“Looking Up” and “Looking Down”: On the Dual Character of Mechanistic Explanations

Mechanistic explanation is at present the received view of scientific explanation. One of its central features is the idea that mechanistic explanations are both “downward looking” and “upward looking”: they explain by offering information about the internal constitution of the mechanism as well as the larger environment in which the mechanism is situated. That is, they offer both constitutive and contextual explanatory information. Adequate mechanistic explanations, on this view, accommodate the full range of explanatory factors both “above” and “below” the target phenomenon. The aim of this paper is to demonstrate that mechanistic explanation cannot furnish both constitutive and contextual information simultaneously, because these are different types of explanation with distinctly different aims. Claims that they can, I argue, depend on several intertwined confusions concerning the nature of explanation. Particularly, such claims tend to conflate mechanistic and functional explanation, which I argue ought to be understood as distinct. Conflating them threatens to oversell the explanatory power of mechanisms and obscures the means by which they explain. I offer two broad reasons in favor of distinguishing mechanistic and functional explanation: the first concerns the direction of explanation of each, and the second concerns the type of questions to which these explanations offer answers. I suggest an alternative picture on which mechanistic explanation is understood as fundamentally constitutive, and according to which an adequate understanding of a phenomenon typically requires supplementing the mechanistic explanation with a functional explanation.     

Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology.     

Rethinking the explanatory power of dynamical models in cognitive science

In this paper I offer an interventionist perspective on the explanatory structure and explanatory power of (some) dynamical models in cognitive science: I argue that some “pure” dynamical models – ones that do not refer to mechanisms at all – in cognitive science are “contextualized causal models” and that this explanatory structure gives such models genuine explanatory power. I contrast this view with several other perspectives on the explanatory power of “pure” dynamical models. One of the main results is that dynamical models need not refer to underlying mechanisms in order to be explanatory. I defend and illustrate this position in terms of dynamical models of the A-not-B error in developmental psychology as elaborated by Thelen and colleagues, and dynamical models of unintentional interpersonal coordination developed by Richardson and colleagues.     

Mechanisms,Experiments, and Theory-Ladenness: A Realist–Perspectivalist View

The terms “perspectivism” and “perspectivalism” have been the focus of an intense philosophical discussion with important repercussions for the debate about the role of mechanisms in scientific explanations. However, leading exponents of the new mechanistic philosophy have conceded more than was necessary to the radically subjectivistic perspectivalism, and fell into the opposite error, by retaining not negligible residues of objectivistic views about mechanisms. In order to remove this vacillation between the subjective-cultural and the objective-natural sides of mechanisms, we shall raise the question about theory-ladenness over again and interpret it in its connection with the technical–experimental nature of scientific knowledge, as affirming the perspectival character of scientific knowledge: It is because of the character at once theory-laden and practice-laden, i.e. technique-laden, of our putting questions to nature that empirical reality must be investigated from particular perspectives: nature can be known scientifically only from a potentially infinite (not determinable a priori) number of perspectives or theoretical points of view, concretely exemplified by mechanisms or experimental ‘machines’ that allow specific access to specific aspects of sensible reality.     

Between mechanical clocks and emergent flocks: complexities in biology

Even though complexity is a concept that is ubiquitously used by biologists and philosophers of biology, it is rarely made precise. I argue that a clarification of the concept is neither trivial nor unachievable, and I propose a unifying framework based on the technical notion of “effective complexity” that allows me to do justice to conflicting intuitions about biological complexity, while taking into account several distinctions in the usage of the concept that are often overlooked. In particular, I propose a distinction between two kinds of complexity, “mechanical” and “emergent”, which can be understood as different ways of relating the effective complexity of mechanisms and of behaviors in biological explanations. I illustrate the adequacy of this framework by discussing different attempts to understand intracellular organization in terms of pathways and networks. My framework provides a different way of thinking about recent philosophical debates, for example, on the difference between mechanistic and topological explanations and about the concept of emergence. Moreover, it can contribute to a proper assessment of metascientific arguments that invoke biological complexity.

