Object substitution without reentry?
Bibliographic record
Abstract
G. Francis and F. Hermens (2002) used computer simulations to claim that many current models of metacontrast masking can account for the findings of V. Di Lollo, J. T. Enns, and R. A. Rensink (2000). They also claimed that notions of reentrant processing are not necessary because all of V. Di Lollo et al. 's data can be explained by feed-forward models. The authors show that G. Francis and F. Hermens's claims are vitiated by inappropriate modeling of attention and by ignoring important aspects of V. Di Lollo et al. 's results. We note with interest Francis and Hermens's (2002) article, which purports to show that the findings reported by Di Lollo, Enns, and Rensink (2000) can be explained by other models of metacontrast masking. To buttress their claim, Francis and Hermens reported computer simulations showing that some of our results can be modeled by the theories of Bridgeman (1978), Francis (2000) and Weisstein (1968). This claim has a good deal of surface appeal because it is parsimonious. It argues that our results can be explained without recourse to the new concept of object substitution. Parsimony, however, is achieved at the cost of inappropriate modeling of attention and modeling an incomplete portion of our masking data. Here, we reiterate our original claim that reentrant modeling is necessary for explaining our findings. We do so by showing that a plausible case for the sufficiency of feed-forward processes has not been made by Francis and Hermens. Modeling of Attention Modeling of the effects of attention in Di Lollo et al. 's (2000) study was based on the large literature on set-size effects in visual perception (Duncan & Humphreys, 1989; Eriksen,1995;
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".