Metaphor and Scientific Explanation

The idea that metaphors can serve an explanatory function, or that they can carry any theoretical weight, has raised the hackles of philosophers of science from Hempel onward. But there seem to be bona fide cases of explanatory metaphors in science. This chapter sketches an account of explanation, grounded in the connection between explanation and understanding. It discusses some potential arguments for this account and shows how it allows us to make sense of the idea that metaphors can explain. The chapter illustrates these ideas by looking at the case of informational metaphors in cell biology.

Can Children Benefit from Thought Experiments?

The “child as scientist” metaphor has been a source of many important insights about how children learn about the world. Extensive research has shown that, like scientists, children construct and test theories about the world through observation, exploration, and experimentation. What is not known, however, is whether children are similar to scientists in their employment of thought experimentation and other rationalistic processes when trying to learn about the world. Although the history of science has documented many instances of thought experiments being central to conceptual revolutions, there have been no empirical studies that ask the same question within developmental psychology. Such empirical studies are needed and warranted. Contrary to popular belief, children’s imagination is not fanciful or poorly disciplined. Instead, their imagination is constrained by knowledge of causal principles across different domains. Thus, engaging children in thought experiments should not produce unrealistic or impossible outcomes; rather, it should produce outcomes consistent with the causal structure of the world. Indeed, the consideration of hitherto unacknowledged implications of such outcomes may teach children something new about the world. This chapter reviews evidence from several studies that were not originally designed to test whether children can benefit from thought experiments but which nonetheless provide encouraging preliminary evidence of such benefit. Somewhat surprisingly, they hint that at least under some circumstances, the benefit from thought experiments may be greater than the benefit from direct observations of the world.

Is Imagination Constrained Enough for Science?

The imagination is a necessary tool for doing science, because it allows scientists to form hypotheses, make predictions about the future, and consider non-actual possibilities. But some have worried that the imagination is too unconstrained to be used in the service of scientific inquiry, which needs to be tied closely to reality. This chapter reviews these arguments and provides empirical evidence that the imagination is constrained enough for science. Both children and adults base their imagined worlds on the real world, and these worlds rarely stray from the causal structure of reality. And although the imagination may be subject to some biases that make certain kinds of worlds easier to imagine, these biases can be identified and corrected through training and enculturation in science. Finally, the conclusions drawn within an imagined context can be brought to bear appropriately on reality, allowing the results of thought experiments and hypothetical scenarios to inform the practice of science.

Abstraction and Representational Capacity in Computational Structures

Michael Weisberg’s book Simulation and Similarity argued that although mathematical models are sometimes described in narrative form, they are best understood as interpreted mathematical structures. But how can a mathematical structure be causal, as many models described in narrative seem to be? This chapter argues that models with apparently narrative form are actually computational structures. It explores this suggestion in detail, examining what computational structure consists of, the resources it offers modelers, and why attempting to re-describe computational models as imaginary concrete systems fails even more dramatically than it does for mathematical models.

Models and Reality

The philosopher Hilary Putnam uses model theory to cast doubt on our ability to engage semantically with an objective world. The role of mathematics for him is to prove this pessimistic conclusion. The present chapter, on the other hand, explores how models can help us to engage semantically with the objective world. Mathematics functions here as an analogy. Among their many other accomplishments, numbers boost the language’s expressive power; they give us access to recondite physical facts. Models, among their many other accomplishments, do the same thing. This is the analogy this chapter attempts to develop.

