Surface Cues Reduce the Latency to Name Rotated Images of Objects
Bibliographic record
Abstract
Jolicoeur (1985, Memory & Cognition 13 289-303) found a linear increase in the latency to name line drawings of objects rotated (0 degrees to 120 degrees) from the upright (0 degrees) in the initial trial block. This effect was much shallower in later blocks. He proposed that the initial effect may indicate that mental rotation is the default process for recognising rotated objects, and that the decrease in this effect, seen with practice, may reflect the increased use of learned orientation-invariant features. Initially, we were interested in whether object-colour associations that may be learned during the initial block, could account for the reduced latency to name rotated objects, seen in later blocks. In experiment 1 we used full-cue colour images of objects that depicted colour and other surface cues. Surprisingly, given that Jolicoeur's findings were replicated several times with line drawings, we found that even the initial linear trend in naming latency was shallow. We replicated this result in follow-up experiments. In contrast, when we used less-realistic depictions of the same objects that had fewer visual cues (ie line drawings, coloured drawings, greyscale images), the results were comparable to those of Jolicoeur. Also, the initial linear trends were steeper for these depictions than for full-cue colour images. The results suggest that, when multiple surface cues are available in the image, mental rotation may not be the default recognition process.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".