Complementary congruent and opposite neurons achieve concurrent multisensory integration and segregation
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Abstract
Abstract Our brain perceives the world by exploiting multiple sensory modalities to extract information about various aspects of external stimuli. If these sensory cues are from the same stimulus of interest, they should be integrated to improve perception; otherwise, they should be segregated to distinguish different stimuli. In reality, however, the brain faces the challenge of recognizing stimuli without knowing in advance whether sensory cues come from the same or different stimuli. To address this challenge and to recognize stimuli rapidly, we argue that the brain should carry out multisensory integration and segregation concurrently with complementary neuron groups. Studying an example of inferring heading-direction via visual and vestibular cues, we develop a concurrent multisensory processing neural model which consists of two reciprocally connected modules, the dorsal medial superior temporal area (MSTd) and the ventral intraparietal area (VIP), and that at each module, there exists two distinguishing groups of neurons, congruent and opposite neurons. Specifically, congruent neurons implement cue integration, while opposite neurons compute the cue disparity, both optimally as described by Bayesian inference. The two groups of neurons provide complementary information which enables the neural system to assess the validity of cue integration and, if necessary, to recover the lost information associated with individual cues without re-gathering new inputs. Through this process, the brain achieves rapid stimulus perception if the cues come from the same stimulus of interest, and differentiates and recognizes stimuli based on individual cues with little time delay if the cues come from different stimuli of interest. Our study unveils the indispensable role of opposite neurons in multisensory processing and sheds light on our understanding of how the brain achieves multisensory processing efficiently and rapidly. Significance Statement Our brain perceives the world by exploiting multiple sensory cues. These cues need to be integrated to improve perception if they come from the same stimulus and otherwise be segregated. To address the challenge of recognizing whether sensory cues come from the same or different stimuli that are unknown in advance, we propose that the brain should carry out multisensory integration and segregation concurrently with two different neuron groups. Specifically, congruent neurons implement cue integration, while opposite neurons compute the cue disparity, and the interplay between them achieves rapid stimulus recognition without information loss. We apply our model to the example of inferring heading-direction based on visual and vestibular cues and reproduce the experimental data successfully.
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Previous theoretical modeling work indicated that opposite neurons in macaque MSTd may be involved in segregating sensory cues.
“Previous theoretical modeling work has indicated that opposite neurons in macaque MSTd may be involved in segregating sensory cues (22, 32, 33).”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [33]
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Cites
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844 · see citing works
Mardia, K. V., & Jupp, P. E. (1999) · Wiley series in probability and statistics · doi:10.1002/9780470316979 · Cited by 3,277 · see citing works
Neuronal Population Coding of Movement Direction
Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986) · Science · doi:10.1126/science.3749885 · Cited by 3,187 · see citing works
The analysis of visual motion: a comparison of neuronal and psychophysical performance
Britten, K., Shadlen, M., Newsome, W., & Movshon, J. (1992) · The Journal of Neuroscience · doi:10.1523/jneurosci.12-12-04745.1992 · Cited by 1,958 · see citing works
The Ventriloquist Effect Results from Near-Optimal Bimodal Integration
Alais, D., & Burr, D. (2004) · Current Biology · doi:10.1016/j.cub.2004.01.029 · Cited by 1,936 · see citing works
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906 · see citing works
Multisensory integration: current issues from the perspective of the single neuron
Stein, B. E., & Stanford, T. R. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2331 · Cited by 1,598 · see citing works
Bayesian inference with probabilistic population codes
Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A. (2006) · Nature Neuroscience · doi:10.1038/nn1790 · Cited by 1,457 · see citing works
Theory of orientation tuning in visual cortex.
