Subspace alignment as a mechanism for binding
Bibliographic record
Abstract
ABSTRACT To choose between options, we must solve two important binding problems. First, the features that determine each options’ values must be appropriately combined and kept separate from the corresponding features of other options. Second, options must be associated with the specific actions needed to select them. We hypothesized that the brain solves these problems through use of aligned (for bound dimensions) and orthogonal (for separated dimensions) population subspaces. We examined responses of single neurons in six putative value-coding regions in rhesus macaques performing a risky choice task. In all areas, single neurons encode the features that define the value of each option (stakes and probability) but only very weakly encode value per se . However, the coding dimensions associated with these features are aligned on a single subspace, from which a strong emergent value signal can be read out. Moreover, all six regions use nearly orthogonal subspaces for the left and right options, thereby linking options to their position in space, implementing functional partitioning, and reducing the possibility of misbinding. These results provide a new solution to the neuroeconomic binding problems and suggest that other forms of binding may work through similar principles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".