Bibliographic record
Abstract
The phenomenon of bistability arises from physical ambiguities in the stimulus that lend themselves to two mutually exclusive interpretations. Many properties have been shown to affect switching rate, including stimulus interruptions (Kornmeier et al., 2007), attention (Meng & Tong, 2004), and eye movements (Ellis & Stark, 1978). However, relatively little research has addressed the role of individual differences. Previous studies have found that subjects fall into two groups: fast switchers and slow switchers (Borsellino et al., 1982). It has been suggested that these differences arise from variations in individual experience with the stimulus (Sakai et al., 1995). In this study, we use a sibling-descendant cascade-correlation neural network (Baluja & Fahlman, 1994; Shultz, 2004) to examine this hypothesis. We trained the network on a set of unambiguous stimuli, then tested it on an ambiguous stimulus, modeled after the Necker cube. Networks with extensive training showed high switching rates, while networks with shorter training regimes showed significantly lower switching rates. In addition, we found that strong positive feedback yielded lower switching rates, while weak positive feedback resulted in higher switching rates. Dynamical models support the latter result, where rivalry depends on a balance between positive self-feedback and mutually inhibitory connections between neural populations (Wilson, 1999). Our model suggests that switching rates may also depend on the underlying neural architecture, which in turn depends on early network training and experience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".