Spontaneous retrieval of sequential non-declarative information: New software-based neuropsychological test and algorithmic implementation of cognitive dissonance principles in serial ordering
Bibliographic record
Abstract
Context : The principles behind the process of creating new, spontaneous sequences out of previously ordered non- declarative stimuli have been scarcely addressed and, for such reason, remain highly unknown. Objective : This paper has four interconnected goals: (1) introduce a new software-based neuropsychological test that can be used as a mean to assess key aspects of the way people order and reorder non-declarative stimuli, based upon cognitive dissonance principles; (2) introduce a mathematical approach to the latter in ordering/re-ordering of non-declarative stimuli; (3) assess whether the principles of cognitive dissonance in ordering/re-ordering hold for a cohort of young adults with upper socio-economic level; (4) access the extent to which the same holds for children and adolescents and trace a curve of maturation of cognitive dissonance in ordering/re-ordering. Methods : Our multi-age and multi-language social Network Test has two stages, first subjects must order figures representing human faces in accordance with their preference; next, different pairs of figures are automatically provided and each subject is asked to fulfill the intermediate arrays that are assumed to interconnect the original pair. Our mathematical model is centered around the relation defined by increases in the distance separating these different pairs of figures in the initial order (distances 1, 5 and 11) and related increases in the mean number of intermediate arrays placed in the re-ordering phase; 105 subjects were tested. Results : The tendency to produce reorders that are consonant to the one produced in the initial phase increases with age (in other words: people feel that there are more intermediate arrays between any two individuals to which they attribute divergent affect than the contrary). This trend inspired us to propose a cognitive dissonance index in spontaneous ordering/reordering of non-declarative stimuli, which may formalize the operation of a previously unknown cognitive dimension of the human mind and may serve as an index of cognitive maturation. To the extent that further studies endorse these perspectives, the tests, formulas, and theoretical principals may support new diagnostic methods and explorations in cognitive science.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".