Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».