In this post, I explore how analogical modes of explanation have been used in empirical psychology to loop together data derived through experiments and descriptive explanation. Metaphor has been used to transcend the limitations of experiments on human subjects because it allows for framing higher-level interpretation of data through notions of purpose, constraints, and goals.
The pursuit of hypothetico-deductive principles and mechanistic explanation in empirical psychology requires analogical thinking via metaphor for the creative, pragmatic unification of data. Analogical reasoning thus serves as a conceptual laboratory for developing schemes by which scientists design (i.e., discover) and interpret (i.e., justify) their research. Analogical thought creates order and necessity (or immanent connection) through two principles of association: resemblance and contiguity.
The pragmatic function of metaphor in empirical psychology is often to help settle intransigent empirical or rational disputes through tracing practical consequences. A metaphor that is pragmatically true will lead to a model that points researchers in an empirically worthwhile direction. For William James, what a scientist seeks in philosophy is intellectual abstraction that maintains a positive connection to the world. By offering superordinate structures to conceive of behavior, interpretation of data through metaphors helps scientists achieve an understanding of human behavior that enables, among other things, predictions about future behavior and reliable frameworks to understand intentional thought.[i]
In his magisterial study of memory, psychologist Kurt Danziger describes how reciprocal confirmation of symbolic structures at different levels of discourse implies a hall of mirrors wherein the root metaphor of an era is formed from taken-for-granted collective representations that serve as the metaphysical background into which we are acculturated.[ii] Metaphor can determine ontology by offering a fundamental re-description that frames our dispositions towards a hidden entity. Indeed, metaphor partially creates what it purports to reveal because it structures how we see what we don’t know.[iii] Analogy is a practical strategy of disambiguation to conceive of unknown domains; we use it in situations when we are confused. Thus, metaphors of mind occupy a unique position between evidence and trust: we collect enough evidence through an analogical structure that we grow to trust it as a guide for prediction and frame the collection of further evidence.[iv]
Psychological theory in the 18th century devolved on metaphors of mind-as-entity characterized by the qualities of tangibility and passivity, with a simplicity of structure.[v] In the late-19th century there was a shift to an emphasis on mind-as-living being wherein ‘mindscapes,’ ‘sentient webs,’ and other generative metaphors for mind-as-substance predominated. In the early part of the twentieth century, animate and spatial metaphors proliferated, while in the latter half of the century systems and computer metaphors were in ascendance.[vi]
Mind as the tenor (i.e., the thing being described) of the metaphor in contemporary experimental psychology draws upon two major vehicles: biology and engineering. Both are modes of scientific naturalism and it is largely through them that contemporary psychology employs epistemology adapted from the natural sciences. By employing them, the following associational fields are brought to bear on the study of mind: evolution, developmental processes, emergence, the body, and machine learning. Through structural correspondence, concepts from these vehicles render the tenor of mind more tractable to mechanistic explanation.
The unifying theories of biology are cell theory, evolution theory, genes, and homeostasis.[vii] Placing psychology in correspondence with biology allows the entailment of principles like probability, ecology, and a broader range of spatial and temporal scales. The key principles of engineering are: testability, maintenance, integrity, external integration, ethics, and management.[viii] Taking such a design stance allows psychologists to consider the adaptive functions of mind, creative solutions to problems faced by the organism, and the modes in which these solutions can be implemented.
Contemporary models illustrate how these metaphors serve pragmatic ends in theoretical and empirical work. Models that emphasize dynamic mechanics in neural systems harken back to the metaphor of mind as machine, portraying the brain as a sophisticated set of mechanisms that produce determinate regularities.[ix] Contemporary dynamic and heuristic models of mind, through associational fields in engineering, emphasize constraints upon computation.
Ideally, the metaphors of mind with which we move forward will neither obfuscate elements of human dignity nor reify our technological achievements. It is the responsibility of the psychological sciences to deliver metaphors that encapsulate the function, context, and purpose of the phenomena. In this context, a discursive, biological model with space for descriptive localized mechanistic exhibition may be the most pragmatic metaphor of mind.
[i] Kagan, J. 2017. Five Constraints on Predicting Behavior. Cambridge. MA: The MIT Press.
Hoffman R. R., Cochran E. L., & Nead J. M. 1990. Cognitive metaphors in the history of experimental psychology. In Leary D. (Ed.), Metaphors in the history of psychology (pp. 173-209). Cambridge, UK: Cambridge University Press.
[ii] Danziger, K. 2008. Marking the Mind: A History of Memory. Cambridge: Cambridge University Press.
[iii] Berggren, D. 1963. The use and abuse of metaphor II. The Review of Metaphysics, 17, 450-472.
[iv] Children are said to use such practical systems as they explore and learn about their environment. Gopnick, 1996
[v] Kearns, M. 1987. Metaphors of mind in fiction and psychology. Lexington: The University press of Kentucky.
[vi] See review and tables in Gentner, D. & Grudin, J. 1985. The evolution of mental metaphors in psychology: A 90-year retrospective. American Psychologist, 40, 181-192.
[vii] Tibell, L.A.E. & and Harms, U. 2017. Biological principles and threshold concepts for understanding natural selection. Science & Education, 26, 953-973.; Ruse, M. 2005. Darwinism and mechanism: Metaphor in science. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36 (2), 285-302
[viii] Hurst, K.S. 1999. Engineering design principles. New York: Butterworth-Heinemann.
[ix] Chemero, T. & Silberstein, M. 2008. After the Philosophy of Mind: Replacing Scholasticism with Science. Philosophy of Science 75 (1):1-27.