Hostname: page-component-6766d58669-nqrmd Total loading time: 0 Render date: 2026-05-19T00:37:08.640Z Has data issue: false hasContentIssue false
  • English
  • Français

Beyond metaphors and semantics: A framework for causal inference in neuroscience

Published online by Cambridge University Press:  28 November 2019

Roberto A. Gulli Open the ORCID record for Roberto A. Gulli [Opens in a new window]
Roberto A. Gulli*
Affiliation:
Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY10027; Center for Theoretical Neuroscience, Columbia University, New York, NY10027; Department of Neuroscience, Columbia University, New York, NY10027r.gulli@columbia.eduhttp://robertogulli.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 42 , 2019 , e230
Check for updates
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Anderson, A. A. (2019) Assessing statistical results: Magnitude, precision, and model uncertainty. The American Statistician 73 (supp1.):118–21. doi:10.1080/00031305.2018.1537889.CrossRefGoogle Scholar
Baayen, R. H., Davidson, D. J. & Bates, D. M. (2008) Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language 59(4):390–412. doi:10.1016/j.jml.2007.12.005.CrossRefGoogle Scholar
Caton, R. (1875) The electric currents of the brain.” In: Proceedings of the Forty-Third Annual Meeting of the British Medical Association, August 1875, Edinburgh. British Medical Journal 2:257. doi:10.1136/bmj.2.765.257.Google Scholar
Dodds, W. J. (1878) Localisation of functions of the brain: Being an historical and critical analysis of the question. Journal of Anatomy and Physiology 12(Pt. 4):636–60.Google ScholarPubMed
Ferrier, D. (1886) Functions of the brain, 2nd edition. G. P. Putnam's Sons.Google Scholar
Goense, J. B. M. & Logothetis, N. K. (2008) Neurophysiology of the BOLD fMRI signal in awake monkeys. Current Biology 18(9):631–40. doi:10.1016/j.cub.2008.03.054.CrossRefGoogle ScholarPubMed
Haugeland, J. (1985) Artificial intelligence. MIT Press.Google Scholar
Hill, A. B. (1965) The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine 58(5):295–300.CrossRefGoogle ScholarPubMed
Jazayeri, M., & Afraz, A. (2017) Navigating the neural space in search of the neural code. Neuron 93(5):1003–14. doi:10.1016/j.neuron.2017.02.019.CrossRefGoogle ScholarPubMed
Marinescu, I. E., Lawlor, P. N.and Konrad, P., Kording, K. P. (2018) Quasi-experimental causality in neuroscience and behavioural research. Nature Human Behaviour 2(12):891–98. doi:10.1038/s41562-018-0466-5.CrossRefGoogle ScholarPubMed
Pearl, J., Glymour, M. & Jewell, N. P. (2016) Causal inference in statistics. Wiley.Google Scholar
Phillips, C.V. & Goodman, K. J. (2004) The missed lessons of Sir Austin Bradford Hill. Epidemiologic Perspectives & Innovations 1:Article 3. doi:10.1186/1742-5573-1-3.Google ScholarPubMed
Rougier, J. (2019) p-Values, Bayes factors, and sufficiency. The American Statistician 73(supp1):148–51. doi:10.1080/00031305.2018.1502684.CrossRefGoogle Scholar
Saxena, S. & Cunningham, J. P. (2019) Towards the neural population doctrine. Current Opinion in Neurobiology 55:103–11. doi:10.1016/j.conb.2019.02.002.CrossRefGoogle ScholarPubMed
Sejnowski, T. J., Churchland, P. S. & Movshon, J. A. (2014) Putting big data to good use in neuroscience. Nature Neuroscience 17(11):1440–1. doi:10.1038/nn.3839.CrossRefGoogle ScholarPubMed
Song, L., Langfelder, P. & Horvath, S. (2013) Random generalized linear model: A highly accurate and interpretable ensemble predictor. BMC Bioinformatics 14(1):5. doi:10.1186/1471-2105-14-5.CrossRefGoogle ScholarPubMed
Wang, H. & Yang, J. (2016) Multiple confounders correction with regularized linear mixed effect models, with application in biological processes. bioRxiv, November. Cold Spring Harbor Laboratory, 089052. doi:10.1101/089052.CrossRefGoogle Scholar