I propose a dynamic causal approach to characterizing the notion of a mechanism. Levy and Bechtel, among others, have pointed out several critical limitations of the new mechanical philosophy, and pointed in a new direction to extend this philosophy. Nevertheless, they have not fully fleshed out what that extended philosophy would look like. Based on a closer look at neuroscientific practice, I propose that a mechanism is a dynamic causal system that involves various components interacting, typically nonlinearly, with one another (...) to produce a phenomenon of interest. (shrink)

In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, all (...) mechanisms are more or less general, and on the other, many design principles are causal systems. (shrink)

This paper examines a case of failed interdisciplinary collaboration, between experimental stem cell research and theoretical systems biology. Recently, two groups of theoretical biologists have proposed dynamical systems models as a basis for understanding stem cells and their distinctive capacities. Experimental stem cell biologists, whose work focuses on manipulation of concrete cells, tissues and organisms, have largely ignored these proposals. I argue that ‘failure to communicate’ in this case is rooted in divergent views of explanation: the theoretically-inclined modelers are committed (...) to a version of the covering-law view, while experimental stem cell biologists aim at mechanistic explanations. I propose a way to reconcile these two explanatory approaches to cell development, and discuss the significance of this result for interdisciplinary collaboration in systems biology and beyond. (shrink)

It is widely assumed that mechanistic explanations are causal explanations. Many prominent new mechanists endorse interventionism as the correct analysis of explanatory causal models in biology and other fields. This article argues that interventionism is not entirely satisfactory in this regard. A case study of Jacob and Monod’s operon model shows that at least some important mechanistic explanations in biology present significant contrasts with the interventionist account. This result motivates a more inclusive approach to mechanistic explanation, allowing for noncausal aspects.

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 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, we further demonstrate it through a concrete example, of reinforcement learning, in the cognitive and neural sciences. We then examine two possible solutions. 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. (shrink)

A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is (...) not clear how to distinguish explanatory from non-explanatory relations. In this paper, I draw from both views and suggest that, in the cognitive sciences, relations of counterfactual dependence that allow manipulation and control can be explanatory even when they are neither causal nor mechanistic. Furthermore, the ability to allow manipulation can determine whether non-causal counterfactual dependence relations are explanatory. I present a preliminary framework for manipulation relations that includes some non-causal relations and use two examples from the cognitive sciences to show how this framework distinguishes between explanatory and non-explanatory, non-causal relations. The proposed framework suggests that, in the cognitive sciences, causal and non-causal relations have the same criterion for explanatory value, namely, whether or not they allow manipulation and control. (shrink)

Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory which are typically based on similarity or rule functions as explanations of how categories emerge. Although these theories work well at modeling highly controlled stimuli in laboratory settings, they often perform less well outside of these settings, such as explaining (...) the emergence of background knowledge processes. In order to explain background knowledge, we present a non-similarity-based post-Skinnerian theory of human language called Relational Frame Theory which is rooted in a philosophical world view called functional contextualism. This theory offers a very different interpretation of how categories emerge through the functions of behavior and through contextual cues, which may be of some benefit to existing categorization theories. Specifically, RFT may be able to offer a novel explanation of how background knowledge arises, and we provide some mathematical considerations in order to identify a formal model. Finally, we discuss much of this work within the broader context of general semantic knowledge and artificial intelligence research. (shrink)

Philosophers have traditionally treated physicalism as an empirically informed metaphysical thesis. This approach faces a well-known problem often referred to as Hempel’s dilemma: formulations of physicalism tend to be either false or indeterminate. The generally preferred strategy to address this problem involves an appeal to a hypothetical complete and ideal physical theory. After demonstrating that this strategy is not viable, I argue that we should redefine physicalism as an interdisciplinary research program seeking to explain the mental in terms of the (...) physical that encompasses the physical sciences, the psychological and brain sciences, and philosophy. Redefining physicalism in this way improves upon previous reconstructive accounts while avoiding the indeterminacy associated with orthodox forms of futurist physicalism. (shrink)

