Are accuracy and reaction time equivalent measures of the attentional blink?
Bibliographic record
Abstract
Perception of the second of two rapidly sequential targets (T1, T2) is impaired when presented soon after the first (attentional blink; AB). AB magnitude is indexed by the difference between performance at short and long inter-target lags. Conventionally, T2 performance is assessed using accuracy as the dependent measure. An inherent problem with this measure, often encountered in AB experiments, is the 100% response ceiling. For example, Visser (2007) reported greater AB magnitude with hard than with easy T1 tasks. That conclusion is questionable, however, because the two functions converged to the ceiling, thereby confounding the effect of T1 difficulty with the ceiling constraint. To avoid this problem, we used reaction time (RT) as the dependent measure and found AB magnitude to be invariant with T1 difficulty (Experiment 1). One interpretation of this result is that the invariance seen with RT would also obtain with accuracy but for the response ceiling. This implies equivalence of the two measures, which is not always the case (Santee & Egeth, 1982). In Experiment 2, we checked the equivalence of RT and accuracy measures of the AB using the phenomenon of lag-1 sparing, which refers to the finding that T2 performance is relatively unimpaired when T2 comes directly after T1 (Lag 1). Using accuracy, Visser et al. (1999) found lag-1 sparing only when T1 and T2 were presented in the same spatial location. Lag-1 deficit occurred otherwise. We replicated Visser et al.’s finding with accuracy; with RT as the dependent measure, however, lag-1 deficit occurred even when T1 and T2 were presented in the same location. This pattern of results suggests that RT and accuracy are not always equivalent measures of the underlying processes involved in the AB. Therefore, RT may not be a good way of avoiding the ceiling problem inherent in accuracy measures. Meeting abstract presented at VSS 2015
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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 | 0.009 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".