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Record W2967241057 · doi:10.1037/cep0000184

Interactive processes in an instance model of memory: A computational analysis of Jacoby’s (1983) dissociation between perception and recognition.

2019· article· en· W2967241057 on OpenAlexaff
Evan T. Curtis

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2019
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBooth University College
Fundersnot available
KeywordsDissociation (chemistry)PsychologyCognitive psychologyPerceptionPsycINFOExperimental psychologyCognitive scienceRecognition memoryCognition

Abstract

fetched live from OpenAlex

In a classic and well-cited work, Jacoby (1983) demonstrated an important dissociation in which conceptual processing at study resulted in high performance on a standard memory test but low performance on a perceptual test. Perceptual processing at study resulted in the opposite pattern. I simulated the dissociation in MINERVA2, a classic instance model of memory. I assumed that stimulus representations are composed of perceptual and contextual features and that different study tasks favor the encoding of some features over others. I also assumed that different test tasks utilize some features more than others. The model successfully produced the dissociation. The simulations provide a formal account of a core principle of memory: Performance is determined by the appropriateness of processing at encoding given the demands of retrieval. In conjunction with previous work, I conclude that the result emerged from the same mechanisms that underlie empirical regularities from other areas of memory research (e.g., the production effect) and argue in favor of incorporating converging evidence across multiple modelling frameworks to provide stronger theoretical foundations. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.084
GPT teacher head0.347
Teacher spread0.264 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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