Extraction of Average Temperature From Visual Scene Ensembles Without High Spatial Frequencies
Bibliographic record
Abstract
We have previously demonstrated that participants can rapidly extract average scene temperature (i.e., how hot or cold scenes would feel on average; VSS, 2024), without reliance on color, contrast, or low spatial frequencies. Furthermore, this occurred without utilizing visual working memory (VWM) resources, a hallmark of ensemble processing. In the present study, we furthered this investigation by examining whether average scene temperature could be extracted without reliance on high spatial frequency content. Given the established importance of low spatial frequencies in the rapid formation of scene gist representations (Oliva, 2005), we predicted that average scene temperature could be extracted when high spatial-frequency information was filtered out of scene images, and, similar to our previous results, this would occur without reliance on VWM. Participants rated the average temperature of scene ensembles that were gray-scaled and had a low spatial frequency filter applied (< 1 cycle/degree). We varied set size by randomly presenting 1, 2, 4, or 6 scenes to participants on each trial, and measured VWM capacity using a 2-AFC task. Participants were able to accurately extract average temperature, with all 6 scenes being integrated into their summary statistics. This occurred without relying on VWM, as fewer than 0.9 scenes were remembered on average. These results reveal that computing cross-modal summary statistics (i.e., average temperature) does not rely on high spatial frequency information or VWM resources, and that abstract multisensory information can be rapidly retrieved from complex visual stimuli.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".