Philosophy and meta-theory

Meta-theoretical myths in psychological science

There is a lot of talk of “meta science” in psychology these days. Meta science is essentially the scientific study of science itself—or, in other words, what has more traditionally been called “science studies”. The realization that psychological science (at least as indexed by articles published in high-prestige journals) is littered with questionable research practices, false positive results, and poorly justified conclusions has undoubtedly sparked an upsurge in this area.

The meta-scientific revolution in psychology is extremely sorely needed. It is, however, really a meta-methodological revolution so far. It has done little to rectify the lack of rigorous meta-theoretical work in psychology, which dates back all the way to the behaviorist expulsion of philosophy from the field (for example, see this paper by Toulmin & Leary, 1985). Psychology is today, as philosopher of psychology André Kukla has remarked (in this book), perhaps more strongly empiricist than any scientific field has been at any point in history. Although many researchers have an extremely advanced knowledge of statistics and measurement, few have more than a superficial familiarity with contemporary philosophy of science, mind, language, and society. When psychologists discuss meta-theoretical issues, they usually do it without engaging with the relevant philosophical literature.

I will describe three meta-theoretical myths that I think are hurting theory and research in psychology. This is not a complete list. I might very well update it later.

1. Scientific explanation is equivalent to the identification of a causal mechanism

This is on all counts an extremely common assumption in psychological science. In this respect, psychological theorizing is remarkably discordant with contemporary philosophical discussions of the nature of scientific explanation. While there can be little doubt that mechanistic explanation is a legitimate form of explanation, the notion that all scientific explanations fall (or should fall) in this category has not been a mainstream view among philosophers for several decades. Even some of the once most vocal proponents of explanatory reductionism abandoned this stance long ago. One of today’s leading philosophers of science, Godfrey-Smith (2001, p. 197) goes as far as to assert (in this book) that “It is a mistake to think there is one basic relation that is the explanatory relation . . . and it is also a mistake to think that there are some definite two or three such relations. The alternative view is to recognize that the idea of explanation operates differently within different parts of science—and differently within the same part of science at different times.”

Psychology is particularly diverse in terms of levels of explanation, ranging from instincts and neurobiology to intentionality and culture-embedment. For example, functional explanations (the existence of success of something is explained in terms of its function) are very popular in cognitive psychology. In my own field, personality and social psychology, a lot of the explanations are implicitly intentional (reason-based) explanations (a mental event or behavior is explained in terms of beliefs, desires, goals, intentions, emotions, and other intentional states of a rational agent). The reasoning is often that it would be rational for people to act in a particular way (people should be inclined to do this or that because they have this or that belief, goal, value, emotion, etc.) and that this explains why they de facto tend to act in this way. Even though the researchers seldom recognize it themselves, this is not a mechanistic explanation. The cause of the action is described in intentional rather than mechanistic terms. Not all causal explanations are mechanistic explanations (a very famous essay by the philosopher Donald Davidson that first made this case can be found here).

It is of course possible to argue that these are not real scientific explanations—that the only real scientific explanations are mechanistic. The important thing to realize is that this is akin to saying that much, perhaps most, of psychological research really is not science. In fact, even the so called causal mechanisms purportedly identified in psychological research are generally quite different from those identified in the natural sciences. Psychological research is usually predicated on a probabilistic, aggregate-level notion of causality (x causes y in the population if and only if x raises the probability of y in the population on average ceteris paribus) and a notion of probabilistic, aggregate-level mediation as mechanistic explanation, while the natural sciences often employ a deterministic notion of causality.

2. Statistical techniques contain assumptions about ontology and causality

I do not know how widespread this myth really is, but I have personally encountered it many times. Certainly, statistical tests can be based on specific assumptions about the ontology (i.e., the nature of an entity or property) of the analyzed elements and the causal relations between them. But the idea that these assumptions would therefore be intrinsic to the statistical tests is fallacious. Statistical tests merely crunch numbers—that is all they do. They are predicated on statistical assumptions (e.g., regarding distributions, measurement levels, and covaration). Assumptions about ontology and causality stem wholly from the researcher who seeks to make inferences from statistical test to theoretical claims. They are, ideally, based on theoretical reasoning and appropriate empirical evidence (or, less ideally, on taken-for-granted conventions and presuppositions).

One common version of this myth is the idea that techniques such as path analysis and structural equation modeling, which fit a structural model to the data, are based on the assumption that the predictor variables cause the outcome variables. This idea is also related to the notion that tests of mediation are inextricably bound up with the pursuit of mechanistic explanation from a reductionist perspective. These ideas are false. Structural models are merely complex models of the statistical relation between variables. Mediation analyses test whether there is an indirect statistical relation between two variables through their joint statistical relation to an intermediate variable. These tests yield valuable information about the change in variable in light of the change in other variables, which is necessary but far from sufficient for making inferences about causality. The conflation of statistical techniques with “causal analysis” in the social sciences is based on historical contingencies (i.e., that it what they were initially used for), rather than rational considerations (for example, see this paper by Denis & Legerski, 2006).

