Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Control of mental activities by internal models in the cerebellum

Abstract

The intricate neuronal circuitry of the cerebellum is thought to encode internal models that reproduce the dynamic properties of body parts. These models are essential for controlling the movement of these body parts: they allow the brain to precisely control the movement without the need for sensory feedback. It is thought that the cerebellum might also encode internal models that reproduce the essential properties of mental representations in the cerebral cortex. This hypothesis suggests a possible mechanism by which intuition and implicit thought might function and explains some of the symptoms that are exhibited by psychiatric patients. This article examines the conceptual bases and experimental evidence for this hypothesis.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Internal-model control systems for voluntary movement and mental activity.
Figure 2: Block diagrams for internal-model control.
Figure 3: The neuronal unit machine of the cerebellum.
Figure 4: Block diagram of a thought system.
Figure 5: Mental activities in the cerebellum.

Similar content being viewed by others

References

  1. Poon, C.-S. & Merfeld, D. M. Internal model: the state of art. J. Neural Eng. 2 (2005).

  2. Ito, M. Neurophysiological basis of the cerebellar motor control system. Int. J. Neurol. 7, 162–176 (1970).

    CAS  PubMed  Google Scholar 

  3. Ito, M. The Cerebellum and Neural Control (Raven, New York, 1984).

    Google Scholar 

  4. Kawato, M., Furukawa, K. & Suzuki, R. A hierarchical neural-network model for control and learning of voluntary movement. Biol. Cybern. 57, 169–185 (1987).

    Article  CAS  PubMed  Google Scholar 

  5. Miall, R. C., Weir, D. J., Wolpert, D. M. & Stein, J. F. Is the cerebellum a Smith predictor? J. Motor Behav. 25, 203–216 (1993).

    Article  CAS  Google Scholar 

  6. Wolpert, D. M. & Kawato, M. Multiple paired forward and inverse models for motor control. Neural Netw. 11, 1317–1329 (1998).

    Article  CAS  PubMed  Google Scholar 

  7. Wolpert, D. M., Miall, R. C. & Kawato, M. Internal models in the cerebellum. Trends Cogn. Sci. 2, 338–347 (1998).

    Article  CAS  PubMed  Google Scholar 

  8. Kawato, M. Cerebellum: models. In New Encyclopedia of Neuroscience (ed. Squire, L.) (Elsevier, in the press 2008).

    Google Scholar 

  9. Schmahmann, J. D. Disorders of the cerebellum: ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. J. Neuropsychiatry Clin. Neurosci. 16, 367–378 (2004).

    Article  PubMed  Google Scholar 

  10. Leiner, H. C., Leiner, A. L. & Dow, R. S. Does the cerebellum contribute to mental skills? Behav. Neurosci. 100, 443–454 (1986).

    Article  CAS  PubMed  Google Scholar 

  11. Leiner, H. C., Leiner, A. L. & Dow, R. S. Cognitive and language functions of the human cerebellum. Trends Neurosci. 16, 444–447 (1993).

    Article  CAS  PubMed  Google Scholar 

  12. Sasaki, K., Kawaguchi, S., Oka, H., Sakai, M. & Mizuno, N. Electrophysiological studies on the cerebellocerebral projections in monkeys. Exp. Brain Res. 24, 495–507 (1976).

    Article  CAS  PubMed  Google Scholar 

  13. Schmahmann, J. D. An emerging concept: the cerebellar contribution to higher function. Arch. Neurol. 48, 1178–1187 (1991).

    Article  CAS  PubMed  Google Scholar 

  14. Ito, M. A new physiological concept on cerebellum. Rev. Neurol. (Paris) 146, 564–569 (1990).

    CAS  Google Scholar 

  15. Ito, M. in The Principles of Design and Operation of the Brain (Experimental Brain Research) (eds Eccles, J. C. & Creutzfeldt, O.) 281–292 (Springer, 1990).

    Google Scholar 

  16. Ito, M. Movement and thought: identical control mechanisms by the cerebellum. Trends Neurosci. 16, 448–450 (1993).

    Article  CAS  PubMed  Google Scholar 

  17. Ito, M. in Cerebellum and Cognition (International Review of Neurobiology) (eds Schmahmann, J. D., Bradley, R. J., Adron Harris, R. & Jenner, P.) 475–487 (Academic, 1997).

