Bibliographic record
Abstract
Are we conscious of more than what's in the “spotlight” of attention, or is consciousness limited to the content of attention? Recently several authors (DeBrigard & Prinz 2011; Prinz 2010; Dennett & Cohen 2012) have defended the view that attention to some object is necessary for conscious perception of that object. For each of these authors, attention acts like more than just a “spotlight on a stage.” But none of them provides a robust account of this new way of attending. My project offers a new theory of diffuse attention that explains the apparent richness of experience. Accepting that there is a diffuse way of attending requires us to abandon the notion of attention as a spotlight. On the view that I offer, attention has degrees. For example, when looking at a landscape, your attention is spread over a broad spatial area and details are more difficult to remember or describe than when you focus attention in greater depth on some object within that landscape. A broad and shallow diffusion of attention nonetheless makes its object available for guiding thought and action, and so should be considered a way of attending rather than merely being conscious. After defending a theory of diffuse attention, I offer a new argument for the view that attention is necessary for conscious perception. My argument is motivated by the phenomenological observation that ordinary perceptual experience has a structure: some objects are in the foreground of experience, while others are in the background. I motivate the claim that this foreground/background structure is necessary for perceptual experience, and argue that focal and diffuse attention provide the foreground/background structure. I conclude that attention is necessary for perceptual experience, since it provides a necessary structure of experience. In making this argument, I draw on phenomenological insight into the structure of consciousness from James (1890), Gurwitsch (1964; 1966) and C.O. Evans (1970). For each of these authors, attention structures the foreground – but not the background – of consciousness. My novel contribution is to provide an account of how attention structures the conscious background. By enriching the concept of attention to include diffuse attention, my account is poised to explain the structure of conscious experience from foreground to background.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".