Neural correlates of reliability-based cue weighting during multisensory integration
Open accessAbstract
Integration of multiple sensory cues is essential for precise and accurate perception and behavioral performance, yet the reliability of sensory signals can vary across modalities and viewing conditions. Human observers typically employ the optimal strategy of weighting each cue in proportion to its reliability, but the neural basis of this computation remains poorly understood. We trained monkeys to perform a heading discrimination task from visual and vestibular cues, varying cue reliability randomly. The monkeys appropriately placed greater weight on the more reliable cue, and population decoding of neural responses in the dorsal medial superior temporal area closely predicted behavioral cue weighting, including modest deviations from optimality. We found that the mathematical combination of visual and vestibular inputs by single neurons is generally consistent with recent theories of optimal probabilistic computation in neural circuits. These results provide direct evidence for a neural mechanism mediating a simple and widespread form of statistical inference.
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?
Congruent neurons combine vestibular-visual signals in a way that assumes a common cause.
“Congruent neurons appear to combine vestibular–visual signals in a way that assumes a common cause, and their activity is highly predictive of behavioral choice on vestibular–visual combination tasks (8–10).”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [9]
The Results section reports that congruent MSTd neurons gain heading sensitivity when visual and vestibular cues are combined. The Online Methods say the cue conflict was kept small enough to discourage inference of separate causes, but the paper does not establish that the neurons themselves assume a common cause. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe network exhibits reliability-based cue weighting.
“Further, the network exhibits key “behavioural” characteristics observed in human and primate studies, such as reliability-based cue weighting (9, 17) and self-motion estimation biased by causal inference (18).”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [9]
Congruent neuron activity is highly predictive of behavioral choice on vestibular-visual combination tasks.
“Congruent neurons appear to combine vestibular–visual signals in a way that assumes a common cause, and their activity is highly predictive of behavioral choice on vestibular–visual combination tasks (8–10).”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [9]
Vestibular and visual signals are combined in macaque MSTd.
“Neurophysiological work in macaque monkeys indicates that vestibular and visual signals are combined on the dorsal subdivision of the medial superior temporal area (MSTd) (6–11) and in the ventral intraparietal area (VIP) (12, 13).”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [9]
The reweighting of cues according to their reliability has been observed in individual MSTd neurons.
“The reweighting of cues according to their reliability has been observed in individual MSTd neurons (SI Appendix, Fig. S2 A and B, Top) (9); the neuronal tuning curves shift up or down depending on both the cue reliability and direction of conflict.”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [9]
MSTd neuronal tuning curves shift up or down depending on both cue reliability and direction of conflict.
“The reweighting of cues according to their reliability has been observed in individual MSTd neurons (SI Appendix, Fig. S2 A and B, Top) (9); the neuronal tuning curves shift up or down depending on both the cue reliability and direction of conflict.”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [9]
Human and nonhuman primates weight multisensory signals according to their reliability.
“We first assessed a key aspect of perceptual integration: when human and nonhuman primates combine multisensory signals, they weight them according to the reliability of each (9, 17).”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [9]
When a small conflict is introduced between two cues, an observer's judgment incorporates both signals but depends more on the more reliable signal.
“In particular, if a small conflict is introduced between two cues, an observer’s judgment will incorporate both signals but depend more on the signal that is more reliable (i.e., has the lowest estimator variance) (9, 10, 21).”
From How multisensory neurons solve causal inference. (2021) · cites this paper as [9]
Stimulus strength, reflected in the width and strength of its sensory responses, can mediate optimal reliability-based cue integration.
“A number of studies have proposed ways in which stimulus strength, reflected in the width and strength of its sensory responses, can mediate optimal reliability-based cue integration (Fetsch et al., 2012; Ma et al., 2008; Vilares and Kording, 2011).”
