Material perception: What can you see in a brief glance?
Bibliographic record
Abstract
People can recognize natural objects and natural scenes with remarkable speed, even when they have never seen the pictures before (Biederman et al., 1974; Potter, 1975, 1976; Thorpe et al., 1996; Greene & Oliva, 2008). But how quickly can people recognize natural materials? We built an image database containing 1000 images of 9 material categories (e.g., paper, fabric, glass, etc). To prevent subjects from simply doing object recognition, we used cropped images in which overall object shape was not a useful cue. To prevent subjects from simply using color, texture, or other low level cues, we chose images with highly diverse appearances. For example, “plastic” includes close-ups of red trash bags, transparent CD cases, and multi-colored toys. Images were obtained from websites like flickr.com. We found that humans can correctly categorize images with very short durations and in challenging conditions (e.g., 40 msec followed by a noise mask, or presented in the middle of an RSVP stream at 40msec per image). When we degraded the images by simple manipulations like removing color, or blurring, or inverting contrast, performance was reduced but was still surprisingly good. We also measured recognition speed with reaction time. To measure baseline RT, we gave subjects very simple visual tasks (e.g., Is this disc red or blue? Is this diagonal line tilted left or right?). We then asked them to make a 3-way material category judgment (e.g., paper or plastic or fabric?). Material categorization was nearly as fast as baseline. Beyond judgments of material category, observers can judge dimensions of material appearance like matte/glossy, opaque/translucent, rigid/non-rigid, soft/rough, warm/cool reliably even in 40 ms presentations. In conclusion, material perception is fast and flexible, and can have the same rapidity as object recognition and scene perception. NTT Communication Science Laboratories, Japan National Science Foundation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".