Psychological Impairment, Eyewitness Testimony, and False Memories: Individual Differences
Bibliographic record
Abstract
This chapter focuses on memory strengths and deficiencies in individuals with intellectual disabilities (i.e., mental retardation, autism) or various psychological impairments (i.e., schizophrenia, Alzheimer’s disease, dissociative disorder, substance abuse, trauma). For each population, we review empirical work on memory and false memory, and discuss implications for eyewitness testimony. Because many of these disorders have been understudied, we also emphasize methodological limitations and suggestions for future research. These disorders generally are marked by complicated patterns of memory strengths and weaknesses that render decisions regarding reliability and validity of memory reports quite difficult. Understanding the ability of these individuals to serve as witnesses is important, because many of these disorders are associated with higher rates of criminal contact, perpetration, and victimization. Although we assume that cognitive impairments resulting from the short-term abuse of substances like alcohol and other drugs are temporary, unlike the chronic nature of other disorders described herein, an understanding of these impairments is nonetheless relevant to legal procedure. Equal justice in legal contexts for all of these populations requires greater understanding of their cognitive abilities and limitations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".