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Record W4399157800 · doi:10.1080/13875868.2024.2359929

Motoric engagement, but not decision-making, during encoding influences route memory

2024· article· en· W4399157800 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSpatial Cognition and Computation · 2024
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
FundersUniversité de BordeauxNatural Sciences and Engineering Research Council of CanadaMitacsInstitut national de recherche en informatique et en automatique (INRIA)
KeywordsEncoding (memory)Computer scienceMovement (music)Virtual realityControl (management)Path (computing)Point (geometry)Path integrationHuman–computer interactionComputer visionVisual memoryArtificial intelligencePsychologySimulationCognitionNeuroscienceMathematics

Abstract

fetched live from OpenAlex

Navigation aids limit the need for decision-making, possibly hindering memory for routes traveled. We manipulated type of navigation at encoding, within virtual-reality. In the Active condition participants self-initiated decision-making about routes, to find a target, and in the Guided condition followed a pre-defined path overlaid onto virtual streets. In both, they had volitional control using hand-held controllers, allowing head and body rotation in a swivel chair. In the Passive condition they viewed a pre-defined route, with no control of movement. At retrieval, participants were asked to reproduce their exact route from the initial starting point. Route memory was better following Active and Guided encoding than Passive. A visual navigation aid does not impair route memory if volitional movement is maintained.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.943
Threshold uncertainty score0.726

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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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