Probing the Neural Basis of Visual Abstraction: Macaques and ANN Models Achieve Similar Sketch Recognition Performance
Bibliographic record
Abstract
Visual abstraction is the process of distilling complex visual scenes into their essential components. Such abstraction is exemplified by the production and recognition of sketches, which can be effective in conveying the content of a scene while omitting many details. To what degree does visual abstraction also manifest in non-human primates, and what neural computations are responsible? To answer this question, we measured how well macaque monkeys (N=2) could identify the visual concept conveyed in human-drawn sketches and evaluated how well their behavior and ventral stream neural responses could be predicted by an artificial neural network (i.e., AlexNet). Both macaques performed a sketch-recognition task using stimuli (1000 images, 10 object categories) from the Google Quick Draw dataset. In each trial, we briefly presented (100 ms) a sketch, followed by a choice screen where monkeys selected which of two object images the sketch represented. Both monkeys achieved accuracies exceeding 70%, demonstrating that even simple sketches convey sufficient information for robust object identification by non-human primates. The monkeys’ image-by-image recognition accuracies significantly correlated with those predicted by AlexNet (R = 0.42, p<0.001). This correlation matched the monkeys’ noise ceiling (~0.4), indicating that AlexNet strongly approximates the abstraction strategies employed by the primate visual systems, given intrinsic variability in the current dataset. We also recorded population activity (384 sites) from inferior temporal (IT) cortex as monkeys viewed line drawings and sketches. Using linear classification on IT responses, we found that distributed neural activity patterns strongly predicted (accuracy~0.83, chance-level=0.5) object identity, providing evidence that IT cortex encodes the abstract visual features underlying sketch recognition. These findings establish macaques as a powerful model for investigating the neural computations that support sketch recognition.
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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 | 0.003 |
| 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 | 0.001 |
| 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".