Bibliographic record
Abstract
It has been suggested that processing concepts with either prototypical spatial information (e.g., hat vs. shoes) or metaphoric-spatial associations (e.g., god vs. devil) engages visual-attentional mechanisms, orienting attention toward regions of the visual field congruent with concept meaning. Interestingly, both facilitatory (Chasteen et al., 2010) and inhibitory (Estes et al., 2008) effects have been reported as consequences of these shifts of attention. Here we examine two possible causes of this discrepancy. One possibility relates to the nature of the task; tasks requiring target detection may receive facilitation from processing congruent concepts while tasks requiring target discrimination may receive inhibitory effects. A second possibility relates to the nature of the concepts that cue attention; abstract concepts (e.g., god, devil) may invoke facilitatory processes, while concrete concepts (e.g., hat, shoes) invoke inhibitory processes. In Experiment 1, a single word at fixation, either an abstract or concrete concept, preceded a peripheral target (above or below fixation) and subjects were asked to detect the targets as quickly as possible. In Experiment 2, the same procedure was used except that subjects were asked to identify the targets as quickly as possible. To ensure semantic processing of the words, subjects were asked to respond only on trials when the word belonged to a pre-specified category (e.g., divine words). Opposite patterns of results were found across the two concept types: for abstract words, responses were faster in both tasks when target location and word meaning were compatible relative to when they were incompatible. This pattern was reversed for concrete concepts, with faster responses during incompatible trials relative to compatible trials. It appears that the nature of concepts underlies the qualitatively different attentional effects previously reported.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".