From Attention, learning, and the value of information (2012) · cites this paper as Fetsch et al., 2012
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
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · see citing works
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
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,412 · see citing works
The Bayesian brain: The role of uncertainty in neural coding and computation
Knill, D. C., & Pouget, A. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.10.007 · Cited by 2,588 · see citing works
Self-citationThe psychometric function: I. Fitting, sampling, and goodness of fit
Wichmann, F. A., & Hill, N. J. (2001) · Perception & Psychophysics · doi:10.3758/bf03194544 · Cited by 2,359 · see citing works
Shadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178 · 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
(1994) · Choice Reviews Online · doi:10.5860/choice.31-4365 · Cited by 1,897 · see citing works
Neural correlations, population coding and computation
Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743 · see citing works
Self-citationMaunsell, J., & van Essen, D. (1983) · The Journal of Neuroscience · doi:10.1523/jneurosci.03-12-02563.1983 · Cited by 1,602 · 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
Self-citationPerception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425 · see citing works
Correlated neuronal discharge rate and its implications for psychophysical performance
Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237 · 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
Measurement and modeling of depth cue combination: in defense of weak fusion
Landy, M. S., Maloney, L. T., Johnston, E. B., & Young, M. (1995) · Vision Research · doi:10.1016/0042-6989(94)00176-m · Cited by 996 · see citing works
The statistical reliability of signals in single neurons in cat and monkey visual cortex
Tolhurst, D. J., Movshon, J. A., & Dean, A. F. (1983) · Vision Research · doi:10.1016/0042-6989(83)90200-6 · Cited by 982 · see citing works
Shadlen, M., Britten, K., Newsome, W., & Movshon, J. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-04-01486.1996 · Cited by 824 · see citing works
Tanaka, K., & Saito, H. (1989) · Journal of Neurophysiology · doi:10.1152/jn.1989.62.3.626 · Cited by 692 · see citing works
Recent development of flow visualization
Aoki, K., & FUJISAWA, N. (2004) · Journal of Visualization · doi:10.1007/bf03181473 · Cited by 651 · see citing works
Probabilistic population codes for Bayesian decision making
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.021 · Cited by 645 · see citing works
Self-citationResponses of neurons in macaque MT to stochastic motion signals
Britten, K. H., Shadlen, M. N., Newsome, W. T., & Movshon, J. A. (1993) · Visual Neuroscience · doi:10.1017/s0952523800010269 · Cited by 594 · see citing works
When Feeling Is More Important Than Seeing in Sensorimotor Adaptation
van Beers, R. J., Wolpert, D. M., & Haggard, P. (2002) · Current Biology · doi:10.1016/s0960-9822(02)00836-9 · Cited by 560 · 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-citationKomatsu, H., & Wurtz, R. H. (1988) · Journal of Neurophysiology · doi:10.1152/jn.1988.60.2.580 · Cited by 545 · see citing works
Optimal 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
Data Fusion for Sensory Information Processing Systems
Clark, J. J., & Yuille, A. (1990) · Springer eBooks · doi:10.1007/978-1-4757-2076-1 · Cited by 519 · see citing works
Problems Arising in the Analysis of a Series of Similar Experiments
Cochran, W. G. (1937) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.2307/2984123 · Cited by 461 · see citing works
Bayesian integration of visual and auditory signals for spatial localization
Battaglia, P., Jacobs, R. A., & Aslin, R. Ν. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.001391 · Cited by 426 · see citing works
Slant from texture and disparity cues: Optimal cue combination
Hillis, J. M., Watt, S. J., Landy, M. S., & Banks, M. S. (2004) · Journal of Vision · doi:10.1167/4.12.1 · Cited by 421 · see citing works
Dynamic Reweighting of Visual and Vestibular Cues during Self-Motion Perception
Fetsch, C. R., Turner, A. H., DeAngelis, G. C., & Angelaki, D. E. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.2574-09.2009 · Cited by 406 · see citing works
Self-citationDo humans optimally integrate stereo and texture information for judgments of surface slant?
