Suffering more in imagination than in reality? Mental imagery and fear generalization
Bibliographic record
Abstract
Mental imagery may represent a weaker form of perception and, thus, mental images may be more ambiguous than visual percepts. If correct, the acquisition of fear would be less specific for imagined fears in comparison to perceptual fears, perhaps facilitating broader fear generalization. To test this idea, a two-day differential fear conditioning experiment (N = 98) was conducted. On day one, two groups of participants underwent differential fear conditioning such that a specific Gabor patch orientation (CS+) was paired with mild shocks (US) while a second Gabor patch of orthogonal orientation (CS-) was never paired with shock. Critically, one group imagined the Gabor patches and the other group was visually presented the Gabor patches. Next, both groups were presented visual Gabor patches of similar orientations (GCS) to the CS+. On day two, to assess the persistence of imagined fear, participants returned to the lab and were tested on the GCS devoid of shock. For day one, in contrast to our primary hypothesis, both self-report and skin conductance response measures did not show a significant interaction between the GCS and groups. On day two, both measures demonstrated a persistence of imagined fear, without US delivery. Taken together, rather than demonstrating an overgeneralization effect, the results from this study suggest that imagery-based fear conditioning generalizes to a similar extent as perceptually acquired fear conditioning. Further, the persistence of imagery-based fear may have unique extinction qualities in comparison to perceptual-based fear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".