A functional network perspective on response inhibition and attentional control
Bibliographic record
Abstract
Inferior frontal cortex (IFC) modules that inhibit dominant behaviours are a popular feature in theories of cognitive dysfunction. However, the paradigms on which these theories are based fail to distinguish between inhibitory and non-inhibitory cognitive demands. Here we use four novel fMRI variants of the classic stop-signal task to test whether the IFC houses unique inhibitory modules. Our results demonstrate that IFC sub-regions are not functionally unique in their sensitivities to inhibitory cognitive demands, but instead form components of spatially distributed networks. These networks are most strongly activated when infrequent stimuli are being processed, regardless of behavioural inhibitory demands, and when novel tasks are being acquired, as opposed to when routine responses must be suppressed. We propose that there are no inhibitory modules within the frontal lobes and that behavioural inhibition is an emergent property of spatially distributed functional networks, each of which supports a broader class of cognitive demands. The right inferior frontal cortex has long been thought to house a neural module that inhibits dominant behaviours. Using brain imaging, Erika-Florence et al.demonstrate that this inhibition is in fact an emergent property of multiple neural networks that support broader classes of cognitive processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".