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Record W2607927061 · doi:10.1037/xhp0000432

Attentional state modulates the effect of an irrelevant stimulus dimension on perception.

2017· article· en· W2607927061 on OpenAlexafffund
Björn Herrmann, Ingrid S. Johnsrude

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2017
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsPerceptionStimulus (psychology)DistractionIllusionPsychologyCognitive psychologyAudiologyNeuroscience

Abstract

fetched live from OpenAlex

Covariations of acoustic features provide redundancy in rapidly changing soundscapes: Hearing one feature enables a listener to infer another if these 2 features normally covary. However, it is unknown whether situational demands affect the degree to which covariations influence perceptual inferences. We exploited a perceptual interdependency between modulation rate and frequency and examined, in 6 experiments, whether challenging situations would alter the degree to which people rely on frequency information to make decisions about modulation rate. Participants listened to amplitude-modulated (AM) sounds with modulation rates (∼5 Hz) either decreasing or increasing over time and identified the direction of the rate change. Participants were instructed to ignore carrier frequency, which either decreased or increased (∼1,300 Hz) over time. We observed that participants were more likely to perceive the modulation rate as slowing down when frequency decreased and as speeding up when frequency increased (AM-rate change illusion). The magnitude of the illusion increased when uninformative cues (compared with informative cues) prohibited regulation of attention to sounds, and under distraction introduced by a concurrent visual motion-tracking task. The evidence suggests that the attentional state affects how strongly people rely on featural covariations to make perceptual inferences. (PsycINFO Database Record

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.992

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.047
GPT teacher head0.419
Teacher spread0.372 · 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.

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

Citations37
Published2017
Admission routes2
Has abstractyes

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