Explanandum Phenomenon Research Articles

Mechanisms and the problem of abstract models

New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply to a wide number of materially diverse systems. In this article, we examine the various attempts to integrate abstract models within the mechanist program, also presenting a further challenge: the Heimburg-Jackson model, which was introduced as an alternative to the Hodgkin-Huxley model. We argue that although the notion of abstraction as the omission of irrelevant mechanistic details appears to give a mechanistic solution for accommodating abstract models, this notion does not suit models whose epistemic strategy is not decompositional. As a result, the mechanist has to choose whether to dilute the mechanistic approach nearly beyond recognition or to claim that many, if not most, abstract theoretical models do not deliver mechanistic explanations, or qualify as explanatory at all.

There Are No Mathematical Explanations

If ontic dependence is the basis of explanation, there cannot be mathematical explanations. Accounting for the explanatory dependency between mathematical properties and empirical phenomena poses insurmountable metaphysical and epistemic difficulties, and the proposed amendments to the counterfactual theory of explanation invariably violate core commitments of the theory. Instead, mathematical explanations are either abstract mechanistic constitutive explanations or reconceptualizations of the explanandum phenomenon in which mathematics as such does not have an explanatory role. Explanation-like reasoning within mathematics, distinction between explanatory and nonexplanatory proofs, and comparative judgments of mathematical depth can be fully accounted for by a concept of formal understanding.

Mechanistic explanations and components of social mechanisms

The past two decades have witnessed an increase in interest in social mechanisms and mechanistic explanations of social macro-phenomena. This paper addresses the question of what the components of social mechanisms in mechanistic explanations of social macro-phenomena must be. Analytical sociology’s initial position and the main new proposals by analytical sociologists are discussed. It is argued that all of them are faced with outstanding difficulties. Subsequently, a minimal requirement regarding the components of social mechanisms is introduced. It is held that a component of a social mechanism in a mechanistic explanation of a social macro-phenomenon must not have the explanandum phenomenon as a part of it.

Integrating computation into the mechanistic hierarchy in the cognitive and neural sciences

It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system (e.g., an automaton) to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the components of the explanandum phenomenon and their causal organization. Moreover, each component in one level of mechanism is constituted and explained by components of an underlying level of mechanism. Hence, it seems, computational variables and functions cannot be mechanistically explained by the medium-dependent states and properties that implement them. How then, do the computational and the implementational integrate to create the mechanistic hierarchy? After explicating the general problem (Sect. 2), we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences (Sects. 3 and 4). We then examine two possible solutions (Sect. 5). On one solution, the mechanistic hierarchy embeds at the same levels computational and implementational properties. This picture fits with the view that computational explanations are mechanistic sketches. On the other solution, there are two separate hierarchies, one computational and another implementational, which are related by the implementation relation. This picture fits with the view that computational explanations are functional and autonomous explanations. It is less clear how these solutions fit with the view that computational explanations are full-fledged mechanistic explanations. Finally, we argue that both pictures are consistent with the reinforcement learning example, but that scientific practice does not align with the view that computational models are merely mechanistic sketches (Sect. 6).

Representing and Explaining: The Eikonic Conception of Scientific Explanation

AbstractThe ontic conception of explanation, according to which explanations are full-bodied things in the world, is fundamentally misguided. I argue instead for what I call the eikonic conception, according to which explanations are the product of an epistemic activity involving representations of the phenomena to be explained. What is explained in the first instance is a particular conceptualization of the explanandum phenomenon, contextualized within a given research program or explanatory project. I conclude that this eikonic conception has a number of benefits, including making better sense of scientific practice and allowing for the full range of normative constraints on explanation.

Mechanism, explanatory pluralism and efficient coding explanation in neuroscience

There is a growing debate within the philosophical community about the unity or disunity of neuroscience. The new Mechanist philosophers claim that neuroscience exhibits a mosaic unity –one in which different explanatory models may contribute to the explanation of some explanandum phenomenon ? by setting causal constraints on the space of possible mechanisms for ?. Non-mechanist philosophers frequently adopt some form or another of explanatory pluralism. In this paper I argue, first, that Mechanism is compatible with a popular version of explanatory pluralism, which I call causally restricted pluralism. Then, I present a liberalized version of explanatory pluralism –one according to which there are models in neuroscience that are explanatory of some phenomenon ? but that do not set any causal constraint on the space of possible mechanisms for ?. Finally, I argue that there is at least one pattern of explanation in neuroscience –namely, efficient coding explanation– that is better accounted for by liberal pluralism.

Mechanisms and Model-Based Functional Magnetic Resonance Imaging

Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance imaging, a novel neuroimaging method whose significance for current debates on psychological models and mechanistic explanation has yet to be explored.

Associationism without associative links: Thomas Brown and the associationist project

There are two roles that association played in 18th–19th century associationism. The first dominates modern understanding of the history of the concept: association is a causal link posited to explain why ideas come in the sequence they do. The second has been ignored: association is merely regularity in the trains of thought, and the target of explanation. The view of association as regularity arose in several forms throughout the tradition, but Thomas Brown (1778–1820) makes the distinction explicit. He argues that there is no associative link, and association is mere sequence. I trace this view of association through the tradition, and consider its implications: Brown's views, in particular, motivate a rethinking of the associationist tradition in psychology. Associationism was a project united by a shared explanandum phenomenon, rather than a theory united by a shared theoretical posit.

Explanatory completeness and idealization in large brain simulations: a mechanistic perspective

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: (1) Blue Brain, used for particular simulations of the cortical column in hybrid models, and (2) Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-a-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler.

Playing with networks: how economists explain

Network theory is applied across the sciences to study phenomena as diverse as the spread of SARS, the topology of the cell, the structure of the Internet and job search behaviour. Underlying the study of networks is graph theory. Whether the graph represents a network of neurons, cells, friends or firms, it displays features that exclusively depend on the mathematical properties of the graph itself. However, the way in which graph theory is implemented to the modelling of networks differs significantly across scientific fields. This article compares the economics variant of network theory with those of other fields. It shows how the methodology employed by economists to model networks is shaped by two explanatory desiderata: that the explanandum phenomenon is based on micro-economic foundations and that the explanation is general.

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.

Constitutive Explanatory Relevance

In what sense are the activities and properties of components in a mechanism explanatorily relevant to the behavior of a mechanism as a whole? I articulate this problem, the problem of constitutive relevance, and I show that it must be solved if we are to understand mechanisms and mechanistic explanation. I argue against some putative solutions to the problem of constitutive relevance, and I sketch a positive account according to which relevance is analyzed in terms of relationships of mutual manipulability between the behavior of a mechanism as a whole and the properties and activities of its components. My account is a causal-mechanical account in the sense that it is a particular expression of the idea that constitutive explanation is a matter of showing how an explanandum phenomenon is situated within the causal structure of the world. It is thus offered as a rival to epistemic (argument-centered) and psychological accounts of interlevel explanation.