Bibliographic record
Abstract
The long-term memory system for what is conscious and can be verbalized – declarative memory- is often separated into memory for general facts and memory for personal events (Squire, 2009; Tulving, 2002). Personal semantics share elements of both semantic memory (i.e., they are facts that can be known) and episodic memory (i.e., they are self-related and idiosyncratic; Renoult, Davidson, Palombo, Moscovitch, & Levine, 2012). According to the taxonomy of personal semantics (Renoult et al., 2012), they vary in proximity to either semantic or episodic memory. Towards one end of the continuum, memory for autobiographical facts such as jobs and names of friends were hypothesized to be closer to general facts. Towards the other end of the continuum, repeated events are summaries of the core elements of similar events that happened more than once (e.g., getting coffee at a coffee shop), and they were hypothesized to be closer to episodic memory (i.e., the recollection of a unique event). Self-knowledge involves self-reflection about one’s own personality traits and preferences; it was thought to be the most distinct from semantic and episodic memory. However, little research had compared personal semantics to both semantic and episodic memory, or to one another, and these proposals needed to be tested experimentally. In this thesis, I compared the neural correlates of three types of personal semantics to semantic memory (study 1, 2, 3) and episodic memory (study 1, 2), and to one another (study 1) using functional magnetic resonance imaging (fMRI; study 1) and event related potentials (ERPs; study 2, 3). Moreover, I examined whether temporal orientation modified the personal semantics’ relationship to the typically atemporal semantic memory (study 2, 3) and to the typically past-oriented episodic memory (study 2). In study 1, general facts, autobiographical facts, repeated events, and unique events were compared using fMRI, in a follow-up to an ERP study (Renoult et al., 2016). In our analyses of the hippocampus (HPC) and posterior medial network (Ritchey, Libby, & Ranganath, 2015), general semantics and autobiographical facts were often not significantly different from one another (except for the left posterior HPC), and repeated events and unique events did not differ from one another in any comparison. I observed a small graded increase of brain activity from general facts to autobiographical facts to repeated events and unique events (with a significant linear trend) in the left posterior HPC. In contrast, no memory type differed in the anterior temporal network (Ritchey et al., 2015). In study 2 and 3, self-knowledge was operationalized as the knowledge of one’s own traits, and could concern past (study 2), present (study 2, 3) and future selves (study 2, 3). A neural correlate of recollection, the Late Positive Component (LPC), had a larger mean amplitude for thinking about the self than others (study 2, 3), and thinking about a past and/or future self than the present self (on average for study 2, and significant for study 3). The amplitude of the LPC for thinking about the past and future selves did not differ from an episodic recognition memory task (or present self-knowledge; study 2). Further, the temporal orientation effect was smaller and not significant when we compared thinking about the present and the future traits of others (study 3). The operationalization of the “other” as a close friend or a group of people did not modify this result (study 3). Together, in addition to Renoult et al. (2016), these findings suggest that: the neural correlates of autobiographical facts, repeated events, and self-knowledge do not overlap perfectly with semantic or episodic memory. Moreover, the temporal orientation of the knowledge is one factor that can influence the proximity of the neural correlates of personal semantics to either semantic or episodic memory.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".