System identification applied to a visuomotor task: Near-optimal human performance in a noisy changing task

Journal of Neuroscience

Volumn 23, Issue 7, 2003, Pages 3066-3075

  Baddeley, R J ^a   Ingram, H A ^a,b   Miall, R C ^b  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Human; Ideal observer analysis; Movement; Performance; System identification; Visuomotor control

Indexed keywords

ADULT; ALGORITHM; ARTICLE; CONTROLLED STUDY; FEEDBACK SYSTEM; FEMALE; HUMAN; HUMAN EXPERIMENT; MALE; NEUROMODULATION; NOISE; NORMAL HUMAN; PRIORITY JOURNAL; TASK PERFORMANCE; VISUOMOTOR COORDINATION;

EID: 0037387043     PISSN: 02706474     EISSN: None     Source Type: Journal    
DOI: 10.1523/jneurosci.23-07-03066.2003     Document Type: Article

References (34)

1. Akaike H (1974) A new look at the statistical model identication. IEEE Trans AC 19:716-723.
2. Baddeley RJ, Tripathy S (1998) Insights into motion perception by observer modelling. J Opt Soc Am [A] 15:289-296.
3. Barlow HB, Tripathy SP (1997) Correspondence noise and signal pooling in the detection of coherent visual motion. J Neurosci 17:7954-7966.
4. Berg BG (1990) Observer efficiency and weights in a multiple observation task. J Acoust Soc Am 88:149-158.
5. Borah J, Young LR, Curry RE (1988) Optimal estimator model for human spatial orientation. Ann NY Acad Sci 545:51-73.
6. Burgess AE, Wagner RF, Jennings RJ, Barlow HB (1981) Efficiency of human visual discrimination. Science 214:93-94.
7. Bhushan N, Shadmehr R (1999) Computational nature of human adaptive control during learning of reaching movements in force fields. Biol Cybern 81:39-60.
8. Cox DR, Miller HD (1965) Theory of stochastic processes. London: Methuen.
9. Creelman CD (1961) Detection of complex signals as a function of signal bandwidth and duration. J Acoust Soc Am 33:89-94.
10. Crowell JA, Banks MS (1996) Ideal observer for heading judgements. Vision Res 36:471-490.
11. Dietterich TG (1998) Approximate statistical tests for comparing supervised classication learning algorithms. Neural Comput 10:1895-1923.
12. Estes WK (1950) Toward a statistical theory of learning. Psychol Rev 57:94-107.
13. Fisher RA (1925) Statistical methods for research workers. Edinburgh: Oliver and Boyd.
14. Geisler WS (1989) Sequential ideal-observer analysis of visual discriminations. Psychol Rev 96:267-314.
15. Ingram H, van Donkelaar P, Cole J, Vercher J-L, Gauthier G, Miall RC (2000) The role ofproprioception and attention in a visuomotor adaptation task. Exp Brain Res 132:114-126.
16. Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixture of local experts. Neural Comput 3:79-87.
17. Kagerer FA, Contreras-Vidal JL, Stelmach GE (1997) Adaptation to gradual as compared with sudden visuo-motor distortions. Exp Brain Res 115:557-561.
18. Kalman RE, Bucy RS (1961) New results in linear filtering and prediction. J Basic Eng (ASME) 83D:95-108.
19. Lackner JR, Dizio P (1994) Rapid adaptation to Coriolis force perturbations of arm trajectory. J Neurophysiol 72:299-313.
20. Liu X, Tubbesing SA, Aziz TZ, Miall RC, Stein JF (1999) Effects of visual feedback on manual tracking and action tremor in Parkinson's disease. Exp Brain Res 129:477-481.
21. Liu Z, Knill DC, Kersten D (1995) Object classification for human and ideal observers. Vision Res 35:549-568.
22. Ljung L (1999) System Identication: Theory for the user, Ed 2. Upper Saddle River, NJ: Prentice Hall.
23. Lutfi RA (1994) Discrimination of random, time-varying spectra with statistical constraints. J Acoust Soc Am 95:1490-1500.
24. Mackay D (1992) Bayesian model comparison and backprop Nets. In: Advances in Neural Information Processing Systems, Vol 4 (Moody JE, Hanson SJ, Lippmann RP, eds), pp 839-846. San Francisco: Morgan Kaufmann.
25. Parish DH, Sperling G (1991) Object spatial frequencies, retinal spatial frequencies, noise, and the efficiency of letter discrimination. Vision Res 31:1399-1415.
26. Rescorla RA, Wagner AR (1972) A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In: Classical conditioning II: current research and theory (Black AH, Prokasy WF, eds), pp 64-99. New York: Appleton-Century-Crofts.
27. Scheidt RA, Dingwell JB, Mussa-Ivaldi FA (2001) Learning to move amid uncertainty. J Neurophysiol 86:971-985.
28. Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14:3208-3224.
29. Sutton RS, Barto AG (1981) Toward a modern theory of adaptive networks: expectation and prediction. Psychol Rev 88:135-170.
30. Tjan BS, Braje WL, Legge GE, Kersten D (1995) Human efficiency for recognizing 3-D objects in luminance noise. Vision Res 35:3053-3069.
31. van Beers RJ, Sittig AC, Gon JJ (1999) Integration of proprioceptive and visual position information: an experimentally supported model. J Neurophysiol 81:1355-1364.
32. Watamaniuk SN (1993) Ideal observer for discrimination of the global direction of dynamic random-dot stimuli. J Opt Soc Am [A] 10:16-28.
33. Welch RB (1986) Adaptation of space perception. In: Handbook of perception and human performance, Vol 1 (Boff KR, Kaufman L, Thomas JP, eds), pp 24.1-24.45. New York: Wiley.
34. Wolpert DM, Ghahramani Z, Jordan MI (1995) An internal model for sensorimotor control. Science 269:1880-1882.