Memory, Mood, and Identity: Episodic Memory Impairments and the Loss of Diachronic Unity in Depression
Bibliographic record
Abstract
Tulving proposed that episodic memory is not merely a record of past events, but a form of mental time travel, characterized by autonoetic consciousness (Tulving, 2005). From this view, episodic memory allows for the diachronic unity of self by preserving the subjective sense that “I” was the one who lived the remembered experience. In contrast, Conway and Pleydell-Pearce argued that continuity of self is actively constructed by the conceptual self, which selects and distorts past experiences to maintain a coherent narrative (Conway, 2005; Conway & Pleydell-Pearce, 2000). Despite offering opposing accounts of how self-continuity arises, both frameworks converge on a central claim: that episodic memory plays a constitutive role in maintaining a unified sense of self over time. But what happens when this system is disrupted? In depressive disorders, episodic recall becomes overgeneral and biased toward negatively valenced content (Williams et al., 2007). Such cognitive distortions may not merely reflect depressive symptoms, but instead contribute to a fragmented self-concept. Given the role of episodic memory in maintaining a continuous sense of self, I argue that the episodic memory impairments associated with depression disrupt continuous self-concept. Moreover, alterations to sleep architecture in major depressive disorder (MDD), including reduced slow-wave sleep (SWS) and disrupted rapid eye movement (REM) patterns, are thought to exacerbate cognitive biases in episodic memory (Harrington et al., 2023). Thus, interventions targeting sleep quality hold promise for alleviating cognitive distortions and strengthening self-continuity in depression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".