The Relationship between Self-Reported Vividness and Latency during Mental Size Scaling of Everyday Items: Phenomenological Evidence of Different Types of Imagery
Bibliographic record
Abstract
We examined how the relationship between ratings of vividness (or image strength) and image latency might reflect the concerted action of two visual imagery pathways hypothesized by Kosslyn (1994): the ventral pathway, processing object properties, and the dorsal pathway, processing locative properties of mental images. Participants formed their images at small or large angular display sizes, varying the amount of size scaling needed. In Experiment 1, display size varied between participants, and images were trial unique. The higher the vividness, the faster the generation of small images (requiring size scaling of less than 10 degrees), which would recruit mainly the ventral pathway. This vivid-is-fast relationship changed for large images (requiring size scaling of 10 degrees or more), which would recruit mainly the dorsal pathway. The size-dependent alteration of the vivid-is-fast relationship was replicated in the first block of Experiment 2. However, when repeated over 3 consecutive blocks, image generation sped up, and gradually the vivid-is-fast relationship tended to occur for all display sizes until complete automatization of image generation occurred. The findings suggest that differential patterns of vividness-latency relationship can reflect the types of images involved, their relative ventral and dorsal contributions, and the involvement of working memory.
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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 | 0.018 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".