MétaCan
Menu
← Back to cohort
Record W4237763465 · doi:10.31234/osf.io/pnbfz

On the relationship between autobiographical episodic memory and spatial navigation

2019· preprint· en· W4237763465 on OpenAlexaff
Carina L. Fan, Hervé Abdi, Brian Levine

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsAutobiographical memoryEpisodic memoryRecallCognitive psychologySpatial memoryPsychologyReconstructive memoryMental imageChildhood memorySemantic memoryCognitionComputer scienceWorking memoryNeuroscience

Abstract

fetched live from OpenAlex

Influential research has focused on identifying the common neural and behavioural substrates underlying episodic memory (the recall of specific details from past experiences) and spatial cognition, with some theories proposing that these are supported by the same mechanisms. However, the similarities and differences between these two forms of memory in humans require further specification. We used an individual-differences approach based on self-reported survey data collected in a large online study (n = 7487), focusing on autobiographical episodic memory and spatial navigation and their relationship to object and spatial imagery abilities. Multivariate analyses replicated prior findings that autobiographical episodic memory abilities dissociated from spatial navigational abilities. Considering imagery, episodic autobiographical memory overlapped with imagery of objects, whereas spatial navigation overlapped with a tendency to focus on spatial schematics and manipulation. These results suggest that episodic autobiographical memory and spatial navigation correspond to distinct mental processes.

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)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.151
GPT teacher head0.328
Teacher spread0.177 · 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 designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicMemory and Neural Mechanisms→French-language works237,207→