Knill, D. C., & Saunders, J. A. (2003) · Vision Research · doi:10.1016/s0042-6989(03)00458-9 · Cited by 403 · see citing works
Topography of projections to posterior cortical areas from the macaque frontal eye fields
Stanton, G. B., Bruce, C. J., & Goldberg, M. E. (1995) · The Journal of Comparative Neurology · doi:10.1002/cne.903530210 · Cited by 370 · see citing works
Variance as a signature of neural computations during decision making
Churchland, A. K., Kiani, R., Chaudhuri, R., Wang, X.-J., Pouget, A., & Shadlen, M. N. (2011) · Neuron · doi:10.1016/j.neuron.2010.12.037 · Cited by 350 · see citing works
Self-citationVisual-vestibular interactive responses in the macaque ventral intraparietal area (VIP)
Bremmer, F., Klam, F., Duhamel, J., Ben Hamed, S., & Graf, W. (2002) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.2002.02206.x · Cited by 331 · see citing works
Bayesian integration of spatial information.
Cheng, K., Shettleworth, S. J., Huttenlocher, J., & Rieser, J. J. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.4.625 · Cited by 326 · 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
A normalization model of multisensory integration
Ohshiro, T., Angelaki, D. E., & DeAngelis, G. C. (2011) · Nature Neuroscience · doi:10.1038/nn.2815 · Cited by 320 · see citing works
Self-citationGu, 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 · see citing works
Self-citationA functional link between area MSTd and heading perception based on vestibular signals
Gu, Y., DeAngelis, G. C., & Angelaki, D. E. (2007) · Nature Neuroscience · doi:10.1038/nn1935 · Cited by 297 · see citing works
Self-citationMST Neurons Respond to Optic Flow and Translational Movement
Duffy, C. J. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.4.1816 · Cited by 277 · see citing works
A perturbation analysis of depth perception from combinations of texture and motion cues
Young, M., Landy, M. S., & Maloney, L. T. (1993) · Vision Research · doi:10.1016/0042-6989(93)90228-o · Cited by 234 · see citing works
Perceptual Learning Reduces Interneuronal Correlations in Macaque Visual Cortex
Gu, Y., Liu, S., Fetsch, C. R., Yang, Y., Fok, S., Sunkara, A., DeAngelis, G. C., & Angelaki, D. E. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.015 · Cited by 218 · see citing works
Self-citationProbability density estimation for the interpretation of neural population codes
Sanger, T. D. (1996) · Journal of Neurophysiology · doi:10.1152/jn.1996.76.4.2790 · Cited by 216 · see citing works
Amplification of Trial-to-Trial Response Variability by Neurons in Visual Cortex
Carandini, M. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020264 · Cited by 210 · 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
Self-citationRepresentation 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-citationKnill, D. C. (2007) · Journal of Vision · doi:10.1167/7.7.5 · Cited by 191 · see citing works
Optic Flow Processing in Monkey STS: A Theoretical and Experimental Approach
Lappe, M., Bremmer, F., Pekel, M., Thiele, A., & Hoffmann, K. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-19-06265.1996 · Cited by 163 · see citing works
Takahashi, K., Gu, Y., May, P. J., Newlands, S. D., DeAngelis, G. C., & Angelaki, D. E. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.0817-07.2007 · Cited by 162 · see citing works
Self-citationDecoding of MSTd Population Activity Accounts for Variations in the Precision of Heading Perception
Gu, Y., Fetsch, C. R., Adeyemo, B., DeAngelis, G. C., & Angelaki, D. E. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.026 · Cited by 159 · see citing works
Self-citationCorticovestibular interactions: anatomy, electrophysiology, and functional considerations
Fukushima, K. (1997) · Experimental Brain Research · doi:10.1007/pl00005786 · Cited by 152 · see citing works
Ideal-Observer Models of Cue Integration
Landy, M. S., Banks, M. S., & Knill, D. C. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195387247.003.0001 · Cited by 144 · see citing works
