Research Article

Experimental Brain Research

, Volume 181, Issue 3, pp 395-408

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Explicit contextual information selectively contributes to predictive switching of internal models

  • Hiroshi ImamizuAffiliated withDepartment of Cognitive Neuroscience, ATR Computational Neuroscience Laboratories Email author 
  • , Norikazu SugimotoAffiliated withDepartment of Computational Neurobiology, ATR Computational Neuroscience Laboratories
  • , Rieko OsuAffiliated withDepartment of Cognitive Neuroscience, ATR Computational Neuroscience LaboratoriesNational Institute of Information and Communication Technology
  • , Kiyoka TsutsuiAffiliated withNagaoka University of Technology
  • , Kouichi SugiyamaAffiliated withNagaoka University of Technology
  • , Yasuhiro WadaAffiliated withNagaoka University of Technology
  • , Mitsuo KawatoAffiliated withDepartment of Cognitive Neuroscience, ATR Computational Neuroscience Laboratories


Many evidences suggest that the central nervous system (CNS) acquires and switches internal models for adaptive control in various environments. However, little is known about the neural mechanisms responsible for the switching. A recent computational model for simultaneous learning and switching of internal models proposes two separate switching mechanisms: a predictive mechanism purely based on contextual information and a postdictive mechanism based on the difference between actual and predicted sensorimotor feedbacks. This model can switch internal models solely based on contextual information in a predictive fashion immediately after alteration of the environment. Here we show that when subjects simultaneously adapted to alternating blocks of opposing visuomotor rotations, explicit contextual information about the rotations improved the initial performance at block alternations and asymptotic levels of performance within each block but not readaptation speeds. Our simulations using separate switching mechanisms duplicated these effects of contextual information on subject performance and suggest that improvement of initial performance was caused by improved accuracy of the predictive switch while adaptation speed corresponds to a switch dependent on sensorimotor feedback. Simulations also suggested that a slow change in output signals from the switching mechanisms causes contamination of motor commands from an internal model used in the previous context (anterograde interference) and partial destruction of internal models (retrograde interference). Explicit contextual information prevents destruction and assists memory retention by improving the changes in output signals. Thus, the asymptotic levels of performance improved.


Sensorimotor learning Predictive switch Sensorimotor feedback Internal model Computational models