Explaining the behaviour of ecosystems is one of the key challenges for the biological sciences. Since 2000, new-mechanicism has been the main model to account for the nature of scientific explanation in biology. The universality of the new-mechanist view in biology has been however put into question due to the existence of explanations that account for some biological phenomena in terms of their mathematical properties (mathematical explanations). Supporters of mathematical explanation have argued that the explanation of the behaviour of ecosystems (...) is usually provided in terms of their mathematical properties, and not in mechanistic terms. They have intensively studied the explanation of the properties of ecosystems that behave following the rules of a non-random network. However, no attention has been devoted to the study of the nature of the explanation in those that form a random network. In this paper, we cover that gap by analysing the explanation of the stability behaviour of the microbiome recently elaborated by Coyte and colleagues, to determine whether it fits with the model of explanation suggested by the new-mechanist or by the defenders of mathematical explanation. Our analysis of this case study supports three theses: (1) that the explanation is not given solely in terms of mechanisms, as the new-mechanists understand the concept; (2) that the mathematical properties that describe the system play an essential explanatory role, but they do not exhaust the explanation; (3) that a non-previously identified appeal to the type of interactions that the entities in the network can exhibit, as well as their abundance, is also necessary for Coyte and colleagues’ account to be fully explanatory. From the combination of these three theses we argue for the necessity of an integrative pluralist view of the nature of behaviour explanation when this is given by appealing to the existence of a random network. (shrink)

Evolutionary developmental biology (evo-devo) is considered a ‘mechanistic science,’ in that it causally explains morphological evolution in terms of changes in developmental mechanisms. Evo-devo is also an interdisciplinary and integrative approach, as its explanations use contributions from many fields and pertain to different levels of organismal organization. Philosophical accounts of mechanistic explanation are currently highly prominent, and have been particularly able to capture the integrative nature of multifield and multilevel explanations. However, I argue that evo-devo demonstrates the need for a (...) broadened philosophical conception of mechanisms and mechanistic explanation. Mechanistic explanation (in terms of the qualitative interactions of the structural parts of a whole) has been developed as an alternative to the traditional idea of explanation as derivation from laws or quantitative principles. Against the picture promoted by Carl Craver, that mathematical models describe but usually do not explain, my discussion of cases from the strand of evo-devo which is concerned with developmental processes points to qualitative phenomena where quantitative mathematical models are an indispensable part of the explanation. While philosophical accounts have focused on the actual organization and operation of mechanisms, properties of developmental mechanisms that are about how a mechanism reacts to modifications are of major evolutionary significance, including robustness, phenotypic plasticity, and modularity. A philosophical conception of mechanisms is needed that takes into account quantitative changes, transient entities and the generation of novel types of entities, feedback loops and complex interaction networks, emergent properties, and, in particular, functional-dynamical aspects of mechanisms, including functional (as opposed to structural) organization and distributed, system-wide phenomena. I conclude with general remarks on philosophical accounts of explanation. (shrink)

The biological sciences have always proven a fertile ground for philosophical analysis, one from which has grown a rich tradition stemming from Aristotle and flowering with Darwin. And although contemporary philosophy is increasingly becoming conceptually entwined with the study of the empirical sciences with the data of the latter now being regularly utilised in the establishment and defence of the frameworks of the former, a practice especially prominent in the philosophy of physics, the development of that tradition hasn’t received the (...) wider attention it so thoroughly deserves. This review will briefly introduce some recent significant topics of debate within the philosophy of biology, focusing on those whose metaphysical themes (in everything from composition to causation) are likely to be of wide-reaching, cross-disciplinary interest. (shrink)

