The effect of stimulus interruptions on "fast switchers" and "slow switchers": a neural model for bistable perception
Bibliographic record
Abstract
Bistable perception is triggered by a physical stimulation that causes fluctuations between two perceptual interpretations. To date, no physiological mechanism has been causally linked to switching events (Einhauser et al., 2008; Hupé et al., 2008), leaving the neural basis of bistability unclear. External interruptions in the stimulus are known to affect perceptual switching rates: with long offsets, stimulus interruptions stabilize the percept, while short offsets trigger destabilization (Noest et al., 2007). The current study explores the latter phenomenon in a Necker cube presented for 600:1200 ms, 900:900 ms, and 1200:600 ms onset:offset durations. Figure-ground contrast varied between 100%, 50%, 25%, and 12.5%. In the flashing conditions, a 100% contrast cube was presented during the “onset phase”, followed by a lower contrast cube during the “offset phase”. Overall results indicate that destabilization occurs for flashing conditions, though individual results varied. In addition, subjects were evenly split between fast and slow switchers. Slow switchers showed strong biases for one percept, and sensitivity to contrast manipulations. These results suggest a dichotomy between low-level rivalry, of orthogonal orientations for example (Yu et al., 2002), and whole-form perception. Similar patterns have been described in binocular rivalry (Kovacs et al., 1996; Lee & Blake, 1999). Previous research implicates experience (Sakai et al., 1995) and genetic differences (Shannon et al., 2009) to explain why certain individuals experience fast or slow perceptual switching. We discuss our results in the context of noisy neural competition (Marr, 1982; Moreno-Bote et al., 2007). Our neural model makes use of a dynamical system developed by Wilson and Cohen (Wilson, 1999), in which two mutually inhibitory neurons interact. Manipulation of input signal strengths yields broadly similar results to those observed in this psychophysical study, suggesting that input strengths at different levels of processing may explain the divergence between fast and slow switchers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".