Bibliographic record
Abstract
How strongly do we associate “God” and “Devil” with our physical world? Humans have long used spatial metaphors for abstract concepts of the divine, ranging from Mt. Olympus and the underground Hades in ancient Greece to the current conceptions of Heaven and Hell. Such metaphors are useful as they provide a common metric, physical space, to which abstract information can be bounded and communicated to other people. Indeed, such spatial metaphors are so pervasive in divine concepts that many religious and cultural traditions have representations in either or both vertical and horizontal space. Given the reliance on spatial metaphors in concepts of the divine, it is possible that merely thinking of concepts of God or Devil might invoke brain activity associated with the processing of spatial information and orient people's attention to associated locations. To examine if exposure to divine concepts shifts visual attention, participants completed a target detection task in which they were first presented with God and Devil-related words. We found faster RTs when targets appeared at locations compatible with the concepts of God (up/right locations) or Devil (down/left locations), and also found that these results do not vary by participants' religiosity. These results demonstrate that even a highly abstract concept such as God can lead individuals to orient their attention to spatially compatible locations. These findings provide further evidence that the traditional view of exogenous and endogenous attentional processes may not be adequate, as divine concepts generated involuntary shifts of attention without any corresponding peripheral events. Moreover, these results add further support to the notion that abstract concepts like the divine rely on metaphors that contain strong spatial components.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".