Spatial scrambling in human vision: investigating efficiency for discriminating scrambled letters using convolutional neural networks and confusion matrices
Bibliographic record
Abstract
One limitation in our ability to discriminate different letters would be any spatial disorganization in the projections between different visual areas. This “scrambling” could be a source of a positional noise limiting human performance. In this study, we explored different forms this scrambling could take. Based on the idea that letter identification is supported by an optimal spatial frequency, we used spatially-bandpass letters. We devised a physiologically-inspired decomposition and resynthesis scheme, to generate letters composed of log Gabor wavelets. The form of these wavelets is similar to that of an oriented “simple cell” receptive field. We then introduced two forms of scrambling. The first was scrambling at the input to the "oriented receptive field" stage (subcortical scrambling of the receptive field). The second was scrambling at the output from that stage (scrambling connections to the higher “cortical” stages). We also performed a bandpass noise control condition. To compare against human performance, we simulated the responses of both a template-matching observer (TMO) and three convolutional neural networks (CNNs). The three CNNs were trained on the letter stimuli to perform each of the three noise conditions. We computed human efficiency relative to CNN performance. We also characterized mistakes using confusion matrices and computed the population stability index (PSI) as a distance measure between mistakes made by human and model observers. We found the CNNs employed distinct strategies for each condition. Human relative efficiency was higher for subcortical than cortical scrambling. In bandpass noise, PSIs for both TMO and CNNs were comparable. For our scrambling conditions however, the PSI of TMO was significantly higher than that of CNNs in all but one comparison. Our results suggest that the human strategy for identifying scrambled letters is better captured by CNNs, which may share more similar strategies for identifying scrambled letters than a simple TMO.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".