title: In praise of "false" models and rich data.
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" models--models 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.
authors.affiliation: Department of Physiology, Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Illinois 60611, USA. hugoguh@gmail.com
authors.forename: Hugo L
authors.initials: HL
authors.lastname: Fernandes
authors.forename: Konrad P
authors.initials: KP
authors.lastname: Kording
mesh_terms: D001696:Biomechanical Phenomena; D003627:Data Interpretation, Statistical; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009043:Motor Activity / Q000502:physiology; D009068:Movement / Q000502:physiology*; D009132:Muscles / Q000294:innervation / Q000502:physiology*; D009488:Neurosciences / Q000379:methods; D012107:Research Design; D013598:Systems Theory