Impact Of Learned Helplessness On Cognitive Performance And Resting-State Connectivity: An fMRI Study
Bibliographic record
Abstract
1.1 Abstract Learned helplessness (LH) is the phenomenon of resignation in the face of a problematic situation and is caused by the feeling of lacking control in a situation due to internal or external factors. This means that an individual does not seek to resolve the situation they are confronted with and consciously or unconsciously chooses to be passive. The Dorsal Raphe Nucleus (DRN) core could be at the root of the persistent action of the LH phenomenon and influences regions such as the striatum and amygdala. It is thought that the LH phenomenon could affect self-perception and it has been shown that the default mode network (DMN) is often associated with self-reflection during the resting state (RS) phase. It is therefore possible that LH influences both self-perception and the DMN. Based on previous studies, we investigated how LH affects participants. We used functional MRI (fMRI) to test this hypothesis. Participants were divided into two groups, subjected to solvable (control group), and solvable plus unsolvable (LH group) cognitive tasks. We also measured electrodermal signals, OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) personality scores, variables related to the anagram resolution, and related these to LH. The study revealed significant differences in the RS contrast between the two groups, with the Posterior Cingulate Cortex (PCC) (an area of the DMN) being more connected to the DRN in the LH group than in the control group, and a portion of the Superior Temporal Gyrus being more connected to the PCC in the control group than in the LH group. These results suggest that LH may have a direct impact on the DMN and could lead to the start of long-term changes.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".