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Reverse Inference, Abduction, and Probability in Cognitive Neuroscience

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Handbook of Abductive Cognition

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Abstract

The chapter presents the problem of reverse inference in cognitive neuroscience, discussing its analysis in terms of abductive and Bayesian reasoning and highlighting the main methodological issues and open problems surrounding this crucial inferential practice with reference to case studies from current research.

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References

  • Adrian, E. D., & Matthews, B. H. (1934a). The interpretation of potential waves in the cortex. The Journal of Physiology, 81(4), 440–471.

    Article  Google Scholar 

  • Adrian, E. D., & Matthews, B. H. (1934b). The Berger rhythm: Potential changes from the occipital lobes in man. Brain, 57(4), 355–385.

    Article  Google Scholar 

  • Alexander, L., & Moore, M. (2021). Deontological ethics. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Winter 2021 edition.

    Google Scholar 

  • Bandettini, P. A. (2012). Twenty years of functional MRI: The science and the stories. Neuroimage, 62(2), 575–588.

    Article  Google Scholar 

  • Bandettini, P. A., Wong, E. C., Hinks, R. S., Tikofsky, R. S., & Hyde, J. S. (1992). Time course EPI of human brain function during task activation. Magnetic Resonance in Medicine, 25(2), 390–397.

    Article  Google Scholar 

  • Beam, E., Potts, C., Poldrack, R. A., & Etkin, A. (2021). A data-driven framework for mapping domains of human neurobiology. Nature Neuroscience, 24(12), 1733–1744.

    Article  Google Scholar 

  • Bechtel, W., & Huang, L. T.-L. (2022). Philosophy of Neuroscience. Cambridge University Press.

    Book  Google Scholar 

  • Belliveau, J., Kennedy, D., McKinstry, R., Buchbinder, B., Weisskoff, R., Cohen, M., Vevea, J., Brady, T., & Rosen, B. (1991). Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 254(5032), 716–719.

    Article  Google Scholar 

  • Bilder, R. M., Sabb, F. W., Parker, D. S., Kalar, D., Chu, W. W., Fox, J., Freimer, N. B., & Poldrack, R. A. (2009). Cognitive ontologies for neuropsychiatric phenomics research. Cognitive Neuropsychiatry, 14(4–5), 419–450.

    Article  Google Scholar 

  • Bourgeois-Gironde, S. (2010). Is neuroeconomics doomed by the reverse inference fallacy? Mind & Society, 9(2), 229–249.

    Article  Google Scholar 

  • Bruers, S., & Braeckman, J. (2014). A review and systematization of the trolley problem. Philosophia, 42(2), 251–269.

    Article  Google Scholar 

  • Calzavarini, F., & Cevolani, G. (2022). Abductive reasoning in cognitive neuroscience: Weak and strong reverse inference. Synthese, 200(2), 1–26.

    Article  Google Scholar 

  • Calzavarini, F., & Viola, M. (2020). Neural Mechanisms: New Challenges in the Philosophy of Neuroscience (Vol. 17). Springer Nature.

    Google Scholar 

  • Cauda, F., Nani, A., Liloia, D., Manuello, J., Premi, E., Duca, S., Fox, P. T., & Costa, T. (2020). Finding specificity in structural brain alterations through bayesian reverse inference. Human Brain Mapping, 41(15), 4155–4172.

    Article  Google Scholar 

  • Christoff, K., & Owen, A. (2006). Improving reverse neuroimaging inference: Cognitive domain versus cognitive complexity. Trends in Cognitive Sciences, 10(8), 352–353.

    Article  Google Scholar 

  • Churchland, P. S. (2008). The impact of neuroscience on philosophy. Neuron, 60(3), 409–411.

    Article  Google Scholar 

  • Costa, T., Manuello, J., Ferraro, M., Liloia, D., Nani, A., Fox, P. T., Lancaster, J., & Cauda, F. (2021). Bacon: A tool for reverse inference in brain activation and alteration. Technical report.

    Google Scholar 

  • Crupi, V. (2020). Confirmation. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Stanford, Spring 2020 edition.

    Google Scholar 

  • Davis, T., LaRocque, K. F., Mumford, J. A., Norman, K. A., Wagner, A. D., & Poldrack, R. A. (2014). What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. Neuroimage, 97, 271–283.

    Article  Google Scholar 

  • Del Pinal, G., & Nathan, M. J. (2013). There and up again: On the uses and misuses of neuroimaging in psychology. Cognitive Neuropsychology, 30(4), 233–252.

    Article  Google Scholar 

  • Del Pinal, G., & Nathan, M. J. (2017). Two kinds of reverse inference in cognitive neuroscience. In The Human Sciences After the Decade of the Brain (pp. 121–139). Elsevier.

