Critical Motor Involvement in Prediction of Human and Non-biological Motion Trajectories

Abstract

Objectives: Adaptive interaction with the environment requires the ability to predict both human and non-biological motion trajectories. Prior accounts of the neurocognitive basis for prediction of these two motion classes may generally be divided into those that posit that non-biological motion trajectories are predicted using the same motor planning and/or simulation mechanisms used for human actions, and those that posit distinct mechanisms for each. Using brain lesion patients and healthy controls, this study examined critical neural substrates and behavioral correlates of human and non-biological motion prediction.

Methods: Twenty-seven left hemisphere stroke patients and 13 neurologically intact controls performed a visual occlusion task requiring prediction of pantomimed tool use, real tool use, and non-biological motion videos. Patients were also assessed with measures of motor strength and speed, praxis, and action recognition.

Results: Prediction impairment for both human and non-biological motion was associated with limb apraxia and, weakly, with the severity of motor production deficits, but not with action recognition ability. Furthermore, impairment for human and non-biological motion prediction was equivalently associated with lesions in the left inferior parietal cortex, left dorsal frontal cortex, and the left insula.

Conclusions: These data suggest that motor planning mechanisms associated with specific loci in the sensorimotor network are critical for prediction of spatiotemporal trajectory information characteristic of both human and non-biological motions. (JINS, 2017, 23, 171-184).

Keywords: Biological motion; Domain-general; Domain-specific; Forward model; Hemiparesis; Insula; Limb apraxia; Motor planning; Parietal; Premotor; Simulation.

Fig. 1

Frames from tool use (A), pantomimed tool use (B), and non-biological (C) motion videos. The human actions in panels A and B consist of (pantomiming) the use of a fork.

Fig. 2

Overlap map of lesions included in the lesion proportion difference analysis (n = 18, i.e., the 9 highest and 9 lowest scoring patients on the prediction task). The map is displayed on the Colin27 template with z-coordinates of horizontal slices corresponding to MNI standardized space. The color bar represents the number of patients with lesions in a particular voxel (min = 2; max = 18). The cortical surface rendering is displayed at a search depth of 8 voxels.

Fig. 3

Observed mean prediction performance as indexed by d’ for Patients (right panel) and Controls (left panel) plotted separately for each level of Motion Type (tool use, pantomimed tool use, non-biological; see legend). Error bars represent ±1 SE.

Fig. 4

Lesion proportion difference map for mean prediction performance (d’) displaying voxels associated with χ² values surpassing a statistical threshold for significance (α = .01). Voxels are displayed on the Colin27 template; z-coordinates of horizontal slices correspond to MNI standardized space. The cortical surface rendering is displayed at a search depth of 8 voxels. Color bars represent χ² value ranges.