Autobiographical Planning and the Brain: Activation and Its Modulation by Qualitative Features
Bibliographic record
Abstract
To engage in purposeful behavior, it is important to make plans, which organize subsequent actions. Most studies of planning involve "look-ahead" puzzle tasks that are unrelated to personal goals. We developed a task to assess autobiographical planning, which involves the formulation of personal plans in response to real-world goals, and examined autobiographical planning in 63 adults during fMRI scanning. Autobiographical planning was found to engage the default network, including medial-temporal lobe and midline structures, and executive control regions in lateral pFC and parietal cortex and caudate. To examine how specific qualitative features of autobiographical plans modulate neural activity, we performed parametric modulation analyses. Ratings of plan detail, novelty, temporal distance, ease of plan formulation, difficulty in goal completion, and confidence in goal accomplishment were used as covariates in six hierarchical linear regression models. This modeling procedure removed shared variance among the ratings, allowing us to determine the independent relationship between ratings of interest and trial-wise BOLD signal. We found that specific autobiographical planning, describing a detailed, achievable, and actionable planning process for attaining a clearly envisioned future, recruited both default and frontoparietal brain regions. In contrast, abstract autobiographical planning, plans that were constructed from more generalized semantic or affective representations of a less tangible and distant future, involved interactions among default, sensory perceptual, and limbic brain structures. Specific qualities of autobiographical plans are important predictors of default and frontoparietal control network engagement during plan formation and reflect the contribution of mnemonic and executive control processes to autobiographical planning.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".