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Record W3046878773 · doi:10.1167/jov.20.7.31

Phase integration bias in a motion grouping task

2020· article· en· W3046878773 on OpenAlexaff
Jessica Cali, Patrick Bennett, Allison B. Sekuler

Bibliographic record

VenueJournal of Vision · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsBaycrest HospitalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsTask (project management)Phase (matter)Motion (physics)Computer scienceCognitive psychologyArtificial intelligencePsychologyPhysicsEconomics

Abstract

fetched live from OpenAlex

The perception of the direction of global motion depends on our ability to integrate local motion signals over space and time. We examined motion binding using a task requiring integration of relative phase. Observers completed multiple tasks involving clockwise and counter clockwise motion in a stimulus comprising four sets of linearly arranged dots, two moving horizontally and two moving vertically along sinusoidal trajectories differing in phase. Noise jitter was added along the trajectory perpendicular to each dot's motion. The noise acts as a global grouping cue that improves direction discrimination, but surprisingly, the absence of noise causes consistent below-chance performance (Lorenceau, 1996). We explore this phenomenon and subsequently test the hypothesis that observers perceive reverse motion because their representation of the relative phase of the motion components is systematically biased. We employ a number of different objective and subjective measures of motion integration and measure the phenomenon in both younger and older adults. Taken together, the results presented in the current article demonstrate that noise can promote global grouping in the stimulus and that confident, incorrect responses can be observed in the absence of correct global grouping. Generally, the current result raises the possibility that an integration bias could exist in other motion tasks.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.161
GPT teacher head0.407
Teacher spread0.246 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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