Fig. 3 | Model dependencies and results of the rSA. a, MDS of the RDMs describing the relationships between pictures in terms of their collective (national news bulletins and reports on World War II), semantic (that is, Wikipedia World War II articles), spatial (that is, Memorial layout) and temporal (that is, acquisition order) properties. Collective and semantic RDMs included six to ten selected topics (Fig. 2c) and their ten iterations. Temporal RDMs included the six possible routes around the Memorial. b, The dmPFC and vmPFC regions of interest (ROIs). c, Similarities between the upper triangular portions of image arrangements (left), dmPFC (middle) and vmPFC (right) RDMs, and collective, semantic and contextual model RDMs. Bar graphs display the mean beta coefficients from the regression model (top) and Spearman's correlation coefficients (bottom) across participants (N = 24). Horizontal lines indicate significant differences at P < 0.05, false discovery rate (FDR)-corrected for multiple comparisons. Error bars reflect 95% bootstrapped CIs (and thus indicate significance when they do not overlap with zero). Dashed horizontal lines indicate the noise ceiling (that is, an estimate of the reliability of the neural data; see Methods), which reflects the expected performance of the (unknown) true model given the noise and variability among study participants 34. Regarding brain imaging data, the collective RDM reaches the noise ceiling, indicating that collective schemas account for a significant amount of the true neural dissimilarity structure. Note that in the context of the current experiment, the small correlation between individuals' brain dissimilarity structures and the expected true model arises from the fact that we have only one measurement of each pattern of memory activity (that is, participants recall the picture only once), making the estimate of the neural dissimilarity noisy.
Changes over 10 years in the retelling of the flashbulb memories of the attack of September 11, 2001A flashbulb memory is a highly detailed and vivid autobiographical memory for the circumstances in which one first learned of a surprising, consequential, and emotionally arousing event. How retelling of different features of a flashbulb memory changes over time is not totally understood. Moreover, little is known about how the emotional feeling experienced by individuals when they learned about the event modulates these changes.In this study, we explored changes over time in American individuals' retelling of their flashbulb memories of the terrorist attack of September 11, 2001. We conducted textual analysis of 824 testimonies collected from the same 206 individuals one week, 11, 25 and again 119 months after the attack. Results showed individuals were more likely to report temporal and emotional details in their retelling early after the event and spatial details in their long-term retelling. In addition, intensity of emotions felt upon hearing the news about the attack influenced how individuals reported their flashbulb memories over time.Overall, this study provides further support for theories suggesting different rates of forgetting for different canonical features of emotional arousal events.
Résumé Deux approches de la constitution de corpus de textes médiévaux se dessinent depuis une dizaine d’années déjà : 1. numérisation d’éditions critiques modernes ; 2. création de transcriptions diplomatiques précises de manuscrits, éventuellement accompagnées des images des originaux. Ces approches sont en réalité plutôt complémentaires qu’opposées, car elles permettent aux chercheurs de faire le choix entre la quantité (représentativité) et la qualité (sûreté et richesse) des données en fonction de la recherche effectuée. Pour les deux types de corpus nous analysons les enjeux de l’utilisation d’une représentation normalisée du texte et de son ‘ancrage’ signalétique (norme XML et conventions de représentation TEI 1 ). Les problèmes méthodologiques qui se posent lors de la création et de l’exploitation des corpus de textes anciens, et leurs solutions sont aussi valables pour d’autres types de corpus linguistiques.
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