26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1
Bibliographic record
Abstract
Perception seems so simple.I look out of the window to see houses, trees, people walking past, the sky above, the grass below.I hear birds in the trees, cars going past, the distant sound of an alarm.The world is full of objects that make their presence known to me through my senses -what could be more simple?Yet the efficacy of perceptual experience hides a host of questions for which we do not yet have the answers.Information reaching our senses is generally incomplete, ambiguous, distributed in space and time and not neatly sorted according to its source, so a key function of our perceptual systems is to discover the likely causes of our sensations.Perception as inference or hypothesis testing, formalised in the predictive coding theory, offers an attractive framework for exploring these issues.From this perspective, regularities or patterns provide perceptual systems with some traction, allowing the formation of expectations and a basis for decomposing the world into discrete objects.But in the dynamic world which we inhabit, object representations must be similarly dynamic, and need to form and dissolve, dominate and yield, in a way that facilitates veridical perception.In this talk I will discuss auditory scene analysis in the context of predictive coding using experimental data, exemplar models, and the phenomenon of perceptual multistability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.149 | 0.081 |
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".