    Google Scholar 

  18. Ito, M. Bases and implications of learning in the cerebellum – adaptive control and internal model mechanism. Prog. Brain Res. 148, 95–109 (2006).

    Article  Google Scholar 

  19. Vandervert, L. R., Schlimp, P. H. & Liu, H. How working memory and the cerebellum collaborate to produce creativity and innovation. Creativity Res. J. 19, 1–18 (2007).

    Article  Google Scholar 

  20. Wolpert, D. M., Doya, K. & Kawato, M. A unifying computational framework for motor control and social interaction. Philos. Trans. R. Soc. Lond. B Biol. Sci. 358, 593–602 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kelly, R. M. & Strick, P. L. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J. Neurosci. 23, 8432–8444 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Dum, R. P. & Strick, P. L. An unfolded map of the cerebellar dentate nucleus and its projections to the cerebral cortex. J. Neurophysiol. 89, 634–639 (2003).

    Article  PubMed  Google Scholar 

  23. Clower, D. M., Dum, R. P. & Strick, P. L., Basal ganglia and cerebellar inputs to 'AIP'. Cereb. Cortex 15, 913–920 (2005).

    Article  PubMed  Google Scholar 

  24. Fang, P.-C., Stepniewska, I. & Kaas, J. H. Ispilateral cortical connections of motor, premotor, frontal eye, and posterior parietal fields in a prosimian primate, Otalemur garnetti. J. Comp. Neurol. 490, 305–333 (2005).

    Article  PubMed  Google Scholar 

  25. Ramnani, N. The primate cortico-cerebellar system: anatomy and function. Nature Rev. Neurosci. 7, 511–522, (2006).

    Article  CAS  Google Scholar 

  26. Fiez, J. A., Raicle, M. E., Balota, D. A., Tallal, P. & Petersen, S. E. PET activation of posterior temporal regions during auditory word presentation and verb generation. Cereb. Cortex 6, 1–10 (1996).

    Article  CAS  PubMed  Google Scholar 

  27. Hanakawa, T., Honda, M., Okada, T., Fukuyama, H. & Shibasaki, H. Neural correlates underlying mental calculation in abacus experts: a functional magnetic resonance imaging study. Neuroimage 19, 296–307 (2003).

    Article  PubMed  Google Scholar 

  28. Baker, S. C. et al. Neural systems engaged by planning: a PET study of the Tower of London task. Neuropsychologia 34, 515–526 (1996).

    Article  CAS  PubMed  Google Scholar 

  29. Ploghaus, A. et al. Learning about pain: the neural substrate of the prediction error for aversive events. Proc. Natl Acad. Sci. USA 97, 9281–9286 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Allen, G. & Courchesne, E. Differential effects of developmental cerebellar abnormality on cognitive and motor functions in the cerebellum: an fMRI study of autism. Am. J. Psychiatry 160, 262–273 (2003).

    Article  PubMed  Google Scholar 

  31. Schlosser, R. et al. Functional magnetic resonance imaging of human brain activity in a verbal fluency task. J. Neurol. Neurosurg. Psychiatr. 64, 492–498 (1998).

    Article  CAS  Google Scholar 

  32. de Zubicaray, G. I. et al. Prefrontal cortex involvement in selective letter generation: a functional magnetic resonance imaging study. Cortex 34, 389–401 (1998).

    Article  CAS  PubMed  Google Scholar 

  33. Desmond, J. E., Gabrieli, J. D. E., Wagner, A. D., Ginier, B. L. & Glove, G. H. Lobular patterns of cerebellar activation in verbal working memory and finger-tapping tasks as revealed by functional MRI. J. Neurosci. 17, 9675–9685 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hayter, A. L., Lamgton, D. W. & Ramnani, N. Cerebellar contributions to working memory. Neuroimage 36, 943–954 (2007).

