In praise of false models and rich data

Hugo L. Fernandes, Konrad P. Kording

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The authors argue that true models that aim at faithfully mimicking or reproducing every property of the sensorimotor system cannot be compact as they need many free parameters. Consequently, most scientists in motor control use what are called false modelsmodels that derive from well-defined approximations. The authors conceptualize these models as a priori limited in scope and approximate. As such, they argue that a quantitative characterization of the deviations between the system and the model, more than the mere act of falsifying, allows scientists to make progress in understanding the sensorimotor system. Ultimately, this process should result in models that explain as much data variance as possible. The authors conclude by arguing that progress in that direction could strongly benefit from databases of experimental results and collections of models.

Original languageEnglish (US)
Pages (from-to)343-349
Number of pages7
JournalJournal of motor behavior
Volume42
Issue number6
DOIs
StatePublished - Nov 2010

Keywords

  • Bayesianism
  • falsifiability
  • motor control

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine
  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience

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