Reverse Predictivity: Going Beyond One-Way Mapping to Compare Artificial Neural Network Models and Brains
Bibliographic record
Abstract
Abstract A major goal in systems neuroscience is to build computational models that capture the primate brain’s internal representations. Standard evaluations of artificial neural networks (ANNs) emphasize forward predictivity —how well model features predict neural responses—without testing whether model representations are themselves recoverable from neural activity. Here we develop the reverse predictivity metric, which quantifies how well macaque inferior temporal (IT) cortex responses predict ANN unit activations. This two-way framework reveals a striking asymmetry: models with high forward predictivity (∼50% variance explained) often contain units unpredictable from neural activity, reflecting biologically inaccessible dimensions. In contrast, monkey-to-monkey mappings are symmetric, confirming that the asymmetry reflects genuine representational mismatch. Reverse predictivity isolates “common” ANN units—shared with IT, behaviorally relevant, and generalizing across species—and “unique” units lacking such alignment. Influenced by feature dimensionality, training objectives, and adversarial robustness, reverse predictivity offers a principled benchmark for guiding next-generation ANNs toward both high task performance and genuine biological plausibility.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".