    Article  CAS  PubMed  Google Scholar 

  35. Nagahama, Y. et al. Cerebral activation during performance of a card sorting test. Brain 119, 1667–1675 (1996).

    Article  PubMed  Google Scholar 

  36. Narita, H., Odawara, T., Iseki, E., Kosaka, K. & Hirayasu, Y. Psychomotor retardation correlates with frontal hypoperfusion and the Modified Stroop Test in patients with major depression under 60-years-old. Psychiat. Clin. Neurosci. 58, 389–395 (2004).

    Article  Google Scholar 

  37. Atherton, M., Zhung, J., Bart, W. M., Hu, X. & He, S. A functional MRI study of high-level cognition. I. The game of chess. Cogn. Brain Res. 16, 26–31 (2003).

    Article  Google Scholar 

  38. Chen, X., Zhang, D., Zhang, X., Li, Z. & Meng, X. A functional MRI study of high-level cognition. II. The game of GO. Cogn. Brain Res. 16, 32–37 (2003).

    Article  Google Scholar 

  39. Szpunar, K. K., Watson, J. M. & McDermott, K. B. Neural substrates of envisioning the future. Proc. Natl Acad. Sci. USA 2, 642–647 (2007).

    Article  Google Scholar 

  40. Collinson, S. L., Anthonisz, B., Courtenay, D. & Winter, C. Frontal executive impairment associated with paraneoplastic cerebellar degeneration: a case study. Neurocase 12, 350–354 (2006).

    Article  PubMed  Google Scholar 

  41. Janssen, G. et al. Cerebellar mutism syndrome. Klin. Padiatr. 210, 243–247 (1998).

    Article  CAS  PubMed  Google Scholar 

  42. Fiez, J. A., Petersen, S. E., Cheney, M. K. & Raichle, M. E. Impaired non-motor learning and error detection associated with cerebellar damage. A single case study. Brain 115, 155–178 (1992).

    Article  PubMed  Google Scholar 

  43. Gebhart, A. L., Petersen, S. E & Thach, W. T. Role of the posterolateral cerebellum in language. Ann. NY Acad. Sci. 978, 318–333 (2002).

    Article  PubMed  Google Scholar 

  44. Leggio, M. G., Silveri, M. C., Petrosini, L. & Molinari, M. Phonological grouping is specifically affected in cerebellar patients; a verbal fluency study. J. Neurol. Neurosurg. Psychiatr. 69, 102–106 (2000).

    Article  CAS  Google Scholar 

  45. Nicolson, R. I., Daum, I., Schugens, M. M., Fawcett, A. J. & Schulz, A. Eyeblink conditioning indicates cerebellar abnormality in dyslexia. Exp. Brain Res. 143, 42–50 (2002).

    Article  PubMed  Google Scholar 

  46. Stoodley, C. J., Fawcett, A. J., Nicolson, R. I. & Stein, J. F. Balancing and pointing tasks in dyslexic and control adults. Dyslexia 12, 276–288 (2006).

    Article  PubMed  Google Scholar 

  47. Bigelow, N. O. et al. Prism adaptation in schizophrenia. Brain Cogn. 61, 235–242 (2006).

    Article  PubMed  Google Scholar 

  48. Blakemore, S.-J., Smith, J., Steel, R., Johnstone, E. C. & Frith, C. D. The perception of self-produced sensory stimuli in patients with auditory hallucinations and passivity experiences: evidence for a breakdown in self-monitoring. Psychol. Med. 30, 1131–1139 (2000).

    Article  CAS  PubMed  Google Scholar 

  49. Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    Article  CAS  PubMed  Google Scholar 

  50. Happaney, K., Zelazo, P. D. & Stuss, D. T. Development of orbitofrontal function: current theme and future directions. Brain Cogn. 55, 1–10 (2004).

    Article  PubMed  Google Scholar 

  51. Baddeley, A. Working memory: looking back and looking forward. Nature Rev. Neurosci. 4, 829–839 (2003).

    Article  CAS  Google Scholar 

  52. Baddeley, A. & Hitch, G. J. in Recent Advances in Learning and Motivation (ed. Bower, G. A.) 47–89 (Academic, New York, 1974).

