Reversal deficits in individuals with psychopathy in explicit but not implicit learning conditions
Bibliographic record
Abstract
BACKGROUND: Psychopathy is a severe personality disorder that has been linked to impaired behavioural adaptation during reinforcement learning. Recent electrophysiological studies have suggested that psychopathy is related to impairments in intentionally using information relevant for adapting behaviour, whereas these impairments remain absent for behaviour relying on automatic use of information. We sought to investigate whether previously found impairments in response reversal in individuals with psychopathy also follow this dichotomy. We expected response reversal to be intact when the automatic use of information was facilitated. In contrast, we expected impaired response reversal when intentional use of information was required. METHODS: We included offenders with psychopathy and matched healthy controls in 2 experiments with a probabilistic cued go/no-go reaction time task. The task implicated the learning and reversal of 2 predictive contingencies. In experiment 1, participants were not informed about the inclusion of a learning component, thus making cue-dependent learning automatic/incidental. In experiment 2, the instructions required participants to actively monitor and learn predictive relationships, giving learning a controlled/intentional nature. RESULTS: While there were no significant group differences in acquisition learning in either experiment, the results revealed impaired response reversal in offenders with psychopathy when controlled learning was facilitated. Interestingly, this impairment was absent when automatic learning was predominant. LIMITATIONS: Possible limitations are the use of a nonforensic control group and of self-report measures for drug use. CONCLUSION: Response reversal deficits in individuals with psychopathy are modulated by the context provided by the instructions, according to the distinction between automatic and controlled processing in these individuals.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".