Hostname: page-component-84b7d79bbc-g5fl4 Total loading time: 0 Render date: 2024-08-04T08:11:11.765Z Has data issue: false hasContentIssue false

Mechanisms and Model-Based Functional Magnetic Resonance Imaging

Published online by Cambridge University Press:  01 January 2022

Abstract

Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance imaging, a novel neuroimaging method whose significance for current debates on psychological models and mechanistic explanation has yet to be explored.

Type
Cognitive Science
Copyright
Copyright © The Philosophy of Science Association

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.)

Footnotes

I thank Carl Craver, Ron Mallon, Gualtiero Piccinini, and Dan Weiskopf for invaluable comments.

References

Batterman, Robert, and Rice, Collin. 2014. “Minimal Model Explanations.” Philosophy of Science 81 (3): 349–76.10.1086/676677CrossRefGoogle Scholar
Bechtel, William. 2009. “Looking Down, Around, and Up: Mechanistic Explanation in Psychology.” Philosophical Psychology 22 (5): 543–64.10.1080/09515080903238948CrossRefGoogle Scholar
Bechtel, William, and Abrahamsen, Adele. 2005. “Explanation: A Mechanist Alternative.” Studies in History and Philosophy of the Biological and Biomedical Sciences 36 (2): 421–41.CrossRefGoogle ScholarPubMed
Biederman, Irving. 2000. “Recognizing Depth-Rotated Objects: A Review of Recent Research and Theory.” Spatial Vision 13 (2–3): 241–53.CrossRefGoogle ScholarPubMed
Biederman, Irving, Cooper, Eric, Hummel, John, and Fiser, Jozsef. 1993. “Geon Theory as an Account of Shape Recognition in Mind, Brain and Machine.” In Proceedings of the 4th British Machine Vision Conference, ed. Illingworth, John, 175–86. London: Springer.Google Scholar
Bogen, Jim. 2005. “Regularities and Causality: Generalizations and Causal Explanations.” Studies in History and Philosophy of Biology and Biomedical Sciences 36:397420.10.1016/j.shpsc.2005.03.009CrossRefGoogle ScholarPubMed
Craver, Carl. 2007. Explaining the Brain. Oxford: Oxford University Press.10.1093/acprof:oso/9780199299317.001.0001CrossRefGoogle Scholar
Davis, Tyler, Love, Bradley, and Preston, Alison. 2012. “Learning the Exception to the Rule: Model-Based fMRI Reveals Specialized Representations for Surprising Category Members.” Cerebral Cortex 22:260–73.10.1093/cercor/bhr036CrossRefGoogle Scholar
Glascher, Jan, and O’Doherty, John. 2010. “Model-Based Approaches to Neuroimaging: Combining Reinforcement Learning Theory with fMRI Data.” WIREs Cognitive Science 1:501–10.10.1002/wcs.57CrossRefGoogle ScholarPubMed
Hayworth, Kenneth, and Biederman, Irving. 2006. “Neural Evidence for Intermediate Representations in Object Recognition.” Vision Research 46:4024–31.CrossRefGoogle ScholarPubMed
Hempel, Carl, and Oppenheim, Paul. 1948. “Studies in the Logic of Explanation.” Philosophy of Science 15:135–75.CrossRefGoogle Scholar
Huettel, Scott, Song, Allen, and McCarthy, Gregory. 2009. Functional Magnetic Resonance Imaging. Sunderland, MA: Sinauer.Google Scholar
Hummel, John, and Biederman, Irving. 1992. “Dynamic Binding in a Neural Network for Shape Recognition.” Psychological Review 99:480517.10.1037/0033-295X.99.3.480CrossRefGoogle Scholar
Kim, Jiye, and Biederman, Irving. 2012. “Greater Sensitivity to Nonaccidental than Metric Changes in the Relations between Simple Shapes in the Lateral Occipital Cortex.” NeuroImage 63:1818–26.10.1016/j.neuroimage.2012.08.066CrossRefGoogle ScholarPubMed
Krekelberg, Bart, Boynton, Geoffrey, and van Wezel, Richard J. A.. 2006. “Adaptation: From Single Cells to BOLD Signals.” TRENDS in Neurosciences 29 (5): 250–56.CrossRefGoogle ScholarPubMed
Kruschke, John. 1992. “ALCOVE: An Exemplar-Based Connectionist Model of Category Learning.” Psychological Review 99:2244.10.1037/0033-295X.99.1.22CrossRefGoogle ScholarPubMed
Love, Bradley, and Gureckis, Todd. 2007. “Models in Search of a Brain.” Cognitive, Affective, and Behavioral Neuroscience 7 (2): 90108.CrossRefGoogle ScholarPubMed
Love, Bradley C., Medin, Douglas L., and Gureckis, Todd M.. 2004. “SUSTAIN: A Network Model of Category Learning.” Psychological Review 111:309–32.CrossRefGoogle ScholarPubMed
O’Doherty, John, Hampton, Alan, and Kim, Hackjin. 2007. “Model-Based fMRI and Its Application to Reward Learning and Decision Making.” Annals of the New York Academy of Sciences 1104:3553.CrossRefGoogle ScholarPubMed
Salmon, Wesley. 1984. Scientific Explanation and the Causal Structure of the World. Princeton, NJ: Princeton University Press.Google Scholar
Salmon, Wesley 1989. “Four Decades of Scientific Explanation.” In Minnesota Studies in the Philosophy of Science, Vol. 13, Scientific Explanation, ed. Salmon, Wesley and Kitcher, Philip, 3219. Minneapolis: University of Minnesota Press.Google Scholar
Weiskopf, Daniel. 2011. “Models and Mechanisms in Psychological Explanation.” Synthese 183 (3): 313–38.CrossRefGoogle Scholar
White, Corey, and Poldrack, Russell. 2013. “Using fMRI to Constrain Theories of Cognition.” Perspectives on Psychological Science 8 (1): 7983.10.1177/1745691612469029CrossRefGoogle ScholarPubMed