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Record W2968598138 · doi:10.1177/1747021819872159

Effect of room size on geometry and features cue preference during reorientation: Modulating encoding strength or cue weighting

2019· article· en· W2968598138 on OpenAlexafffund
Lin Wang, Weimin Mou

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPreferenceWeightingEncoding (memory)GeometryPsychologyCommunicationMathematicsCognitive psychologyPhysicsStatisticsAcoustics

Abstract

fetched live from OpenAlex

Three experiments investigated how the room size affects preferential use of geometric and non-geometric cues during reorientation inside a room. We hypothesised that room size may affect preferential use of geometric and non-geometric cues by affecting the encoding of the cues (the encoding hypothesis), the retrieval of the cues (the retrieval hypothesis), or both the encoding and retrieval of the cues (the encoding-plus-retrieval hypothesis). In immersive virtual rectangular rooms, participants learned objects' locations with respect to geometric (room shape) and non-geometric cues (features on walls or isolated objects). During the test, participants localised objects with the geometric cue only, non-geometric cues only, or both. The two cues were placed at the original locations or displaced relative to each other (conflicting cues) when both were presented at testing. We manipulated the room size between participants within each experiment. The results showed that the room size affected cue preference using conflicting cues but did not affect response accuracy using single cues at testing. These results support the retrieval hypothesis. The results were discussed in terms of the effects of cue salience and stability on cue interaction in reorientation.

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)0.000
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 score0.014
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.033
GPT teacher head0.370
Teacher spread0.336 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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