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Record W1982078292 · doi:10.1167/13.9.1227

Transsaccadic memory for multiple features

2013· article· en· W1982078292 on OpenAlexaff
Amira Sayed Khan, K. YoungWook, Yunju Nam, Gunnar Blohm

Bibliographic record

VenueJournal of Vision · 2013
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsQueen's University
Fundersnot available
KeywordsSaccadeSaccadic maskingEye movementFixation (population genetics)PsychologyMemorizationMeridian (astronomy)ClockwiseCommunicationCognitive psychologyArtificial intelligenceComputer scienceComputer visionMedicine

Abstract

fetched live from OpenAlex

Transaccadic integration refers to the integration of information across saccadic eye movements, considered to be crucial for spatial constancy. Accurate integration requires two components, 1) memorization of the object features and 2) updating of these features across eye movements, i.e. we need to remember where an object was and it’s features at that location. However, not much is known about how we remember multiple features across eye movements. Here, we tested how accurately participants remembered multiple features of an object across saccadic eye movements. We asked seven subjects to compare two bars, each varying in location (1.6° left of center to 1.6°right in 0.4° intervals on the horizontal meridian), orientation (5° counterclockwise to 5° clockwise in 2° intervals) and size (1.8° to 2.3° in 0.1° increments). Both bars were viewed peripherally and sequentially with an intervening delay during which they either remained fixated or made a saccade to the opposite side. Participants reported how the second bar was different from the first for 1) all three attributes in each trial or 2) only one attribute within a block of trials. We found that remembering three attributes increased uncertainty about each attribute for all three features (p<0.01). Participants were most uncertain about the bar location following a saccade compared to when they remained fixated (p<0.01), mostly resulting from a bias in remembering the first bar to be closer to final fixation after the saccade. The intervening saccade also degraded the certainty of the orientation of the bar (p<0.01) and induced a reduction in the remembered size of the first bar (p<0.05). Based on the findings, we conclude that updating of objects across saccades introduces uncertainty in their remembered attributes. When more attributes required memorization, uncertainty increased which points towards memory interactions between different visual features and saccadic eye movements. Meeting abstract presented at VSS 2013

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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 designObservational
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

Citations2
Published2013
Admission routes1
Has abstractyes

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