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Probabilistic models of state estimation predict visuomotor transformations during prism adaptation

Published online by Cambridge University Press:  01 March 2012

MASAKI YAMAMOTO  and
HIROSHI ANDO
MASAKI YAMAMOTO*
Affiliation:
Division of Human Biology, Department of Rehabilitation Science, Graduate School of Health Sciences, Kobe University, Kobe, Japan
HIROSHI ANDO
Affiliation:
Division of Human Biology, Department of Rehabilitation Science, Faculty of Health Sciences, School of Medicine, Kobe University, Kobe, Japan
*
*Address correspondence and reprint requests to: Masaki Yamamoto, Graduate School of Health Sciences, Kobe University, 10-2, 7, Tomogaoka, Suma, Kobe 654-0142, Japan. E-mail: yamamotoma@kawasakigakuen.ac.jp
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Abstract

This study aims to create a prediction model for state-space estimation and to elucidate the required information processing for identifying an external space in prism adaptation. Subjects were 57 healthy students. The subjects were instructed to rapidly perform reaching movements to one of the randomly illuminating light-emitting diode lights. Their movements were measured while wearing prism glasses and after removing that. We provided the following four conditions and control. In target condition, reaching error distance was visually fed back to the subject. In trajectory condition, the trajectory of fingertip movement could be seen, and the final reaching error was not fed back. Two restricted visual feedback conditions were prepared based on a different presentation timing (on-time and late-time conditions). We set up a linear parametric model and an estimation model using Kalman filtering. The goodness of fit between the estimated and observed values in each model was examined using Akaike information criterion (AIC). AIC would be one way to evaluate two models with different number of parameters. In the control, the value of AIC was 179.0 and 154.0 for the linear model and Kalman filtering, respectively, while these values were 173.6 and 161.1 for the target condition, 202.8 and 159.7 for the trajectory condition, 192.7 and 180.8 for the on-time condition, and 206.9 and 174.0 for the late-time condition. Kalman gain in the control was 0.07–0.26. Kalman gain relies on the prior estimation distribution when its value is below 0.5. Kalman gain in the trajectory and late-time conditions was 0.03–0.60 and 0.08–0.95, respectively. The Kalman filter, a state estimation model based on Bayesian theory, expressed the dynamics of the internal model under uncertain feedback information better than the linear parametric model. The probabilistic estimation model can clearly simulate state estimation according to the reliability of the visual feedback.

Keywords

Sensorimotor transformationMotor adaptationProbabilistic prediction modelSystem identificationKalman filtering
Type
Research Articles
Information
Visual Neuroscience , Volume 29 , Issue 2 , March 2012 , pp. 119 - 129
Copyright
Copyright © Cambridge University Press 2012

