Reading motor intention through mental imagery

Lewkowicz, Daniel; Delevoye-Turrell, Yvonne; Bailly, David; Andry, Pierre; Gaussier, Philippe

doi:10.1177/1059712313501347

Cited by 21 publications

(23 citation statements)

References 77 publications

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“…Because this situation led to a less “optimized” motor performance, one may speculate that this strategy would be employed as an external signal during social interaction to show the agent’s social intention to share the object (Sartori et al, 2009a). Previous studies have supported this interpretation by showing that humans are sensitive to external kinematic characteristics of a movement, and especially trajectory height (Manera et al, 2011; Sartori et al, 2011; Lewkowicz et al, 2013). The new findings reported here demonstrate that even preparatory actions reflect the agent’s social intention and thus, movement properties may be read by perceivers for whom understanding motor intention from early kinematics is important.…”

Section: Discussionmentioning

confidence: 85%

“…Indeed, during social interaction, Boucher et al (2012) showed that human agents placed in a cooperative context are sensitive to the predictive information provided by the direction of gaze of their partners, even when interacting with robots. Furthermore, motor intention influences movement kinematics in such a way that not only the goal of individual actions can be anticipated by a perceiver (Lewkowicz et al, 2013), but also coordinated actions involving several agents can be performed (Knoblich and Sebanz, 2008; Vesper et al, 2010). Thus, it seems important for artificial social intelligence to develop (1) our knowledge of the specific effects that motor intention has on movement kinematics during a true social interactive task and (2) to provide solid guidelines for the development of optimal control models that will be able to implement intention in action in those artificial agents that need to cooperate intuitively with biological organisms.…”

Section: Introductionmentioning

confidence: 99%

“…More recent studies have finally shown that the final purpose of a grasping action strongly influences the kinematics of both the transport phase and the characteristics of the hand shaping, i.e., the manipulation component (Ansuini et al, 2006). As a consequence, when observing an action performed by someone else, it seems possible from early kinematics to anticipate the goal of the action, i.e., much before the entire action is accomplished (Méary et al, 2005; Manera et al, 2011; Sartori et al, 2011; Lewkowicz et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

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Effects of social intention on movement kinematics in cooperative actions

Quesque¹,

Lewkowicz²,

Delevoye-Turrell³

et al. 2013

Front. Neurorobot.

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Optimal control models of biological movements are used to account for those internal variables that constrain voluntary goal-directed actions. They, however, do not take into account external environmental constraints as those associated to social intention. We investigated here the effects of the social context on kinematic characteristics of sequential actions consisting in placing an object on an initial pad (preparatory action) before reaching and grasping as fast as possible the object to move it to another location (main action). Reach-to-grasp actions were performed either in an isolated condition or in the presence of a partner (audience effect), located in the near or far space (effect of shared reachable space), and who could intervene on the object in a systematic fashion (effect of social intention effect) or not (effect of social uncertainty). Results showed an absence of audience effect but nevertheless an influence of the social context both on the main and the preparatory actions. In particular, a “localized” effect of shared reachable space was observed on the main action, which was smoother when performed within the reachable space of the partner. Furthermore, a “global” effect of social uncertainty was observed on both actions with faster and jerkier movements. Finally, social intention affected the preparatory action with higher wrist displacements and slower movements when the object was placed for the partner rather than placed for self-use. Overall, these results demonstrate specific effects of action space, social uncertainty and social intention on the planning of reach-to-grasp actions, in particular on the preparatory action, which was performed with no specific execution constraint. These findings underline the importance of considering the social context in optimal models of action control for human–robot interactions, in particular when focusing on the implementation of motor parameters required to afford intuitive interactions.

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Section: Discussionmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of social intention on movement kinematics in cooperative actions

Quesque¹,

Lewkowicz²,

Delevoye-Turrell³

et al. 2013

Front. Neurorobot.

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“…This result was also tested on tool-use where the spatial receptive fields of the parietal neurons associated to the hand extended to entail the tool (Goldenberg & Iriki, 2007;Maravita & Iriki, 2004). In terms of social cognition, this transformation mechanism is considered to take a central place in the process of understanding others as a mean to transform someone else visuo-motor perception into our own thus simulating their actions (Fogassi et al, 2005;Lewkowicz, Delevoye-Turrell, Bailly, Andry, & Gaussier, 2013;Meltzoff, 2007;Rizzolatti et al, 2001), see Fig. 1.…”

