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The Ventriloquist Effect is not Consistently Affected by Stimulus Realism

2021· article· en· W3199464750 on OpenAlexaff
Thirsa Huisman, Torsten Dau, Tobias Piechowiak, Ewen MacDonald

Bibliographic record

VenueJournal of Perceptual Imaging · 2021
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStimulus (psychology)PsychologyRealismCognitive psychologyPhilosophyEpistemology

Abstract

fetched live from OpenAlex

Despite more than 60 years of research, it has remained uncertain if and how realism affects the ventriloquist effect. Here, a sound localization experiment was run using spatially disparate audio-visual stimuli. The visual stimuli were presented using virtual reality, allowing for easy manipulation of the degree of realism of the stimuli. Starting from stimuli commonly used in ventriloquist experiments, i.e., a light flash and noise burst, a new factor was added or changed in each condition to investigate the effect of movement and realism without confounding the effects of an increased temporal correlation of the audio-visual stimuli. First, a distractor task was introduced to ensure that participants fixated their eye gaze during the experiment. Next, movement was added to the visual stimuli while maintaining a similar temporal correlation between the stimuli. Finally, by changing the stimuli from the flash and noise stimuli to the visuals of a bouncing ball that made a matching impact sound, the effect of realism was assessed. No evidence for an effect of realism and movement of the stimuli was found, suggesting that, in simple scenarios, the ventriloquist effect might not be affected by stimulus realism.

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)0.020
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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.020
GPT teacher head0.341
Teacher spread0.321 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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