Sequential search asymmetry: Behavioral and psychophysiological evidence from a dual oddball task
Bibliographic record
Abstract
We conducted five experiments in order to explore the generalizability of a new type of search asymmetry, which we have termed sequential search asymmetry, across sensory modalities, and to better understand its origin. In all five experiments rare oddballs occurred randomly within longer sequences of more frequent standards. Oddballs and standards all consisted of rapidly-presented runs of five pure tones (Experiments 1 and 5) or five colored annuli (Experiments 2 through 4) somewhat analogous to simultaneously-presented feature-present and feature-absent stimuli in typical visual search tasks. In easy tasks feature-present reaction times and P300 latencies were shorter than feature-absent ones, similar to findings in search tasks with simultaneously-presented stimuli. Moreover the P3a subcomponent of the P300 ERP was strongly apparent only in the feature-present condition. In more difficult tasks requiring focused attention, however, RT and P300 latency differences disappeared but the P300 amplitude difference was significant. Importantly in all five experiments d' for feature-present targets was larger than that for feature-absent targets. These results imply that sequential search asymmetry arises from discriminability differences between feature-present and feature-absent targets. Response time and P300 latency differences can be attributed to the use of different attention strategies in search for feature-present and feature-absent targets, indexed by the presence of a dominant P3a subcomponent in the feature-present target-evoked P300s that is lacking in the P300s to the feature-absent targets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".