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Record W4388280878 · doi:10.1037/dev0001643

Schema formation and stimulus–schema discrepancy: A basic unit and its properties.

2023· review· en· W4388280878 on OpenAlexafffund
Philip R. Zelazo

Bibliographic record

VenueDevelopmental Psychology · 2023
Typereview
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsMcGill University
FundersNational Research Council CanadaNational Institute of Child Health and Human DevelopmentQueen's UniversityCarnegie Corporation of New York
KeywordsPsychologySchema (genetic algorithms)CognitionDevelopmental psychologyPsycINFOCognitive psychologyConceptual schemaNoveltySocial psychologyGender schema theoryInformation retrievalNeuroscience

Abstract

fetched live from OpenAlex

Research with 2-day-old neonates shows that they create mental representations-schemata-for their experiences and that this cognitive ability is hardwired and functional at birth. This research and studies with older infants indicate that both the formation and the expansion of schemata occur through moderate discrepancies, a concept that Jerome Kagan promoted conceptually and through his research. Discrepancy, as distinct from novelty, is insufficiently acknowledged in the literature on schema theory. The schema is both cognitive and affective and develops in unison in a curvilinear pattern with a gradual onset and exponential expansion. Optimal attentiveness and positive affect occur at the peak of formation and to moderate discrepancies. Redundancy beyond the optimal level produces decreasing interest and positive affect and increasing negative affect resulting in boredom and avoidance. These characteristics of schema development are difficult to study with older children and adults. Rumelhart (1980) regarded the schema as the "building block of cognition" and Kagan (2002) called its expansion through moderate discrepancies an "engine of change" implying widespread application for cognition and behavior throughout life. Kagan urged the search for structure (form) as opposed to function in cognition, and the curvilinear pattern of schema development and its characteristics, it is argued, is the structure he sought. Implications and select applications of schema development and expansion are presented. (PsycInfo Database Record (c) 2024 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.474
GPT teacher head0.524
Teacher spread0.050 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2023
Admission routes2
Has abstractyes

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