Mental Time Travel in Depression: Disentangling Cue Valence, Temporal Orientation and Task Instructions
Bibliographic record
Abstract
Unique patterns in thinking about events from one’s personal past (episodic memory) or events that could feasibly occur in one’s future (episodic FT) have been linked to depression symptoms. Extensive research has examined reductions in reporting of specific memories in depression and recent interventions are designed to improve retrieval of specific memories. Reduced positive future fluency, or reduced speeded reporting of positive events plausible in one’s personal future, is another characteristic of depression. While the Mental Time Travel (MTT) Model predicts a relationship between memory for past events and future thinking, there is no consensus regarding their relation in depression. This study examined the effects of Temporal Orientation (Past, Future) and Cue Valence (Negative, Neutral, Positive) within and between Fluency and Specificity Instruction Tasks (FIT and SIT) in Depressed (n=44) and Never Depressed (n=27) undergraduate student samples. Performance on Future conditions of the SIT was significantly positively correlated with performance on Past and Future FIT task conditions. The Past conditions of the SIT correlated negatively with the Future conditions of the SIT and inconsistently with the FIT. Surprisingly, neither performance on FIT nor SIT correlated significantly with depression as assessed using the Beck Depression Inventory, second edition (BDI-II). On the FIT, more events were reported in the Positive and Negative than Neutral conditions, but there were no significant differences between Past and Future conditions. On the SIT, more specific events were reported in the Neutral and Positive conditions. Lack of positive correlations within and between conditions of the FIT and SIT suggests that these tasks involve different cognitive processes. In addition, the lack of correlation between the BDI-II and both the FIT and SIT and lack of group effects suggests that more research is needed to determine moderators of reporting fluency and specificity in depression. Implications of my dissertation include adding to the considerable support of the MTT Model and its applicability to depressed samples, highlighting the need to conduct carefully controlled studies and clarify the influence of sample selection, use of prompts and scoring criteria on effects in the literature, and translate knowledge and methodology from basic research, such as models from brain imaging studies, to inform clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".