What does cognitive control feel like? Effective and ineffective cognitive control is associated with divergent phenomenology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cognitive control is accompanied by observable negative affect. But how is this negative affect experienced subjectively, and are these feelings related to variation in cognitive control? To address these questions, 42 participants performed a punished inhibitory control task while periodically reporting their subjective experience. We found that within-subject variation in subjective experience predicted control implementation, but not neural monitoring (i.e., the error-related negativity, ERN). Specifically, anxiety and frustration predicted increased and decreased response caution, respectively, while hopelessness accompanied reduced inhibitory control, and subjective effort coincided with the increased ability to inhibit prepotent responses. Clarifying the nature of these phenomenological results, the effects of frustration, effort, and hopelessness-but not anxiety-were statistically independent from the punishment manipulation. Conversely, while the ERN was increased by punishment, the lack of association between this component and phenomenology suggests that early monitoring signals might precede the development of control-related subjective experience. Our results indicate that the types of feelings experienced during cognitively demanding tasks are related to different aspects of controlled performance, critically suggesting that the relationship between emotion and cognitive control extends beyond the dimension of valence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it