Object-Based Attention is Modulated by Shifts Across the Meridians
Bibliographic record
Abstract
Object-based attention yields a performance advantage for targets on the same object compared to targets on different objects. Although most published reports find evidence of a same-object advantage (SA), certain situations evoke a same-object cost (Davis & Holmes, 2005). Pilz et al. (2012) reported that most individuals do not show SA, and SA is more prevalent for rectangular objects oriented horizontally than vertically; consistent with attention being more efficiently allocated along the horizontal meridian. To explore these object-based effects when explicitly controlling for shifts of attention across either the horizontal or vertical meridian, we reanalyzed published data from four experiments (Pilz et al., 2012; Greenberg, 2009). In each experiment, rectangle orientation (horizontal vs. vertical) was a factor, allowing computation of SA while controlling for attention shift direction. We controlled for shifts across meridians by subtracting the different-object RTs for one object orientation from the same-object RTs for the other object orientation, thus ensuring that subjects' attention focus never crossed a specific meridian. Results showed that (1) controlling for meridian shifts failed to produce a same-object cost at the group level, (2) shifts within one side of the vertical meridian produced larger SA than for the horizontal meridian, and (3) a smaller proportion of individual subjects showed same-object costs when controlling for meridian shifts. Furthermore, when the target was part of the object (cf. Watson & Kramer, 1999), there was no difference in SA between our meridian analyses, unlike when the target was placed on the object. We conclude that controlling for attention shifts across meridians provides an important perspective on SA. Object-based effects are stronger when attention shifts are confined to one side of the vertical meridian (vs. horizontal). Examining orientation differences confounds meridian shifts (and therefore brain hemisphere representations), which may explain inconsistent SA observations in the literature. Meeting abstract presented at VSS 2014
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".