Discriminating emotions from point-light walkers in persons with Schizophrenia
Bibliographic record
Abstract
The visual system is well adapted to recognize human motion from point lights attached to the major joints of an actor. Moreover, individuals are able to recognize emotions based on the visual information in such dynamic point-light displays. This ability is important because humans utilize both biological motion and affect recognition for understanding the intentions of people in the environment. It is not clear, however, whether atypical observers, including people with schizophrenia, also process visual information about emotion in point-light walkers in the same way. There is evidence that people with schizophrenia are impaired in recognizing emotional expressions. Furthermore, people with schizophrenia are known to have deficits in social perception. For these reasons, we investigated whether the ability to recognize emotions from point-light displays is altered in people with schizophrenia. In the current study, groups of healthy community-based controls (N=33) and people with schizophrenia (N=33) were asked to discriminate the emotions of four types of affective point-light walkers: upright, inverted, scrambled (which contained only local form information), and random-position (which contained only global form information). The point-light walkers were presented in three emotional conditions: happy, sad, and angry. Both healthy controls and people with schizophrenia were able to discriminate emotions from point-light walkers, where performance was best for upright walkers, worst with scrambled walkers, and intermediate with random-position and inverted walkers. Overall, performance was worse for people with schizophrenia compared to healthy observers. These results suggest that both healthy controls and people with schizophrenia are able to recognize emotions from point-light walkers on the basis of local motion or global form information alone. However, performance is best when both form and motion information are presented simultaneously, and, although they do perform above chance, people with schizophrenia are relatively impaired in all conditions. Meeting abstract presented at VSS 2012
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".