From Wide Cognition to Mechanisms: A Silent Revolution

In this paper, we argue that several recent ‘wide’ perspectives on cognition (embodied, embedded, extended, enactive, and distributed) are only partially relevant to the study of cognition. While these wide accounts override traditional methodological individualism, the study of cognition has already progressed beyond these proposed perspectives toward building integrated explanations of the mechanisms involved, including not only internal submechanisms but also interactions with others, groups, cognitive artifacts, and their environment. Wide perspectives are essentially research heuristics for building mechanistic explanations. The claim is substantiated with reference to recent developments in the study of “mindreading” and debates on emotions. We argue that the current practice in cognitive (neuro)science has undergone, in effect, a silent mechanistic revolution, and has turned from initial binary oppositions and abstract proposals toward the integration of wide perspectives with the rest of the cognitive (neuro)sciences.

The Network Theory of Psychiatric Disorders: A Critical Assessment of the Inclusion of Environmental Factors

Borsboom and colleagues have recently proposed a "network theory" of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms. They have also claimed that the network theory should include non-symptom variables such as environmental factors. How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an "extended" network theory provide? The aim of this article is to critically examine what explanatory strategies the network theory that includes both symptoms and environmental factors can accommodate. We first analyze how proponents of the network theory conceptualize the relations between symptoms and between symptoms and environmental factors. Their claims suggest that the network theory could provide insight into the causal mechanisms underlying psychiatric disorders. We assess these claims in light of network analysis, Woodward's interventionist theory, and mechanistic explanation, and show that they can only be satisfied with additional assumptions and requirements. Then, we examine their claim that network characteristics may explain the dynamics of psychiatric disorders by means of a topological explanatory strategy. We argue that the network theory could accommodate topological explanations of symptom networks, but we also point out that this poses some difficulties. Finally, we suggest that a multilayer network account of psychiatric disorders might allow for the integration of symptoms and non-symptom factors related to psychiatric disorders and could accommodate both causal/mechanistic and topological explanations.

Entrainment and motor emulation approaches to joint action: Alternatives or complementary approaches?

Joint actions, such as music and dance, rely crucially on the ability of two, or more, agents to align their actions with great temporal precision. Within the literature that seeks to explain how this action alignment is possible, two broad approaches have appeared. The first, what we term the entrainment approach, has sought to explain these alignment phenomena in terms of the behavioral dynamics of the system of two agents. The second, what we term the emulator approach, has sought to explain these alignment phenomena in terms of mechanisms, such as forward and inverse models, that are implemented in the brain. They have often been pitched as alternative explanations of the same phenomena; however, we argue that this view is mistaken, because, as we show, these two approaches are engaged in distinct, and not mutually exclusive, explanatory tasks. While the entrainment approach seeks to uncover the general laws that govern behavior the emulator approach seeks to uncover mechanisms. We argue that is possible to do both and that the entrainment approach must pay greater attention to the mechanisms that support the behavioral dynamics of interest. In short, the entrainment approach must be transformed into a neuroentrainment approach by adopting a mechanistic view of explanation and by seeking mechanisms that are implemented in the brain.

