Post unilateral lesion response biases modulate memory: Crossed double dissociation of hemispheric specialisations
Bibliographic record
Abstract
We propose that what appears to be hemispheric specialisation in the memory domain, as indexed by effects of unilateral brain lesions, is to a great extent explainable as response bias: left hemisphere lesions result in an omissive response bias or error pattern whereas right hemisphere lesions result in a commissive response bias or error pattern. To test this prediction a group of 40 non-confabulatory cases with a verbal and non-verbal retention deficit (hypomnesia), subsequent to a unilateral lesion, was assembled from the literature. A group of non-amnesic cases with confabulation, paramnesia, false memories or memory-laden hallucination (dysfunctional hypermnesia), due to a unilateral lesion, was also assembled from the literature (N=72). Most of the hypomnesic patients had left hemisphere lesions (73%, p<.005, two tailed) while most of the hypermnesic patients had right hemisphere lesions (78%, p<.0005, two tailed). This crossed double dissociation held good despite statistical control of the lesion's locus within the hemisphere, its size or its aetiology, presence of aphasic symptoms, psychiatric comorbidity, the patient's age, gender, or hand preference, and several other potentially confounding variables.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".