Semantic–episodic interactions in the neuropsychology of disbelief
Bibliographic record
Abstract
INTRODUCTION: The purpose of this paper is to outline ways in which characteristics of memory functioning determine truth judgements regarding verbally transmitted information. METHOD: Findings on belief formation from several areas of psychology were reviewed in order to identify general principles that appear to underlie the designation of information in memory as "true" or "false". RESULTS: Studies on belief formation have demonstrated that individuals have a tendency to encode information as "true" and that an additional encoding step is required to tag information as "false". This additional step can involve acquisition and later recall of semantic-episodic associations between message content and contextual cues that signal that information is "false". Semantic-episodic interactions also appear to prevent new information from being accepted as "true" through encoding bias or the assignment of a "false" tag to data that is incompatible with prior knowledge. CONCLUSIONS: It is proposed that truth judgements are made through a combined weighting of the reliability of the information source and the compatibility of this information with already stored data. This requires interactions in memory. Failure to integrate different types of memories, such as semantic and episodic memories, can arise from mild hippocampal dysfunction and might result in delusions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".