Are vividness judgments in mental imagery correlated with perceptual thresholds?
Bibliographic record
Abstract
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). However, it is still unclear to what extent vividness judgments and perceptual thresholds are determined by this overlap. We measured perceptual thresholds and mental imagery vividness for a set of 20 images from the Natural Scene Dataset (Allen et al., 2022). In the imagination task, we asked participants to report, for each individual trial, the vividness of their mental image (40 trials per image). In the perceptual threshold ABX task, we asked participants to recognise among two unaltered visual scenes (A and B), a target image (X) embedded in Gaussian noise. The Quest procedure (Watson & Pelli, 1983) was used in order to establish the contrast threshold per image (40 trials per image). Preliminary results (N = 8) indicate no, or very weak, within-subject correlations between the contrast thresholds and the mean vividness scores (mean correlation = -0.072; SD = 0.168; min = -0.240; and max = 0.250). This suggests that most of the variance in perceptual thresholds and vividness judgments of mental images is not determined by the same mechanism. These findings contribute to ongoing modeling and theoretical efforts aimed at deciphering how the brain can generate our rich and vivid mental images.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".