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  1. Abstract

    The posterior superior temporal sulcus (pSTS) is a brain region characterized by perceptual representations of human body actions that promote the understanding of observed behavior. Increasingly, action observation is recognized as being strongly shaped by the expectations of the observer (Kilner 2011; Koster-Hale and Saxe 2013; Patel et al. 2019). Therefore, to characterize top-down influences on action observation, we evaluated the statistical structure of multivariate activation patterns from the action observation network (AON) while observers attended to the different dimensions of action vignettes (the action kinematics, goal, or identity of avatars jumping or crouching). Decoding accuracy varied as a function of attention instruction in the right pSTS and left inferior frontal cortex (IFC), with the right pSTS classifying actions most accurately when observers attended to the action kinematics and the left IFC classifying most accurately when observed attended to the actor’s goal. Functional connectivity also increased between the right pSTS and right IFC when observers attended to the actions portrayed in the vignettes. Our findings are evidence that the attentive state of the viewer modulates sensory representations in the pSTS, consistent with proposals that the pSTS occupies an interstitial zone mediating top-down context and bottom-up perceptual cues during action observation.

     
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  2. Observing the actions of others engages a core action observation network (AON) that includes the bilateral inferior frontal cortex (IFC), posterior superior temporal sulcus (pSTS) and inferior parietal lobule (IPL) (Caspers et al., 2010). Each region in the AON has functional properties that are heterogeneous and include representing the perceptual properties of action, predicting action outcomes and making inferences as to the goals of the actor. Critically, recent evidence shows that neural representations within the pSTS are sharpened when attending to the kinematics of the actor, such that the top-down guided attention reshapes underlying neural representations. In this study we evaluate how attention alters network connectivity within the AON as a system. Cues directed participant's attention to the goal, kinematics, or identity depicted in short action animations while brain responses were measured by fMRI. We identified those parcels within the AON with functional connectivity modulated by task. Results show that connectivity between the right pSTS and right IFC, and bilateral extended STS (STS+) were modulated during action observation such that connections were strengthened when the participant was attending to the action than goal. This finding is contrasted by the univariate results, which no univariate modulations in these brain regions except for right IFC. Using the functional networks defined by Yeo et al. (2011), we identified the parcels that are modulated by the attention to consist mainly of the fronto-parietal control network and default mode networks. These results are consistent with models of top-down feedback from executive system in the IFC to pSTS and implicates a right lateralized dual pathway model for action observation when focused on whole-body kinematics. 
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    Free, publicly-accessible full text available December 1, 2024
  3. Background: Multivariate pattern analysis (MVPA or pattern decoding) has attracted considerable attention as a sensitive analytic tool for investigations using functional magnetic resonance imaging (fMRI) data. With the introduction of MVPA, however, has come a proliferation of methodological choices confronting the researcher, with few studies to date offering guidance from the vantage point of controlled datasets detached from specific experimental hypotheses. New method: We investigated the impact of four data processing steps on support vector machine (SVM) classification performance aimed at maximizing information capture in the presence of common noise sources. The four techniques included: trial averaging (classifying on separate trial estimates versus condition-based averages), within-run mean centering (centering the data or not), method of cost selection (using a fixed or tuned cost value), and motion-related denoising approach (comparing no denoising versus a variety of nuisance regressions capturing motion-related reference signals). The impact of these approaches was evaluated on real fMRI data from two control ROIs, as well as on simulated pattern data constructed with carefully controlled voxel- and trial-level noise components. Results: We find significant improvements in classification performance across both real and simulated datasets with run-wise trial averaging and mean centering. When averaging trials within conditions of each run, we note a simultaneous increase in the between-subject variability of SVM classification accuracies which we attribute to the reduced size of the test set used to assess the classifier's prediction error. Therefore, we propose a hybrid technique whereby randomly sampled subsets of trials are averaged per run and demonstrate that it helps mitigate the tradeoff between improving signal-to-noise ratio by averaging and losing exemplars in the test set. Comparison with existing methods: Though a handful of empirical studies have employed run-based trial averaging, mean centering, or their combination, such studies have done so without theoretical justification or rigorous testing using control ROIs. Conclusions: Therefore, we intend this study to serve as a practical guide for researchers wishing to optimize pattern decoding without risk of introducing spurious results. 
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