Bibliographic record
Abstract
At the heart of creativity is the forging of new concept combinations and the adapting of existing ideas to new situations. However, these processes have resisted mathematical description; concepts violate the rules of classical logic when they interact, e.g., concept combinations can exhibit emergent features not possessed by their constituent concepts. These challenges can be addressed using the quantum cognition framework, wherein nonclassical behavior is described in terms of superposition, entanglement, and interference. While in classical probability theory events are drawn from a common sample space, in quantum models events are defined only with respect to a measurement, or (in quantum cognition) a context, and the probabilities reflect the underlying reality. The measurement (or context) causes collapse from a superposition state to a definite eigenstate. The paper explains how creativity can be modelled using quantum cognition approach with an illustrative example, and discuss how the approach could be implemented computationally. Quantum computing is widely expected to revolutionize many fields in the near future through immense increases in speed and computing power. The time may be ripe to explore the potential of quantum computational creativity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".