When Weak Imagery is Worse Than None: Core Aphantasia and Hypophantasia Relate Differently to Mental Health, Mediated by Subjective Interoception and Alexithymia
Bibliographic record
Abstract
Aphantasia, characterized by absent or reduced visual mental imagery, has been associated with alexithymia and negative mental health outcomes. However, the underlying mechanisms driving these associations remain unclear. Since mental health is closely linked to interoception, we examined whether interoceptive processing plays a role in these relationships, assessing self-reported imagery vividness (VVIQ), subjective interoceptive accuracy and attention, alexithymia, and mental health, including anxiety and depression, in individuals with (n = 153, VVIQ = 16–32) and without aphantasia (n = 680, VVIQ = 33–80). The results revealed distinct patterns between these groups. In the aphantasia group, higher VVIQ was associated with greater alexithymia and poorer mental health outcomes, suggesting that individuals with weak imagery (hypophantasia) exhibited more emotional difficulties than those with almost no imagery (core aphantasia). In contrast, in the non-aphantasia group, VVIQ correlated negatively with alexithymia and positively with mental health, aligning with previous research indicating a protective role of vivid imagery. Structural equation modelling confirmed that alexithymia strongly mediated the link between VVIQ and negative mental health outcomes in both groups, reinforcing alexithymia as a mediator between imagery ability and well-being. Another key factor in these relationships was the ratio between subjective interoceptive accuracy and attention, which mediated the relationship between VVIQ and alexithymia and mental health outcomes. These findings highlight the heterogeneity within aphantasia, distinguishing hypophantasia from core aphantasia and revealing distinct interoceptive and mental health profiles.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".