Online research in philosophy

Craver’s (2007) account of explanation in neurobiology offers one of the most sophisticated explications of the mechanism concept. This paper argues that despite groundbreaking advances in understanding mechanistic explanation, serious challenges remain. The first goal of this paper is to address the notorious problem of explanatory relevance concerning mechanistic explanation. I argue that Craver underestimates the importance of pragmatic constraints on the individuation of mechanisms, and that his suggestion for a solution of the explanatory relevance problem is therefore insufficient on several counts. My second goal is to develop an alternative that explicitly incorporates both pragmatic and ontic aspects of mechanism individuation.

This paper serves as an introduction to the special issue on “Reconciling Nature and Nurture in Behavior and Cognition Research” and sets its agenda to resolve the 'interactionist' dichotomy of nature as the genetic, and stable, factors of development, and nurture as the environmental, and plastic influences. In contrast to this received view it promotes the idea that all traits, no matter how developmentally fixed or universal they seem, contingently develop out of a single-cell state through the interaction of a multitude of developmental resources that defies any easy, dichotomous separation. It goes on to analyze the necessary ingredients for such a radical, epigenetic account of development, heredity and evolution: 1. A detailed understanding of the epigenetic nature of the regulatory mechanisms of gene expression; 2. The systematical questioning of preconceptions of 'explanatory' categories of behavior, such as 'innate' or 'programmed'; 3. Especially in psychological research the integration of the concepts of 'development' and 'learning', and a richer classification of the concept of 'environment' in the production of behavior; 4. A fuller understanding of the nature of inheritance that transcends the restriction to the genetic material as the sole hereditary unit, and the study of the process of developmental niche construction; and last 5. Taking serious the role of ecology in development and evolution. I hope that an accomplishment of the above task will then lead to a 'postgenomic' synthesis of nature and nurture that conceptualizes 'nature' as the natural phenotypic outcome 'nurtured' by the natural developmental process leading to it.

Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, they frequently find that the components of a mechanism interact in complex ways involving positive and negative feedback and that the organization often exhibits highly interactive local networks linked by a few long-range connections (small-worlds organization) and power law distributions of connections. The mechanisms are themselves active systems that are perturbed by inputs but not set in motion by them. Researchers also need to look up —situate a mechanism in its context, which may be a larger mechanism that modulates its behavior. When looking down is combined with looking around and up, mechanistic research results in an integrated, multi-level perspective.

S. Oyama’s prominent account of the Parity Thesis states that one cannot distinguish in a meaningful way between nature-based (i.e. gene-based) and nurture-based (i.e. environment-based) characteristics in development because the information necessary for the resulting characteristics is contained at both levels. Oyama as well as P. E. Griffiths and K. Stotz argue that the Parity Thesis has far-reaching implications for developmental psychology in that both nativist and interactionist developmental accounts of psychological capacities that presuppose a substantial nature/nurture dichotomy are inadequate. We argue that well-motivated abandoning of the nature/nurture dichotomy, as advocated in converging versions of the Parity Thesis in biology, does not necessarily entail abandoning the distinction between biologically given abilities necessary for the development of higher psychological capacities and the learning process they enable. Thus, contrary to the claims of the aforementioned authors, developmental psychologists need not discard a substantial distinction between innate (biologically given) characteristics and those acquired by learning, even if they accept the Parity Thesis. We suggest a two-stage account of development: the first stage is maturational and involves interaction of genetic, epigenetic and environmental causes, resulting in the endogenous biological ‘machinery’ (e.g. language acquisition device), responsible for learning in the subsequent stage of the developmental process by determining the organism’s responses to the environment. This account retains the crux of nativism (the endogenous biological structure determines the way the organism learns/responds to an environment) whilst adopting the developmentalist view of biology by characterizing environments as distinctly different in terms of structure and function in two developmental stages.

Carl Craver’s recent book offers an account of the explanatory and theoretical structure of neuroscience. It depicts it as centered around the idea of achieving mechanistic understanding, i.e., obtaining knowledge of how a set of underlying components interacts to produce a given function of the brain. Its core account of mechanistic explanation and relevance is causal-manipulationist in spirit, and offers substantial insight into casual explanation in brain science and the associated notion of levels of explanation. However, the focus on mechanistic explanation leaves some open questions regarding the role of computation and cognition.

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists.

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is required of an adequate mechanistic model. Mechanistic models are explanatory.

The first part of this paper deals with the relations between mechanistic explanation and reduction. It is argued that there is no insuperable conflict between the two, but that the mechanistic framework adds requirements that are not acknowledged in the model of property reduction. The second part concerns the relations between organization and environmental factors. Internal organization may be so tightly linked to external context that both have to be considered together.

We provide an account of mechanistic representation and explanation that has several advantages over previous proposals. In our view, explaining mechanistically is not simply giving an explanation of a mechanism. Rather, an explanation is mechanistic because of particular relations that hold between a mechanical representation, or model, and the target of explanation. Under this interpretation, mechanistic explanation is possible even when the explanatory target is not a mechanism. We argue that taking this view is not only coherent and plausible, it gives a more sophisticated view of the relationship between mechanical models and their targets. This allows us to address some ambiguities within the mechanist framework, and delivers a more intuitive way to interpret scientists' use of the term "mechanism".

As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding.