Individual differences in the Pd component support object-file updating as the source of the same-location cost in attentional orienting
Bibliographic record
Abstract
Spatial cues that mismatch the colour of a subsequent target have recently been shown to slow responses to that target. The source of this ‘same location cost’ (SLC) is currently unknown. Two potential sources are attentional signal suppression and object-file updating. Here, we tested these accounts by reanalysing data from a previously published spatial-cueing study in which we recorded brain activity using electroencephalography (EEG), and focusing on the event-related PD component, which is thought to index attentional signal suppression. Correlating PD component amplitude with SLC magnitude gives rise to two opposing predictions. If attentional signal suppression is the source of the SLC, then the SLC should be positively correlated with PD amplitude. Alternatively, if object-file updating is the source of the SLC, the SLC should be negatively correlated with PD magnitude, as a more suppressed signal should be easier to update with subsequent target features. Forty-eight participants performed a colour-based spatial-cueing task, and showed a pattern of reaction times consistent with an SLC. Across participants, SLC and PD magnitudes were negatively correlated (r=-.41, p=.004). This finding is not compatible with an attentional suppression account of the SLC, and instead supports object-file updating as the source of the SLC.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".