Average Temperature from Visual Scene Ensembles Without Reliance on Color, Contrast or Low Spatial Frequencies
Bibliographic record
Abstract
Summary statistics for groups (i.e., ensembles) of faces or objects can be rapidly extracted to optimize visual processing, without reliance on visual working memory (VWM). We have previously demonstrated that this ability extends to complex groups of scenes. Namely, participants were able to extract average scene content and spatial boundary from scene ensembles. In the present study we tested whether this ability extends to scene features that are not solely attributable to visual processing and are instead computed cross-modally. Specifically, we examined ensemble processing of scene temperature. Given that the apparent temperature (i.e., how hot or cold a scene would feel) of single scenes is accurately rated by observers (Jung & Walther, 2021), we predicted that average scene temperature could be extracted by observers, without reliance on VWM. Crucially, across 4 experiments, we tested if this ability would depend on low-level visual features. Participants rated the average temperature of scene ensembles, with either colored stimuli (Exp 1), gray-scaled stimuli (Exp. 2), gray-scaled stimuli with a 75% contrast reduction (Exp. 3), or gray-scaled high spatial frequency filtered stimuli (> 6 cycles/degree, Exp. 4). In all experiments, 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 in all experiments, with all 6 scenes being integrated into their summary statistics. This occurred without relying on VWM, as fewer than 1.2 scenes were remembered on average. These results reveal that computing cross-modal summary statistics (i.e., average temperature) does not rely on lower-level visual features. Overall, these results reveal that with minimal low-level visual information available, abstract multisensory information can be rapidly retrieved and combined from long term memory to form statistical representations.
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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 | 0.001 | 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 | 0.000 |
| 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".