Collective memory shapes the organization of individual memories in the medial prefrontal cortex

Abstract

It has long been hypothesized that individual recollection fits collective memory. To look for a collective schema, we analysed the content of 30 years of media coverage of World War II on French national television. We recorded human brain activity using functional magnetic resonance imaging as participants recalled World War II displays at the Caen Memorial Museum following an initial tour. We focused on the medial prefrontal cortex, a key region for social cognition and memory schemas. The organization of individual memories captured using the distribution of the functional magnetic resonance imaging signal in the dorsal part of the medial prefrontal cortex was more accurately predicted by the structure of the collective schema than by various control models of contextual or semantic memory. Collective memory, which exists outside and beyond individuals, can also organize individual memories and constitutes a common mental model that connects people’s memories across time and space.

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Fig. 1: Experimental design for recall of individual memories.
Fig. 2: Measuring collective, shared and contextual memory.
Fig. 3: Model dependencies and results of the RSA.
Fig. 4: Disentangling the contributions of individual and collective schemas.

Data availability

All raw behavioural and imaging data are archived at the GIP Cyceron Centre in Caen. The collective memory corpus is archived at the INAthèque (Bibliothèque Nationale de France) in Paris, which has the legal deposit. This corpus was collected by the MATRICE project (http://www.matricememory.fr/?lang=en), a multidisciplinary and technological consortium of research units, whose aim is to provide tools and technological and theoretical background to understand the relationship between collective and individual memory. Scientists interested in the analysis of this corpus are welcome to submit a project to the MATRICE project and join the consortium. The tagged Wikipedia corpus used for these analyses is available at http://redac.univ-tlse2.fr/corpus/wikipedia.html.

Code availability

The text processing and topic analysis of the collective memory and Wikipedia corpora were conducted using TXM (http://textometrie.ens-lyon.fr/) and MALLET (http://mallet.cs.umass.edu/topics.php) software. The MATLAB implementation based on SPM12 (https://www.fil.ion.ucl.ac.uk/spm/) and the RSA toolbox (https://github.com/rsagroup/rsatoolbox) of the first level and RSAs are available from the corresponding author on request.

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Acknowledgements

We thank R. Benoit and M. Wimber for helpful corrections and comments, as well as R. Henson for helpful discussions, on an earlier version of this manuscript. We thank B. Pincemin for her help in the early stages of this project, as well as for her final comments on this manuscript. We thank S. Grimaldi, head of the Caen Memorial, for giving us full access and clearance to proceed with our experiment. We thank “l’observatoire B2V des Mémoires” and M. Morel for help with data collection for the image arrangement task, as well as H. Abdi for guidance on data analysis. We thank D. Maréchal for his help with the selection of television news, as well as K. Dauchot and M. Dufossé for their help during the initial piloting of the experiment. We thank E. Portier and T. Chan for editing the English of the main text. We also thank all participants for volunteering in this study. The study was supported by an EQUIPEX (MATRICE) led by D.P. and funded by the French National Research Agency (grant no. 10-EQPX-21-01), and by a three-year postdoctoral fellowship from the Normandy region to P.G. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

P.G., D.P. and F.E. designed the experiment and wrote the article. P.G. analysed the data. T.V. designed and programmed the image arrangement task, constructed experimental material, processed the corpus and collected the data. S.H. and M.D. programmed the script embedded in TXM to lexically process the corpus. J.-L.G. and A.L. designed and programmed the speech-to-text conversion algorithm to transform the corpus of news into text. C.K.-P. and D.P. supervised the collection of the collective memory corpus and its storing, manual editing and trimming. F.V. supervised the MRI data collection.

Correspondence to Pierre Gagnepain.

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Extended data

Extended Data Fig. 1 Results of searchlight analysis.

A searchlight analysis following the surface curvature of the brain was ran to test the effects of collective schema, and contextual and semantic memory. At each searchlight, brain RDM was extracted and compared to RDM models using a regression model, thus producing a beta map for each participant (n = 24) and RDM model. To correct for multiple comparisons, the group-level beta map was submitted to maximal permutation testing using threshold-free cluster enhancement (TFCE58). Blue regions indicate clusters surviving TFCE correction across the whole brain (P corrected < .05) for collective schema. There was no statistically significant evidence that brain regions were related to other RDM models using the searchlight approach.

Extended Data Fig. 2 Graph network of topic model.

To illustrate the semantic model and the connections between words derived from our topic models, we computed a Lemmas x Lemmas correlation matrix using the estimated distribution of probabilities over 10 topics. This correlation matrix was then thresholded and transformed into a binary adjacency matrix by keeping the top 10% of the strongest connections between lemmas. The adjacency matrix is visualized here using a force vector algorithm proposed with the Gephi software (https://gephi.org/). Each node represents one of the 6,240 lemmas. The color and the size of the node is determined by its maximal topic assignment and probability, respectively. Amongst the 6,240 nodes, only key words describing Memorial pictures and specifically translated into English, are displayed here for visualization purpose. The size of the label is proportional to its topic probability. The distribution of topic probabilities for each Memorial pictures is directly derived from the topic probabilities associated with these key words.

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Supplementary Tables 1–2.

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Gagnepain, P., Vallée, T., Heiden, S. et al. Collective memory shapes the organization of individual memories in the medial prefrontal cortex. Nat Hum Behav (2019). https://doi.org/10.1038/s41562-019-0779-z

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