The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses
Földiák, P. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3254-5_9 · Cited by 87 · see citing works
Perrone, J. A., & Stone, L. S. (1998) · Journal of Neuroscience · doi:10.1523/jneurosci.18-15-05958.1998 · Cited by 85 · see citing works
Rosas, P., Wagemans, J. P., Ernst, M. O., & Wichmann, F. A. (2005) · Journal of the Optical Society of America A · doi:10.1364/josaa.22.000801 · Cited by 77 · see citing works
Beck, J. M., Bejjanki, V. R., & Pouget, A. (2011) · Neural Computation · doi:10.1162/neco_a_00125 · Cited by 67 · see citing works
Self-citationRadial motion bias in macaque frontal eye field
XIAO, Q., Barborică, A., & Ferrera, V. P. (2006) · Visual Neuroscience · doi:10.1017/s0952523806231055 · Cited by 44 · see citing works
Probabilistic population codes and the exponential family of distributions
Beck, J. M., Ma, W. J., Latham, P. E., & Pouget, A. (2007) · Progress in brain research · doi:10.1016/s0079-6123(06)65032-2 · Cited by 40 · see citing works
Self-citationSpatiotemporal Properties of Vestibular Responses in Area MSTd
Fetsch, C. R., Rajguru, S. M., Karunaratne, A., Gu, Y., Angelaki, D. E., & DeAngelis, G. C. (2010) · Journal of Neurophysiology · doi:10.1152/jn.91247.2008 · Cited by 25 · see citing works
Self-citationLinear Responses to Stochastic Motion Signals in Area MST
Heuer, H. W., & Britten, K. H. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00083.2007 · Cited by 22 · see citing works
Extraction of visual motion and optic flow
Fukushima, K. (2008) · Neural Networks · doi:10.1016/j.neunet.2007.12.049 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [22]Dayan P Abbott LF Theoretical Neuroscience MIT press Cambridge, MA 2001
Cited by
Sorted by citations
Behavioral, Neural, and Computational Principles of Bodily Self-Consciousness
Blanke, O., Slater, M., & Serino, A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.029 · Cited by 698 · see citing works
Probabilistic brains: knowns and unknowns
Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013) · Nature Neuroscience · doi:10.1038/nn.3495 · Cited by 615 · see citing works
“Experimental results in a multisensory integration task involving the visual and vestibular systems are consistent with this prediction 55 .”
ResultSelf-citationConfidence and certainty: Distinct probabilistic quantities for different goals
Pouget, A., Drugowitsch, J., & Kepecs, A. (2016) · Nature Neuroscience · doi:10.1038/nn.4240 · Cited by 528 · see citing works
Self-citationA multilevel multimodal circuit enhances action selection in drosophila
Ohyama, T., Schneider-Mizell, C. M., Fetter, R. D., Aleman, J. V., Franconville, R., Rivera-Alba, M., Mensh, B. D., Branson, K. M., Simpson, J. H., Truman, J. W., et al. (2015) · Nature · doi:10.1038/nature14297 · Cited by 502 · see citing works
Suboptimality in perceptual decision making
Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342 · see citing works
Knowing how much you don't know: a neural organization of uncertainty estimates
Bach, D. R., & Dolan, R. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3289 · Cited by 339 · see citing works
Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception
Rohe, T., & Noppeney, U. (2015) · PLOS Biology · doi:10.1371/journal.pbio.1002073 · Cited by 344 · see citing works
Neural Coding of Uncertainty and Probability
Ma, W. J., & Jazayeri, M. (2014) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014017 · Cited by 290 · see citing works
Heeger, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619788114 · Cited by 220 · see citing works
Attention, learning, and the value of information
Gottlieb, J. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.034 · Cited by 241 · see citing works
“A number of studies have proposed ways in which stimulus strength, reflected in the width and strength of its sensory responses, can mediate optimal reliability-based cue integration (Fetsch et al., 2012; Ma et al., 2008; Vilares and Kording, 2011).”
BackgroundGriffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012) · Psychological Bulletin · doi:10.1037/a0026884 · Cited by 192 · see citing works
Self-citationThe neural dynamics of hierarchical Bayesian causal inference in multisensory perception
Rohe, T., Ehlis, A.-C., & Noppeney, U. (2019) · Nature Communications · doi:10.1038/s41467-019-09664-2 · Cited by 187 · see citing works
Cue combination in human spatial navigation
Chen, X., McNamara, T. P., Kelly, J. W., & Wolbers, T. (2017) · Cognitive Psychology · doi:10.1016/j.cogpsych.2017.04.003 · Cited by 122 · see citing works
Diverse Spatial Reference Frames of Vestibular Signals in Parietal Cortex
Chen, X., DeAngelis, G. C., & Angelaki, D. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.006 · Cited by 94 · see citing works
Self-citationCausal inference accounts for heading perception in the presence of object motion
Dokka, K., Park, H., Jansen, M., DeAngelis, G. C., & Angelaki, D. E. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820373116 · Cited by 80 · see citing works
Self-citationRelation between belief and performance in perceptual decision making
Drugowitsch, J., Moreno-Bote, R., & Pouget, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096511 · Cited by 72 · see citing works
Self-citationEfficient probabilistic inference in generic neural networks trained with non-probabilistic feedback
Orhan, A. E., & Ma, W. J. (2017) · Nature Communications · doi:10.1038/s41467-017-00181-8 · Cited by 77 · 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
Cognition as a Window into Neuronal Population Space
Ruff, D. A., Ni, A. M., & Cohen, M. R. (2018) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061936 · Cited by 63 · see citing works
Khalvati, K., Kiani, R., & Rao, R. P. N. (2021) · Nature Communications · doi:10.1038/s41467-021-25419-4 · Cited by 64 · see citing works
Learning Multisensory Integration and Coordinate Transformation via Density Estimation
Makin, J. G., Fellows, M. R., & Sabes, P. N. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003035 · Cited by 53 · see citing works
Studying the neural representations of uncertainty
Walker, E. Y., Pohl, S., Denison, R. N., Barack, D. L., Lee, J., Block, N., Ma, W. J., & Meyniel, F. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01444-y · Cited by 56 · see citing works
Spatial Patterns of Persistent Neural Activity Vary with the Behavioral Context of Short-Term Memory
Daie, K., Goldman, M. S., & Aksay, E. R. F. (2015) · Neuron · doi:10.1016/j.neuron.2015.01.006 · Cited by 42 · 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
Self-citationPennartz, C. M. A., Oude Lohuis, M. N., & Olcese, U. (2023) · Philosophical Transactions of the Royal Society B · doi:10.1098/rstb.2022.0336 · Cited by 38 · 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
Self-citationSuboptimal human multisensory cue combination
Arnold, D. H., Petrie, K., Murray, C., & Johnston, A. (2019) · Scientific Reports · doi:10.1038/s41598-018-37888-7 · Cited by 23 · see citing works
Weighted cue integration for straight-line orientation
Shaverdian, S., Dirlik, E., Mitchell, R., Tocco, C., Webb, B., & Dacke, M. (2022) · iScience · doi:10.1016/j.isci.2022.105207 · Cited by 9 · see citing works
- Authors
- Fetsch, C. R., Pouget, A., DeAngelis, G. C., & Angelaki, D. E.
- Affiliations
- Washington University in St. Louis (US)
- University of Geneva (CH)
- Baylor College of Medicine (US)
- Corresponding author
- Fetsch, Christopher R
- Venue
- Nature Neuroscience
- Published
- 2011-11-20
- Volume
- 15
- Issue
- 1
- Pages
- 146-154
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC3398428
- OpenAlex
- W2072262638
- Cited by
- 459
- References
- 62
- Citations by year
- 2026: 26 · 2025: 47 · 2024: 36 · 2023: 35 · 2022: 22 · 2021: 27 · 2020: 26 · 2019: 30 · 2018: 35 · 2017: 48 · 2016: 33 · 2015: 27 · 2014: 22 · 2013: 30 · 2012: 15
- FWCI
- 27.62
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Archive ouverte UNIGE (University of Geneva) (submittedVersion, cc-by)
- Funders
- National Science Foundation
- National Institute for Health and Care Research
- National Institutes of Health
- National Eye Institute (5-T32-EY13360-07, R01EY019087, EY-019087, EY-016178, T32EY013360, R01EY016178)
- 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 › Motor Control and Adaptation
- Keywords
- multisensory integration, cue weighting, cue reliability, visual-vestibular integration, population decoding, statistical inference, cue conflict, non-human primates
- MeSH terms
- Animals, Brain, Conflict, Psychological, Cues, Macaca mulatta, Models, Neurological, Motion Perception, Neurons, Proprioception, 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 ›
Are you an author, or do you see a problem with this page? Request a review
