Do visual mental imagery and exteroceptive perception rely on the same mechanisms?
Bibliographic record
Abstract
Subjective visual experience can be achieved with or without external stimuli. Previous work in neuroimaging suggests that exteroceptive visual perception and mental imagery activate similar brain areas within the ventral visual stream (e.g., Horikawa & Kamitani, 2017). It is still unclear to what extent visual mental imagery and exteroceptive visual perception rely on the same mechanisms. We tested a total of 98 individuals (60 men; age range 18-66 years; M = 34.22, SD = 13.65) recruited via Prolific. Mental imagery abilities were assessed using four self-report questionnaires: Object-spatial Imagery Questionnaire (OSIQ; Blajenkova et al., 2006), Vividness of Visual Imagery Questionnaire (VVIQ; Marks, 1973), Spontaneous Use of Imagery Scale (SUIS; Kosslyn et al., 1998), and Questionnaire upon Mental Imagery (QMI; Sheehan, 1967). Face recognition ability was evaluated using the Cambridge Face Perception Test (CFPT; Duchaine et al., 2007) and the extended version of the Cambridge Face Memory test (CFMT+; Duchaine & Nakayama, 2006; Russell et al., 2009). All tests were conducted online using Meadows (https://meadows-research.com). We computed a global imagery score for each participant as the sum of the z-scores of the four questionnaires. Similarly, the global perception score was computed as the sum of the z-scores of the accuracy of the CFMT+ and of the upright faces trials of the CFPT. No correlation was observed between the two global scores (r = -0.018, p = 0.85; and r = 0.081, p = 0.43, restricting the correlation to face items in the mental imagery questionnaires). We also examined the association between VVIQ and the CFMT+, both gold-standard in their fields. The lack of significant correlation (r = -0.035, p = 0.72) further suggests that visual mental imagery and face recognition stem from only partly overlapping mechanisms.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".