Bibliographic record
Abstract
Abstract Hebb included a version of his neural postulate of learning in his MA thesis (1932). It seems to be a translation of Pavlovian conditioning into neural terms. The version that appears in his book, The Organization of Behavior (1949), speaks of synapses rather than routes, but the idea of simultaneous firing of afferent and efferent elements is common to both versions. The postulate was adopted by groups interested in programming computers to learn and think. It was also one of the hypotheses tested by neurophysiologists in their search for the synaptic mechanism of learning. So far the neurophysiologists have had more success than the logicians; the NMDA synapse appears to explain the main features of Hebb's postulate. In his monograph, The Organization of Behavior, Hebb (1949) presented a theory concerning the way stimuli might be represented in the brain. It was the first such attempt to be widely accepted by psychologists and it has had a strong influence on subsequent theories. Commonsense decrees that brain representations must be learned, at least in the case of representations of the shapes of human artefacts such as the letters of the alphabet, tools, and buildings. The radical empiricism of early 20th century psychology led Hebb to go the whole hog and base his model on the assumption that the neurons of the newborn visual cortex are randomly interconnected. In order to proceed, Hebb then had to specify the conditions under which visual input might organize these connections. In 1932, when Hebb was a part-time graduate student at McGill, he submitted an MA thesis entitled Conditioned and Unconditioned Reflexes and Inhibition. The gist of the thesis is that spinal reflexes are the result of prenatal Pavlovian conditioning; in his more mature years Hebb referred to it as nonsense (Hebb, 1980). The McGill library had, in the meantime, lost its copy of the thesis, so Hebb's evaluation could not readily be verified. Recently, however, Professor Richard Brown of Dalhousie University, in connection with a presentation he made to the Society of Neuroscience, tracked down another copy of the thesis. The part of it dealing with the ontogeny of spinal reflexes may well be nonsense, as Hebb maintained, but Brown (2001, 2002) made the interesting discovery that the thesis includes an analysis of the neural learning mechanism underlying Pavlovian conditioning that foreshadows the one he later presented to explain the learning of visual representations. Based on the observations of Pavlov and others that a stimulus occurring repeatedly at about the same time as a response, acquires a connection to that response, Hebb concluded that: An excited neuron tends to decrease its discharge to inactive neurons, and to increase this discharge to any active neuron, and therefore to form a route to it, whether there are intervening neurons between the two or not. With repetition this tendency is prepotent in the formation of neural routes. (Hebb, 1932, p. 8) An accompanying diagram makes it clear that this postulate was indeed a transposition of Pavlov's conditioning paradigm to the neural level. When, some dozen or so years later, Hebb needed to specify the neural learning mechanism responsible for the acquisition of shape representations in the brain, it seems that he consciously or unconsciously returned to the formulation in his thesis. The neurological postulate was presented in The Organization of Behavior as follows: Let us assume then that the persistence or repetition of a reverberatory activity (or trace) tends to induce lasting cellular changes that add to its stability. The assumption can be precisely stated as follows: When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes a part in in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased. …
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".