Bibliographic record
Abstract
Abstract This book explores the nature and mechanisms of consciousness from the perspectives of neuroscience and philosophy. Thagard proposes the NBC (Neural representation, Binding, Coherence, and Competition) theory as a comprehensive explanation for human consciousness. He addresses external perceptions such as smell, internal sensations such as hunger, emotions such as loneliness, and abstract thoughts such as the self. The book explains how complex conscious experiences emerge from the interactions of neural mechanisms. It highlights the integration of neural and cultural factors, showing how consciousness results from both biological processes and social influences. It uses ideas about neural representation and coherence to produce powerful new theories of dreaming, humour, and musical experience. Other applications include religion, morality, sports, romantic chemistry, and drugs. Consciousness has many psychological functions, especially action focus, combining senses with emotions, and increasing social understanding. Chapters also explore awareness of time, consciousness in non-human animals, the feasibility of machine consciousness, and how NBC compares to alternative theories. NBC justifies attributing some kinds of consciousness to advanced animals such as mammals and birds, and maybe even to fish, crabs, and bees; but not to plants, bacteria, or rocks. Thagard’s work bridges the gap between scientific mechanisms and the qualitative nature of experience, offering a new materialist solution to the mind–body problem.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".