Revisiting shape versus texture bias in primate vision: contrasting human vs. monkey perceptual strategies
Bibliographic record
Abstract
Visual perception in artificial and biological systems often diverges in the processing of complex stimuli. Geirhos et al. (2019) underscored this distinction, demonstrating that unlike convolutional neural networks (CNNs) trained on ImageNet, human vision is biased by shape over texture in object recognition. To delineate the mechanistic differences in visual perception between CNNs and biological systems, we probed the object shape versus texture biases of rhesus macaques—an animal model where finer-grained neural measurements are feasible. We trained two macaques on binary object discrimination tasks using the Microsoft COCO dataset across ten object categories. They were subsequently tested on cue-conflict images (from Geirhos et al. 2019), wherein images featured either texture-shape congruence or conflict – designed to assess whether macaques exhibit shape-bias like humans. Our results revealed a nuanced perceptual strategy in macaques. Consistent with previous studies, we observed high accuracy when the images contained no shape-texture conflicts –indicating that macaques are adept at shape-based recognition, with performances ranging from 0.80 to 0.89 across shapes. However, the introduction of conflicting textures led to variable outcomes. In particular, the accuracy for recognizing 'Elephant' shapes with 'Chair' textures dropped sharply to 0.14, highlighting a substantial influence of texture on the recognition process. The performance gradient across various shape-texture pairings suggests a complex interplay in the macaques' visual processing, differing significantly from the consistent human shape bias reported earlier. Next, we asked how these behavioral biases were driven by activity in the macaque IT cortex (critical for object recognition). We observed a significant alignment (consistency of 0.36) between neural activity and cue-conflict confusion pattern. In conclusion, our results reveal that macaques' reliance on shape versus texture is context-dependent and less robust than in humans. These insights motivate further exploration of the factors influencing distinct perceptual biases and the evolution of visual processing across species.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".