Functional data analysis of mammalian feeding What are we missing from traditional analyses of masticatory kinematics


Meeting Abstract

P1.113  Saturday, Jan. 4 15:30  Functional data analysis of mammalian feeding: What are we missing from traditional analyses of masticatory kinematics? CRANE, E.; GERSTNER, G.E.; ROTHMAN, E.; MONTUELLE, S.J.*; WILLIAMS, S.H.; University of Michigan; University of Michigan; University of Michigan; Ohio University; Ohio University willias7@ohio.edu

In most studies of mammalian masticatory kinematics, the analysis focuses on extracting a discrete number of variables to represent the gape cycle (e.g., phase durations) and provide insight into the dynamics of the behavior. However, mastication involves rhythmic and coordinated movements of the jaw and tongue which are complex and difficult to characterize with a discrete set of kinematic variables. These analyses also leave the bulk of the data unanalyzed. Here, we compare traditional analyses of mammalian mastication to a new approach called Functional Data Analysis (FDA) utilizing sample chewing cycles from goats and pigs. FDA is a potentially informative approach because it utilizes the entire movement profile instead of extracting specific kinematic variables. We use the results of traditional event-driven kinematic analyses and those of FDA to discuss the benefits and limitations of each method for detecting differences in the amplitude and timing of chewing movements, as well as in their respective variability. FDA analyzes the entire gape cycle movement trajectory and identifies portions of the gape cycle that significantly differ between species, which may not be identified with traditional analyses. Our data also show that significant differences in the variability of the chewing movements exist between species, confirming the power of FDA for the study of movements. As such, FDA is a potentially transformative approach for understanding the dynamics of continuous and complex movements in functional morphological research.

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