symposium on motivation · doi:10.1007/978-1-4614-4794-8 · Cited by 37 · see citing works
de Beeck, H. O., Wagemans, J. P., & Vogels, R. (2007) · Neuroscience · doi:10.1016/j.neuroscience.2006.12.058 · Cited by 35 · see citing works
Self-citationDissociable behavioural outcomes of visual statistical learning
Bays, B. C., Turk‐Browne, N. B., & Seitz, A. R. (2015) · Visual Cognition · doi:10.1080/13506285.2016.1139647 · Cited by 29 · see citing works
Networks that learn the precise timing of event sequences
Veliz‐Cuba, A., Shouval, H. Z., Josić́, K., & Kilpatrick, Z. P. (2015) · Journal of Computational Neuroscience · doi:10.1007/s10827-015-0574-4 · Cited by 23 · see citing works
Otsuka, S., & Saiki, J. (2015) · Cognition · doi:10.1016/j.cognition.2015.11.004 · Cited by 21 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Suppressed Sensory Response to Predictable Object Stimuli throughout the Ventral Visual Stream
Richter, D., Ekman, M., & de Lange, F. P. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.3421-17.2018 · Cited by 150 · see citing works
Tang, M. F., Smout, C. A., Arabzadeh, E., & Mattingley, J. B. (2018) · eLife · doi:10.7554/elife.33123 · Cited by 83 · see citing works
“poral cortex show an enhanced response to unexpected relative to expected stimuli (Kaposvari et al., 2018), and population decoding accuracy is higher for unexpected compared”
Encoding of predictable and unpredictable stimuli by inferior temporal cortical neurons
Kumar, S., Kaposvari, P., & Vogels, R. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01135 · Cited by 75 · see citing works
Self-citationRepetition 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
Sleep strengthens predictive sequence coding
Lutz, N. D., Wolf, I., Hübner, S., Born, J., & Rauss, K. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1352-18.2018 · Cited by 27 · see citing works
- Authors
- Kaposvari, P., Kumar, S., & Vogels, R.
- Affiliations
- KU Leuven (BE)
- Corresponding author
- Vogels, Rufin
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-12-01
- Volume
- 28
- Issue
- 1
- Pages
- 250-266
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2560516988
- Cited by
- 91
- References
- 59
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 28 · 2023: 13 · 2022: 8 · 2021: 6 · 2020: 3 · 2019: 6 · 2018: 6 · 2017: 6
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cerebral Cortex (publishedVersion)
- PubMed (publishedVersion)
- SZTE Publicatio Repozitórium (University of Szeged) (acceptedVersion, other-oa)
- Lirias (submittedVersion)
- 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, inferior temporal cortex, rhesus monkeys, visual prediction, sequence prediction, transition probabilities, stimulus order, response suppression, response enhancement, predictive coding
- MeSH terms
- Animals, Association Learning, Macaca mulatta, Male, Microelectrodes, Neurons, Neuropsychological Tests, Photic Stimulation, Probability Learning, Reward, Temporal Lobe, Time Factors, Time Perception, Visual Perception
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 ›
Are you an author, or do you see a problem with this page? Request a review
