Abstract
Linking models and brain measures offers a number of advantages over standard analyses. Models that have been evaluated on previous datasets can provide theoretical constraints and assist in integrating findings across studies. Model-based analyses can be more sensitive and allow for evaluation of hypotheses that would not otherwise be addressable. For example, a cognitive model that is informed from several behavioural studies could be used to examine how multiple cognitive processes unfold across time in the brain. Models can be linked to brain measures in a number of ways. The information flow and constraints can be from model to brain, brain to model, or reciprocal. Likewise, the linkage from model and brain can be univariate or multivariate, as in studies that relate patterns of brain activity with model states. Models have multiple aspects that can be related to different facets of brain activity. This is well illustrated by deep learning models that have multiple layers or representations that can be aligned with different brain regions.
Model-based approaches offer a lens on brain data that is complementary to popular multivariate decoding and representational similarity analysis approaches. Indeed, these approaches can realise greater theoretical significance when situated within a model-based approach.
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Acknowledgements
This work was supported by the NIH Grant 1P01HD080679, ESRC grant (ES/W007347/1), Wellcome Trust Investigator Award WT106931MA, and Royal Society Wolfson Fellowship 183029 to B.C.L. Although mostly original, this paper draws on some previously published work (Love, 2020a, b; Turner et al., 2017). Thanks to Sebastian Bobadilla-Suarez for helpful comments on a previous draft.
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Appendices
Questions for Consideration
Model-based analyses can offer additional theoretical constraints but can also introduce degrees of freedom when choosing which model-based analysis to conduct. How should one choose which model-based analysis to conduct?
How much should we demand of researchers in terms of verifying their models before conducting a model-based analysis given that the analysis is only as good as the model used?
Will behavioural studies be increasingly valued as one avenue to verify models for model-based neuroscience?
The motivation for a model-based analysis can involve more than the model itself to include the bridge theory that links model components to brain regions. How does one choose between this focused, top-down approach to model application and a bottom-up, data-driven approach?
Models can be specified at multiple levels of abstraction (see “levels of mechanism” discussion). Why is it rare to have multiple models for the same task that differ in their level of abstraction?
Further Reading
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Love, B. C. (2020a). Levels of biological plausibility. Philosophical Transactions of the Royal Society B. https://doi.org/10.1098/rstb.2019.0632
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Love, B. C. (2020b). Model-based fMRI analysis of memory. Current Opinion in Behavioral Sciences, 32, 88–93. https://doi.org/10.1016/j.cobeha.2020.02.012
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Turner, B. M., Forstmann, B. U., Love, B. C., Palmeri, T. J., & Van Maanen, L. (2017). Approaches to analysis in model-based cognitive neuroscience. Journal of Mathematical Psychology, 76, 65–79.
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Turner, B. M., Forstmann, B. U., & Steyvers, M. (2019). Joint models of neural and behavioral data. Springer International Publishing. https://doi.org/10.1007/978-3-030-03688-1
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Love, B.C. (2024). Linking Models with Brain Measures. In: Forstmann, B.U., Turner, B.M. (eds) An Introduction to Model-Based Cognitive Neuroscience. Springer, Cham. https://doi.org/10.1007/978-3-031-45271-0_2
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