Assessment of skin conductance in African American and Non–African American participants in studies of conditioned fear
Bibliographic record
Abstract
Skin conductance (SC) is a psychophysiological measure of sympathetic nervous system activity that is commonly used in research to assess conditioned fear responses. A portion of individuals evidence very low or unmeasurable SC levels (SCL) and/or response (SCR) during fear conditioning, which precludes the use of their SC data. The reason that some individuals do not produce measurable SCL and/or SCR is not clear; some early research suggested that race may be an influencing factor. In the current article, archival data from five fear conditioning samples collected from four different laboratories were examined to explore SCL and SCR magnitude in African American (AA) and non-African American (non-AA) participants. Across studies, the aggregate group difference for exclusion due to unmeasurable SCL or no measurable SCR to an unconditioned stimulus reflected a significant medium effect size (d = 0.54). Furthermore, 24.3% (range: 0-48.3%) of AA participants met SC exclusion criteria versus 14.3% (range: 4.3-24.2%) of non-AA participants. AA participants also displayed significantly lower SCL during habituation (d = 0.58). The low SC levels and responses in AA individuals and the consequent exclusion of their contributions to fear conditioning study results impacts the generalizability of findings across races. Given higher rates of posttraumatic stress disorder (PTSD) and chronic anxiety in AA individuals, it is important that AA individuals not be excluded from fear conditioning research, which informs the treatment of anxiety and PTSD. Examination of the basis of very low SCL and/or SCR is a potentially informative direction for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".