Quantum affective processes for multidimensional decision-making
Bibliographic record
Abstract
In modeling the human affective system and applying lessons learned to human-robot interaction, the challenge is to handle ambiguous emotional states of an agency (whether human or artificial), probabilistic decisions, and freedom of choice in affective and behavioral patterns. Moreover, many cognitive processes seem to run in parallel whereas seriality is the standard in conventional computation. Representation of contextual aspects of behavior and processes and of self-directed neuroplasticity are still wanted and so we attempt a quantum-computational construction of robot affect, which theoretically should be able to account for indefinite and ambiguous states as well as parallelism. Our Quantum Coppélia (Q-Coppélia) is a translation into quantum logics of the fuzzy-based Silicon Coppélia system, which simulates the progression of a robot's attitude towards its user. We show the entire circuitry of the Q-Coppélia framework, aiming at contemporary descriptions of (neuro)psychological processes. Arguably, our work provides a system for simulating and handling affective interactions among various agencies from an understanding of the relations between quantum algorithms and the fundamental nature of psychology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".