An eye-tracking investigation of intentional motion perception in patients with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Schizophrenia has been characterized by an impaired attribution of intentions in social interactions. However, it remains unclear to what extent poor performance may be due to low-level processes or to later, higher-level stages or to what extent the deficit reflects an over- (hypermentalization) or underattribution of intentions (hypomentalization). METHODS: We evaluated intentional motion perception using a chasing detection paradigm in individuals with schizophrenia or schizoaffective disorder and in healthy controls while eye movements were recorded. Smooth pursuit was measured as a control task. Eye-tracking was used to dissociate ocular from cognitive stages of processing. RESULTS: We included 27 patients with schizophrenia, 2 with schizoaffective disorder and 29 controls in our analysis. As a group, patients had lower sensitivity to the detection of chasing than controls, but showed no bias toward the chasing present response. Patients showed a slightly different visual exploration strategy, which affected their ocular sensitivity to chasing. They also showed a decreased cognitive sensitivity to chasing that was not explained by differences in smooth pursuit ability, in visual exploration strategy or in general cognitive abilities. LIMITATIONS: It is not clear whether the deficit in intentional motion detection demonstrated in this study might be explained by a general deficit in motion perception in individuals with schizophrenia or whether it is specific to the social domain. CONCLUSION: Participants with schizophrenia showed a hypomentalization deficit: they adopted suboptimal visual exploration strategies and had difficulties deciding whether a chase was present or not, even when their eye movement revealed that chasing information had been seen correctly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".