In its pragmatic project, empirical psychology employs metaphors to interpret data and deliver descriptive explanations.[i] Insofar as it is a positivist project, empirical psychology pursues reductive explanations which have the semblance of taking phenomena designated by natural language to be composed of more ‘real’ elements. Reductive descriptions of complex systems generally emphasize decomposition into structural and functional aspects amenable to computational modeling.[ii] Ideally, biological principles provide the bridge between functional models and neuroanatomy.
In A Suspicious Science, I review arguments about the limits of reductionism. For example, nonfundamental kinds like folk psychology concepts are multiply realizable. Furthermore, reductionism has trouble with nonderivational and causal forms of scientific explanation.[iii] As a heuristic strategy, reduction tends to misplace boundaries between vaguely defined levels and wholly ignores what happens outside the phenomena.[iv] I argue that new mechanical philosophy more accurately reflects research practices in neuroscience because it suggest the goal of explanation is not regulative deductive modeling but rather a description of the local organization of a phenomena.[v]
New mechanical philosophy posits that mechanical explanation is satisfied through the description of four basic features: (1) a phenomenon, (2) its parts, (3) its causal structure, and (4) its organization.[vi] The goal of this approach is to assess mechanistic inter-field integrations such as multi-level interdisciplinary neuroscience so as to deliver a description of the productive flow of a mechanism. This is achieved when a researcher can demonstrate the constraints, such as structural and temporal elements like stages and components, that mediate the relations between levels of the model.[vii] This approach can build from intra-level to inter-level relations toward ascertaining abstract mechanistic structures which may scaffold inter-field integration.[viii] This mosaic approach to biological phenomena theories can be assessed by its reliability, discrimination, efficiency, sensitivity, and robustness. This approach is better suited to modeling biological creatures who differ from idealized models due to design limitations, tolerances, and failure rates due to the contingencies of matter.[ix]
Scientific models in empirical psychology, like metaphors of mind and reductive schematics, are partial and imperfect representational entities. They seek realism but often imperfectly correspond to the phenomenon. They thus end up offering conditional/instrumental systems for pragmatic purposes.[x] At best, the positivist-pragmatist pursuit of hypothetico-deductive principles in empirical psychology has led to probabilistic models. In the book, I survey how context and the value-ladeness of empirical research render the discovery of hypothetico-deductive principles difficult if not impossible in mind sciences. In response, I espouse a perspectival realism whereby a psychologist must take into account both the constraints of their value-laden perspective and how much their models are underdetermined by the data.[xi]
Unlike reductionism, the use of expansionary analytic forms like the multi-level analysis employed in the new mechanical philosophy seems to admit the interlevel complexity of mind. For example, there is evidence that adversity caused by socioeconomic factors constrains genetic variations in serotonin and dopamine pathways.[xii] In describing the constraints that emerge from multilevel integration, descriptive explanations may depict nomothetic predictive structures. This interlevel modelling is akin to causal explanations used in both the social sciences and in probabilistic modeling. In this case, indicators of one level, say the social conditions in which an individual exists, serve as the ‘prior’ which would then allow the researcher to posit a set of probabilities concerning an individual’s behavior in a salient interaction. In light of this, I argue an integration rather than unity of science is a more pragmatic aspiration.
[i] Shaffner, K. (1993). Discovery and Explanation in Biology and Medicine, Chicago: The University of Chicago Press. Of course, reality can simultaneously sustain a range of diverging accounts we give of it (Shapin, 1982). The boundaries we choose determine the bias in reductionism: “This systematic bias operates relative to the system boundary chosen, so changing the system boundary will change what simplifications are made and appear acceptable.” (Wimsatt, 2006, p. 468). Wimsatt describes 20 biases in reductionist heuristics that affect model-building, observation and experimental design, functional localization, and other elements of the scientific process, see Wimsatt, 2006: 469-473.
[ii] Bechtel, W. & Richardson, R. C. (1992). Emergent phenomena and complex systems. In A. Beckermann, H. Flohr, & J. Kim (Eds.) Emergence or Reduction? Essays on the prospects of nonreductive physicalism, pp. 257-288. Berlin: Walter de Gruyter Verlag.; Bechtel, W. & Abrahamsen, A. (2010). Dynamic mechanistic explanation: Computational modeling of circadian rhythms as an exemplar for cognitive science. Studies in History and Philosophy of Science Part A.,1, 321-333.
[iii] Fodor, 1974.; Salmon, W. C. 1989. Four decades of scientific explanation. In P. Kitcher, & W. C. Salmon (Eds.), Scientific explanation (pp. 3–219). Minnesota Studies in the Philosophy of Science, XVIII. Minneapolis: University of Minnesota Press.
[iv] Wimsatt, 2006.
[v] Machamer, P., Darden, L., & Craver, C. F. 2000. Thinking about mechanisms. Philosophy of Science 67 (1):1-25.
[vi] Craver, C. & Tabery, J. “Mechanisms in Science”, The Stanford Encyclopedia of Philosophy (Summer 2019 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/sum2019/entries/science-mechanisms/>.
[vii] Craver C. F. 2005. Beyond reduction: mechanisms, multifield integration and the unity of neuroscience. Studies in history and philosophy of biological and biomedical sciences, 36(2), 373–395. https://doi.org/10.1016/j.shpsc.2005.03.008
[viii] Craver, 2005: 393. Wimsatt, W. C. (1976). Reductionism, levels of organization, and the mind-body problem. In Gordon G. Globus (ed.), Consciousness and the Brain. Plenum Press.
[ix] Wimsatt, 2021. Evolution and the Metabolism of Error: Biological Practice as Foundation for a Scientific Metaphysics
[x] Giere, R. 1999. Science without laws. Chicago: University of Chicago Press.
[xi] Giere, R. (2006). Scientific Perspectivism. Chicago, IL: University of Chicago Press.
Shapin, S. (1982). History of science and its sociological reconstructions. History of Science 20: 157-211.
Osbeck, L.M. (2019). Values in Psychological Sciences. Cambridge: Cambridge University Press.
[xii] Mitchell, C., Hobcraft, J., McLanahan, S. S., Siegel, S. R., Berg, A., Brooks-Gunn, J., Garfinkel, I., & Notterman, D. (2014). Social disadvantage, genetic sensitivity, and children’s telomere length. Proceedings of the National Academy of Sciences of the United States of America, 111(16), 5944–5949. https://doi.org/10.1073/pnas.1404293111