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Record W4307283378 · doi:10.1163/22134468-bja10065

Discrimination of Brief Empty Time Intervals when the First Marker Is Tactile

2022· article· en· W4307283378 on OpenAlexaff
Leila Azari, Joanie Drouin, G. Plante, Simon Grondin

Bibliographic record

VenueTiming & Time Perception · 2022
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAudiologyBisectionTime perceptionModality (human–computer interaction)Duration (music)PsychologyStimulus modalitySensory systemMathematicsPerceptionCognitive psychologyMedicineAcousticsComputer scienceArtificial intelligencePhysicsNeuroscience

Abstract

fetched live from OpenAlex

Abstract The present study investigated the discrimination of brief empty time intervals and compared three different sensory modality conditions for marking time: when two successive signals are tactile (intramodal condition, TT), and when a first tactile signal is followed by a either a visual (V) or an auditory (A) signal (intermodal conditions: TV and TA). Twelve participants completed a bisection task over the course of eight experimental sessions. Three factors were manipulated: the marker modality (TT, TV, and TA), the duration range (300 and 900 ms), and the preparation (certainty, uncertainty). In the latter case, participants knew that the first marker was T, but were uncertain about the modality of the second marker. In general, TT intervals were better discriminated than intermodal intervals at 300 ms, but this effect fades at 900 ms. No difference in the discrimination level was observed between the intermodal conditions. For the perceived duration, there was no difference between TT and TA conditions, but the TV intervals were perceived as longer than the TT intervals. As well, the TV intervals were perceived as longer than the TA intervals. These results are interpreted in terms of attentional and sensory effects caused by the signals themselves (onset and alerting properties, prior entry).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.4960.007

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 head0.047
GPT teacher head0.320
Teacher spread0.273 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations6
Published2022
Admission routes1
Has abstractyes

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