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Record W2145538818 · doi:10.3389/fpsyg.2010.00228

Semantic Elaboration in Auditory and Visual Spatial Memory

2010· article· en· W2145538818 on OpenAlexafffund
Meghan Taevs, Louisa Dahmani, Robert J. Zatorre, Véronique D. Bohbot

Bibliographic record

VenueFrontiers in Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsMontreal Neurological Institute and HospitalDouglas Mental Health University InstituteDouglas College
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElaborationPsychologyCognitive psychologyCognitive scienceSemantic memoryCommunicationCognitionNeuroscienceHumanities

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the hypothesis that semantic information facilitates auditory and visual spatial learning and memory. An auditory spatial task was administered, whereby healthy participants were placed in the center of a semi-circle that contained an array of speakers where the locations of nameable and non-nameable sounds were learned. In the visual spatial task, locations of pictures of abstract art intermixed with nameable objects were learned by presenting these items in specific locations on a computer screen. Participants took part in both the auditory and visual spatial tasks, which were counterbalanced for order and were learned at the same rate. Results showed that learning and memory for the spatial locations of nameable sounds and pictures was significantly better than for non-nameable stimuli. Interestingly, there was a cross-modal learning effect such that the auditory task facilitated learning of the visual task and vice versa. In conclusion, our results support the hypotheses that the semantic representation of items, as well as the presentation of items in different modalities, facilitate spatial learning and memory.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.353
Teacher spread0.338 · 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 designObservational
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

Citations23
Published2010
Admission routes2
Has abstractyes

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