Complexity Level Analysis Revisited: What Can 30 Years of Hindsight Tell Us about How the Brain Might Represent Visual Information?
Bibliographic record
Abstract
In a series of papers spanning 1987-2012, we examined the inherent computational difficulty of visual information processing using theoretical and empirical methods. The main goal of this activity had three components: to understand the deep nature of the computational problem of visual information processing; to discover how well the computational difficulty of vision matches to the fixed resources of biological seeing systems; and, to abstract from the matching exercise the key principles that lead to the observed characteristics of biological visual performance. The problem is clearly of interest to each of the communities studying vision, namely machine vision, neuroscience, psychology, cognitive science, artificial intelligence and robotics. This paper revisits those principles with the advantage that decades of hindsight can provide. It is clear, for example, that the current leading computational approaches to machine vision that employ deep learning have achieved their success in part due to conforming to the results of that analysis. It is also apparent that the problem of signal interference within a hierarchical network is significant and requires deeper examination. In order to deal with complexity issues and signal interference, we assert that the generality that human vision - and likely intelligence in general - exhibits is enabled by dynamic tuning of the brain's neural machinery based on task, environment and moment-by-moment requirements, and this is what attention accomplishes. And in order for this to occur, the underlying representations must be of a particular form: hierarchical, space and time limited, pyramidal, bidirectional, and dynamically tunable by task and context, which further requires each tunable representation to be semantically transparent.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.024 |
| Scholarly communication | 0.008 | 0.029 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".