Bibliographic record
Abstract
Is attention necessary for phenomenal representation?Can a cognitive system phenomenally represent a stimulus without first paying attention to the stimulus?Within the literature on phenomenality and attention, one may identify two general camps.One camp argues for a negative answer and cites inattentional blindness studies as evidence.The second camp argues the opposite: that attention is unnecessary for phenomenal representation.Researchers in this camp cite studies using natural scenes and masked primes as evidence.However, there are some issues with the evidence for either position.The crucial issue is that researchers cannot guarantee their studies use tasks that fully occupy attention.Some attention might spill over from a distracting task to a critical image, which would mean that their studies do not do what they claim.As a result, a gap exists in our understanding of attention and phenomenal representation.This dissertation aims to close that gap by not only arguing that attention is unnecessary for phenomenal representation but also offering an alternative cognitive basis to ground phenomenal representation in cognition and representation called quality space cognition.I use evidence taken from the relevant literature and argue using traditional philosophical methods.Chapter 1 defines phenomenal representation and contrasts it with other forms of representation.A representation is phenomenal, I argue, when it represents a stimulus using traditional phenomenal properties and the relations among them so that the stimulus appears to the system as an object.Chapter 2 surveys the literature on attention and phenomenality and argues that our current understanding is inconclusive.iii Chapter 3 argues that attention is unnecessary for phenomenal representation and uses evidence from attention disorders, such as unilateral neglect, aphantasia, and phobias.Chapter 4 grounds pre-attentive phenomenal representation in a quality space, a model of cognition for the qualitative parts of phenomenal representation.The concluding chapter, Chapter 5, introduces some future empirical work to continue the research.In summation, I argue cognitive systems can phenomenally represent stimuli without first paying attention to the stimuli.Furthermore, the quality space approach models the cognitive and representational basis of phenomenal representation without attentional cognition.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.024 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".