title: Quantifying effects of stochasticity in reference frame transformations on posterior distributions.
abstract: Reference frame transformations are usually considered to be deterministic. However, translations, scaling or rotation angles could be stochastic. Indeed, variability of these entities often originates from noisy estimation processes. The impact of transformation noise on the statistics of the transformed signals is unknown and a quantification of these effects is the goal of this study. We first quantify analytically and numerically how stochastic reference frame transformations (SRFT) alter the posterior distribution of the transformed signals. We then propose an new empirical measure to quantify deviations from a given distribution when only limited data is available. We apply this empirical measure to an example in sensory-motor neuroscience to quantify how different head roll angles change the distribution of reach endpoints away from the normal distribution.
authors.affiliation: Centre for Neuroscience Studies, Queen's University Kingston, ON, Canada ; Canadian Action and Perception Network Kingston, ON, Canada.
authors.forename: Hooman
authors.initials: H
authors.lastname: Alikhanian
authors.affiliation: Centre for Neuroscience Studies, Queen's University Kingston, ON, Canada ; Canadian Action and Perception Network Kingston, ON, Canada.
authors.forename: Schubert R
authors.initials: SR
authors.lastname: de Carvalho
authors.affiliation: Centre for Neuroscience Studies, Queen's University Kingston, ON, Canada ; Canadian Action and Perception Network Kingston, ON, Canada ; Association for Canadian Neuroinformatics and Computational Neuroscience Kingston, ON, Canada.
authors.forename: Gunnar
authors.initials: G
authors.lastname: Blohm
keywords: Stochastic noise; deviation from normality; reaching; reference frame transformation; sensory-motor transformation