    Google Scholar 

  53. Smith, E. E. & Jonides, J. Working memory: a view from neuroimaging. Cognit. Psychol. 33, 5–42 (1997).

    Article  CAS  PubMed  Google Scholar 

  54. Stuss, D. T. & Knight, R. T. Principles of Frontal Lobe Function (Oxford Univ. Press, New York, 2002).

    Book  Google Scholar 

  55. Baddeley, A. D. & Logie, R. H. in Models of Working Memory (eds Miyake, A. & Shah, P.) 28–61 (Cambridge Univ. Press, Cambridge, UK, 1999).

    Book  Google Scholar 

  56. Craik, K. J. W. The Nature of Explanation (Cambridge Univ. Press, Cambridge, UK, 1943).

    Google Scholar 

  57. Johnson-Raird, P. N. Mental Models (Cambridge Univ. Press, 1983).

    Google Scholar 

  58. Held, C., Knauff, M. & Vosgerau, G. Mental Models and the Mind: Current Developments in Cognitive Psychology, Neuroscience, and Philosophy of Mind (Advances in Psychology) (Elsevier, 2006).

    Google Scholar 

  59. Tanaka, K. Inferotemporal cortex and object vision. Ann. Rev. Neurosci. 19, 109–139 (1996).

    Article  CAS  PubMed  Google Scholar 

  60. Tsunoda, K., Yamane, Y., Nishizaka, M. & Tanifuji, M. Complex objects are represented in the macaque inferotemporal cortex by the combination of feature columns. Nature Neurosci. 4, 832–838 (2001).

    Article  CAS  PubMed  Google Scholar 

  61. Sheinberg., D. L. & Logothetis, N.K. The role of temporal areas in perceptual organization. Proc. Natl Acad. Sci. USA 94, 3408–3413 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Tarok, L. & Srinivasan, R. Transient MEG frequency-tagging response induced by switching stimulus salience during binocular rivalry. (Abstr.) 201.9/BBB20 (Society for Neuroscience Annual Meeting, San Diego, 2007).

  63. Kosslyn, S. M., Ganis, G. & Thompson, W. L. Neural foundations of imagery. Nature Rev. Neurosci. 2, 635–642 (2001).

    Article  CAS  Google Scholar 

  64. Pearson, D., De Beni, R. & Cornoldi, C. in Imagery, Language and Visuo-Spatial Thinking (eds Denis, M., Logie, R. H., Cornoldi, C., De Vega, M. & Engelkamp, J.) 1–27 (Psychological Press, 2001).

    Google Scholar 

  65. Hesslow, G. Conscious thought as simulation of behaviour and perception. Trends Cogn. Sci. 6, 242–247 (2002).

    Article  PubMed  Google Scholar 

  66. Le Bihan, D. et al. Activation of human primary visual cortex during visual recall: a magnetic resonance imaging study. Proc. Natl Acad. Sci. USA 90, 11802–11805 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Miyashita, Y. Cognitive memory: cellular and network machineries and their top-down control. Science 306, 435–440 (2004).

    Article  CAS  PubMed  Google Scholar 

  68. Hasegawa, I., Fukushima, T., Ihara, T. & Miyashita, Y. Callosal window between prefrontal cortices: cognitive interaction to retrieve long-term memory. Science 281, 814–818 (1998).

    Article  CAS  PubMed  Google Scholar 

  69. Penfield, W. & Perot, P. The brain's record of auditory and visual experience; a final summary and discussion. Brain 86, 595–696 (1963).

    Article  CAS  PubMed  Google Scholar 

  70. Fuster, J. M. The Prefrontal Cortex: Anatomy, Physiology, and Neuropsychology of the Frontal Lobe (Raven, New York, 1997).

    Google Scholar 

  71. Ito, M. Cerebellar long-term depression: characterization, signal transduction and functional roles. Physiol. Rev. 81, 1143–1195 (2001).

    Article  CAS  PubMed  Google Scholar 

  72. Ito, M. Cerebellar circuitry as a neuronal machine. Prog. Neurobiol. 78, 272–303 (2006).

    Article  PubMed  Google Scholar 

  73. Ralston, D. D. Corticorubral synaptic organization in Macaca fascicularis: a study utilizing degeneration, anterograde transport of WGA-HRP, and combined immuno-GABA-gold technique and computer-assisted reconstruction. J. Comp. Neurol. 350, 657–673 (1994).

