Conflict neurons in cingulate cortex of macaques
Bibliographic record
Abstract
Abstract Conflict—the magnitude of co-activation of mutually incompatible response processes—was proposed to explain how cognitive control is invoked (Botvinick et al. 2001) and continues to engage debate (Becker et al. 2024). Original observations consistent with this construct emphasized the primary contribution of cingulate cortex (CC) based on human functional imaging (Botvinick et al., 1999; Carter et al., 2000) and electroencephalogram (Yeung, Botvinick, & Cohen, 2004). In countermanding tasks conflict arises through co-activation of competing GO and STOP processes (Boucher et al. 2007; Schall & Boucher 2007; Sajad et al. 2022). Single neuron activity representing conflict has been described in the supplementary motor cortex of human epilepsy patients (Fu et al., 2019; Sheth et al., 2012) and of macaque monkeys (Sajad, Errington, & Schall, 2022; Stuphorn, Taylor, & Schall, 2000) and in human cingulate cortex (Fu et al., 2019; Sheth et al., 2012) but not in monkey cingulate cortex (Ebitz & Platt, 2015; Ito, Stuphorn, Brown, & Schall, 2003; Nakamura, Roesch, & Olson, 2005). This lack of homology generated debate about the utility of macaques for investigation of cognitive control (Cole et al. 2009; Schall & Emeric 2010). With higher-resolution, less-biased samples, we re-examined the presence of a conflict signal in cingulate cortex of monkeys. Neurons modulating specifically when response conflict was maximal were found in cingulate cortex— more commonly in the dorsal than the ventral bank. However, such neurons were much more common in supplementary motor cortex. These data confirm the presence of a conflict signal in medial frontal cortex and demonstrate that it can be found in a small fraction of neurons in cingulate cortex. Further research is needed to determine if the weak response conflict signal in cingulate cortex is sufficient or negligible.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".