Schema formation and stimulus–schema discrepancy: A basic unit and its properties.
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".