Representation of Change: Separate Electrophysiological Markers of Attention, Awareness, and Implicit Processing
Bibliographic record
Abstract
Awareness of change within a visual scene only occurs in the presence of focused attention. When two versions of a complex scene are presented in alternating sequence separated by a blank mask, unattended changes usually remain undetected, although they may be represented implicitly. To test whether awareness of change and focused attention had the same or separable neurophysiological substrates, and to search for the neural substrates of implicit representation of change, we recorded event-related brain potentials (ERPs) during a change blindness task. Relative to active search, focusing attention in the absence of a change enhanced an ERP component over frontal sites around 100-300 msec after stimulus onset, and in posterior sites at the 150-300 msec window. Focusing attention to the location of a change that subjects were aware of, replicated those attentional effects, but also produced a unique positive deflection in the 350-600 msec window, broadly distributed with its epicenter in mediocentral areas. The unique topography and time course of this latter modulation, together with its dependence on the aware perception of change, distinguishes this "awareness of change" electrophysiological response from the electrophysiological effects of focused attention. Finally, implicit representation of change elicited a distinct electrophysiological event: Unaware changes triggered a positive deflection at the 240-300 msec window, relative to trials with no change. Overall, the present data suggest that attention, awareness of change, and implicit representation of change may be mediated by separate underlying systems.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".