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Record W3198065882 · doi:10.32920/ryerson.14646684.v1

A Virtual Reality Investigation of Spontaneous Navigation Strategies and Spatial Memory Performance in Schizophrenia

2021· preprint· en· W3198065882 on OpenAlexaff
Leanne K. Wilkins

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsSpatial memoryCognitive psychologySchizophrenia (object-oriented programming)Latency (audio)Virtual realityPsychologyCognitionCognitive mapLocale (computer software)Spatial abilitySpatial cognitionTask (project management)Episodic memoryWorking memoryComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

There is strong evidence that schizophrenia (SCZ) patients perform poorly on spatial memory tasks. We investigated whether these deficits were associated with subdivisions of spatial memory (locale/cognitive map and taxon/response) or whether these deficits represented a general cognitive decline. This study investigated the types of spontaneous navigation strategies used by individuals living with SCZ to solve the 4 on 8 task. It was predicted that SCZ participants who spontaneously chose a spatial strategy would have the longest latencies and make the most errors. Four of five measures of latency and errors produced a medium magnitude effect size (

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.927

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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations0
Published2021
Admission routes1
Has abstractyes

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