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Record W2574374661

Reconciling Competing Accounts of Picture Perception from Art Theory and Perceptual Psychology via the Dual Route Hypothesis.

2011· article· en· W2574374661 on OpenAlexaboutno aff
Peter Coppin

Bibliographic record

VenueCognitive Science · 2011
Typearticle
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionDual (grammatical number)Cognitive scienceVisual perceptionPsychologyCognitive psychologyExperimental psychologyCognitionLinguisticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Reconciling Competing Accounts of Picture Perception from Art Theory and Perceptual Psychology via the Dual Route Hypothesis Coppin Peter The University of Toronto Abstract: The fine and applied visual arts and perceptual psychology use conflicting accounts of picture perception. In the arts, the human ability to perceive pictured objects is characterized as learned, or conventionalized, like a ”visual language” (Gombrich, 1960; Goodman, 1976; Kulvicki, 2010). In perceptual psychology, picture perception is characterized as an unlearned, biologically grounded, ability. In this account, optical properties of light produced by pictures make use of biologically evolved capabilities to perceive surfaces and edges in actual environments (J. J. Gibson, 1978; Kennedy, 1974; Lee et al., 1980; Hammad et al., 2008). The purpose of this paper is to reconcile these competing claims through Goodale et al.’s (2005) dual route hypothesis. It includes a role for learning and memory in visual processing via the ventrally located ”what/how” stream, in addition to a role for visual processes that rely less on memory and learning, via the dorsally located ”what” stream.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.011
Scholarly communication0.0060.017
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.086
GPT teacher head0.309
Teacher spread0.222 · 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 designTheoretical or conceptual
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

Citations3
Published2011
Admission routes1
Has abstractyes

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