    Google Scholar 

  • Dockès, J., Poldrack, R. A., Primet, R., Gözükan, H., Yarkoni, T., Suchanek, F., Thirion, B., & Varoquaux, G. (2020). NeuroQuery, comprehensive meta-analysis of human brain mapping. Elife, 9, e53385.

    Article  Google Scholar 

  • Douven, I. (2021). Abduction. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Summer 2021 edition.

    Google Scholar 

  • Edwards, A. W. F. (1976). Likelihood: An Account of the Statistical Concept of “Likelihood” and Its Application to Scientific Inference. Cambridge University Press.

    Google Scholar 

  • Ferris, S. H., de Leon, M. J., Wolf, A. P., Farkas, T., Christman, D. R., Reisberg, B., Fowler, J. S., MacGregor, R., Goldman, A., George, A. E., et al. (1980). Positron emission tomography in the study of aging and senile dementia. Neurobiology of Aging, 1(2), 127–131.

    Article  Google Scholar 

  • Foot, P. (1978). The Problem of Abortion and the Doctrine of Double Effect, in Her Virtues and Vices (pp. 19–32). Berkeley/Los Angeles: University of California Press.

    Google Scholar 

  • Forster, M., & Sober, E. (2010). Why likelihood? In The Nature of Scientific Evidence: Statistical, Philosophical, and Empirical Considerations (pp. 153–165). University of Chicago Press.

    Google Scholar 

  • Forster, M. R. (2006). Counterexamples to a likelihood theory of evidence. Minds and Machines, 16(3), 319–338.

    Article  Google Scholar 

  • Fox, P. T., Laird, A. R., Fox, S. P., Fox, P. M., Uecker, A. M., Crank, M., Koenig, S. F., & Lancaster, J. L. (2005). BrainMap taxonomy of experimental design: Description and evaluation. Human Brain Mapping, 25(1), 185–198.

    Article  Google Scholar 

  • Fox, P. T., & Lancaster, J. L. (2002). Mapping context and content: The brainmap model. Nature Reviews Neuroscience, 3(4), 319–321.

    Article  Google Scholar 

  • Gandenberger, G. (2016). Why I Am Not a Likelihoodist. Ann Arbor, MI: Michigan Publishing, University of Michigan Library.

    Google Scholar 

  • Gelman, A. (2017). Is the dorsal anterior cingulate cortex “selective for pain”?

    Google Scholar 

  • Glymour, C., & Hanson, C. (2016). Reverse Inference in Neuropsychology. The British Journal for the Philosophy of Science, 67(4), 1139–1153.

    Article  Google Scholar 

  • Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001). An fMRI investigation of emotional engagement in moral judgment. Science, 293(5537), 2105–2108.

    Article  Google Scholar 

  • Harman, G. H. (1965). The inference to the best explanation. The Philosophical Review, 74(1), 88–95.

    Article  Google Scholar 

  • Haynes, J.-D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7(7), 523–534.

    Article  Google Scholar 

  • Hebart, M. N., & Baker, C. I. (2018). Deconstructing multivariate decoding for the study of brain function. Neuroimage, 180, 4–18.

    Article  Google Scholar 

  • Henson, R. (2006). Forward inference using functional neuroimaging: Dissociations versus associations. Trends in Cognitive Sciences, 10(2), 64–69.

    Article  MathSciNet  Google Scholar 

  • Hutzler, F. (2014). Reverse inference is not a fallacy per se: Cognitive processes can be inferred from functional imaging data. NeuroImage, 84, 1061–1069.

    Article  Google Scholar 

  • Jeffreys, H. (1961). Theory of Probability. Clarendon.

    MATH  Google Scholar 

  • Klein, C. (2010). Philosophical issues in neuroimaging. Philosophy Compass, 5(2), 186–198.

    Article  Google Scholar 

  • Klein, C. (2011). The dual track theory of moral decision-making: A critique of the neuroimaging evidence. Neuroethics, 4(2), 143–162.

    Article  Google Scholar 

  • Klein, C. (2012). Cognitive ontology and region-versus network-oriented analyses. Philosophy of Science, 79(5), 952–960.

    Article  Google Scholar 

  • Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., & Turner, R. (1992). Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proceedings of the National Academy of Sciences, 89(12), 5675–5679.

    Article  Google Scholar 

  • Lieberman, M. D., & Eisenberger, N. I. (2015). The dorsal anterior cingulate cortex is selective for pain: Results from large-scale reverse inference. Proceedings of the National Academy of Sciences, 112(49), 15250–15255.

