Isolating the top-down component of perceptual learning
Bibliographic record
Abstract
Perceptual learning (i.e. an improvement in performance in a perceptual task following practice) is known to be driven by low-level, “bottom-up” processes (e.g. Crist, Li & Gilbert, 2001; Gold, Bennett & Sekuler, 1999, Karni & Sagi, 1991). It has even been shown to occur in the absence of high-level, presumably “top-down” factors such as awareness (Watanabe, Nañez & Sasaki, 2001, 2002). Several experiments, however, suggest that “top-down” processes can modulate perceptual learning to a certain extent (e.g. Shiu & Pashler, 1992; Ahissar & Hochstein, 1993; Ito, Westheimer & Gilbert, 1998). Last year, we tried to isolate “top-down” processes in perceptual learning; unfortunately, the results of our experiment were ambiguous (Dupuis-Roy & Gosselin, 2003). Here, we report a better experiment based on the same logic. Four participants were submitted to 36 sessions of testing over a period of about two months. A session consisted in 250 trials; in addition, two 1/f2 target textures (T1 and T2) were presented twice per session (exposure to T1 and T2 was identical and minimal). On each trial, subjects had to indicate which one of two white Gaussian noise fields was more similar to either T1 or T2. Trials thus contained no “bottom-up” signal (Gosselin & Schyns, 2003). Crucially, the experiment comprised 24 times more T1- than T2-trials. The only difference between T1- and T2-trials was thus “top-down” practice. We compared the percentage of agreement between our human observers and an ideal observer over the logarithm of blocks of 10 successive trials. The slope of the best linear fit is significantly (p < .01) greater for T1 (R2 = 8.69%, slope = .0145) than for T2 (R2 = 5.98%, slope = −.0148). Therefore, perceptual learning can occur in the absence of “bottom-up” signal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".