Ben-Yishai, R., Bar‐Or, R. L., & Sompolinsky, H. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.9.3844 · Cited by 1,091 · see citing works
Chklovskii, D. B. (2002) · Neuron · doi:10.1016/s0896-6273(02)00703-1 · Cited by 1,074 · see citing works
Causal Inference in Multisensory Perception
Körding, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., & Shams, L. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000943 · Cited by 1,069 · see citing works
Ceramide-orchestrated signalling in cancer cells
Morad, S. A. F., & Cabot, M. C. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3398 · Cited by 1,018 · see citing works
Zhang, K. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-06-02112.1996 · Cited by 1,001 · see citing works
Path integration and cognitive mapping in a continuous attractor neural network model
Samsonovich, A., & McNaughton, B. L. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-15-05900.1997 · Cited by 989 · see citing works
Integration of Proprioceptive and Visual Position-Information: An Experimentally Supported Model
van Beers, R. J., Sittig, A. C., & Gon, J. J. D. V. D. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.81.3.1355 · Cited by 742 · see citing works
Decision Making in Recurrent Neuronal Circuits
Wang, X.-J. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.034 · Cited by 679 · cited 1× · see citing works
“…will sustain active responses after competition to represent the choice; alternatively, the activities of congruent and opposite neurons provide competing inputs to a decision-making network, and the latter generates the choice by accumulating evidence over time (Wang, 2008; Engel and Wang, 2011).”
BackgroundBoussaoud, D., Ungerleider, L. G., & Desimone, R. (1990) · Journal of Comparative Neurology · doi:10.1002/cne.902960311 · Cited by 634 · see citing works
Organization of visual inputs to the inferior temporal and posterior parietal cortex in macaques
Baizer, J. S., Ungerleider, L. G., & Desimone, R. (1991) · Journal of Neuroscience · doi:10.1523/jneurosci.11-01-00168.1991 · Cited by 626 · see citing works
Neural correlates of multisensory cue integration in macaque MSTd
Gu, Y., Angelaki, D. E., & DeAngelis, G. C. (2008) · Nature Neuroscience · doi:10.1038/nn.2191 · Cited by 554 · see citing works
Self-citationOptimal representation of sensory information by neural populations
Jazayeri, M., & Movshon, J. A. (2006) · Nature Neuroscience · doi:10.1038/nn1691 · Cited by 530 · see citing works
Causal inference in perception
Shams, L., & Beierholm, U. R. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.07.001 · Cited by 403 · see citing works
Reading population codes: a neural implementation of ideal observers
Denève, S., Latham, P. E., & Pouget, A. (1999) · Nature Neuroscience · doi:10.1038/11205 · Cited by 392 · see citing works
Norm-based face encoding by single neurons in the monkey inferotemporal cortex
Leopold, D. A., Bondar, I. V., & Giese, M. A. (2006) · Nature · doi:10.1038/nature04951 · Cited by 379 · see citing works
Optimal integration of texture and motion cues to depth
Jacobs, R. A. (1999) · Vision Research · doi:10.1016/s0042-6989(99)00088-7 · Cited by 324 · see citing works
Gu, Y., Watkins, P. V., Angelaki, D. E., & DeAngelis, G. C. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.2356-05.2006 · Cited by 316 · cited 5× · see citing works
“At each module, there exist two groups of excitatory neurons: congruent and opposite neurons (blue and red circles in Figure 4A respectively), and they have the same number of neurons, as supported by experiments (Figure 2C) (Chen et al., 2011; Gu et al., 2006).”
