Absence of conditioned responding in humans: A bad measure or individual differences?
Bibliographic record
Abstract
Skin conductance response (SCR) is often used as an index of conditioned fear. SCR has been shown to be highly variable, and absence of SC reactivity is sometimes used as criteria for excluding data. It is, however, possible that low or no SC reactivity is the result of a distinct biological signature that underlies individual differences in SCR reactivity. This study examined neural correlates associated with the near absence of SCR conditionability. Archival data from 109 healthy adults aged 18-60 years were pooled. All individuals had participated in a fear conditioning protocol in a fMRI environment, during which two cues were partially reinforced (CS+) with a shock and a third cue was not (CS-). Using SCR to the conditioned stimuli and differential SCR (CS+ minus CS-), we created two groups of 30 individuals: low conditioners (defined as those showing the smallest SCR to the CS+ and smallest differential SCR) and high conditioners (defined as those showing the largest SCR to the CS+ and largest differential SCR). Our analyses showed differences in patterns of brain activations between these two groups during conditioning in the following regions: dorsal anterior cingulate cortex, amygdala, subgenual anterior cingulate cortex, and insular cortex. Our findings suggest that low or absent SCR conditionability is associated with hypoactivation of brain regions involved in fear learning and expression. This highlights the need to be cautious when excluding SCR nonconditioners and to consider the potential implications of such exclusion when interpreting the findings from studies of conditioned fear.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".