The perception of emotion from body movement in point-light displays of interpersonal dialogue

We examined whether it is possible to identify the emotional content of behaviour from point-light displays where pairs of actors are engaged in interpersonal communication. These actors displayed a series of emotions, which included sadness, anger, joy, disgust, fear, and romantic love. In experiment 1, subjects viewed brief clips of these point-light displays presented the right way up and upside down. In experiment 2, the importance of the interaction between the two figures in the recognition of emotion was examined. Subjects were shown upright versions of (i) the original pairs (dyads), (ii) a single actor (monad), and (iii) a dyad comprising a single actor and his/her mirror image (reflected dyad). In each experiment, the subjects rated the emotional content of the displays by moving a slider along a horizontal scale. All of the emotions received a rating for every clip. In experiment 1, when the displays were upright, the correct emotions were identified in each case except disgust; but, when the displays were inverted, performance was significantly diminished for some ernotions. In experiment 2, the recognition of love and joy was impaired by the absence of the acting partner, and the recognition of sadness, joy, and fear was impaired in the non-veridical (mirror image) displays. These findings both support and extend previous research by showing that biological motion is sufficient for the perception of emotion, although inversion affects performance. Moreover, emotion perception from biological motion can be affected by the veridical or non-veridical social context within the displays.

Measuring the effects of manipulating stimulus presentation time on sensorimotor alpha and low beta reactivity during hand movement observation

The present study addresses three methodological questions that have been ignored in previous research on EEG indices of the human mirror neuron system (hMNS), particularly in regard to autistic individuals. The first question regards how to elicit the EEG indexed hMNS during movement observation: Is hMNS activation best elicited using long stimulus presentations or multiple short repetitions? The second question regards what EEG sensorimotor frequency bands reflect sensorimotor reactivity during hand movement observation? The third question regards how widespread is the EEG reactivity over the sensorimotor cortex during movement observation? The present study explored sensorimotor alpha and low beta reactivity during hand movement versus static hand or bouncing balls observation and compared two experimental protocols (long exposure vs. multiple repetitions) in the same participants. Results using the multiple repetitions protocol indicated a greater low beta desynchronisation over the sensorimotor cortex during hand movement compared to static hand and bouncing balls observation. This result was not achieved using the long exposure protocol. Therefore, the present study suggests that the multiple repetitions protocol is a more robust protocol to use when exploring the sensorimotor reactivity induced by hand action observation. In addition, sensorimotor low beta desynchronisation was differently modulated during hand movement, static hand and bouncing balls observation (non-biological motion) while it was not the case for sensorimotor alpha and that suggest that low beta may be a more sensitive index of hMNS activation during biological motion observation. In conclusion the present study indicates that sensorimotor reactivity of low beta during hand movement observation was found to be more widespread over the sensorimotor cortex than previously thought.

An eye for an eye: robust motion segmentation by applying the latest in human vision research

Automatically extracting interesting objects from videos is a very challenging task and is applicable to many research areas such robotics, medical imaging, content based indexing and visual surveillance. Automated visual surveillance is a major research area in computational vision and a commonly applied technique in an attempt to extract objects of interest is that of motion segmentation. Motion segmentation relies on the temporal changes that occur in video sequences to detect objects, but as a technique it presents many challenges that researchers have yet to surmount. Changes in real-time video sequences not only include interesting objects, environmental conditions such as wind, cloud cover, rain and snow may be present, in addition to rapid lighting changes, poor footage quality, moving shadows and reflections. The list provides only a sample of the challenges present. This thesis explores the use of motion segmentation as part of a computational vision system and provides solutions for a practical, generic approach with robust performance, using current neuro-biological, physiological and psychological research in primate vision as inspiration.