BackgroundMethodResultInfluentialSelf-citationFetsch, C. R., DeAngelis, G. C., & Angelaki, D. E. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3503 · Cited by 304 · see citing works
Unifying multisensory signals across time and space
Wallace, M. T., Roberson, G. E., Hairston, W. D., Stein, B. E., Vaughan, J. W., & Schirillo, J. A. (2004) · Experimental Brain Research · doi:10.1007/s00221-004-1899-9 · Cited by 291 · see citing works
Neural Mechanisms of Saccadic Suppression
Thiele, A., Henning, P., Kubischik, M., & Hoffmann, K.-P. (2002) · Science · doi:10.1126/science.1068788 · Cited by 278 · see citing works
Roach, N. W., Heron, J., & McGraw, P. V. (2006) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2006.3578 · Cited by 210 · see citing works
A Bayesian View on Multimodal Cue Integration
Ernst, M. O. (2005) · Human Body Perception From The Inside Out · doi:10.1093/oso/9780195178371.003.0006 · Cited by 209 · see citing works
Multisensory Integration in Macaque Visual Cortex Depends on Cue Reliability
Morgan, M. L., DeAngelis, G. C., & Angelaki, D. E. (2008) · Neuron · doi:10.1016/j.neuron.2008.06.024 · Cited by 208 · see citing works
Representation of Vestibular and Visual Cues to Self-Motion in Ventral Intraparietal Cortex
Chen, A., DeAngelis, G. C., & Angelaki, D. E. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.0395-11.2011 · Cited by 192 · see citing works
Self-citationSatō, Y., Toyoizumi, T., & Aihara, K. (2007) · Neural Computation · doi:10.1162/neco.2007.19.12.3335 · Cited by 187 · see citing works
Vision and touch are automatically integrated for the perception of sequences of events
Bresciani, J., Dammeier, F., & Ernst, M. O. (2006) · Journal of Vision · doi:10.1167/6.5.2 · Cited by 180 · see citing works
Center-Surround Interactions in the Middle Temporal Visual Area of the Owl Monkey
Born, R. T. (2000) · Journal of Neurophysiology · doi:10.1152/jn.2000.84.5.2658 · Cited by 177 · see citing works
Human trimodal perception follows optimal statistical inference
Wozny, D. R., Beierholm, U., & Shams, L. B. (2008) · Journal of Vision · doi:10.1167/8.3.24 · Cited by 169 · see citing works
Inverting faces elicits sensitivity to race on the N170 component: A cross-cultural study
Vizioli, L., Foreman, K., Rousselet, G. A., & Caldara, R. (2010) · Journal of Vision · doi:10.1167/10.1.15 · Cited by 150 · see citing works
Characterizing global and local mechanisms in biological motion perception.
Chang, D. H. F., & Troje, N. (2009) · Journal of Vision · doi:10.1167/9.5.8 · Cited by 141 · see citing works
Population Coding and Decoding in a Neural Field: A Computational Study
Wu, S., Амари, Ш.-И., & Nakahara, H. (2002) · Neural Computation · doi:10.1162/089976602753633367 · Cited by 138 · see citing works
Self-citationFunctional Specializations of the Ventral Intraparietal Area for Multisensory Heading Discrimination
Chen, A., DeAngelis, G. C., & Angelaki, D. E. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4522-12.2013 · Cited by 129 · see citing works
Self-citationGu, Y., DeAngelis, G. C., & Angelaki, D. E. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.5154-11.2012 · Cited by 128 · see citing works
Self-citationDynamics and Computation of Continuous Attractors
Wu, S., Hamaguchi, K., & Amari, S.-I. (2008) · Neural Computation · doi:10.1162/neco.2008.10-06-378 · Cited by 121 · see citing works
Self-citationAccumulation and persistence of memory for natural scenes
Melcher, D. (2006) · Journal of Vision · doi:10.1167/6.1.2 · Cited by 114 · see citing works
Same or Different? A Neural Circuit Mechanism of Similarity-Based Pattern Match Decision Making
Engel, T. A., & Wang, X. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6150-10.2011 · Cited by 82 · see citing works
Girshick, A. R., & Banks, M. S. (2009) · Journal of Vision · doi:10.1167/9.9.8 · Cited by 76 · see citing works
Fung, C. C. A., Wong, K. Y. M., & Wu, S. (2009) · Neural Computation · doi:10.1162/neco.2009.07-08-824 · Cited by 76 · see citing works
Self-citation“What Not” Detectors Help the Brain See in Depth
Goncalves, N. R., & Welchman, A. E. (2017) · Current Biology · doi:10.1016/j.cub.2017.03.074 · Cited by 66 · see citing works
Gu, Y., Cheng, Z., Yang, L., DeAngelis, G. C., & Angelaki, D. E. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv183 · Cited by 62 · see citing works
Self-citationFung, C. C. A., Wong, K. Y. M., & Wu, S. (2009) · Cited by 62 · see citing works
Self-citationDecentralized Multisensory Information Integration in Neural Systems
Zhang, W., Chen, A., Rasch, M. J., & Wu, S. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0578-15.2016 · Cited by 55 · see citing works
