Biologically plausible feature selection through relative correlation
Bibliographic record
Abstract
Biological organisms need to accurately infer which features of their environment predict future rewards and punishments for survival sake. This problem resembles linear regression, which finds parameter values expressing the linear relationship between features and an outcome. The least mean squares regression method generalizes well when there is little system noise and at least as many training data points (experiences) as input features. When this is not the case, feature selection may be applied to eliminate irrelevant features and improve generalization. Here, we show a biologically plausible approach to feature selection that computes the maximum likelihood estimate of Pearl's “Noisy OR” model. We show that this results in highlighting the features that are most correlated with the outcome at the expense of the least correlated. We extend this “relative correlation” approach to represent global inhibitory features and show that as additive noise and the number of irrelevant features are increased, relative correlation leads to substantially less prediction error on test data than does least means squares in a simple linear regression task. We demonstrate how relative correlation can be implemented in a dual pathway neural network and discuss some similarities between it and the basal ganglia.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".