Using guitar learning to probe the Action Observation Network's response to visuomotor familiarity

Tom Gardner,Aidas Aglinskas,Emily S Cross

doi:10.1016/j.neuroimage.2017.04.060

Abstract

Watching other people move elicits engagement of a collection of sensorimotor brain regions collectively termed the Action Observation Network (AON). An extensive literature documents more robust AON responses when observing or executing familiar compared to unfamiliar actions, as well as a positive correlation between amplitude of AON response and an observer's familiarity with an observed or executed movement. On the other hand, emerging evidence shows patterns of AON activity counter to these findings, whereby in some circumstances, unfamiliar actions lead to greater AON engagement than familiar actions. In an attempt to reconcile these conflicting findings, some have proposed that the relationship between AON response amplitude and action familiarity is nonlinear in nature. In the present study, we used an elaborate guitar training intervention to probe the relationship between movement familiarity and AON engagement during action execution and action observation tasks. Participants underwent fMRI scanning while executing one set of guitar sequences with a scanner-compatible bass guitar and observing a second set of sequences. Participants then acquired further physical practice or observational experience with half of these stimuli outside the scanner across 3 days. Participants then returned for an identical scanning session, wherein they executed and observed equal numbers of familiar (trained) and unfamiliar (untrained) guitar sequences. Via region of interest analyses, we extracted activity within AON regions engaged during both scanning sessions, and then fit linear, quadratic and cubic regression models to these data. The data best support the cubic regression models, suggesting that the response profile within key sensorimotor brain regions associated with the AON respond to action familiarity in a nonlinear manner. Moreover, by probing the subjective nature of the prediction error signal, we show results consistent with a predictive coding account of AON engagement during action observation and execution that also takes into account effects of changes in neural efficiency.

Highlights

Watching others in action provides important information about other people's goals, intentions, and desires
We were interested in testing whether a direct matching account (Rizzolatti et al, 2001; Gallese and Goldman, 1998; Wolpert et al, 2003), a predictive coding-inspired account (Keysers and Perrett, 2004; Kilner et al, 2007a, 2007b; Gazzola and Keysers, 2009; Schippers and Keysers, 2011), or a predictive coding-inspired account that takes into consideration effects of neural efficiency (Babiloni et al, 2010; Kelly and Garavan, 2005; Wiestler and Diedrichsen, 2013) would best explain the impact of increasing familiarity on Action Observation Network (AON) engagement
By performing region of interest analyses on areas that exhibited activity during both action observation and execution, we addressed two distinct questions, : (1) is the relationship between AON response amplitude and increasing familiarity better captured by a linear or nonlinear response profile?; and (2) how do subjective measures of familiarity compare to objective measures of familiarity in terms of influencing the response amplitude within core AON regions based on increasing familarity?

Summary

Introduction

Watching others in action provides important information about other people's goals, intentions, and desires. The core brain regions that compose the AON include occipitotemporal regions associated with observing bodies in motion, as well as the premotor cortex and inferior parietal lobule These latter two brain regions have been shown to contain so-called mirror neurons in the non-human primate brain (di Pellegrino et al, 1992; Gallese et al, 1996; Rizzolatti et al, 2001; Umiltà et al, 2001), and demonstrate a similar response profile during action observation and execution in the human brain (Gazzola and Keysers, 2009; for a review see Molenberghs et al, 2012).

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