Reading motor intention through mental imagery. (October 2013)

• Record Type: Journal Article
• Authors: Lewkowicz, Daniel; Delevoye-Turrell, Yvonne; Bailly, David; Andry, Pierre; Gaussier, Philippe
• Is Part Of: Adaptive behavior. Volume 21:Number 5(2013)
• Page Start: 315
• Page End: 327
• Publication Date: 2013-10
• Subjects: Motor sequence -- classifier -- kinematics -- intentionality -- optimal control -- biological movement; Animal behavior -- Periodicals; Animals -- Adaptation -- Periodicals; Adaptability (Psychology) -- Periodicals; Adaptation, Psychological -- Periodicals; Artificial intelligence -- Periodicals; 591.5
• DOI: 10.1177/1059712313501347
• Languages: English
• ISSNs: 1741-2633

Abstract

Motor imagery is defined as a dynamic state during which the representation of a given motor act is internally rehearsed without overt motor output. Some evidence in experimental psychology has suggested that imagery ability is crucial for the correct understanding of social intention. The present study was conducted first to confirm that the nature of the motor intention leads to early modulations of movement kinematics. Secondly, we tested whether humans use imagery to read an agent's intention when observing the very first element of a complex action sequence. Results revealed early variations in movement kinematics between three different social actions and further showed that human agents can use these early deviants to anticipate above chance level the end-result before seeing the second half of the sequence. Response times in the observation task were similar in duration to those measured in the true production task, suggesting the use of motor imagery for trial categorization. Nevertheless, in a third study, the use of an artificial (neural network) classifier demonstrated that classification within the first 500 ms is possible without cognitive imagery processing. Hence, our results suggest that low-level motor indices afford intention reading without need for motor imagery but that human agents may use imaging beyond simulation to create an embodied sense of interactivity.