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Record W2036602990 · doi:10.1167/10.7.882

Learning arbitrary visuoauditory mappings during interception of moving targets

2010· article· en· W2036602990 on OpenAlexaff
Thomas A. Reh, Joost C. Dessing, J. Douglas Crawford, Frank Bremmer

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsYork University
Fundersnot available
KeywordsFixation (population genetics)InterceptionComputer scienceArtificial intelligenceComputer visionCommunicationAcousticsPhysicsPsychologyMedicine

Abstract

fetched live from OpenAlex

The brain represents multisensory mappings relevant for interaction with the world. These mappings mostly involve intrinsically relevant signals, such as vision and proprioception of the hand in reaching. Here, we studied how more arbitrary maps are learned. The employed visuoauditory map coupled visual target position to the pitch of an accompanying sound. Our participants thus had to reach to intercept a moving target. The pitch of the accompanying sound was a function of target position either on the screen or relative to the fixation direction (in different subsets of participants, n = 5 for both, so far), which was also varied in the experiment. Participants sat in front of a monitor with their heads immobilized by a bite-bar. Targets appeared on a variety of positions and moved with a variety of velocities (left or right). After 500 ms the fixation point changed size and color, indicating that the reaching movement could be initiated. Our design involved a pre-test (intercepting visual targets), a learning phase (intercepting visual and audible targets, while the duration of target visibility was progressively reduced), and a testing phase (intercepting audible targets). Finger position at the moment of contact with the screen was determined using Optotrak, and fixation quality was assessed using EyeLink II. Participants in both groups could perform the task reasonably: even for the audible targets the pointing positions were significantly correlated with the target position at interception. We are currently analyzing the pointing errors within subjects as a function of fixation direction, initial target position and target velocity. This will provide a general idea of factors playing into the control of interception. More importantly, however, we will test the effect of mapping (screen versus gaze-centered) between participants, in order to examine whether the arbitrary mapping was better represented in screen-(/world-) or gaze-centered coordinates.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0010.000

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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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 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

Citations0
Published2010
Admission routes1
Has abstractyes

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