    Article  Google Scholar 

  • Lierberman, M. D. (2015). Comparing pain, cognitive, and salience accounts of dacc.

    Google Scholar 

  • Lipton, P. (2003). Inference to the Best Explanation. Routledge.

    Book  Google Scholar 

  • Machery, E. (2014). In Defense of Reverse Inference. The British Journal for the Philosophy of Science, 65(2), 251–267.

    Article  Google Scholar 

  • Magnani, L. (2009). Creative abduction and hypothesis withdrawal. In Models of Discovery and Creativity (pp. 95–126). Springer.

    Google Scholar 

  • Magnani, L. (2011). Abduction, Reason and Science: Processes of Discovery and Explanation. Springer Science & Business Media.

    Google Scholar 

  • Nathan, M. J., & Del Pinal, G. (2017). The future of cognitive neuroscience? Reverse inference in focus. Philosophy Compass, 12(7), e12427.

    Article  Google Scholar 

  • Nathan, M. J., & Pinal, G. D. (2016). Mapping the mind: Bridge laws and the psycho-neural interface. Synthese, 193(2), 637–657.

    Article  Google Scholar 

  • Niiniluoto, I. (2018). Truth-Seeking by Abduction. Berlin/Heidelberg: Springer.

    Book  MATH  Google Scholar 

  • Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430.

    Article  Google Scholar 

  • Pauli, W. M., O’Reilly, R. C., Yarkoni, T., & Wager, T. D. (2016). Regional specialization within the human striatum for diverse psychological functions. Proceedings of the National Academy of Sciences, 113(7), 1907–1912.

    Article  Google Scholar 

  • Peirce, C. S. (1931). Collected Papers of Charles Sanders Peirce. Harvard University Press.

    Google Scholar 

  • Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59–63.

    Article  Google Scholar 

  • Poldrack, R. A. (2010). Mapping mental function to brain structure: How can cognitive neuroimaging succeed? Perspectives on Psychological Science, 5(6), 753–761.

    Article  Google Scholar 

  • Poldrack, R. A. (2011). Inferring mental states from neuroimaging data: From reverse inference to large-scale decoding. Neuron, 72(5), 692–697.

    Article  Google Scholar 

  • Poldrack, R. A. (2014). Is reverse inference a fallacy? A comment on Hutzler.

    Google Scholar 

  • Poldrack, R. A., Kittur, A., Kalar, D., Miller, E., Seppa, C., Gil, Y., Parker, D. S., Sabb, F. W., & Bilder, R. M. (2011). The Cognitive Atlas: Toward a knowledge foundation for cognitive neuroscience. Frontiers in Neuroinformatics, 5, 17.

    Article  Google Scholar 

  • Poldrack, R. A., & Yarkoni, T. (2016). From brain maps to cognitive ontologies: Informatics and the search for mental structure. Annual Review of Psychology, 67, 587–612.

    Article  Google Scholar 

  • Price, C. J., & Friston, K. J. (2005). Functional ontologies for cognition: The systematic definition of structure and function. Cognitive Neuropsychology, 22(3–4), 262–275.

    Article  Google Scholar 

  • Ramsey, J. D., Hanson, S. J., Hanson, C., Halchenko, Y. O., Poldrack, R. A., & Glymour, C. (2010). Six problems for causal inference from fMRI. NeuroImage, 49(2), 1545–1558.

    Article  Google Scholar 

  • Ritchie, J. B., Kaplan, D. M., & Klein, C. (2019). Decoding the brain: Neural representation and the limits of multivariate pattern analysis in cognitive neuroscience. The British Journal for the Philosophy of Science, 70(2), 581–607.

    Article  Google Scholar 

  • Roskies, A. L. (2007). Are neuroimages like photographs of the brain? Philosophy of Science, 74(5), 860–872.

    Article  Google Scholar 

  • Roskies, A. L. (2008). Neuroimaging and inferential distance. Neuroethics, 1(1), 19–30.

    Article  Google Scholar 

  • Royall, R. (1997). Statistical Evidence: A Likelihood Paradigm. Routledge.

    MATH  Google Scholar 

  • Rubin, T. N., Koyejo, O., Gorgolewski, K. J., Jones, M. N., Poldrack, R. A., & Yarkoni, T. (2017). Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition. PLOS Computational Biology, 13(10), e1005649.

    Article  Google Scholar 

  • Salmon, W. C. (2001). Explanation and confirmation: A Bayesian critique of inference to the best explanation. In Explanation (pp. 61–91). Springer.

    Google Scholar 

  • Schurz, G. (2017). Patterns of abductive inference. In Springer Handbook of Model-Based Science (pp. 151–173). Springer.

