Bibliographic record
Abstract
A scene is the major visual input for real-world perception, consisting of various complex features that assist numerous actions and cognitive processes we routinely perform in daily life. Conventionally, researchers assumed that scenes are processed in a multi-dimensional representational space where each dimension corresponds to a feature relevant to perceiving scenes. Previous studies identified and listed critical features that comprise such a space with rich behavioural and neural evidence. However, this dissertation demonstrates that such an approach that manually identifies scene features is limited in its ability to understand the mechanism of scene representation. Instead, I propose a different type of multi-dimensional space that compresses the regularity of perceptual experiences a perceiver accumulates throughout their life. To empirically assess this space, I introduce a novel stimulus generation tool utilizing generative adversarial networks (GANs), deep generative networks that compress the regularity of a large training dataset into a relatively lower-dimensional probability distribution. In Chapter 2, I explained how to create various stimulus sets composed of continuously transitioning real-world scenes, by sophisticatedly sampling the datapoints from the distribution. In Chapter 3, I validated the usage of this novel stimulus generation tool in psychophysics, especially for perceptual similarity rating and continuous memory reconstruction paradigms. In Chapters 4 and 5, I utilized this stimulus set to investigate the nature of the meaningful scene features emerging from the probability space. Lastly, in Chapter 6, I explored the neural mapping for the fine-grained similarity spaces corresponding to the stimulus spaces created from GANs. The GAN-based stimulus generation tool provides the pathway to achieve the controllability and ecological validity of real-world psychophysics. The findings leveraging this tool contribute to understanding the mechanisms of how to adaptively organize the multidimensional space for real-world scene representations.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".