Over the past three decades, philosophy of science has grown increasingly “local.” Concerns have switched from general features of scientific practice to concepts, issues, and puzzles specific to particular disciplines. Philosophy of neuroscience is a natural result. This emerging area was also spurred by remarkable recent growth in the neurosciences. Cognitive and computational neuroscience continues to encroach upon issues traditionally addressed within the humanities, including the nature of consciousness, action, knowledge, and normativity. Empirical discoveries about brain structure and function suggest (...) ways that “naturalistic” programs might develop in detail, beyond the abstract philosophical considerations in their favor. -/- The literature distinguishes “philosophy of neuroscience” and “neurophilosophy.” The former concerns foundational issues within the neurosciences. The latter concerns application of neuroscientific concepts to traditional philosophical questions. Exploring various concepts of representation employed in neuroscientific theories is an example of the former. Examining implications of neurological syndromes for the concept of a unified self is an example of the latter. In this entry, we will assume this distinction and discuss examples of both. (shrink)

In this article a neo-Jamesian approach to the self is developed within a naturalistic, bottom-up, and systemic-relational framework. In this approach, consciousness of the body as one’s own body is a necessary precondition of self-consciousness as psychological self-awareness, and hence of a socially and historically situated narrative self. Thus we take on board the criticism of those accounts of the narrative self that pay little attention to embodiment, or go to the extreme of stating that the narrative self is abstract (...) and hence not embodied. But at the same time, we reject the idea that the embodiment of the narrative self is provided by a pre-reflective self-consciousness. By contrast, we view self-consciousness as a construction all the way down, which develops from automatic and pre-reflective processing of representations of objects (object-consciousness), through awareness and then self-awareness of the body, up to introspective self-awareness and then narrative identity. This form of constructivism is a naturalistic reinterpretation of the Hegelian idea that selfhood is socially constructed and self-experience intersubjectively mediated. However, it enables to move away from that blend of narrativism and epiphenomenism that characterizes some strands of the socio-constructivist approach to the narrative self. The storied Me that the selfing process makes is not “empty chatter”, but rather a causally efficacious layer of personality viewed as a self-unifying system. (shrink)

Philosophers of science have recently begun to pay more attention to scientific practice, moving away from the discipline’s focus on theories. The creation of the Society for Philosophy of Science in Practice in 2006, as well as the emergence of scholarship on experimental practice (e.g. Sullivan 2009; 2010; 2016) as well as on the tools scientists use to construct explanations and theories (e.g. Feest 2011) all point to a disciplinary shift towards a more practice-conscious philosophy of science. In addition, scholars (...) are realizing the potential social relevance of philosophy of science and identifying the obstacles that stand in the way of realizing said potential (e.g. Douglas 2010). The aim of this dissertation is to propose a novel descriptive framework for philosophy of science, one that rests on the idea that scientific practice should be described as accurately as possible so that philosophical discussions of science rest on a solid descriptive base and can more easily be of use in engaging users and practitioners of science. The dissertation is organized along the stages of developing what I call the dynamic-iterative model of scientific practice. In chapter 1, I clarify the project’s philosophical background and outline six conditions of adequacy on a descriptive model of science. These conditions allow me to evaluate the three descriptive models reviewed in chapter 2. Chapter 3 combines Nickles’ problem-solving view of science (1981) with core insights from the heuristics and biases literature into a workable version of the dynamic-iterative model. I then apply the model to the case of place cell research in chapter 4 as a test of the model’s usefulness and applicability. In chapter 5 I evaluate the model with respect to my six conditions and assess its strengths and weaknesses. The dynamic-iterative model is a flexible descriptive framework that not only allows for more complete philosophical analyses of cases than other current models, but also supports important practical applications. (shrink)

