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Record W6906614365 · doi:10.17632/dtz5tvy66r.2

embodiedPerspective

2025· dataset· en· W6906614365 on OpenAlexaff

Bibliographic record

VenueMendeley Data · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPrecuneusAngular gyrusPerspective (graphical)Embodied cognitionEncoding (memory)RecallVisual memoryScripting language

Abstract

fetched live from OpenAlex

This folder contains scripts and results detailed in "An embodied perspective: angular gyrus and precuneus decode selfhood in memories of naturalistic events" by Heather Iriye and Peggy L. St. Jacques. We investigated the interaction between embodiment and visual perspective during encoding, and how this interplay shapes the recall of past events. We hypothesized that the angular gyrus, precuneus, and hippocampus would be involved in integrating the visual perspective and sense of embodiment initially present during memory encoding within memories for naturalistic events as they were retrieved from memory. Patterns of activity during retrieval in the left angular gyrus and bilateral precuneus predicted embodiment on its own separated from visual perspective. In contrast, we observed only inconclusive evidence that these posterior parietal regions predicted visual perspective independent of embodiment. While the left angular gyrus distinguished between in-body and out-of-body perspectives during the retrieval of events associated with both strong and weak embodiment, decoding accuracy predicting visual perspective was only above chance for events encoded with strong embodiment in the precuneus bilaterally. Our results suggest that the contribution of posterior parietal regions in establishing visual perspectives within memories is tightly interconnected with embodiment. Encoding events from an embodied in-body perspective compared to embodied out-of-body perspective led to higher memory accuracy following repeated retrieval. These results elucidate how fundamental feelings of being located in and experiencing the world from our own body’s perspective are integrated within memory. scripts subfolder: run_crossvalidation_measure_ROI_ST_final.m - Matlab script to perform ROI decoding analyses using the CoSMoMVPA toolbox. ROIs subfolder: Contains binarized ROI masks of the left and right angular gyrus, precuneus, and hippocampus. results subfolder: behavioralResults.csv - results of the illusion induction questionnaire and cued recall test/subjective ratings from immediate testing, scanning, and post-scanning sessions. decodingAccuraciesSingleHemiAll.xlsx - results of the ROI decoding analyses with one tab per analysis Abbreviations: Sess1 = session one (immediate testing) Scan = scanning session Sess2 = session 2 (post-scanning) illMinCont = average illusion minus control statement ratings Corr = cued recall accuracy (proportion correct) Viv = vividness EI = emotional intensity Belief = belief in memory accuracy InBodyRat = average 1PP rating OutofBodyRat = average 3PP rating accLDA = LDA classifier accuracy accMinChLDA = accuracy minus chance LDA classifier sscore

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.483
Threshold uncertainty score0.688

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5170.230

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 head0.072
GPT teacher head0.372
Teacher spread0.300 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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