Bibliographic record
Abstract
Very simple psychophysiological visual tests suggest that the brain, instead of processing visual information in a passive way as was classically thought, in fact actively evaluates probabilities of the causes of visual data and continuously proposes to the mind the ones that are more likely to account for sensory inputs. In the past few years, Karl Friston, a researcher from University College of London, and his group have proposed a mechanism by which the brain successfully performs with great precision the inversion of probability densities necessary for this Bayesian computation. This mechanism would account for several anatomic structures of the cortex, explaining in particular the abundance of backwards interneuronal connections. The proposed picture of brain functioning is that of a dynamical process, far from the static image of a photographic plate. The result is an emergence, for the final picture of the world is a coherent vision where the more likely causes are proposed in a coherent manner. Although the theory accounts for the automatic, infraconscious side of the processing of information in the brain, it is in good accord with Roger Sperry’s theory of consciousness as a theory of strong emergence. It is too soon to evaluate the solidity of the law of “minimization of free energy” proposed by Friston not only as ruling the automatisms of the brain but as a general law of biology. This law is similar (although in contradistinction) to the second law of thermodynamics of increase of entropy (insofar as it explains the tendency of living beings for self-organization), and it is already looked at by some neuroscientists as a big step forward in deciphering the mysteries of the brain.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".