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References

Akaike, H. (1992). Data analysis by statistical models. No to hattatsu. Brain and Development 24, 127–133.Google Scholar
Baddeley, R.J., Ingram, H.A. & Miall, R.C. (2003). System identification applied to a visuomotor task: Near optimal human performance in a noisy changing task. The Journal of Neuroscience 23, 3066–3075.Google Scholar
Bayes, T. & Price, R. (1763). An essay towards solving a problem in the doctrine of chance. By the late Rev. Mr. Bayes, communicated by Mr. Price, in a letter to John, Canton, M.A. & F.R.S. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 53, 370–418.Google Scholar
Burge, J., Ernst, M.O. & Banks, M.S. (2008). The statistical determinants of adaptation rate in human reaching. Journal of Vision 8, 1–19.Google Scholar
Burge, J., Girshick, A.R. & Banks, M.S. (2010). Visual-haptic adaptation is determined by relative reliability. The Journal of Neuroscience 30, 7714–7721.CrossRefGoogle ScholarPubMed
Cisek, P. & Kalaska, J.F. (2005). Neural correlates of reaching decisions in dorsal premotor cortex: Specification of multiple direction choices and final selection of action. Neuron 45, 801–814.Google Scholar
Deneve, S. (2008). Bayesian spiking neurons I: Inference. Neural Computation 20, 91–117.CrossRefGoogle ScholarPubMed
Ernst, M.O. & Banks, M.S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433.Google Scholar
Ernst, M.O. & Bülthoff, H.H. (2004). Merging the senses into a robust percept. Trends in Cognitive Sciences 8, 162–169.Google Scholar
Gold, J.I. & Shadlen, M.N. (2001). Neural computations that underlie decisions about sensory stimuli. Trends in Cognitive Sciences 5, 10–16.Google Scholar
Gold, J.I. & Shadlen, M.N. (2003). The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. The Journal of Neuroscience 23, 632–651.CrossRefGoogle ScholarPubMed
Helbig, H.B. & Ernst, M.O. (2008). Visual-haptic cue weighting is independent of modality-specific attention. Journal of Vision 8, 1–16.Google Scholar
Izawa, J. & Shadmehr, R. (2008). On-line processing of uncertain information in visuomotor control. The Journal of Neuroscience 28, 11360–11368.Google Scholar
Jakobson, L.S. & Goodale, M.A. (1991). Factors affecting higher-order movement planning: A kinematic analysis of human prehension. Experimental Brain Research 86, 199–208.CrossRefGoogle ScholarPubMed
Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82, 35–45.CrossRefGoogle Scholar
Kalman, R.E. & Bucy, R.S. (1961). New results in linear filtering and prediction theory. Journal of Basic Engineering 83, 95–107.Google Scholar
Kawato, M. (1999). Internal models for motor control and trajectory planning. Current Opinion in Neurobiology 9, 718–727.CrossRefGoogle ScholarPubMed
Kersten, D. & Yuille, A. (2003). Bayesian models of object perception. Current Opinion in Neurobiology 13, 150–158.Google Scholar
Kiani, R. & Shadlen, M.N. (2009). Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324, 759–764.CrossRefGoogle ScholarPubMed
Kitazawa, S., Kohno, T. & Uka, T. (1995). Effects of delayed visual information on the rate and amount of prism adaptation in the human. The Journal of Neuroscience 15, 7644–7652.CrossRefGoogle ScholarPubMed
Körding, K.P. & Wolpert, D.M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences 10, 319–326.CrossRefGoogle ScholarPubMed
Ma, W.J., Beck, J.M., Latham, P.E. & Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nature Neuroscience 9, 1432–1438.Google Scholar
MacNeilage, P.R., Ganesan, N. & Angelaki, D.E. (2008). Computational approaches to spatial orientation: From transfer functions to dynamic Bayesian inference. Journal of Neurophysiology 100, 2981–2996.CrossRefGoogle ScholarPubMed
Marr, D. & Vaina, L. (1982). Representation and recognition of the movements of shapes. Proceedings of the Royal Society of London. Series B, Biological Sciences 214, 501–524.Google ScholarPubMed
Norris, S.A., Greger, B.E., Martin, T.A. & Thach, W.T. (2001). Prism adaptation of reaching is dependent on the type of visual feedback of hand and target position. Brain Research 905, 207–219.Google Scholar
Pouget, A., Dayan, P. & Zemel, R.S. (2003). Inference and computation with population codes. Annual Review of Neuroscience 26, 381–410.CrossRefGoogle ScholarPubMed
Prablanc, C., Echallier, J.F., Komilis, E. & Jeannerod, M. (1979). Optimal response of eye and hand motor systems in pointing at a visual target. I. Spatio-temporal characteristics of eye and hand movements and their relationships when varying the amount of visual information. Biological Cybernetics 35, 113–124.CrossRefGoogle Scholar
Previc, F. (1998). The neuropsychology of 3-D space. Psychological Bulletin 124, 123–164.CrossRefGoogle ScholarPubMed
Rao, R.P. (1999). An optimal estimation approach to visual perception and learning. Vision Research 39, 1963–1989.Google Scholar
Redding, G.M. & Wallace, B. (1978). Sources of ‘overadditivity’ in prism adaptation. Perception & Psychophysics 24, 58–62.Google Scholar
Redding, G.M.B. & Wallace, B. (1988). Components of prism adaptation in terminal and concurrent exposure: Organization of the eye–hand coordination loop. Perception & Psychophysics 44, 59–68.CrossRefGoogle ScholarPubMed
Redding, G.M. & Wallace, B. (1990). Effects on prism adaptation of duration and timing of visual feedback during pointing. Journal of Motor Behavior 22, 209–224.CrossRefGoogle ScholarPubMed
Scheidt, R.A. & Ghez, C. (2007). Separate adaptive mechanisms for controlling trajectory and final position in reaching. Journal of Neurophysiology 98, 3600–3613.Google Scholar
Smeets, J.B., van den Dobbelsteen, J.J., de Grave, D.D., van Beers, R.J. & Brenner, E. (2006). Sensory integration does not lead to sensory calibration. Proceedings of the National Academy of Sciences of the United States of America 103, 18781–18786.CrossRefGoogle Scholar
Stevenson, I.H., Fernandes, H.L., Vilares, I., Wei, K. & Körding, K.P. (2009). Bayesian integration and non-linear feedback control in a full-body motor task. PLoS Computational Biology 5, e1000629.CrossRefGoogle Scholar
Tassinari, H., Hudson, T.E. & Landy, M.S. (2006). Combining priors and noisy visual cues in a rapid pointing task. The Journal of Neuroscience 26, 10154–10163.Google Scholar
Tin, C. & Poon, C.S. (2005). Internal models in sensorimotor integration: Perspectives from adaptive control theory. Journal of Neural Engineering 2, S147–S163.Google Scholar
Trommershäuser, J., Maloney, L.T. & Landy, M.S. (2003). Statistical decision theory and the selection of rapid, goal-directed movements. Journal of the Optical Society of America. A, Optics, Image Science, and Vision 20, 1419–1433.Google Scholar
von Helmholtz, H. (1867). Handbuch der physiologischen optik. Leipzig, Germany: Leopold Voss.Google Scholar
Wei, K. & Körding, K. (2009). Relevance of error: What drives motor adaptation? Journal of Neurophysiology 101, 655–664.CrossRefGoogle ScholarPubMed
Wei, K. & Körding, K. (2010). Uncertainty of feedback and state estimation determines the speed of motor adaptation. Frontiers in Computational Neuroscience 4, 1–9.Google Scholar
Welch, R.B., Choe, C.S. & Heinrich, D.R. (1974). Evidence for a three-component model of prism adaptation. Journal of Experimental Psychology 103, 700–705.Google Scholar
Wolpert, D.M. (2007). Probabilistic models in human sensorimotor control. Human Movement Science 26, 511–524.CrossRefGoogle ScholarPubMed
Zemel, R.S., Dayan, P. & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Computation 10, 403–430.CrossRefGoogle ScholarPubMed