27 research outputs found
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
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
Doing It Your Way: How Individual Movement Styles Affect Action Prediction
<div>Flow diagram of the approach for the paper : </div><div>Doing It Your Way: How Individual Movement Styles Affect Action Prediction (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165297) </div><div><a><br></a></div><div><a>Atesh Koul, Andrea Cavallo,</a><a>Caterina Ansuini and </a><a>Cristina Becchio</a></div><div><br></div><div><br></div><div><br></div><div><br></div><div>Abstract:</div><div><br></div><div>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 (<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165297#sec002">Experiment 1</a>).
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 <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165297#sec009">2</a> and <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165297#sec016">3</a>).
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.</div