Bibliographic record
Abstract
Learning is controlled by two interacting processes, cognitive and habitual learning. How these two systems are used while we learn in our everyday lives depends on an individual’s context. For example, stress is one contextual factor that consistently has created a shift toward habitual learning. In addition, there is evidence that ovarian hormones can also influence learning processes. However, research investigating these hormonal influences has resulted in inconsistent findings. While there is evidence that both of these contextual factors influence learning processes, there is little research on what effects result from their interaction. Further, while the menstrual cycle is often used to approximate ovarian hormone levels in such studies, it is conceptualized strictly as a biological phenomenon, despite evidence supporting its biopsychosocial characterization. Thus, the current study investigated the individual and interactive effects of chronic stress and ovarian hormones on learning processes, while using a biopsychosocial understanding of the menstrual cycle. Participants (N = 32) completed a probabilistic classification learning task. They also provided salivary measures of estradiol and progesterone, and completed measures of chronic stress and menstrual-related attitudes and beliefs. Results revealed a trending association between progesterone and learning processes. Further, there was an interaction between chronic stress and estradiol in predicting learning process use. Lastly, there were significant correlations between learning processes and various menstrual beliefs. As such, these preliminary results revealed how ovarian hormones and chronic stress interact to influence learning processes, and menstrual attitudes and beliefs can provide a more detailed understanding of these effects.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".