Error- and inhibitory-related brain activity associated with political ideology: A multi-site replication study
Bibliographic record
Abstract
The relationship between political ideology and brain activity has captured the fascination of scientists and the public alike. Using approaches from cognitive neuroscience to provide insights into deeply held and personal beliefs requires careful navigation, with the application of robust methods that generate replicable results. A hallmark study in this area from Amodio et al. (2007) reported that brain components reflective of conflict monitoring and inhibition (namely the ERN [error-related negativity] and N2) are heightened in individuals who self-identify as liberal compared to conservative. While the study is highly influential and well-cited in the scientific literature, no direct replications of their findings exist and as such, this work was selected as a target replication for the #EEGManyLabs initiative. This cross-cultural multi-site study (N=320) will conduct a thorough replication of the Amodio et al. (2007) study, strictly adhering to the original protocol, namely by administering a Go/No-Go task with simultaneous EEG recording and a one-item scale asking participants to rate the extent to which they are liberal or conservative. We will supplement the original study with new measures that may better correspond to political identity in non-US contexts, such as religiosity, dogmatism, and traditionalism. In line with the original study, we will conduct correlational analyses between self-identified liberalism and ERN/N2 amplitudes. In addition, Bayesian linear regressions will be used to provide robust estimates of the strength of association between other components of political ideology and electrophysiological signals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".