A computerised test of perceptual ability for learning endoscopic and laparoscopic surgery and other image guided procedures: Score norms for PicSOr.

BACKGROUND: The aptitude to infer the shape of 3-D structures, such as internal organs from 2-D monitor displays, in image guided endoscopic and laparoscopic procedures varies. We sought both to validate a computer-generated task Pictorial Surface Orientation (PicSOr), which assesses this aptitude, and to identify norm referenced scores. METHODS: 400 subjects (339 surgeons and 61 controls) completed the PicSOr test. 50 subjects completed it again one year afterwards. RESULTS: Complete data was available on 396 of 400 subjects (99%). PicSOr demonstrated high test and re-test reliability (r = 0.807, p < 0.000). Surgeons performed better than controls' (surgeons = 0.874 V controls = 0.747, p < 0.000). Some surgeons (n = 22-5.5%) performed atypically on the test. CONCLUSIONS: PicSOr has population distribution scores that are negatively skewed. PicSOr quantitatively characterises an aptitude strongly correlated to the learning and performance of image guided medical tasks. Most can do the PicSOr task almost perfectly, but a substantial minority do so atypically, and this is probably relevant to learning and performing endoscopic tasks.

The movements made by performers in a skilled quartet: a distinctive pattern, and the function that it serves.

When people perform a task as part of a joint action, their behavior is not the same as it would be if they were performing the same task alone, since it has to be adapted to facilitate shared understanding (or sometimes to prevent it). Joint performance of music offers a test bed for ecologically valid investigations of the way non-verbal behavior facilitates joint action. Here we compare the expressive movement of violinists when playing in solo and ensemble conditions. The first violinists of two string quartets (SQs), professional and student, were asked to play the same musical fragments in a solo condition and with the quartet. Synchronized multimodal recordings were created from the performances, using a specially developed software platform. Different patterns of head movement were observed. By quantifying them using an appropriate measure of entropy, we showed that head movements are more predictable in the quartet scenario. Rater evaluations showed that the change does not, as might be assumed, entail markedly reduced expression. They showed some ability to discriminate between solo and ensemble performances, but did not distinguish them in terms of emotional content or expressiveness. The data raise provocative questions about joint action in realistically complex scenarios.

Perceiving emotion: towards a realistic understanding of the task.

A decade ago, perceiving emotion was generally equated with taking a sample (a still photograph or a few seconds of speech) that unquestionably signified an archetypal emotional state, and attaching the appropriate label. Computational research has shifted that paradigm in multiple ways. Concern with realism is key. Emotion generally colours ongoing action and interaction: describing that colouring is a different problem from categorizing brief episodes of relatively pure emotion. Multiple challenges flow from that. Describing emotional colouring is a challenge in itself. One approach is to use everyday categories describing states that are partly emotional and partly cognitive. Another approach is to use dimensions. Both approaches need ways to deal with gradual changes over time and mixed emotions. Attaching target descriptions to a sample poses problems of both procedure and validation. Cues are likely to be distributed both in time and across modalities, and key decisions may depend heavily on context. The usefulness of acted data is limited because it tends not to reproduce these features. By engaging with these challenging issues, research is not only achieving impressive results, but also offering a much deeper understanding of the problem.

Beyond emotion archetypes: databases for emotion modelling using neural networks.

There has been rapid development in conceptions of the kind of database that is needed for emotion research. Familiar archetypes are still influential, but the state of the art has moved beyond them. There is concern to capture emotion as it occurs in action and interaction ('pervasive emotion') as well as in short episodes dominated by emotion, and therefore in a range of contexts, which shape the way it is expressed. Context links to modality-different contexts favour different modalities. The strategy of using acted data is not suited to those aims, and has been supplemented by work on both fully natural emotion and emotion induced by various technique that allow more controlled records. Applications for that kind of work go far beyond the 'trouble shooting' that has been the focus for application: 'really natural language processing' is a key goal. The descriptions included in such a database ideally cover quality, emotional content, emotion-related signals and signs, and context. Several schemes are emerging as candidates for describing pervasive emotion. The major contemporary databases are listed, emphasising those which are naturalistic or induced, multimodal, and influential.