An investigation of the reliability and relationships among global-local processing measures
Bibliographic record
Abstract
Global-local stimuli have been shown to be useful for examining a variety of different concepts, such as how human faces are processed (e.g. Hillger & Koenig), how affect can alter an individual's judgement of a stimulus (Fredrickson & Branigan, 2005), how dispositional global/local precedence can alter an individual's perception of time (Liberman & Förster, 2009), and how dispositional global/local precedence can affect the direction of selective attention (Dale & Arnell, 2010). Throughout the literature, multiple different versions of global-local stimuli have been used, such as traditional hierarchical letters and numbers (i.e. Navon letters), abstract hierarchical shapes, and high and low spatial frequency gratings. However, it is currently unclear how reliable or stable performance is on these measures within individuals over time, and whether these seemingly different measures are tapping into the same underlying process. This study sought to examine the reliability of, and relationships among, three distinct hierarchical measures previously used in research: standard Navon letters with a traditional interference task (e.g. Navon, 1977), hierarchical shapes with a paper-and-pencil free-choice task (e.g. Fredrickson & Branigan, 2005), and superimposed high and low-pass spatial frequency faces with a free-choice task (e.g. Deruelle et al., 2008). Fifty-five undergraduate participants completed all three global-local tasks, and returned 7–10 days later to again complete the same tasks. The degree of global-local bias within an individual was found to be highly reliable in the hierarchical shape task and the spatial frequency face task. Global interference in the Navon task was also reliable, although to a lesser degree. Interestingly, when the relationships among the three measures of global bias were examined, it was found that none of the measures significantly correlated with each other. Therefore, while these measures do appear to be reliable over time, they may be tapping into distinct aspects of global-local processing.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".