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Record W2769862770

Part-Set Cuing: A Connectionist Approach to Strategy Disruption

2004· article· en· W2769862770 on OpenAlexaboutno aff
Edward T. Clokely, Roy W. Roring

Bibliographic record

VenueeScholarship (California Digital Library) · 2004
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsConnectionismSet (abstract data type)PsychologyCued speechCognitive scienceAssociative propertyCognitive psychologyArtificial intelligenceArtificial neural networkComputer scienceInterpretation (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

Part-Set Cuing: A Connectionist Approach to Strategy Disruption Edward T. Cokely (cokely@psy.fsu.edu) Department of Psychology, Florida State University Tallahassee, FL 32306 Roy W. Roring (roring@psy.fsu.edu) Department of Psychology, Florida State University Tallahassee, FL 32306 Part-Set Cuing Experiment 2: Simulation Results In a part-set cuing paradigm, when part of a previously studied list of words is provided as a memory aid, a reliable and robust impairment of the non-cued list items results. Since its discovery (Slamecka, 1968; as cited in Nickerson, 1984), this paradoxical phenomenon has been characterized as a persisting enigma in memory research (Nickerson, 1984), of both theoretical and practical concern. One leading informal model, the strategy disruption interpretation (Basden & Basden, 1995), suggests that the part-set cuing impairment results because one’s retrieval strategy is changed and differs from the original encoding strategy, following the presentation of cues. The strategy disruption account is thoroughly supported by empirical evidence; however, it has been criticized as theoretically vague and poorly defined. In contrast, the other leading account, a formal model using SAM (Raaijmaker & Shiffrin, 1981), while precise, has been criticized as overly defined, theoretically inconsistent, and unable to account for the full range of findings (Roediger & Neely, 1982). In an attempt to more precisely identify and extend the strategy disruption interpretation, we examine and compare both neural network simulations and human experiments in a part-set cuing paradigm. The Neural Network The artificial neural network used was a fully-connected, auto-associative, three-layer perceptron, using a backpropagation algorithm with a learning rate set to 0.1. The network used 15 input and 15 output nodes with a bias, 10 hidden, and 10 context units. The context layer used a 1 to 1 association from hidden units to context units and was fully connected from context to hidden units. Experiment 1: Human Results A within-participant (N=24) design was used and counterbalanced for list-order, list-cue-order, and randomized part-set cuing. A typical and robust part-set cuing impairment was observed for cued (M=.35) verses non-cued (M=.40) items, F (1,23) = 4.99, p < .05. A within-simulated-participant (N=24) part-set cuing design was used and counterbalanced for list-order and list-cue- order, with randomized part-set cues. Output vector error served as the dependent variable and was summed and analyzed for cued and non-cued states. A typical and robust part-set cuing impairment was observed, F (1, 23) = 24.00, p < .05, without evidence of catastrophic interference. Conclusion & Discussion The neural network was consistent with the observed human performance, providing a good fit across a number of analyses. The findings suggest that the neural network formalism is consistent with and may serve as an extension of the Basden and Basden strategy disruption account of part-set cuing. That is, following cuing, different study and activation patterns disrupt the subsequent process of recall. This disruption is caused by a change in the availability and accessibility of cued items, altering the retrieval process and thus the retrieval strategy. Although the experimental evidence is from a small set, results suggest that the neural network can provide an increasingly precise mechanistic account of part-set cuing impairment that is consistent with the leading informal theoretical account. Future simulations should attempt to replicate key findings including part-set cuing facilitation and category-cuing impairment. References Basden, D. R., & Basden, B. H. (1995). Some tests of the strategy disruption interpretation of part-list cuing inhibition. Journal of Experimental Psycholgoy: Learning, Memory, and Cognition, 21, 1656-1669. Nickerson, R. S. (1984). Retrieval inhibition from part-set cuing: A persisting enigma in memory research. Memory and Cognition, 12, 531-552. Raaijmakers, G.W., & Shiffrin, R. M. (1981). Search of associative memory. Psychological Review, 88, 93-134. Roediger III, H. L., & Neely, J. H. (1982). Retrieval blocks in episodic and semantic memory. Canadian Journal of Psychology, 36, 213-242.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0030.006
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.052
GPT teacher head0.263
Teacher spread0.211 · 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.

Study designTheoretical or conceptual
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".

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Citations0
Published2004
Admission routes1
Has abstractyes

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