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Record W1764902502 · doi:10.1002/hipo.22539

Memory and navigation: Compression of space varies with route length and turns

2015· article· en· W1764902502 on OpenAlexafffund
Kyra Bonasia, Joseph Blommesteyn, Morris Moscovitch

Bibliographic record

VenueHippocampus · 2015
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsNeuroscienceSpace (punctuation)Compression (physics)Computer sciencePsychologyPhysics

Abstract

fetched live from OpenAlex

For memory to be efficient and useful during recall, problem-solving, and planning, retrieval must be compressed in time. Evidence from rodents suggests that neural compression during replay of spatial memories varies widely, with a range of compression ratios reported from 6:1 to 64:1. Anecdotal evidence suggests that similar compression occurs during mental navigation in humans: we recall how to get from one place to another countless times almost every day of our lives, and this recall never takes as long as physically travelling those routes would take. In this experiment we sought to determine whether this behavioural compression could be measured during mental navigation in humans (spatial memory replay), and which factors might affect the compression of such spatial memories. To this end, thirty participants mentally navigated routes between two landmarks, which varied in length and number of turns, as we measured replay times and recorded ratings of familiarity, detail, and presence. A multi-level model was used to determine which factors were associated with variation in compression. Route length and number of turns emerged from this model as significantly correlated with compression, such that longer routes were more compressed while compression was attenuated as the number of turns in a route increased. This suggests that compression during recall may be affected by specific features of a route, especially those that may act to segment the space or event being represented.

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 score0.012
Threshold uncertainty score0.304

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 head0.068
GPT teacher head0.285
Teacher spread0.217 · 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

Citations92
Published2015
Admission routes2
Has abstractyes

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