Power spectrum classification image analysis reveals localized mechanisms underlying nonlinear detection of narrowband stimuli
Bibliographic record
Abstract
Prior measurements of detection thresholds for gratings in noise have suggested that detection may be based upon linear cross-correlation with relatively small broadband filters (Kersten, 1984). Recent direct measurements of spatial summation using a classification image technique have yielded contrary results, showing broad spatial summation over many cycles of the narrowband stimulus (Morgenstern & Elder, 2005). Here we report a computational model that partially resolves this contradiction. We show that under the standard linear cross-correlator model, classification images derived from signal-present trials yield broad summation fields locked to signal frequency and phase. However, classification images derived from signal-absent trials reveal no peak at signal frequency, indicating that the linear detection model does not apply (Ahumada & Beard, 1999). We test a number of alternative models, and show that an energy model based on broad spatial pooling of responses from local broadband mechanisms is most consistent with the human data. We show that this model is linear in the power spectrum domain, and introduce a novel classification image analysis technique that allows direct estimation of the two-dimensional bandpass transfer function of the underlying local mechanisms. These mechanisms are found to be highly localized in space, quantitatively similar in their spatial and spatial-frequency properties to neurons in early visual cortex of primate, and qualitatively similar to the broadband mechanisms inferred indirectly by Kersten (1984).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".