Self-citationSasaki, R., Angelaki, D. E., & DeAngelis, G. C. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1177-17.2017 · Cited by 38 · see citing works
Joint Representation of Depth from Motion Parallax and Binocular Disparity Cues in Macaque Area MT
Nadler, J. W., Barbash, D., Kim, H. R., Shimpi, S., Angelaki, D. E., & DeAngelis, G. C. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0251-13.2013 · Cited by 37 · see citing works
Cue combination on the circle and the sphere
Murray, R. F., & Morgenstern, Y. (2010) · Journal of Vision · doi:10.1167/10.11.15 · Cited by 32 · see citing works
Kim, H. R., Pitkow, X., Angelaki, D. E., & DeAngelis, G. C. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00005.2016 · Cited by 32 · see citing works
Proscription supports robust perceptual integration by suppression in human visual cortex
Rideaux, R., & Welchman, A. E. (2018) · Nature Communications · doi:10.1038/s41467-018-03400-y · Cited by 27 · see citing works
Neural Information Processing with Feedback Modulations
Zhang, W., & Wu, S. (2012) · Neural Computation · doi:10.1162/neco_a_00296 · Cited by 21 · see citing works
Self-citationMotion grouping/segmentation via velocity gradients
Clarke, A., & Rainville, S. (2010) · Journal of Vision · doi:10.1167/8.6.24 · Cited by 8 · see citing works
Flagged first, then by citations
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Sorted by citations
Bayesian causal inference: A unifying neuroscience theory
Shams, L. B., & Beierholm, U. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104619 · Cited by 134 · see citing works
How multisensory neurons solve causal inference.
Rideaux, R., Storrs, K. R., Maiello, G., & Welchman, A. E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2106235118 · Cited by 65 · see citing works
“Previous theoretical modeling work has indicated that opposite neurons in macaque MSTd may be involved in segregating sensory cues (22, 32, 33).”
Multisensory neural processing: from cue integration to causal inference
French, R. L., & DeAngelis, G. C. (2020) · Current Opinion in Physiology · doi:10.1016/j.cophys.2020.04.004 · Cited by 41 · see citing works
Cognitive, Systems, and Computational Neurosciences of the Self in Motion
Noel, J., & Angelaki, D. E. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-021021-103038 · Cited by 41 · see citing works
Gallagher, M., Choi, R., & Ferrè, E. R. (2020) · Multisensory Research · doi:10.1163/22134808-20201487 · Cited by 34 · see citing works
Proscription supports robust perceptual integration by suppression in human visual cortex
Rideaux, R., & Welchman, A. E. (2018) · Nature Communications · doi:10.1038/s41467-018-03400-y · Cited by 27 · see citing works
Causal inference during closed-loop navigation: parsing of self- and object-motion
Noel, J., Bill, J., Ding, H., Vastola, J. J., DeAngelis, G. C., Angelaki, D. E., & Drugowitsch, J. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0344 · Cited by 19 · see citing works
Dynamic changes of brain networks during standing balance control under visual conflict
Wang, G., Yang, Y., Wang, J., Hao, Z., Luo, X., & Liu, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1003996 · Cited by 19 · see citing works
Cortical Mechanisms of Multisensory Linear Self-motion Perception
Zhou, L., & Gu, Y. (2022) · Neuroscience Bulletin · doi:10.1007/s12264-022-00916-8 · Cited by 17 · see citing works
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Causal inference and the evolution of opposite neurons
Badde, S., Hong, F., & Landy, M. S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112686118 · Cited by 13 · see citing works
Multisensory coding of self-motion and its contribution to navigation
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Self-citationVestibular and active self-motion signals drive visual perception in binocular rivalry
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Rapid cross-sensory adaptation of self-motion perception
Shalom-Sperber, S., Chen, A., & Zaidel, A. (2022) · Cortex · doi:10.1016/j.cortex.2021.11.018 · Cited by 9 · see citing works
Self-citationCausal contribution of optic flow signal in Macaque extrastriate visual cortex for roll perception
Li, W., Lu, J., Zhu, Z., & Gu, Y. (2022) · Nature Communications · doi:10.1038/s41467-022-33245-5 · Cited by 9 · see citing works
Self-citationThe role of auditory source and action representations in segmenting experience into events
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Hou, H., & Gu, Y. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.23879-0 · Cited by 7 · see citing works
