Attention alters decision criteria but not appearance: A reanalysis of Anton-Erxleben, Abrams, and Carrasco (2010)
Bibliographic record
Abstract
Paying attention to a stimulus affords it many behavioral advantages, but whether attention also changes its subjective appearance is controversial. K. A. Schneider and M. Komlos (2008) demonstrated that the results of previous studies suggesting that attention increased perceived contrast could also be explained by a biased decision mechanism. This bias could be neutralized by altering the methodology to ask subjects whether two stimuli were equal in contrast or not rather than which had the higher contrast. K. Anton-Erxleben, J. Abrams, and M. Carrasco (2010) claimed that, even using this equality judgment, attention could still be shown to increase perceived contrast. In this reply, we analyze their data and conclude that the effects that they reported resulted from fitting symmetric functions that poorly characterized the individual subject data, which exhibited significant asymmetries between the high- and low-contrast tails. The strength of the effect attributed to attentional enhancement in each subject was strongly correlated with this skew. By refitting the data with a response model that included a non-zero asymptotic response in the low-contrast regime, we show that the reported attentional effects are better explained as changes in subjective criteria. Thus, the conclusion of Schneider and Komlos that attention biases the decision mechanism but does not alter appearance is still valid and is in fact supported by the data from Anton-Erxleben et al.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".