    Article  CAS  PubMed  Google Scholar 

  74. Burman, K., Darian-Smith, C. & Darian-Smith, I. Macaque red nucleus: origins of spinal and olivary projections and terminations of cortical inputs. J. Comp. Neurol. 423, 178–196 (2000).

    Google Scholar 

  75. Burman, K., Darian-Smith, C. & Darian-Smith, I. Geometry of rubrospinal, rubroolivary, and local circuit neurons in the macaque red nucleus. J. Comp. Neurol. 423, 197–219, (2000).

    Article  CAS  PubMed  Google Scholar 

  76. Oka, H., Jinnai, K. & Yamamoto, T. The parieto-rubro-olivary pathway in the cat. Exp. Brain Res. 37, 115–125 (1979).

    Article  CAS  PubMed  Google Scholar 

  77. Yttri, E., Smith, A., Reid, E. & Thach, W. T. Inactivation of parvocellular red nucleus impairs prism adaptation to and learning of contralateral gaze-reach shift. (Abstr.) 440.15/K8 (Society for Neuroscience Annual Meeting, Atlanta, 2006).

  78. Schmahmann, J. D. & Pandya, D. N. Anatomical investigation of projections to the basis pontis from posterior parietal association cortices in rhesus monkey. J. Comp. Neurol. 289, 53–73 (1989).

    Article  CAS  PubMed  Google Scholar 

  79. Shidara, M., Kawano, M., Gomi, H. & Kawato, M. Inverse-dynamics encoding of eye movements by Purkinje cells in the cerebellum. Nature 365, 50–52 (1993).

    Article  CAS  PubMed  Google Scholar 

  80. Miles, G. B., Cerminara, N. L. & Marple-Horvat, D. E. Purkinje cells in the lateral cerebellum of the cat encode visual events and target motion during visually guided reaching. J. Physiol. 571, 619–637 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Pasalar, S., Roitman, A. V., Durfee, W. K. & Ebner, T. J. Force field effects on cerebellar Purkinje cell discharge with implications for internal models. Nature Neurosci. 9, 1404–1411 (2006).

    Article  CAS  PubMed  Google Scholar 

  82. Yamamoto, K., Kawato, M., Kotosaka, S. & Kitazawa, S. Encoding of movement dynamics by Purkinje cell simple spike activity during fast arm movements under resistive and assistive force fields. J. Neurophysiol. 97, 1588–1599 (2007).

    Article  PubMed  Google Scholar 

  83. Schmajuk, N. A., Lam, Y.-W. & Gray, J. A. Latent inhibition: a neural network approach. J. Exp. Psychol. 22, 321–349 (1996).

    CAS  Google Scholar 

  84. Lisman, J. E. & Grace, A. A. The hippocampal-VTA loop: controlling the entry of information into long-term memory. Neuron 46, 703–713 (2005).

    Article  CAS  PubMed  Google Scholar 

  85. Miller, E. K., Erickson, C. A. & Desimone, R. Neural mechanisms of visual working memory in prefrontal cortex of the macaque. J. Neurosci. 16, 5154–5167 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Einstein, A. Letters a Maurice Solvince (Gauthier-Villars, Paris, 1956).

    Google Scholar 

  87. Spriner, S. P. & Deutsch, G. Left Brain, Right Brain: Perspectives on Cognitive Neuroscience 5th edn (Freeman, New York, 1998).

    Google Scholar 

  88. Diamond, A. Close interrelation of motor development and cognitive development and the cerebellum and prefrontal cortex. Child Develop. 71, 44–56 (2000).

    Article  CAS  PubMed  Google Scholar 

  89. Carper, R. A. & Courchesne, E. Inverse correlation between frontal lobe and cerebellum sizes in children with autism. Brain 123, 836–844 (2000).

    Article  PubMed  Google Scholar 

  90. Fetami, S. H. et al. Purkinje cell size is reduced in cerebellum of patients with autism. Cell. Mol. Neurosci. 22, 171–175 (2002).

