The error-related negativity and error-related temporal binding: Different predictors of task performance?
Bibliographic record
Abstract
When performing goal-directed tasks, mistakes can motivate changes in our choices and behaviours. This process of behavioural adaptation is assumed to be at least partly driven by error processing mechanisms in the brain marked by the error-related negativity (ERN). A recently observed perceptual consequence of errors is a temporal binding effect, which is the perceived compression of time between actions and outcomes and is commonly claimed to be an implicit marker for the sense of agency. Given that both phenomena are triggered by errors, we sought to investigate the relationship between ERN amplitude and error-related temporal binding and assess the extent to which each of these predicted several measures of task performance. Utilising a modified Eriksen Flanker task to increase error rates, we measured error-related changes in ERP amplitude (ERN difference wave) and action-outcome interval estimates (error-related temporal binding). Both measures were significantly affected by erroneous responses, and this was correlated between measures - participants with larger ERN amplitude also exhibited stronger error-related binding. When controlling for each other as predictors of task performance, ERN amplitude was shown to independently predict overall error rates, while error-related binding was shown to independently predict the rate of improvement. To our knowledge, this is the first study to observe error-related changes in temporal binding in a flanker task, and the first to measure the relationship between ERN amplitude and temporal binding. We discuss the distinction (and overlap) between the ERN and error-related binding, along with the potential links to the sense of agency.
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.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".