     

Competing Explanations and Explaining-Away Arguments

An “explaining-away argument” [EAA] aims to discredit some explanatory hypothesis by appealing to the explanatory power of an alternative hypothesis. Nietzsche's genealogical argument against theism and Darwin's case against Paley's “old argument of design in nature” are famous examples. In order for EAAs to have their negative force, they must satisfy several conditions. After clarifying these conditions, I focus in on one in particular: the two hypotheses in question offer potential explanations that compete with one another. I develop a formal account of what it takes for potential explanations to compete, and I use this account to argue that EAAs are often misapplied today. This is due to the fact that philosophers often fail to appreciate the subtle line dividing competing from non-competing explanations.     

The wonderful adventures of Nils: or Taking leave of childhood

The relationship between neuroscience and psychoanalysis is studied by taking “the new mechanism of neuroscience” under scrutiny. That new trend stresses that neuroscientific explanations are mechanistic explanations in particular. Since the issue of psychological mechanisms lies at the core of psychoanalysis, it is crucial to study the relationship between neural and psychological mechanisms. The authors argue that neuroscience cannot verify psychoanalytic theories. However, by combining neuroscientific and psychoanalytic (psychological) viewpoints, it will be possible to approach a more holistic picture of psychological phenomena, here suggested by a new conception of defense mechanisms.     

A narrative synthesis of the applicability of the CaR–FA–X model in child and adolescent populations: a systematic review

The CaR–FA–X model [Williams, J. M. G., Barnhofer, T., Crane, C., Hermans, D., Raes, F., Watkins, E.,?…?Dalgleish, T. (2007). Autobiographical memory specificity and emotional disorder. Psychological Bulletin, 133(1), 122–148. doi:10.1037/0033-2909.133.1.122] is the most prominent and comprehensive model of overgeneral autobiographical memory (OGM) and provides a framework for OGM. The model comprises of three mechanisms, capture and rumination, functional avoidance and impaired executive control. These can independently, or in interaction, account for OGM. This systematic review aims to evaluate the existing research on the CaR–FA–X model, and trauma exposure studies specific to child and adolescent populations. The following databases were searched: “PsychInfo”, “PsychArticles”, “PubMed”, “Web of Science”, “Medline”, “SCOPUS” and “Embase” for English-language, peer-reviewed papers with samples <M?=?18 years, published since 1986. Support was reported for a relationship between trauma exposure and OGM as well as for capture errors and OGM. Limited support was found for rumination, avoidance and impaired executive control in isolation. No support was found for interacting mechanisms and OGM. Partial support for the CaR–FA–X model was found for child and adolescent populations. Recommendations, proposals for future research and plausible explanations for the mixed findings are discussed.     

Appraisal of certain methodologies in cognitive science based on Lakatos’s methodology of scientific research programmes

Attempts to apply the mathematical tools of dynamical systems theory to cognition in a systematic way has been well under way since the early 90s and has been recognised as a “third contender” to computationalist and connectionist approaches (Eliasmith in Philos Psychol 9(4):441–463, 1996). Nevertheless, it was also realised that such an application will not lead to a solid paradigm as straightforwardly as was initially hoped (Eliasmith 1996; van Leeuwen in Minds Mach 15:271–333, 2005). In this paper I explicate a method for assessing such proposals by drawing upon Lakatos’s (in: Lakatos, Musgrave (eds) Criticism and the growth of knowledge, Cambridge University Press, London, pp 91–195, 1970) methodology of scientific research programs (hereafter: “MSRP”). MSRP focuses on the heuristics of a particular field and gauges the model/theory building stratagems by reference to theoretical and empirical progress, on the one hand, and the continuity and the autonomy of the way the field’s heuristic generates its series of models/theories, on the other. The requirement of continuity and autonomy afford distinct senses of ad hoc-ness, which serve as an effective tool to detect various subtleties which may otherwise be missed: the present approach identifies shortcomings missed by Chemero’s (Radical embodied cognitive science, The MIT Press, Cambridge, 2009) radical embodied cognitive science and falsifies Chemero’s claim that the methodological powers of his model-based account is on a par with computationalism. In general, I claim that MSRP is relevant to current methodological issues in cognitive science and can supplement debates regarding “local” assessments of methodologies, such as that between mechanical versus covering-law explanations. MSRP must at least be viewed as a necessary constraint for any methodological considerations in cognitive science.

