Bibliographic record
Abstract
Abstract Attention, the awareness and selection of elements in our physical or mental environments, is a central concept in neuroscience. Michael I Posner and colleagues have proposed a three‐network model of attention. ‘Alerting’ involves increased readiness for immanent stimuli, ‘orienting’ refers to selecting amid various stimuli; whereas ‘executive attention’ links attention to decision making, planning and other higher cognitive functions. Though ignorant of the neural mechanisms underlying human attention, magicians are skilled at exploiting human attention to achieve their effects. Recent interest in the neuroscience of magic has built bridges between the practice of magic and the study of attention. However, beyond illustrating how our attention systems can be tricked, magic can be employed in research to explore otherwise unachievable conditions. Such methods provide a unique opportunity to study atypical attention, providing important insights into the function of human attention and other key cognitive domains. Key Concepts Attention refers to the preparedness for and selection of particular aspects of our environment or of ideas in our mind. Attention can be overt, that is tied to fixation, or covert, like when we attend to something we are not looking at. There are three attentional networks – alerting, orienting and executive – each with distinct neural correlates. Magicians exploit change blindness, inattentional blindness and choice blindness to achieve many effects. Neuroscientists have recruited magic as a tool for uncovering the nature of many cognitive processes, most notably attention. Pushing healthy individuals towards atypical attention – via hypnosis, deception and other methods – introduces unique experimental opportunities. Aside from helping direct investigations of attention, magic tricks can be effectively incorporated into certain experimental designs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".