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Record W2734549751 · doi:10.1111/mila.12148

Attention and Mental Primer

2017· article· en· W2734549751 on OpenAlexaff
Jacob Beck, Keith A. Schneider

Bibliographic record

VenueMind & Language · 2017
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
Fundersnot available
KeywordsPremiseSalience (neuroscience)Argument (complex analysis)EpistemologyRealismPsychologyMental representationDirect and indirect realismPerceptionSocial psychologyPhilosophyCognitive psychologyCognition

Abstract

fetched live from OpenAlex

Abstract Drawing on the empirical premise that attention makes objects look more intense (bigger, faster, higher in contrast), Ned Block has argued for mental paint, a phenomenal residue that cannot be reduced to what is perceived or represented. If sound, Block's argument would undermine direct realism and representationism, two widely held views about the nature of conscious perception. We argue that Block's argument fails because the empirical premise it is based upon is false. Attending to an object alters its salience, but not its perceived intensity. We also argue that salience should be equated with mental primer, a close cousin of mental paint that reintroduces difficulties for direct realism and representationism. The upshot is that direct realism and representationism are still in trouble, but not for the reason that Block thinks.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.007
Scholarly communication0.0040.005
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.049
GPT teacher head0.356
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2017
Admission routes1
Has abstractyes

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