Thinking versus feeling: How interoception and cognition influence emotion recognition in behavioural-variant frontotemporal dementia, Alzheimer's disease, and Parkinson's disease
Bibliographic record
Abstract
Disease-specific mechanisms underlying emotion recognition difficulties in behavioural-variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD) are unknown. Interoceptive accuracy, accurately detecting internal cues (e.g., one's heart beating), and cognitive abilities are candidate mechanisms underlying emotion recognition. One hundred and sixty-eight participants (52 bvFTD; 41 AD; 24 PD; 51 controls) were recruited. Emotion recognition was measured via the Facial Affect Selection Task or the Mini-Social and Emotional Assessment Emotion Recognition Task. Interoception was assessed with a heartbeat detection task. Participants pressed a button each time they: 1) felt their heartbeat (Interoception); or 2) heard a recorded heartbeat (Exteroception-control). Cognition was measured via the Addenbrooke's Cognitive Examination-III or the Montreal Cognitive Assessment. Voxel-based morphometry analyses identified neural correlates associated with emotion recognition and interoceptive accuracy. All patient groups showed worse emotion recognition and cognition than controls (all P's ≤ .008). Only the bvFTD showed worse interoceptive accuracy than controls (P < .001). Regression analyses revealed that in bvFTD worse interoceptive accuracy predicted worse emotion recognition (P = .008). Whereas worse cognition predicted worse emotion recognition overall (P < .001). Neuroimaging analyses revealed that the insula, orbitofrontal cortex, and amygdala were involved in emotion recognition and interoceptive accuracy in bvFTD. Here, we provide evidence for disease-specific mechanisms for emotion recognition difficulties. In bvFTD, emotion recognition impairment is driven by inaccurate perception of the internal milieu. Whereas, in AD and PD, cognitive impairment likely underlies emotion recognition deficits. The current study furthers our theoretical understanding of emotion and highlights the need for targeted interventions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".