Bibliographic record
Abstract
Brain diseases and their treatment may help or hurt creativity in ways that shape quality of life. Increased creative drive is associated with bipolar disorder, depression, psychosis, temporal lobe epilepsy, frontotemporal dementia, Parkinson disease treatments, and autism. Creativity depends on goal-driven approach motivation from midbrain dopaminergic systems. Fear-driven avoidance motivation is of less aid to creativity. When serotonin and norepinephrine lower motivation and flexible behaviour, they can inhibit creativity. Hemispheric lateralization and frontotemporal connections must interact to create new ideas and conceptual schemes. The right brain and temporal lobe contribute skill in novelty detection, while the left brain and frontal lobe foster approach motivation and more easily generate new patterns of action from the novel perceptions. Genes and phenotypes that increase plasticity and creativity in tolerant environments with relaxed selection pressure may confer risk in rigorous environments. Few papers substantively address this important but fraught topic. Antidepressants (ADs) that inhibit fear-driven motivation, such as selective serotonin reuptake inhibitors, sometimes inhibit goal-oriented motivation as well. ADs that boost goal-directed motivation, such as bupropion, may remediate this effect. Benzodiazepines and alcohol may be counterproductive. Although dopaminergic agonists sometimes stimulate creativity, their doing so may inappropriately disinhibit behaviour. Dopamine antagonists may suppress creative motivation; lithium and anticonvulsant mood stabilizers may do so less. Physical exercise and REM sleep may help creativity. Art therapy and psychotherapy are not well studied. Preserving creative motivation can help creativity and other aspects of well-being in all patients, not just artists or researchers.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".