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Record W2315291046 · doi:10.1037/xlm0000069

Disruption of relational processing underlies poor memory for order.

2014· article· en· W2315291046 on OpenAlexaff
Tanya R. Jonker, Colin M. MacLeod

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2014
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEncoding (memory)Task (project management)Cognitive psychologySemantic memoryLevels-of-processing effectPsychologyDistractionComputer scienceOrder (exchange)Memory errorsEpisodic memoryImpaired memoryRecallCognitionNeuroscience

Abstract

fetched live from OpenAlex

McDaniel and Bugg (2008) proposed that relatively uncommon stimuli and encoding tasks encourage elaborative encoding of individual items (item-specific processing), whereas relatively typical or common encoding tasks encourage encoding of associations among list items (relational processing). It is this relational processing that is thought to result in better memory for the serial order of a study list. We report 4 experiments examining memory for order demonstrating that (a) both semantic and orthographic tasks can impair memory for order when performed on individual items, (b) item-specific processing is not necessary for impairment because even an item-generic key press task harms memory for order, (c) impaired memory for order is due primarily to distraction during the processing of an item rather than between list items, and (d) even an unusual processing task will preserve memory for order as long as that task encourages the encoding of item-to-item relations. These findings suggest that an encoding task will disrupt order memory only when it is both attention grabbing (either through its atypicality or by requiring an overt response) and nonrelational.

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)0.001
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 score0.024
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.062
GPT teacher head0.363
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; 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

Citations15
Published2014
Admission routes1
Has abstractyes

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