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Quantifying effects of stochasticity in reference frame transformations on posterior distributions.
Hooman Alikhanian1, Schubert R de Carvalho1, Gunnar Blohm2
1Centre for Neuroscience Studies, Queen's University Kingston, ON, Canada ; Canadian Action and Perception Network Kingston, ON, Canada.
Frontiers in Computational Neuroscience
|July 21, 2015
Summary
Stochastic reference frame transformations (SRFT) can alter signal distributions due to estimation noise. This study quantifies these effects and introduces a new measure for distribution deviations with limited data.
Area of Science:
- Neuroscience
- Signal Processing
- Statistics
Background:
- Reference frame transformations are typically deterministic.
- Variability in translations, scaling, or rotation angles can arise from noisy estimation.
- The impact of transformation noise on signal statistics is not well understood.
Purpose of the Study:
- To quantify how stochastic reference frame transformations (SRFT) affect the posterior distribution of transformed signals.
- To develop an empirical measure for quantifying distribution deviations with limited data.
- To apply this measure to sensory-motor neuroscience data.
Main Methods:
- Analytical and numerical quantification of SRFT effects on posterior distributions.
- Development of a novel empirical measure for distribution deviation.
- Application to sensory-motor neuroscience data concerning head roll angles and reach endpoint distributions.
Main Results:
- SRFT demonstrably alters the posterior distribution of transformed signals.
- The proposed empirical measure effectively quantifies deviations from a target distribution using limited data.
- Analysis revealed how head roll angles influence reach endpoint distributions in sensory-motor tasks.
Conclusions:
- Stochasticity in reference frame transformations has a quantifiable impact on signal statistics.
- The new empirical measure provides a valuable tool for analyzing distribution changes in data-limited scenarios.
- Understanding these effects is crucial for applications in fields like sensory-motor neuroscience.
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