Markus I. Eronen

Heating up the measurement debate: What psychologists can learn from the history of physics

Discussions of psychological measurement are largely disconnected from issues of measurement in the natural sciences. We show that there are interesting parallels and connections between the two, by focusing on a real and detailed example (temperature) from the history of science. More specifically, our novel approach is to study the issue of validity based on the history of measurement in physics, which will lead to three concrete points that are relevant for the validity debate in psychology. First of all, studying the causal mechanisms underlying the measurements can be crucial for evaluating whether the measurements are valid. Secondly, psychologists would benefit from focusing more on the robustness of measurements. Finally, we argue that it is possible to make good science based on (relatively) bad measurements, and that the explanatory success of science can contribute to justifying the validity of measurements.

Robustness and reality

Robustness is often presented as a guideline for distinguishing the true or real from mere appearances or artifacts. Most of recent discussions of robustness have focused on the kind of derivational robustness analysis introduced by Levins, while the related but distinct idea of robustness as multiple accessibility, defended by Wimsatt, has received less attention. In this paper, I argue that the latter kind of robustness, when properly understood, can provide justification for ontological commitments. The idea is that we are justified in believing that things studied by science are real insofar as we have robust evidence for them. I develop and analyze this idea in detail, and based on concrete examples show that it plays an important role in science. Finally, I demonstrate how robustness can be used to clarify the debate on scientific realism and to formulate new arguments.

Interventionism and Supervenience: A New Problem and Provisional Solution

The causal exclusion argument suggests that mental causes are excluded in favour of the underlying physical causes that do all the causal work. Recently, a debate has emerged concerning the possibility of avoiding this conclusion by adopting Woodwards interventionist theory of causation. Both proponents and opponents of the interventionist solution crucially rely on the notion of supervenience when formulating their positions. In this article, we consider the relation between interventionism and supervenience in detail and argue that importing supervenience relations into the interventionist framework is deeply problematic. However, rather than reject interventionist solutions to exclusion wholesale, we wish to propose that the problem lies with the concept of supervenience. This would open the door for a moderate defence of the interventionist solution to the exclusion argument.

The significance of levels of organization for scientific research: A heuristic approach 1

The concept of levels of organization has come under fire recently as being useless for scientific and philosophical purposes. In this paper, we show that levels is actually a remarkably resilient and constructive conceptual tool that can be, and in fact is, used for a variety of purposes. To this effect, we articulate an account of the importance of the levels concept seen in light of its status as a major organizing concept of biology. We argue that the usefulness of levels is best seen in the heuristic contributions the concept makes to treating and structuring scientific problems. We illustrate this with two examples from biological research.

Don't blame the model : Reconsidering the network approach to psychopathology

The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue that this rationale for the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders. As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches that can provide a better framework for conceptualizing mental disorders. Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward. (PsycINFO Database Record

Robust Realism for the Life Sciences

Although scientific realism is the default position in the life sciences, philosophical accounts of realism are geared towards physics and run into trouble when applied to fields such as biology or neuroscience. In this paper, I formulate a new robustness-based version of entity realism, and show that it provides a plausible account of realism for the life sciences that is also continuous with scientific practice. It is based on the idea that if there are several independent ways of measuring, detecting or deriving something, then we are justified in believing that it is real. I also consider several possible objections to robustness-based entity realism, discuss its relationship to ontic structural realism, and show how it has the potential to provide a novel response to the pessimistic induction argument.

Understanding through modeling: the explanatory power of inadequate representation

The claim that models are representationally inadequate, as the title of this special issue tentatively suggests, is provocative. Isn’t it the case that, by their very nature, models aim at idealization, approximation, and simplification? These features are often seen as merits rather than defects of models. Pragmatists and instrumentalists have argued extensively that this kind of “inadequacy” does not matter, as long as models serve their descriptive or predictive purposes. However, models also seem to play a vital role in understanding and explaining reality and in giving us descriptions of what there is; prima facie, their function does not reduce to merely enabling us to somehow get along. Given their representational “deficiencies”, it is not at all clear to which extent and how models can help us understand the world, or how they can possibly exhibit something like explanatory power.