Racial Stigma and Sense of agency: Implications for neurocognitive and social-cognitive research
Bibliographic record
Abstract
As social creatures, our social encounters matter. They matter for how we experience the world, as well as ourselves. The role of psycho-social experiences has recently been recognized in the neurocognitive literature on the sense of agency. Defined as the experience of control over one’s actions and outcomes, researchers have begun exploring how social interactions and contextual cues modulate this experience, using an implicit task known as intentional binding. This task claims to capture the sense of agency by assessing differences in perception of time across conditions that are theoretically considered to be higher in sense of agency as compared to those that are lower. Drawing inspiration from this new literature, this thesis explores, across five studies, the impact of different psycho-social experiences, particularly those related to stigmatized racial minority groups, on the sense of agency. Our first two studies (n= 36, n=123) indicate that reflection on both negative and positive psycho-social experiences, including racial stigma, bias, and acceptance, reduces the sense of agency, as indexed by lower action-effect interval estimates. Further, our latter three studies (n=45, n=44, n=44), which focus on North American and international samples, suggest that expectations of racial bias reduce the sense of agency and that this reduction is greatest amongst people who experience a threat to their identity because of the event, as well as people who are low-self monitors. Insights from these studies are used to advance neurocognitive and social cognitive work, including psycho-social modulates of intentional binding and psychological mechanisms that affect racial minorities.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".