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Geometric models reveal behavioural and neural signatures of transforming experiences into memories


How do we preserve and distort our ongoing experiences when encoding them into episodic memories? The mental contexts in which we interpret experiences are often person-specific, even when the experiences themselves are shared. Here we develop a geometric framework for mathematically characterizing the subjective conceptual content of dynamic naturalistic experiences. We model experiences and memories as trajectories through word-embedding spaces whose coordinates reflect the universe of thoughts under consideration. Memory encoding can then be modelled as geometrically preserving or distorting the ‘shape’ of the original experience. We applied our approach to data collected as participants watched and verbally recounted a television episode while undergoing functional neuroimaging. Participants’ recountings preserved coarse spatial properties (essential narrative elements) but not fine spatial scale (low-level) details of the episode’s trajectory. We also identified networks of brain structures sensitive to these trajectory shapes.

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Fig. 1: Topic weights in episode and recall content.
Fig. 2: Modelling naturalistic stimuli and recalls.
Fig. 3: Naturalistic extensions of classic list-learning memory analyses.
Fig. 4: Novel content-based metrics of naturalistic memory: precision and distinctiveness.
Fig. 5: Precision reflects the completeness of recall, whereas distinctiveness reflects recall specificity.
Fig. 6: Trajectories through topic space capture the dynamic content of the episode and recalls.
Fig. 7: Language used in the most and least precisely remembered events.
Fig. 8: Brain structures that underlie the transformation of experience into memory.

Data availability

The fMRI data we analysed are available online at The behavioural data are available at

Code availability

All of our analysis code can be downloaded from


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We thank L. Chang, J. Chen, C. Honey, C. Lee, L. Owen, E. Whitaker, X. Xu and K. Ziman for feedback and scientific discussions, and we thank J. Chen, Y. C. Leong, C. Honey, C. Yong, K. Norman and U. Hasson for sharing the data used in our study. Our work was supported in part by National Science Foundation Established Program to Stimulate Competitive Research Award number 1632738. The content is solely the responsibility of the authors and does not necessarily represent the official views of our supporting organizations. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information




Conceptualization: A.C.H. and J.R.M.; methodology: A.C.H., P.C.F. and J.R.M.; software: A.C.H., P.C.F. and J.R.M.; analysis: A.C.H., P.C.F. and J.R.M.; writing, reviewing and editing: A.C.H., P.C.F. and J.R.M.; and supervision: J.R.M.

Corresponding author

Correspondence to Jeremy R. Manning.

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The authors declare no competing interests.

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Peer review information Primary Handling Editor: Marike Schiffer.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Methods detail for recall trajectory analysis displayed in Figure 6B.

A. This panel replicates Figure 6B, but with two additions. First, individual participants’ recall trajectories are displayed (faintly) as light gray lines. Second, three locations on the trajectory have been highlighted (blue, yellow, and red circles). B. These zoomed-in views of the locations highlighted in Panel A show the average trajectory (black) and individual participants’ trajectories (gray lines) that intersect the given region of topic space. C. For each circular region of topic space tiling the 2D embedding plane displayed in Panel A, we compute the distribution of angles formed between each participant’s trajectory segment (that is, the point at which the trajectory enters and exists the region of topic space) and the x-axis. The distributions of angles for these three example regions are displayed in the colored rose plots. We use Rayleigh tests to assign an arrow direction, length, and color for that region of topic space. Arrows displayed in color are significant at the p < 0.05 level (corrected). The arrow directions are oriented according to the circular means of each distribution, and the arrow lengths are proportional to the lengths of those mean vectors. The example regions have been oriented from left to right in decreasing order of consistency across participants.

Extended Data Fig. 2 Recall temporal correlation matrices and event segmentation fits.

Each panel is in the same format as Figure 2E. The yellow boxes indicate HMM-identified event boundaries.

Extended Data Fig. 3 Episode-recall event correlation matrices.

Each panel is in the same format as Figure 2G. The yellow boxes mark the matched episode event for each recall event (that is, the maximum correlation in each column).

Extended Data Fig. 4 Episode and recall topic proportions matrix K-optimization functions.

We selected the optimal K-value for the episode and each recall topic proportions matrix using the formula described in Methods. This computation resulted in a curve for each matrix, describing the Wasserstein distance between the distributions of within-event and across-event topic vector correlations, as a function of K.

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Supplementary Methods, Supplementary Figs. 1–3 and Supplementary References.

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Heusser, A.C., Fitzpatrick, P.C. & Manning, J.R. Geometric models reveal behavioural and neural signatures of transforming experiences into memories. Nat Hum Behav 5, 905–919 (2021).

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