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Record W2796621789 · doi:10.1037/xlm0000498

Two sources of information in reconstructing event sequence.

2018· article· en· W2796621789 on OpenAlexafffund
Tanya R. Jonker, Colin M. MacLeod

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2018
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEncoding (memory)Computer scienceEpisodic memoryContext (archaeology)Event (particle physics)Sequence (biology)Task (project management)Artificial intelligenceNatural language processingPsychologyCognition

Abstract

fetched live from OpenAlex

Reconstructing memory for sequences is a complex process, likely involving multiple sources of information. In 3 experiments, we examined the source(s) of information that might underlie the ability to accurately place an event within a temporal context. The task was to estimate, after studying each list, the temporal position of a single test word within that list. In the first 2 experiments, we demonstrated that memory for temporal location was better following semantic encoding than silent reading of the list, which in turn was better than orthographic encoding of the list. Although other measures of sequence retention have revealed impaired memory for order with greater item-level encoding, these experiments demonstrated that item-level encoding improved memory for temporal-location. A 3rd experiment extended these findings by measuring interitem associations in addition to item memory, demonstrating that memory for temporal location within a list was more closely related to item information than to interitem relational information. It is now clear that reconstructing an event sequence can involve at least 2 distinct sources of information-both item and relational encoding can play important roles, depending on the nature of the test for order. (PsycINFO Database Record

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 score0.023
Threshold uncertainty score0.256

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 communication0.0000.001
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 head0.054
GPT teacher head0.370
Teacher spread0.316 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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