Bibliographic record
Abstract
Many cultures have long been instilled with positive and negative concepts, which are associated with spatial metaphors, for instance,“I am feeling down” implies that I feel upset. Recent research (Chasteen, Burdzy & Pratt, 2009; Meier & Robinson, 2005) has suggested that some concepts are strongly ingrained such that they influence how we attend to the environment. In particular, certain positive concepts such as ‘almighty’ and ‘happiness’ bias attention upward and to the right, respectively and negative words‘lucifer’ and ‘mournful’ to the downward and to the left, respectively. Using a larger variety of positive and negative concepts than in previous studies, the current study seeks to determine whether concepts derived from pictures as well as words will produce a shift in attention. We present participants with words or pictures depicting either positive (e.g., smiling baby) or negative concepts (e.g., drug addict). We expect that a target displayed at the top or right‐side of the screen will be detected more quickly for positive concepts and targets displayed at the bottom or left‐side of the screen will be faster for negative. However, whether the response times to targets in the valid conditions will be quicker for words than pictures is unknown. Words could be faster than pictures because previous research has demonstrated that pictures can access concepts in our minds directly, whereas words access indirectly. On the other hand, another theory suggests that both words and pictures access concepts directly; therefore, there may be no difference between pictures and words.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".