     

MATTERING AND MECHANISM: MUST A MECHANISTIC UNIVERSE BE DEPRESSING?

There is an intuition to the effect that, if human actions are explicable in scientific terms – that is, if mechanism holds – then our lives and actions do not matter. “Mattering” depends on successful intentional explanations of human actions. The intuition springs from an intuitive analogy between manipulation and mechanism: just as a manipulated agent's actions are not successfully explained in intentional terms, neither are the actions of a mechanistic agent. I explore ways to avoid the conclusion of this argument. Some of these ways are more promising than others, but all have non‐trivial philosophical consequences.     

Models and mechanisms in network neuroscience

This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a long-standing research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation, and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal of network neuroscience, two concerns are addressed. In response to the concern that functional network models are nonexplanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general.     

Representations and Cognitive Explanations: Assessing the Dynamicist's Challenge in Cognitive Science

Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations.     

Topological explanations and robustness in biological sciences

This paper argues that besides mechanistic explanations, there is a kind of explanation that relies upon “topological” properties of systems in order to derive the explanandum as a consequence, and which does not consider mechanisms or causal processes. I first investigate topological explanations in the case of ecological research on the stability of ecosystems. Then I contrast them with mechanistic explanations, thereby distinguishing the kind of realization they involve from the realization relations entailed by mechanistic explanations, and explain how both kinds of explanations may be articulated in practice. The second section, expanding on the case of ecological stability, considers the phenomenon of robustness at all levels of the biological hierarchy in order to show that topological explanations are indeed pervasive there. Reasons are suggested for this, in which “neutral network” explanations are singled out as a form of topological explanation that spans across many levels. Finally, I appeal to the distinction of explanatory regimes to cast light on a controversy in philosophy of biology, the issue of contingence in evolution, which is shown to essentially involve issues about realization.     

The methodological role of mechanistic-computational models in cognitive science

This paper discusses the relevance of models for cognitive science that integrate mechanistic and computational aspects. Its main hypothesis is that a model of a cognitive system is satisfactory and explanatory to the extent that it bridges phenomena at multiple mechanistic levels, such that at least several of these mechanistic levels are shown to implement computational processes. The relevant parts of the computation must be mapped onto distinguishable entities and activities of the mechanism. The ideal is contrasted with two other accounts of modeling in cognitive science. The first has been presented by David Marr in combination with a distinction of “levels of computation”. The second builds on a hierarchy of “mechanistic levels” in the sense of Carl Craver. It is argued that neither of the two accounts secures satisfactory explanations of cognitive systems. The mechanistic-computational ideal can be thought of as resulting from a fusion of Marr’s and Craver’s ideals. It is defended as adequate and plausible in light of scientific practice, and certain metaphysical background assumptions are discussed.

     

Contextual utility affects the perceived quality of explanations

Are explanations of different kinds (formal, mechanistic, teleological) judged differently depending on their contextual utility, defined as the extent to which they support the kinds of inferences required for a given task? We report three studies demonstrating that the perceived “goodness” of an explanation depends on the evaluator’s current task: Explanations receive a relative boost when they support task-relevant inferences, even when all three explanation types are warranted. For example, mechanistic explanations receive higher ratings when participants anticipate making further inferences on the basis of proximate causes than when they anticipate making further inferences on the basis of category membership or functions. These findings shed light on the functions of explanation and support pragmatic and pluralist approaches to explanation.     