    Article  Google Scholar 

  91. Lee, M. et al. Nicotinic receptor abnormalities in the cerebellar cortex in autism. Brain 125, 1483–1495 (2002).

    Article  CAS  PubMed  Google Scholar 

  92. Sadakata, T. et al. Autistic-like phenotypes in Cadps 2-knockout mice and aberrant CADPS2 splicing in austistic patients. J. Clin. Invest. 117, 931–943 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Sadakata, T. et al. Impaired cerebellar development and function in mice lacking CAPS2, a protein involved in neurotrophin release. J. Neurosci. 27, 2472–2482 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Deuel, R. K. Autism: a cognitive developmental riddle. Pediatr. Neurol. 26, 349–357 (2002).

    Article  PubMed  Google Scholar 

  95. Ito, M. Nurturing the brain as an emerging research field involving child neurology. Brain Dev. 26, 429–433 (2004).

    Article  PubMed  Google Scholar 

  96. Sperling, A. J., Lu, Z. L., Manis, F. R. & Seidenberg, M. S. Deficits in perceptual noise exclusion in developmental dyslexia. Nature Neurosci. 8, 862–863 (2005).

    Article  CAS  PubMed  Google Scholar 

  97. Galaburda, A. M., Sherman, G. F., Rosen, G. D., Aboitiz, F. & Geschwind, N. Developmental dyslexia: four consecutive patients with cortical anomalies. Ann. Neurol. 18, 222–233 (1985).

    Article  CAS  PubMed  Google Scholar 

  98. Stein, J. & Walsh, V. To see but not to read: the magnocellular theory of dyslexia. Trends Neurosci. 20, 147–152 (1997).

    Article  CAS  PubMed  Google Scholar 

  99. Blakemore, S.-J., Wolpert, D. M. & Frith, C. D. Central cancellation of self-produced tickle sensation. Nature Neurosci. 1, 635–640 (1998).

    Article  CAS  PubMed  Google Scholar 

  100. Blakemore, S. J. & Sirigu, A. Action prediction in the cerebellum and in the parietal lobe. Exp. Brain Res. 153, 239–245 (2003).

    Article  PubMed  Google Scholar 

  101. Solms, M. & Turnbull, O. The Brain and the Inner World. (OTHER, 2002).

    Google Scholar 

  102. Wegner, D. M. The Illusion of Conscious Will (MIT Press, Cambridge, Massachusetts, 2002).

    Book  Google Scholar 

  103. Crick, F. & Koch, C. Towards a neurobiological theory of consciousness. Semin. Neurosci. 2, 263–275 (1990).

    Google Scholar 

  104. Crick, F., Koch, C., Kreiman, G. & Fried, I. Consciousness and neurosurgery. Neurosurgery 55, 273–282 (2004).

    Article  PubMed  Google Scholar 

  105. Barlow, J. S. The Cerebellum and Adaptive Control (Cambridge Univ. Press, New York, 2002).

    Book  Google Scholar 

  106. Marr, D. A theory of cerebellar cortex. J. Physiol. 202, 437–470 (1969).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Albus, J. S. A theory of cerebellar function. Math. Biosci. 10, 25–61 (1971).

    Article  Google Scholar 

  108. Fujita, M. Adaptive filter model of the cerebellum. Biol. Cybern. 45, 195–206 (1982).

    Article  CAS  PubMed  Google Scholar 

  109. Mauk, M. D. & Donegan, N. H. A model of Pavlovian eyelid conditioning based on the synaptic organization of the cerebellum. Learn. Mem. 4, 130–158 (1997).

    Article  CAS  PubMed  Google Scholar 

  110. Yamazaki, T. & Tanaka, S. Neural modeling of an internal clock. Neural Comput. 17, 1–27 (2005).

    Article  Google Scholar 

  111. Yamazaki, T. & Tanaka, S. The cerebellum as a liquid state machine. Neural Netw. 20, 290–297 (2007).

    Article  PubMed  Google Scholar 

  112. Schweighofer, N., Doya, K. & Kuroda, S. Cerebellar aminergic neuromodulation: towards a functional understanding. Brain Res. Rev. 44, 103–116 (2004).

