MétaCan
Menu
Back to cohort
Record W3083014912 · doi:10.3758/s13414-020-02106-y

Influences of luminance contrast and ambient lighting on visual context learning and retrieval

2020· article· en· W3083014912 on OpenAlexaff
Xuelian Zang, Lingyun Huang, Xiuna Zhu, Hermann J. Müller, Zhuanghua Shi

Bibliographic record

VenueAttention Perception & Psychophysics · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersLudwig-Maximilians-Universität München
KeywordsMesopic visionPhotopic visionContrast (vision)LuminanceVisual searchFacilitationPsychologyContext (archaeology)Cognitive psychologyCommunicationComputer visionArtificial intelligenceComputer scienceNeuroscienceGeography

Abstract

fetched live from OpenAlex

Invariant spatial context can guide attention and facilitate visual search, an effect referred to as "contextual cueing." Most previous studies on contextual cueing were conducted under conditions of photopic vision and high search item to background luminance contrast, leaving open the question whether the learning and/or retrieval of context cues depends on luminance contrast and ambient lighting. Given this, we conducted three experiments (each contains two subexperiments) to compare contextual cueing under different combinations of luminance contrast (high/low) and ambient lighting (photopic/mesopic). With high-contrast displays, we found robust contextual cueing in both photopic and mesopic environments, but the acquired contextual cueing could not be transferred when the display contrast changed from high to low in the photopic environment. By contrast, with low-contrast displays, contextual facilitation manifested only in mesopic vision, and the acquired cues remained effective following a switch to high-contrast displays. This pattern suggests that, with low display contrast, contextual cueing benefited from a more global search mode, aided by the activation of the peripheral rod system in mesopic vision, but was impeded by a more local, fovea-centered search mode in photopic vision.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.325
Teacher spread0.286 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations14
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAttention Perception & PsychophysicsSame topicVisual perception and processing mechanismsFrench-language works237,207