Psychoanalytic action explanation

Psychoanalysis is concerned with neurotic behaviour that counts as an action if one takes into account “repressed” mental states. Freud's paradigmatic examples are a challenge for philosophical theories of action explanation. The main problem is that such symptomatic behaviour is, in a characteristic way, irrational. In line with standard interpretations, I will recap that psychoanalytic action explanation is not in accordance with Davidson's classical reason-explanation model, and I will recall that Freud's unconsciousness is not a second mind with its own rationality but that it is non-propositional in character. However, I then will argue that this characterization is not discriminating enough to explain the dynamical unconscious and overlooks the crucial role of “counter-cathexis”. With counter-cathexis the relevant desire turns out to be a complex with two inseparable aspects (“double-aspect view”), so that the causing belief–desire pair is still part of the space of reasons, although it cannot rationalize the behaviour. Psychoanalytic action explanation is hence still Davidsonian, albeit in a modified way.     

Einstein's theory of theories and types of theoretical explanation

In this paper I draw on Einstein's distinction between “principle” and “constructive” theories to isolate two levels of physical theory that can be found in both classical and (special) relativistic physics. I then argue that when we focus on theoretical explanations in physics, i.e. explanations of physical laws, the two leading views on explanation, Salmon's “bottom‐up” view and Kitcher's “top‐down” view, accurately describe theoretical explanations for a given level of theory. I arrive at this conclusion through an analysis of explanations of mass—energy equivalence in special relativity.     

Nominalistic content,grounding, and covering generalizations: Reply to ‘Grounding and the indispensability argument’

‘Grounding and the indispensability argument’ presents a number of ways in which nominalists can use the notion of grounding to rebut the indispensability argument for the existence of mathematical objects. I will begin by considering the strategy that puts grounding to the service of easy-road nominalists (“Nominalistic content meets grounding” section). I will give some support to this strategy by addressing a worry some may have about it (“A misguided worry about the grounding strategy” section). I will then consider a problem for the fast-lane strategy (“Grounding and generality: a problem for the fast lane” section) and a problem for easy-road nominalists willing to accept Liggins’ grounding strategy (“More on the grounding strategy and easy-road nominalism” section). Both are related to the problem of formulating nominalistic explanations at the right level of generality. I will then consider a problem that Liggins only hints at (“Mathematics and covering generalizations” section). This problem has to do with mathematics’ function of providing the sort of covering generalizations we need in scientific explanations.     

Equivalent explanations and mathematical realism. Reply to “Evidence,Explanation, and Enhanced Indispensability”

The author of “Evidence, Explanation, Enhanced Indispensability” advances a criticism to the Enhanced Indispensability Argument and the use of Inference to the Best Explanation in order to draw ontological conclusions from mathematical explanations in science. His argument relies on the availability of equivalent though competing explanations, and a pluralist stance on explanation. I discuss whether pluralism emerges as a stable position, and focus here on two main points: whether cases of equivalent explanations have been actually offered, and which ontological consequences should follow from these.     

Intentional concepts in cognitive neuroscience

In this article, I develop an account of the use of intentional predicates in cognitive neuroscience explanations. As pointed out by Maxwell Bennett and Peter Hacker, intentional language abounds in neuroscience theories. According to Bennett and Hacker, the subpersonal use of intentional predicates results in conceptual confusion. I argue against this overly strong conclusion by evaluating the contested language use in light of its explanatory function. By employing conceptual resources from the contemporary philosophy of science, I show that although the use of intentional predicates in mechanistic explanations sometimes leads to explanatorily inert claims, intentional predicates can also successfully feature in mechanistic explanations as tools for the functional analysis of the explanandum phenomenon. Despite the similarities between my account and Daniel Dennett's intentional-stance approach, I argue that intentional stance should not be understood as a theory of subpersonal causal explanation, and therefore cannot be used to assess the explanatory role of intentional predicates in neuroscience. Finally, I outline a general strategy for answering the question of what kind of language can be employed in mechanistic explanations.