A cultural training for the improvement of cognitive and affective Theory of Mind in people with Multiple Sclerosis: a pilot randomized controlled study
Bibliographic record
Abstract
Theory of Mind (ToM), the ability to understand and attribute mental states to ourselves and others, could be impaired in Multiple Sclerosis (MS), a neurodegenerative disease affecting young adults. Considering that ToM is strictly connected to Quality of Life (QoL) in MS and that could enhance the social support network -which is particularly important for this population-, we aimed to design and implement a novel ToM rehabilitation training. To make the training as much ecological as possible, we have devised a protocol enhancing ToM through stimuli depicting real-world conditions (video-clips taken from cinema movies, literary fictions, and audio voices). We test training’s effect on both cognitive and affective components of ToM in a sample of 13 subjects, randomly assigned to the ToM training Group and to the Control Group. The following ToM tasks were administered: the Reading the Mind in the Eyes (RMET), the Strange Stories task, the Faux Pas Task and the False Belief First- and Second - Order Task (FB II and III order). We also administered a psycho-behavioral assessment through the Toronto Alexithymia Scale (TAS-20). Results show that our novel ToM training is useful in enhancing ToM abilities measured by the following tasks: the RMET (affective task, p = 0.015) and the FB II-order task (FB, cognitive task, p = 0.032). Our ToM training had also a significant effect on the total score of the TAS-20 Scale (p = 0.018) and on its “Difficulty Describing Feelings subscale” (p = 0.018), indicating a reduction of the alexithymia traits. Future works with larger samples could investigate the ToM training effectiveness in a more representative MS populations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".