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Reverse-engineering the cortical architecture for controlled semantic cognition

Abstract

We employ a reverse-engineering approach to illuminate the neurocomputational building blocks that combine to support controlled semantic cognition: the storage and context-appropriate use of conceptual knowledge. By systematically varying the structure of a computational model and assessing the functional consequences, we identified the architectural properties that best promote some core functions of the semantic system. Semantic cognition presents a challenging test case, as the brain must achieve two seemingly contradictory functions: abstracting context-invariant conceptual representations across time and modalities, while producing specific context-sensitive behaviours appropriate for the immediate task. These functions were best achieved in models possessing a single, deep multimodal hub with sparse connections from modality-specific regions, and control systems acting on peripheral rather than deep network layers. The reverse-engineered model provides a unifying account of core findings in the cognitive neuroscience of controlled semantic cognition, including evidence from anatomy, neuropsychology and functional brain imaging.

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Fig. 1: The seven different architectures.
Fig. 2: The model environment.
Fig. 3: Comparing the conceptual abstraction across the architectures without (in Phase 1) and with (in Phase 2) the additional demand of context-appropriate output.
Fig. 4: Comparing the training time across the architectures without (in Phase 1) and with (in Phase 2) the additional demand of context-appropriate output.
Fig. 5: Consequences of the location of the connection to control.
Fig. 6: Simulating different error patterns in SD versus SA.
Fig. 7: Simulating dynamic changes in univariate activation and functional connectivity across contexts.

Data availability

The data are available upon request or can be generated using the code provided.

Code availability

The code for replicating all the simulations is available in the Supplementary Information and online at https://github.com/JacksonBecky/reverse-engineered-semantics. The code for further analysis is available online.

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Acknowledgements

This work was supported by a British Academy Postdoctoral Fellowship awarded to R.L.J. (no. pf170068), a programme grant to M.A.L.R. and T.T.R. from the Medical Research Council (grant no. MR/R023883/1), an Advanced Grant from the European Research Council to M.A.L.R. (GAP: 670428) and Medical Research Council intramural funding (no. MC_UU_00005/18). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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R.L.J., T.T.R. and M.A.L.R. made substantial contributions to the conception and design of the work, the interpretation of the data and the manuscript revisions. R.L.J. acquired the results and drafted the manuscript.

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Correspondence to Rebecca L. Jackson or Matthew A. Lambon Ralph.

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The authors declare no competing interests.

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Peer review information Nature Human Behaviour thanks Peter Hagoort, Andrew Saxe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Marike Schiffer.

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Supplementary Tables 1–7, Notes 1–10 (including Figs. 1–12) and Methods 1 and 2.

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Jackson, R.L., Rogers, T.T. & Lambon Ralph, M.A. Reverse-engineering the cortical architecture for controlled semantic cognition. Nat Hum Behav 5, 774–786 (2021). https://doi.org/10.1038/s41562-020-01034-z

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