Self-citationZhang, J., Gu, Y., Chen, A., & Yu, Y. (2022) · Research · doi:10.34133/2022/9787040 · Cited by 6 · see citing works
Self-citationSolving the causal inference problem
Noppeney, U. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.09.004 · Cited by 6 · see citing works
Temporal and spatial properties of vestibular signals for perception of self-motion
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Self-citationInterhemispheric multisensory perception and Bayesian causal inference
Huo, H., Liu, X., Tang, Z., Dong, Y., Zhao, D., Chen, D., Tang, M., Qiao, X., Du, X., Guo, J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106706 · Cited by 3 · see citing works
Neuroscience-inspired information-integration system based on stochastic magnetic tunnel junctions
Zhao, L., Zhang, M., Zhang, Y., Mi, Y., Yuan, Z., & Xia, K. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.21.064040 · Cited by 3 · see citing works
Adaptive neurons compute confidence in a decision network
Li, L., & Wang, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-01523-9 · Cited by 3 · see citing works
Bimodular continuous attractor neural networks with static and moving stimuli
Yan, M., Zhang, W., Wang, H., & Wong, K. Y. M. (2023) · Physical review. E · doi:10.1103/physreve.107.064302 · Cited by 3 · see citing works
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Wang, Y., Wei, Y., So, R. H. Y., Okazaki, Y. O., & Kitajo, K. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10354-7 · Cited by 1 · see citing works
Equilibrium States and Their Stability in the Head-Direction Ring Network
Wang, C., & Zhang, K. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00096 · Cited by 1 · see citing works
Li Zhaoping, A. (2024) · Vision Research · doi:10.1016/j.visres.2024.108489 · Cited by 1 · see citing works
Emergence of opposite neurons in a decentralized firing-rate model of multisensory integrationPreprint
Niu, X., Chau, H. Y., Lee, T. S., & Zhang, W. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/845743 · Cited by 1 · see citing works
Self-citationEmergence of opposite neurons in a firing-rate model of multisensory integrationPreprint
Chau, H. Y., Zhang, W., & Lee, T. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/814483
Self-citationA spiking neural circuit model for learning multi-sensory integrationPreprint
Song, D., Niu, X., Zhang, W., & Lee, T. S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.27.401216
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Jané, M. B., Pisupati, S., Smith, K. E., Tonelli, L. C., Melo-Thomas, L., Schwarting, R. K. W., Wöhr, M., Chrobak, J. J., & Read, H. L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.29.493898
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Ma, X., Wang, H., Zhang, W., & Wong, K. Y. M. (2022) · Conference on Cognitive Computational Neuroscience (CCN) · doi:10.32470/ccn.2022.1200-0
Self-citationZhang, J., Huang, M., Gu, Y., Chen, A., & Yu, Y. (2022) · Brain Sciences · doi:10.3390/brainsci12101387
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- Authors
- Zhang, W.-H., Wang, H., Chen, A., Gu, Y., Lee, T. S., Wong, K. M., & Wu, S.
- Affiliations
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong
- Hong Kong University of Science and Technology (HK)
- East China Normal University (CN)
- Chinese Academy of Sciences (CN)
- Center for the Neural Basis of Cognition (US)
- Corresponding author
- Wu, Si
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2019-05-23
- Volume
- 8
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC6565362
- OpenAlex
- W2946207583
- Cited by
- 45
- References
- 57
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 10 · 2021: 7 · 2020: 6 · 2019: 3 · 2018: 1
- FWCI
- 7.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Research Grants Council, University Grants Committee (N_HKUST606/12, 605813, 16322616, 16306817)
- National Basic Research Program of China (2014CB846101)
- Natural Science Foundation of China (31261160495)
- National Science Foundation (1816568)
- Intelligence Advanced Research Projects Activity (D16PC00007)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- multisensory integration, visual-vestibular integration, reciprocal connections, neural network model
- MeSH terms
- Animals, Brain, Humans, Macaca mulatta, Motion Perception, Neurons, Photic Stimulation, Temporal Lobe, Vestibule, Labyrinth, Visual Perception, Head Movements
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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