Yet another related idea is that statistical tests are based on presuppositions regarding the reality of the variables that are analyzed. It is true in a trivial sense that there is little point in performing a statistical test unless you assume that the analyzed variables have at least some reference to something out there in the world—or, in other words, that something is causing variation in scores on the variable. But the critical assumption is just that something is measured (much like science in general presupposes that there is something there to be studied). Assumptions about the ontology of what is measured are up to the researcher. For example, statistical analyses of “Big Five” trait data are consistent with a wide variety of assumptions regarding the ontology of the Big Five (e.g., that they are internal causal properties, behavioral regularities, abstract statistical patterns, instrumentalist fictions, socially constructed personae). Furthermore, the finding that scores on an instrument have (or do not have) desirable statistical properties does not tell us whether the constructs it purportedly measures are in some sense real or not. A simple realistic ontology is not necessary; nor is it usually reasonable, which brings us to the third myth.

3. Psychological constructs have a simple realistic ontology

At least some versions of this myth appear to be very common in psychological science. In its extreme form, it amounts to the idea that even abstract psychological constructs correspond to real internal properties under the skin, like organs, cells, or synapses, that are cut into the joints of nature in a determinate way. There are several fundamental problems here.

First, scientific descriptions in general are replete with indeterminacy.  There are often multiple equally valid descriptions that are useful for different purposes. In biology, for example, there are several different notions of ‘species’ (morphological, genetic, phylogenetic, allopatric), with somewhat different extension, that are used in different branches of the field. In chemistry, even the period table of elements—the paradigmatic  example of a scientific taxonomy—may be less determinately “cut into the joints of nature” that popular opinion would suggest (see this paper by the philosopher of science John Dupré). In psychology, the indeterminacy is much greater still. The empirical bodies of data are often difficult to overview and assess, both the phenomena themselves and the process of measurement may be complicated, and particularly intentional descriptions have messy properties. Debates over whether, for example, personality traits, political proclivities, or emotions “really” are one-, two-, or n-dimensional are therefore, from a philosophical perspective, misguided (and, by the way, another common mistake is to confuse conceptual representations such as these ones, which can have referents but not truth values, with theories, which have truth values!) What matters is whether the models are useful. Sometimes it may be the case that multiple models have legitimate uses, for example by describing a phenomenon with different levels of granularity and bandwidth. There are practical benefits in having the scientific community unite around a common model, but this is often not motivated by the genuine superiority of one model over the competitors.

Second, psychological constructs are commonly identified in terms of individual differences between persons. They are, in this sense, statistical idealizations or convenient fictions (“the average person”) that are useful for describing between-person variation in a group. The differences exist between persons rather within any particular person (as particularly James Lamiell has argued for decades, for example in this paper). It is of course possible to study psychological attributes that we have good reasons for ascribing to individuals in terms of between-person constructs. But the opposite chain of reasoning is fallacious; it is not possible to directly infer the existence or structure of an attribute at the level of the individual from models or constructs that usefully represent between-person variation at the level of the group aggregate (see, for example, this recent paper by Fisher, Medaglia, & Jeronimus, 2018). For example, it is misleading to describe personality traits such as the “Big Five” as internal causal properties, as has often been the case (see also this interesting paper by Simon Boag). This does not (contrary to what some critics have argued) necessarily imply that suchlike between-person constructs are useless for describing the psychology of individuals, but only that a naïve realistic ontology of the phenomena that they identify is precluded.

Third, at least insofar as we employ intentional descriptions (and possibly other descriptions as well), portraying persons as basically rational agents that harbor beliefs, desires, emotions, intentions, and other intentional states, we are faced with an additional problem. On this level of description, a person’s ontology is not just causally impacted by the external world; it is in part constituted by his or her relation to the world (this is often called the ‘externalism’ of the mental). This is because intentional states derive a part of their content from those aspects of the world they represent. The world affords both the raw materials that can be represented and acted upon and frameworks for how to represent and organize these raw materials. It is, in this sense, necessary for making different kinds of intentional thought and action possible. Therefore, at least some psychological attributes exist in the person’s embedment in the world—fully understanding them requires an understanding of both the person’s international psychological properties and his or her world, including both personal circumstances of life and the collective systems of meaning that actions (both behavioral and mental) are embedded within (see, for example, this classical paper by Fay & Moon, 1977).

On top of this, we have the problem most thoroughly explicated by the philosopher of science Ian Hacking (in this book) that many psychological attributes are moving targets with an interactive ontology. This means that the labels we place on the attributes (e.g., that certain sexual orientations have been viewed as pathological, immoral, or forbidden) elicit reactions in those who have the attributes and responses from the surrounding social environment that, in turn, change the attributes.

Swedish associate professor of psychology blogging about research and theoretical issues. Web page: Contact:

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