Section: Introductionmentioning

confidence: 91%

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

et al. 2015

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The so-called self-other correspondence problem in imitation demands to find the transformation that maps the motor dynamics of one partner to our own. This requires a general purpose sensorimotor mechanism that transforms an external fixation-point (partner's shoulder) reference frame to one's own body-centered reference frame. We propose that the mechanism of gain-modulation observed in parietal neurons may generally serve these types of transformations by binding the sensory signals across the modalities with radial basis functions (tensor products) on the one hand and by permitting the learning of contextual reference frames on the other hand. In a shoulder-elbow robotic experiment, gain-field neurons (GF) intertwine the visuo-motor variables so that their amplitude depends on them all. In situations of modification of the body-centered reference frame, the error detected in the visuo-motor mapping can serve then to learn the transformation between the robot's current sensorimotor space and the new one. These situations occur for instance when we turn the head on its axis (visual transformation), when we use a tool (body modification), or when we interact with a partner (embodied simulation). Our results defend the idea that the biologically-inspired mechanism of gain modulation found in parietal neurons can serve as a basic structure for achieving nonlinear mapping in spatial tasks as well as in cooperative and social functions.

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“…But more importantly, these findings are in line with a current motor cognition hypothesis (Ansuini, Cavallo, Bertone, & Becchio, 2014;Becchio, Manera, Sartori, Cavallo, & Castiello, 2012) that suggests that during our everyday actions, our intentions in action may be revealed in early trajectory patterns of simple movements. During social interaction, external observers would thus infer motor and social intention through the simple observation of the kinematic patterns of body motion (Herbort, Koning, van Uem, & Meulenbroek, 2012;Lewkowicz, Delevoye-Turrell, Bailly, Andry, & Gaussier, 2013;Sartori, Becchio, & Castiello, 2011). But to further test this hypothesis, interactivity for whole body motion is necessary.…”

Section: General Principlesmentioning

confidence: 99%

Real-Time Motion Capture Toolbox (RTMocap): an open-source code for recording 3-D motion kinematics to study action–effect anticipations during motor and social interactions

Lewkowicz¹,

Delevoye-Turrell

2015

Behav Res

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We present here a toolbox for the real-time motion capture of biological movements that runs in the crossplatform MATLAB environment (The MathWorks, Inc., Natick, MA). It provides instantaneous processing of the 3-D movement coordinates of up to 20 markers at a single instant. Available functions include (1)the setting of reference positions, areas, and trajectories of interest; (2)recording of the 3-D coordinates for each marker over the trial duration; and (3)the detection of events to use as triggers for external reinforcers (e.g., lights, sounds, or odors). Through fast online communication between the hardware controller and RTMocap, automatic trial selection is possible by means of either a preset or an adaptive criterion. Rapid preprocessing of signals is also provided, which includes artifact rejection, filtering, spline interpolation, and averaging. A key example is detailed, and three typical variations are developed (1)to provide a clear understanding of the importance of real-time control for 3-D motion in cognitive sciences and (2) The Real-Time Motion Capture (RTMocap) Toolbox is a MATLAB toolbox dedicated to the instantaneous control and processing of 3-D motion capture data. It was developed to automatically trigger reinforcement sounds during reachand-grasp object-related movements, but it is potentially useful in a wide range of other interactive situations-for instance, when directing voluntary movements to places in space (e.g., triggering a light on when the hand reaches a predefined 3-D position in a room), when performing actions toward stationary or moving objects (e.g., triggering a sound when an object is grasped with the correct body posture), or simply as a way to reinforce social interactions (e.g., turning music on when a child looks at a person by moving the head in the proper direction). The RTMocap Toolbox is created from open-source code, distributed under the GPL license, and freely available for download at http://sites. google.com/site/RTMocap/.The RTMocap Toolbox is mainly intended to work with recordings made with an infrared marker-based optical motion capture system. Such motion capture systems are based on an actively emitting source that pulses infrared light at a very high frequency, which is then reflected by small, usually circular markers attached to the tracked body parts and objects. With each camera capturing the position of the reflective markers in two dimensions (Fig. 1), a network of several cameras can be used to obtain position data in 3-D. The RTMocap Toolbox was developed with the Qualysis motion capture system, but it can be adapted, by means of slight changes in code lines, to other 3-D motion capture systems that provide real-time information in the MATLAB environment-for instance,

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Reading motor intention through mental imagery

Abstract: International audienc

Cited by 21 publications

References 77 publications

Effects of social intention on movement kinematics in cooperative actions

Effects of social intention on movement kinematics in cooperative actions

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

Real-Time Motion Capture Toolbox (RTMocap): an open-source code for recording 3-D motion kinematics to study action–effect anticipations during motor and social interactions

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