Cognitive and formal bases of perspectival models
Much of the work on “perspectivalism” about scientific theories and objects has focused on ways that a scientific model could be “perspectival” in some interesting sense, and the impacts on scientific practice or interpretation. In this talk, I instead explore whether it might be, in some sense, inevitable that we have perspectival scientific models. That is, I focus on the causes (rather than effects) of such models, though not contingent causal factors such as specific technologies or power relations within particular scientific communities. In contrast, I am principally concerned with cognitive and formal features (of human scientists investigating scientific problems) that are largely universal or ubiquitous in scientific inquiry, and that (nearly) inevitably lead to perspectival models. My talk considers multiple such factors of each type; in this abstract, I give just one example of each.
On the cognitive side, consider the cognitive necessity of conceptualization. Humans simply cannot function unless they understand the world in terms of concepts and categories that group together individuals with shared features, structures, or roles. The use of concepts to understand our world is not a contingent matter; we cannot do otherwise. Unsurprisingly, our scientific models are also based on our scientific concepts. Interestingly, however, a number of recent cognitive science experiments have revealed that our concepts—both everyday and scientific—do not simply “mirror” the structure or natural kinds of the world. Instead, our concepts are heavily shaped by our goals: the reason why we need some conceptual scheme influences both the structure and content of the resulting concepts. For example, the plant-centric concepts of a landscape designer/architect are importantly different from those of an evolutionary biologist. And since our scientific concepts are goal-relative (because all of our concepts are goal-relative in this way), then the resulting scientific models will have a measure of goal-relativity; they will be perspectival.
On the formal side, one standard view is that we have strong (though perhaps defeasible) reason to be realists about the objects of our best scientific theories. The notion of ‘best’ here is, however, relative to an evaluation standard: different theories will be superior depending on the metric by which we judge them. This goal-relativity of theories does not necessarily imply that they are perspectival, however, as we might be able to reconcile them in some way. However, one can prove that, for a wide range of formal conditions, the objects of different “best” theories for some domain will be incompatible (i.e., the objects that cannot be inter-translated). In these cases, we have no reason to privilege one of the evaluation standards, and so the formal constraints of the scientific learning problem lead directly to perspectival models.
Other cognitive and formal factors lead to the same overarching conclusion: our scientific models should almost certainly be perspectival, in the sense that the scientists’ goals and desired functions for the models matter. More generally, these considerations provide a normative justification for perspectival pluralism.