The functions of consciousness in visual processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Conscious experiences form a relatively diverse class of psychological phenomena, supported by a range of distinct neurobiological mechanisms. This diversity suggests that consciousness occupies a variety of different functional roles across different task domains, individuals, and species; a position I call functional pluralism. In this paper, I begin to tease out some of the functional contributions that consciousness makes to (human) visual processing. Consolidating research from across the cognitive sciences, I discuss semantic and spatiotemporal processing as specific points of comparison between the functional capabilities of the visual system in the presence and absence of conscious awareness. I argue that consciousness contributes a cluster of functions to visual processing; facilitating, among other things, (i) increased capacities for semantically processing informationally complex visual stimuli, (ii) increased spatiotemporal precision, and (iii) increased capacities for representational integration over large spatiotemporal intervals. This sort of analysis should ultimately yield a plurality of functional markers that can be used to guide future research in the philosophy and science of consciousness, some of which are not captured by popular theoretical frameworks like global workspace theory and information integration theory.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it