Epiphenomenal neural activity in the primate cortex
Bibliographic record
Abstract
Abstract When neuroscientists record neural activity from the brain, they often conclude that neural responses observed during task performance are indicative of the functional role of the brain area(s) studied. In humans and nonhuman primates, it is often hard to combine recordings and causal techniques within the same experiment, leaving the possibility that the activity recorded may be epiphenomenal rather than reflecting a specific functional role. Currently, the prevalence of epiphenomenal neural activity in the cortex is unknown. To estimate the extent of such activity in primates, we chronically recorded neural activity in the prefrontal cortex of the same monkeys using the same neural implants during the performance of four different cognitive tasks. The four tasks were carefully selected such that only one of them causally depends on the brain area recorded, as demonstrated by previous double dissociation studies. Using the four most common single neuron analyses methods in the field, we found that the prevalence and strength of neural correlates were just as high across all four tasks, including for the three tasks that do not depend on this brain area. These results suggest that the probability of observing epiphenomenal activity in primate cortex is high, which can mislead investigators relying on neural recording or imaging to map brain function. One-Sentence Summary Tremblay, Testard and colleagues show that inferring a brain area’s function from neural recordings alone could be misleading.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".