Impairment in acquisition of conditioned fear in schizophrenia: a pooled analysis of four studies
Bibliographic record
Abstract
Abstract Background Individuals with schizophrenia show impairments in associative learning. One well-studied, quantifiable form of associative learning is Pavlovian fear conditioning. However, to date, studies of fear conditioning in schizophrenia have been inconclusive, possibly because they lacked sufficient power. Methods To address this issue, data were pooled from 4 independent fear conditioning studies that included a total of 77 individuals with schizophrenia and 74 control subjects. Skin conductance responses (SCRs) during fear conditioning to stimuli that were paired (the CS+) and not paired (CS-) with an aversive, unconditioned stimulus were measured, and the success of acquisition of differential conditioning (the magnitude of CS+ vs. CS-SCRs) and responses to CS+ and CS-separately were assessed. Results Acquisition of differential conditioned fear responses was significantly lower in individuals with schizophrenia than in healthy controls (Cohen’s d = 0.53). This effect was primarily related to a significantly higher response to the CS-stimulus in the schizophrenia compared to the control group. The magnitude of this response to the CS- in the schizophrenia group was correlated with the severity of delusional ideation. Other symptoms or antipsychotic dose were not associated with fear conditioning measures. Conclusions Individuals with schizophrenia who endorse delusional beliefs are over-responsive to neutral stimuli during fear conditioning. This finding is consistent with prior models of aberrant learning in psychosis.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".