Top-down Approach to the Investigation of the Neural Basis of Geometric-optical Illusions: Understanding the Brain as a Theoretical Entity
Bibliographic record
Abstract
: Geometric-optical illusions have been the subjects of research interest in a number of disciplines in science. Moreover, investigation of the patients’ reactions to illusory configurations has been somewhat instrumental in the understanding of impaired neuro-cognitive processes underlying some of the neurological and/or psychiatric disorders. Recently, neuroscientists have made some progress in understanding the neural underpinning of the geometric-optical illusions. However, a closer collaboration between psychology and neuroscience may lead to a better understanding of not only the neural basis of the illusions but the function of the brain in general. The purpose of the present analysis is to outline a sound epistemological ground for such a relationship and to demonstrate how psychological theories may potentially play a guiding role in the context of scientific discoveries in the neuroscience of illusory phenomena. In order to do so, two concepts of the “many-one” relationship between the mental and the neural states and “context-sensitivity” will be described with regard to the possible relationships between perception and brain in the context of research on illusions. In addition, the implications of the top-down strategy for research in Psychiatry will be explained and the strategy will be discussed as a path towards the unification of scientific explanations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".