Action simulation: time course and representational mechanisms

Abstract

The notion of action simulation refers to the ability to re-enact foreign actions (i.e., actions observed in other individuals). Simulating others' actions implies a mirroring of their activities, based on one's own sensorimotor competencies. Here, we discuss theoretical and experimental approaches to action simulation and the study of its representational underpinnings. One focus of our discussion is on the timing of internal simulation and its relation to the timing of external action, and a paradigm that requires participants to predict the future course of actions that are temporarily occluded from view. We address transitions between perceptual mechanisms (referring to action representation before and after occlusion) and simulation mechanisms (referring to action representation during occlusion). Findings suggest that action simulation runs in real-time; acting on newly created action representations rather than relying on continuous visual extrapolations. A further focus of our discussion pertains to the functional characteristics of the mechanisms involved in predicting other people's actions. We propose that two processes are engaged, dynamic updating and static matching, which may draw on both semantic and motor information. In a concluding section, we discuss these findings in the context of broader theoretical issues related to action and event representation, arguing that a detailed functional analysis of action simulation in cognitive, neural, and computational terms may help to further advance our understanding of action cognition and motor control.

Keywords: action simulation; internal forward models; occlusion; point-light action; predictive coding; static matching.

Figure 1

Illustration of the stimuli utilized in Graf et al. (2007). Point light actions were presented and then occluded for a variable time (100, 400, or 700 ms). Occlusion was followed by a test pose that was rotated or in the correct orientation. Pose time was also varied (100, 400, or 700 ms).

Figure 2

A schematic illustration of a trial in which the PLA was presented with variable visibility against the background. (A) Shows how varying white pixel ratio in the actor's joints increases visibility against the noise background, (B) shows a basic trial sequence, and (C) depicts a schematic showing how different sections of the action were shown as the prime motion to manipulate motion congruency with the same test motion section. Figure adapted from Parkinson et al. (, p. 1466). Copyright © The Experimental Psychology Society. Adapted with permission of Taylor and Francis Ltd., www.tandfonline.com on behalf of The Experimental Psychology Society, with permission from the authors.

Figure 3

A schematic illustration of a trial with “inserted motion” during the occlusion phase.

Figure 4

Percentage correct judgments for the “inserted motion” experiment. Black asterisks connected with solid lines indicate significance levels of between-condition t-test comparisons. Asterisks in gray connected with dotted lines indicate significance levels of one-sample t-test comparisons to chance performance (50%), ^*p < 0.05, ^**p < 0.01, ^***p < 0.001. Figure reproduced from Parkinson et al. (, p. 428). Copyright © Springer Science+Business Media. Reproduced with permission.

Figure 5

Illustration of the experimental setting as seen through the eyes of the participant. On each trial the actor (sitting behind an occluding object) transported a teapot from a home position to a target position. Figure adapted from Prinz and Rapinett (2008) (p. 226). Copyright by IOS Press. Adapted with permission.

Figure 6

Panel (A) shows the actual movement of an object behind the occluder (black lines) and the action simulation (gray line) illustrating lag error. Panel (B) shows two sources of the lag error: intercept (dotted gray) and slope (solid gray) lines. Panel (C) shows the different predictions of the two sources of lag error when occluder duration changes. Panel (D) shows the different predictions of the two sources of lag error when motion speed changes. See text for detailed explanations. Figure adapted from Prinz and Rapinett (2008) (p. 226). Copyright by IOS Press. Adapted with permission.

Figure 7

Panel (A) shows the velocity profile of the action as it accelerates from the start and decelerates at the target (black solid line) with the occluded portion dotted. The regenerated action simulation is shown in gray. Panel (B) shows how this regenerated simulation hypothesis provides different predictions when occluder duration and action speed change. Panel (C) shows how action simulations might be affected by the implied goal of the action. Figure adapted from Prinz and Rapinett (2008) (p. 226). Copyright by IOS Press. Adapted with permission.

Figure 8

Bar graphs of lag error in action simulation motion judgments when different durations of motion are shown prior to occlusion. Figure adapted from Parkinson et al. (, p. 426). Copyright © Springer Science + Business Media. Adapted with permission.