    Google Scholar 

  • Shackman, A. J. (2015). The importance of respecting variation in cingulate anatomy: Comment on lieberman & eisenberger 2015 and yarkoni.

    Google Scholar 

  • Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011). Network modelling methods for fMRI. Neuroimage, 54(2), 875–891.

    Article  Google Scholar 

  • Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., et al. (2013). Functional connectomics from resting-state fMRI. Trends in Cognitive Sciences, 17(12), 666–682.

    Article  Google Scholar 

  • Sprenger, J., & Hartmann, S. (2019). Bayesian Philosophy of Science. Oxford University Press.

    Book  Google Scholar 

  • Teplan, M. et al. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2(2), 1–11.

    Google Scholar 

  • Thomson, J. J. (1976). Killing, letting die, and the trolley problem. The Monist, 59(2), 204–217.

    Article  Google Scholar 

  • Tomaiuolo, F., Cecchetti, L., Gibson, R. M., Logi, F., Owen, A. M., Malasoma, F., Cozza, S., Pietrini, P., & Ricciardi, E. (2016). Progression from vegetative to minimally conscious state is associated with changes in brain neural response to passive tasks: A longitudinal single-case functional MRI study. Journal of the International Neuropsychological Society, 22(6), 620–630.

    Article  Google Scholar 

  • Tudor, M., Tudor, L., & Tudor, K. I. (2005). Hans Berger (1873–1941) – The history of electroencephalography. Acta medica Croatica: casopis Hravatske akademije medicinskih znanosti, 59(4), 307–313.

    Google Scholar 

  • Turner, J. A., & Turner, M. D. (2021). Re-conceptualizing domains in neuroscience, hopes and utopias aside. Nature Neuroscience, 24(12), 1643–1644.

    Article  Google Scholar 

  • Uttal, W. R. (2001). The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain. The MIT Press.

    Google Scholar 

  • Wager, T. D., Atlas, L. Y., Botvinick, M. M., Chang, L. J., Coghill, R. C., Davis, K. D., Iannetti, G. D., Poldrack, R. A., Shackman, A. J., & Yarkoni, T. (2016). Pain in the ACC? Proceedings of the National Academy of Sciences, 113(18), E2474–E2475.

    Article  Google Scholar 

  • Weiskopf, D. A. (2021). Data mining the brain to decode the mind. In Neural Mechanisms (pp. 85–110). Springer.

    Google Scholar 

  • Yarkoni, T. (2015a). No, the dorsal anterior cingulate is not selective for pain: Comment on Lieberman and Eisenberger.

    Google Scholar 

  • Yarkoni, T. (2015b). Still not selective: comment on comment on comment on Lieberman & Eisenberger.

    Google Scholar 

  • Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Tommaso Brischetto Costa, Davide Bottari, Luca Cecchetti, Vincenzo Crupi, Igor Douven, Vincenzo Fano, Jordi Manuello, Jan Sprenger, and Marco Viola for very useful discussions on the topics of the chapter. Davide Coraci acknowledges funding from an ERASMUS+ Mobility Grant 2020/2021 and Gustavo Cevolani from the PRIN 2017 grant (n. 201743F9YE) “From Models to Decisions” of the Italian Ministry of Education, University and Research.

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Authors and Affiliations

  1. Models, Inference, and Decisions (MInD) Group, MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy

    Davide Coraci

  2. Institut d’Histoire et de Philosophie des Sciences et des Techniques (IHPST), Paris 1 Panthéon-Sorbonne University, Paris, France

    Davide Coraci

  3. Department of Letter, Philosophy, Communication, University of Bergamo, Bergamo, Italy

    Fabrizio Calzavarini

  4. Center for Logic, Language, and Cognition (LLC), University of Turin, Turin, Italy

    Fabrizio Calzavarini

  5. Models, Inference, and Decisions (MInD) Group, MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy

    Gustavo Cevolani

  6. Center for Logic, Language, and Cognition (LLC), University of Turin, Turin, Italy

    Gustavo Cevolani

Authors
  1. Davide Coraci
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  2. Fabrizio Calzavarini
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  3. Gustavo Cevolani
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Correspondence to Davide Coraci .

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Editors and Affiliations

  1. Department of Humanities, University of Pavia, Pavia, Italy

    Lorenzo Magnani

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Coraci, D., Calzavarini, F., Cevolani, G. (2023). Reverse Inference, Abduction, and Probability in Cognitive Neuroscience. In: Magnani, L. (eds) Handbook of Abductive Cognition. Springer, Cham. https://doi.org/10.1007/978-3-031-10135-9_60

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