Imagining Mechanisms with Diagrams

Philosophy of science has long focused on how scientists achieve successful explanations of a phenomenon. But much of scientific work is aimed at something more basic: successfully and coherently imagining how a phenomenon might be explained—for example, hypothesizing a mechanism that could possibly produce the phenomenon. This chapter examines the graphics and diagrams that scientists in the field of circadian biology have generated and used to externalize and stabilize their imaginative reasoning. In particular, it examines how scientists revise their graphics as they sharpen and constrain their imaginative construal of a hypothetical mechanism. This analysis examines published diagrams that reflect the community’s developing understanding of the mechanism responsible for circadian rhythms in cyanobacteria and zeroes in on unpublished graphics from a single lab as they developed one operation in the mechanism. The goal is to understand how circadian biologists rely on graphics to overcome the difficulties of imagining the complex working of hypothetical mechanisms over time. Throughout, the chapter emphasizes that pursuing imaginative success is a scientific endeavor governed by its own internal norms, distinct from the norms of successful explanation. The aim is to direct philosophical analysis to scientists’ imaginings and to encourage integrating this understudied dimension of scientific practice with traditional philosophical analysis of normativity in scientific practice.

Imaginative Frames for Scientific Inquiry

Philosophers of science in the last half century have emphasized that scientific theories are not sets of transparently interpretable, logically connected true descriptions; rather, they involve implicit appeal to only partially articulated theoretical, practical, and empirical assumptions, and depart from stating the truth in various ways. One influential trend treats scientific theorizing as largely a process of model construction, and analyzes models as fictions. While this chapter embraces the increased role accorded to imagination and interpretation in scientific practice by the models-as-fictions view, it argues that different scientific representations relate to the world in importantly different ways. It distinguishes among a range of distinct representational tropes, or “frames,” all of which function to provide a perspective: an overarching intuitive principle for noticing, explaining, and responding to some subject. Starting with Max Black’s metaphor of metaphor as a pattern of etched lines on smoked glass, the chapter explains what makes frames in general powerful cognitive tools. It then distinguishes metaphor from some of its close cousins, especially telling details, just-so fictions, and analogies, first in the context of ordinary cognition and then in application to science, focusing on the different sorts of gaps that frames or models can open up between scientific representations and reality.

“Learning by Thinking” in Science and in Everyday Life

This chapter introduces “learning by thinking” (LbT) as a form of learning distinct from familiar forms of learning through observation. When learning by thinking, the learner gains genuinely new insight in the absence of novel observations “outside the head.” Scientific thought experiments are canonical examples, but the phenomenon is much more widespread, and includes learning by explaining to oneself, through analogical reasoning, or through mental simulation. The chapter argues that episodes of LbT can be re-expressed as explicit arguments or inferences but are neither psychologically nor epistemically reducible to explicit arguments or inferences, and that this partially explains the novelty of the conclusions reached through LbT. It also introduces a new perspective on the epistemic value of LbT processes as practices with potentially beneficial epistemic consequences, even when the commitments they invoke and the conclusions they immediately deliver are not themselves true.

Models, Fictions, and Conditionals

This chapter discusses recent debates about scientific models and fictional or imaginary systems. Model-based science often apparently deals in non-actual or fictional systems, and does so by design. This practice raises questions about the relationships that can exist between such models and their real-world targets, especially about the evident empirical utility of some highly idealized scientific models. The chapter considers a range of recent treatments of models and offers an account that gives a central role to counterfactual conditionals. This chapter takes these conditionals to be the typical output of scientific modeling. These conditionals raise many problems of their own, but understanding models in these terms does enable some progress.

The Fictional Character of Scientific Models

Many philosophers have drawn parallels between scientific models and fictions. This chapter is concerned with a recent version of this analogy, which compares models to the imagined characters of fictional literature. Though versions of the position differ, the shared idea is that modeling essentially involves imagining concrete systems analogously to the way that we imagine characters and events in response to works of fiction. Advocates of this view argue that imagining concrete systems plays an ineliminable role in the practice of modeling that cannot be captured by other accounts. The approach thus leaves open what we should say about the ontological status of model systems, and here advocates differ among themselves, defending a variety of realist or anti-realist positions. I argue that this debate over the ontological status of model systems is misguided. If model systems are the kinds of objects fictional realists posit, they can play no role in explaining the epistemology of modeling for an advocate of this approach. So they are at best superfluous. Defenders of the approach should focus on developing an account of the epistemological role of imagining model systems.