In this thesis, I defend the explanatory force of algorithmic information processing models in cognitive neuroscience. I describe the algorithmic approach to cognitive explanation, its relation to Shea’s theory of cognitive representation, and challenges stemming from neuronal population analysis and dimensionality reduction. I then consider competing interpretations of some neuroscientific data that have been central to the debate. I argue in favor of a sequenced computational explanation of the phenomenon, contra Burnston. Finally, I argue that insights from theoretical neuroscience allow (...) us to understand why dimensionality reduction does not militate against localizing distinct contents to distinct components of functioning brain systems. (shrink)

Proponents of mechanistic explanation have argued that dynamical models are mere phenomenal models, in that they describe rather than explain the scientific phenomena produced by complex systems. I argue instead that dynamical models can, in fact, be explanatory. Using an example from neuroscientific research on epilepsy, I show that dynamical models can meet the explanatory demands met by mechanistic models, and as such occupy their own unique place within the space of explanatory scientific models.

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions (...) can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

The aim of this paper lies in characterizing the explanations and models used in the field of evolutionary developmental biology throughout its history. While manipulative experiments in controlled conditions have been useful to set the bases of the discipline and are still routinely performed, this approach supposes a tension between the reliability and the representativity of the conclusions. Given the recent changes in the understanding of evolutionary phenomena, different authors currently emphasize the need of avoiding excessive simplifications in experimental approaches, (...) incorporating the complexity of the analyzed systems as a relevant trait in the study of biological diversity. On the other hand, the fragile and contingent nature of evolutionary processes and the impossibility of intervening some of these phenomena underscore the importance of non-manipulative models that allow the elucidation of how-possibly mechanisms. We argue that a pluralism characterized by the integration of different kinds of explanations, models and metaphors used in evo-devo allows to exploit their respective advantages, favours interdisciplinarity and can contribute to form a representative description of evolutionary processes without relinquishing the local and detailed study of actual mechanisms. (shrink)

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.

This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system.

Philippe Huneman has recently questioned the widespread application of mechanistic models of scientific explanation based on the existence of structural explanations, i.e. explanations that account for the phenomenon to be explained in virtue of the mathematical properties of the system where the phenomenon obtains, rather than in terms of the mechanisms that causally produce the phenomenon. Structural explanations are very diverse, including cases like explanations in terms of bowtie structures, in terms of the topological properties of the system, or in (...) terms of equilibrium. The role of mathematics in bowtie structured systems and in topologically constrained systems has recently been examined in different papers. However, the specific role that mathematical properties play in equilibrium explanations requires further examination, as different authors defend different interpretations, some of them closer to the new-mechanistic approach than to the structural model advocated by Huneman. In this paper, we cover this gap by investigating the explanatory role that mathematics play in Blaser and Kirschner’s nested equilibrium model of the stability of persistent long-term human-microbe associations. We argue that their model is explanatory because: i) it provides a mathematical structure in the form of a set of differential equations that together satisfy an ESS; ii) that the nested nature of the ESSs makes the explanation of host-microbe persistent associations robust to any perturbation; iii) that this is so because the properties of the ESS directly mirror the properties of the biological system in a non-causal way. The combination of these three theses make equilibrium explanations look more similar to structural explanations than to causal-mechanistic explanation. (shrink)

Whereas most branches of neuroscience are thought to provide mechanistic explanations, systems neuroscience is not. Two reasons are traditionally cited in support of this conclusion. First, systems neuroscientists rarely, if ever, rely on the dual strategies of decomposition and localization. Second, they typically emphasize organizational properties over the properties of individual components. In this paper, I argue that neither reason is conclusive: researchers might rely on alternative strategies for mechanism discovery, and focusing on organization is often appropriate and consistent with (...) the norms of mechanistic explanation. Thus, many explanations in systems neuroscience can also be viewed as mechanistic explanations. (shrink)

The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer simulations and mathematical (...) representations in the epistemic practices of mechanism discovery and mechanism description. These examples also indicate that the scope of mechanistic explanation must be re-examined: With new and increasingly powerful methods of discovery and description comes the possibility of describing mechanisms far more complex than traditionally assumed. (shrink)