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Record W2567944975 · doi:10.1016/j.jarmac.2016.11.002

Event perception: Translations and applications.

2017· article· en· W2567944975 on OpenAlex
Lauren L. Richmond, David Gold, Jeffrey M. Zacks

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Research in Memory and Cognition · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsToronto Western HospitalUniversity Health Network
FundersDefense Advanced Research Projects AgencyNational Institute on AgingNational Institutes of Health
KeywordsPsychologyCognitive psychologyEvent (particle physics)NormativePsychological interventionSegmentationMarket segmentationPerceptionEveryday lifeArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Event segmentation is the parsing of ongoing activity into meaningful events. Segmenting in a normative fashion-identifying event boundaries similar to others' boundaries-is associated with better memory for and better performance of naturalistic actions. Given this, a reasonable hypothesis is that interventions that improve memory and attention for everyday events could lead to improvement in domains that are important for independent living, particularly in older populations. Event segmentation and memory measures may also be effective diagnostic tools for estimating people's ability to carry out tasks of daily living. Such measures preserve the rich, naturalistic character of everyday activity, but are easy to quantify in a laboratory or clinical setting. Therefore, event segmentation and memory measures may be a useful proxy for clinicians to assess everyday functioning in patient populations and an appropriate target for interventions aimed at improving everyday memory and tasks of daily living.

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.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.070
GPT teacher head0.420
Teacher spread0.351 · 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