Detecting Adaptive Inverse Models in the Central Nervous System

Abstract

This study aimed to find evidence for the formation of an internal inverse model of a novel visuomotor relationship for feedforward control in the brain. An experiment was carried out involving 20 normal adult subjects who performed a pursuit random tracking task with a steering wheel for input. During learning, the response cursor was periodically blanked, removing all feedback about the external system (i.e., about the relationship between hand motion and response cursor motion). Results showed a transfer of learning from the unblanked runs to the blanked runs for a static nonlinear system (linear trend RMS error F(1,19) 5.05, p .037) thereby demonstrating adaptive feedforward control in the nervous system. This result provides the strongest evidence to date that the brain adaptively tunes inverse models of external controlled systems during motor learning. No such transfer was observed for a dynamic linear system, indicating a dominant adaptive feedback control component. Results are consistent with inverse modeling and suggest a combination of feedforward and feedback adaptive control in the brain.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412134

Entities

People

  • Harsha R. Sirisena
  • John H. Andreae
  • Paul R. Davidson
  • Richard D. Jones

Organizations

  • University of Canterbury

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Biomedical Engineering
  • Brain
  • Central Nervous System
  • Closed Loop Systems
  • Control Systems
  • Economic Forecasting
  • Engineering
  • Feedback
  • Frequency
  • Health Services
  • Learning
  • Linear Systems
  • Models
  • Nervous System
  • Neural Networks
  • New Zealand
  • Nonlinear Systems

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Control Systems Engineering.