Modulation of corticospinal output during goal-directed actions: Evidence for a contingent coding hypothesis.

Abstract Seeing a person perform an action activates the observer's motor system. The present study aimed at investigating the temporal relationship between execution and observation of goal-directed actions. One possibility is that the corticospinal excitability (CSE) follows the dynamic evolution of the pattern of muscle activity in the executed action. Alternatively, CSE may anticipate the future course of the observed action, prospectively extrapolating future states. Our study was designed to test these alternative hypotheses by directly comparing the time course of muscle recruitment during the execution and observation of reach-to-grasp movements. We found that the time course of CSE during action observation followed the time course of the EMG signal during action execution. This contingent coding was observed despite the outcome of the observed motor act being predictable from the earliest phases of the movement. These findings challenge the view that CSE serves to predict the target of an observed action

PredPsych: A toolbox for predictive machine learning-based approach in experimental psychology research

Recent years have seen an increased interest in machine learning based predictive methods for analysing quantitative behavioural data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible software framework. The goal of this work was to build an open-source toolbox – “PredPsych” – that could make these methods readily available to all psychologists. PredPsych is a user-friendly, R toolbox based on machine learning predictive algorithms. In this paper, we present the framework of PredPsych via the analysis of a recently published multiple-subject motion capture data set. In addition, we discuss examples of possible research questions that can be addressed with the machine learning algorithms implemented in PredPsych and cannot be easily investigated with mass univariate statistical analysis. We anticipate that PredPsych will be of use to researchers with limited programming experience not only in the field of psychology, but also in that of clinical neuroscience, enabling computational assessment of putative bio-behavioral markers for both prognosis and diagnosis

What Will I Do Next? The Intention from Motion Experiment

In computer vision, video-based approaches have been widely explored for the early classification and the prediction of actions or activities. However, it remains unclear whether this modality (as compared to 3D kinematics) can still be reliable for the prediction of human intentions, defined as the overarching goal embedded in an action sequence. Since the same action can be performed with different intentions, this problem is more challenging but yet affordable as proved by quantitative cognitive studies which exploit the 3D kinematics acquired through motion capture systems. In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions. Precisely, we propose Intention from Motion, a new paradigm where, without using any contextual information, we consider instantaneous grasping motor acts involving a bottle in order to forecast why the bottle itself has been reached (to pass it or to place in a box, or to pour or to drink the liquid inside). We process only the grasping onsets casting intention prediction as a classification framework. Leveraging on our multimodal acquisition (3D motion capture data and 2D optical videos), we compare the most commonly used 3D descriptors from cognitive studies with state-of-the-art video-based techniques. Since the two analyses achieve an equivalent performance, we demonstrate that computer vision tools are effective in capturing the kinematics and facing the cognitive problem of human intention prediction.Comment: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshop

Doing it your way: How individual movement styles affect action prediction

Individuals show significant variations in performing a motor act. Previous studies in the action observation literature have largely ignored this ubiquitous, if often unwanted, characteristic of motor performance, assuming movement patterns to be highly similar across repetitions and individuals. In the present study, we examined the possibility that individual variations in motor style directly influence the ability to understand and predict others’ actions. To this end, we first recorded grasping movements performed with different intents and used a two-step cluster analysis to identify quantitatively ‘clusters’ of movements performed with similar movement styles (Experiment 1). Next, using videos of the same movements, we proceeded to examine the influence of these styles on the ability to judge intention from action observation (Experiments 2 and 3). We found that motor styles directly influenced observers’ ability to ‘read’ others’ intention, with some styles always being less ‘readable’ than others. These results provide experimental support for the significance of motor variability for action prediction, suggesting that the ability to predict what another person is likely to do next directly depends on her individual movement style

Decoding intentions from movement kinematics

How do we understand the intentions of other people? There has been a longstanding controversy over whether it is possible to understand others’ intentions by simply observing their movements. Here, we show that indeed movement kinematics can form the basis for intention detection. By combining kinematics and psychophysical methods with classification and regression tree (CART) modeling, we found that observers utilized a subset of discriminant kinematic features over the total kinematic pattern in order to detect intention from observation of simple motor acts. Intention discriminability covaried with movement kinematics on a trial-by-trial basis, and was directly related to the expression of discriminative features in the observed movements. These findings demonstrate a definable and measurable relationship between the specific features of observed movements and the ability to discriminate intention, providing quantitative evidence of the significance of movement kinematics for anticipating others’ intentional actions

Simulation Theory: An introduction

Navigating through social environment requires understanding and predicting complex interactions around us. This social understanding is in contrast to the understanding and predictions for inanimate objects that are governed by a set of fixed laws. Interactions with other individuals involves not only concrete observations like their height or physical appearance but their abstract states as emotions, beliefs, desires, intentions etc. Also most social interactions are dictated by judgments on such mental states than mere physical appearance. But how are humans able to achieve this? Considering that one individual doesn’t have any physical access to other’s mental states. And yet most individuals are highly adept at recognizing others’ mental states. In the recent years, multiple insights in this direction have been provided by cognitive psychology and especially by cognitive neuroscience. Broadly categorizing, three main propositions have been suggested to explain how humans understand others - a theory-theory based account, a simulation and an interactionistic approach. The current work provides a brief account of the simulation theory