Consciousness of Error: How Origin Shapes Awareness and Learning
Bibliographic record
Abstract
Errors are often regarded as obstacles to be minimized, yet psychological and neuroscience research suggests they may serve as critical signals for learning. In Being Wrong, Kathryn Schulz (2010) framed error as a universal cognitive condition, but left unanswered whether all errors equally contribute to conscious awareness and adaptation. Building on this gap, the present paper examines how the origin and confidence of errors influence memory and learning. Recent work shows that high-confidence errors are tied to perceptual processing (Alilović et al., 2023), while low-confidence errors reflect later decisional mechanisms; complementary evidence suggests that predictions and errors are anatomically segregated in V1 (Thomas et al., 2024). A behavioural experiment is proposed to test whether perceptual (high-confidence) versus cognitive (low-confidence) errors differ in their likelihood of being consciously encoded and remembered. It is predicted that high-confidence errors will be more accessible to memory and thus more likely to facilitate learning. Counter-evidence shows that confidence and awareness can diverge (Lau & Passingham, 2006; Overgaard et al., 2006) and that learning can occur without awareness (Tsushima et al., 2006), suggesting Schulz’s framing requires refinement. Taken together, confidence appears to act as a functional gatekeeper of error-based learning, but unconscious mechanisms also shape adaptation.
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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 | 0.013 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".