    Article  PubMed  Google Scholar 

  113. Nagao, S., Ito, M. & Karachot, L. Eye field in the cerebellar flocculus of pigmented rabbits determined with local electrical stimulation. Neurosci. Res. 3, 39–51 (1985).

    Article  CAS  PubMed  Google Scholar 

  114. Christian, K. M. & Thompson, R. F. Neural substrates of eyeblink conditioning: acquisition and retention. Learn. Mem. 10, 427–455 (2003).

    Article  PubMed  Google Scholar 

  115. Apps, R. & Garwicz, M. Anatomical and physiological foundations of cerebellar information processing. Nature Rev. Neurosci. 6, 297–311 (2005).

    Article  CAS  Google Scholar 

  116. Riklan, M., Cullinan, T., Shulman, M. & Cooper, I. S. A psychometric study of chronic cerebellar stimulation in man. Biol. Psychiatry 11, 543–574 (1976).

    CAS  PubMed  Google Scholar 

  117. Koch, G. et al. Repetitive TMS of cerebellum interferes with millisecond time processing. Exp. Brain Res. 179, 291–299 (2007).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

I would like to thank the RIKEN Brain Science Institute for their continuous support of my research on the cerebellum.

Author information

Authors and Affiliations

Authors

Related links

Related links

FURTHER INFORMATION

Masao Ito's homepage

Glossary

Body-part dynamics

The dynamic properties of a body part that are determined by physical factors such as weight, length, centre of gravity and viscosity. The dynamic properties in turn determine the movement of the body part in response to command signals.

Column

A basic functional unit of the cerebral cortex. Each column is approximately 0.5 mm wide and 2 mm high and contains approximately 10,000 neurons. These units operate much like microcircuits in a computer. The human cerebral cortex is thought to have approximately a million columns.

Control system

A term that was originally used to refer to a mechanical or chemical system equipped with a mechanism for manipulating an object or regulating a process. The term now broadly applies to an informational, biological, neural, psychological or social system.

Controlled object

A key part of a control system, a controlled object converts a command into an output action. For example, a muscle converts signals in nerves into a contraction.

Controller

A key part of a control system, a controller converts a given instruction into a command. For example, the brain converts an instructed spatial position of a target into a command, which consists of signals in the nerves that innervate muscles.

Error signals

Signals representing errors in a system. The errors are discrepancies in the performance of a control system from either the instruction (consequence errors) or the prediction by an internal model (internal errors).

Instructor

The part of a control system that supplies an instruction to the controller. The instructor gives a goal towards which a control system should work.

Internal model

A functional dummy of a body part or of a mental representation in the cerebral cortex. Internal models are encoded in the neuronal circuitry of the cerebellum and mimic the essential properties of a body part or mental representation.

Paced auditory serial-addition test

(PASAT). A test that is used to impose a high cognitive load on the working memory. Subjects receive a pseudo-random auditory presentation of a number between 1 and 9 every 3 seconds and are asked to add consecutive numbers and provide the answer to each addition verbally.

Stroop Task

A task in which subjects are instructed to either read words or name the colour in which the words are written. Subjects must selectively attend to one attribute, particularly when naming the colour of a conflict stimulus (for example, the word 'green' displayed in red).

Tower of London Task

A test of planning capability. Typically, starting from an initial condition in which three differently coloured rings are distributed to three poles, the subject is asked to gather all of the rings to one particular pole by moving one at a time and not making a total of more than five moves. A modified version is used in neuroimaging to avoid contamination of the results with activity relating to movements.

Unit learning machine

The cerebellum contains numerous modular units, each of which consists of a uniform set of neuronal circuits that is capable of learning. Each unit learning machine is inserted into a neural control system and carries out the role of an internal model.

Wisconsin Card-Sorting Task

(WCST). In this task, participants are given cards that can be sorted by colour, shape or name, and must deduce the correct sorting criterion. After several consecutive correct sorts, the correct sorting criterion is changed without warning.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ito, M. Control of mental activities by internal models in the cerebellum. Nat Rev Neurosci 9, 304–313 (2008). https://doi.org/10.1038/nrn2332

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrn2332

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing