A recursive bayesian updating model of haptic stiffness perception

Bing Wu, Roberta L. Klatzky

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Stiffness of many materials follows Hooke's Law, but the mechanism underlying the haptic perception of stiffness is not as simple as it seems in the physical definition. The present experiments support a model by which stiffness perception is adaptively updated during dynamic interaction. Participants actively explored virtual springs and estimated their stiffness relative to a reference. The stimuli were simulations of linear springs or nonlinear springs created by modulating a linear counterpart with low-amplitude, half-cycle (Experiment 1) or full-cycle (Experiment 2) sinusoidal force. Experiment 1 showed that subjective stiffness increased (decreased) as a linear spring was positively (negatively) modulated by a half-sinewave force. In Experiment 2, an opposite pattern was observed for full-sinewave modulations. Modeling showed that the results were best described by an adaptive process that sequentially and recursively updated an estimate of stiffness using the force and displacement information sampled over trajectory and time.

Original languageEnglish (US)
Pages (from-to)941-952
Number of pages12
JournalJournal of Experimental Psychology: Human Perception and Performance
Volume44
Issue number6
DOIs
StatePublished - Jun 1 2018

Keywords

  • Bayesian updating
  • Haptic perception
  • Magnitude estimation
  • Recursive estimation
  • Stiffness

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)
  • Behavioral Neuroscience

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