Toward an Embodied, Embedded Predictive Processing Account

In this paper, I argue for an embodied, embedded approach to predictive processing and thus align the framework with situated cognition. The recent popularity of theories conceiving of the brain as a predictive organ has given rise to two broad camps in the literature that I call free energy enactivism and cognitivist predictive processing. The two approaches vary in scope and methodology. The scope of cognitivist predictive processing is narrow and restricts cognition to brain processes and structures; it does not consider the body-beyond-brain and the environment as constituents of cognitive processes. Free energy enactivism, on the other hand, includes all self-organizing systems that minimize free energy (including non-living systems) and thus does not offer any unique explanations for more complex cognitive phenomena that are unique to human cognition. Furthermore, because of its strong commitment to the mind-life continuity thesis, it does not provide an explanation of what distinguishes more sophisticated cognitive systems from simple systems. The account that I develop in this paper rejects both of these radical extremes. Instead, I propose a compromise that highlights the necessary components of predictive processing by making use of a mechanistic methodology of explanation. The starting point of the argument in this paper is that despite the interchangeable use of the terms, prediction error minimization and the free energy principle are not identical. But this distinction does not need to disrupt the status quo of the literature if we consider an alternative approach: Embodied, Embedded Predictive Processing (EEPP). EEPP accommodates the free energy principle, as argued for by free energy enactivism, but it also allows for mental representations in its explanation of cognition. Furthermore, EEPP explains how prediction error minimization is realized but, unlike cognitivist PP, it allocates a constitutive role to the body in cognition. Despite highlighting concerns regarding cognitivist PP, I do not wish to discredit the role of the neural domain or representations as free energy enactivism does. Neural structures and processes undeniably contribute to the minimization of prediction error but the role of the body is equally important. On my account, prediction error minimization and free energy minimization are deeply dependent on the body of an agent, such that the body-beyond-brain plays a constitutive role in cognitive processing. I suggest that the body plays three constitutive roles in prediction error minimization: The body regulates cognitive activity, ensuring that cognition and action are intricately linked. The body acts as distributor in the sense that it carries some of the cognitive load by fulfilling the function of minimizing prediction error. Finally, the body serves to constrain the information that is processed by an agent. In fulfilling these three roles, the agent and environment enter into a bidirectional relation through influencing and modeling the structure of the other. This connects EEPP to the free energy principle because the whole embodied agent minimizes free energy in virtue of being a model of its econiche. This grants the body a constitutive role as part of the collection of mechanisms that minimize prediction error and free energy. The body can only fulfill its role when embedded in an environment, of which it is a model. In this sense, EEPP offers the most promising alternative to cognitivist predictive processing and free energy enactivism.

Closing in on the constitution of consciousness

The science of consciousness is a nascent and thriving field of research that is founded on identifying the minimally sufficient neural correlates of consciousness. However, I have argued that it is the neural constitution of consciousness that science seeks to understand and that there are no evident strategies for distinguishing the correlates and constitution of (phenomenal) consciousness. Here I review this correlation/constitution distinction problem and challenge the existing foundations of consciousness science. I present the main analyses from a longer paper in press on this issue, focusing on recording, inhibition, stimulation, and combined inhibition/stimulation strategies, including proposal of the Jenga analogy to illustrate why identifying the minimally sufficient neural correlates of consciousness should not be considered the ultimate target of consciousness science. Thereafter I suggest that while combined inhibition and stimulation strategies might identify some constitutive neural activities—indeed minimally sufficient constitutive neural activities—such strategies fail to identify the whole neural constitution of consciousness and thus the correlation/constitution distinction problem is not fully solved. Various clarifications, potential objections and related scientific and philosophical issues are also discussed and I conclude by proposing new foundational claims for consciousness science.

Hierarchy and levels: analysing networks to study mechanisms in molecular biology

Network representations are flat while mechanisms are organized into a hierarchy of levels, suggesting that the two are fundamentally opposed. I challenge this opposition by focusing on two aspects of the ways in which large-scale networks constructed from high-throughput data are analysed in systems biology: identifying clusters of nodes that operate as modules or mechanisms and using bio-ontologies such as gene ontology (GO) to annotate nodes with information about where entities appear in cells and the biological functions in which they participate. Of particular importance, GO organizes biological knowledge about cell components and functions hierarchically. I illustrate how this supports mechanistic interpretation of networks with two examples of network studies, one using epistatic interactions among genes to identify mechanisms and their parts and the other using deep learning to predict phenotypes. As illustrated in these examples, when network research draws upon hierarchical information such as provided by GO, the results not only can be interpreted mechanistically but provide new mechanistic knowledge. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Mechanisms in practice: A methodological approach

In this paper, we offer a minimal characterization of the concept of mechanism in biomedicine, according to which a mechanism is a theoretically described causal pathway. We argue that this conception can be drawn from scientific practice, as illustrated by how a central biological and biomedical mechanism, the mechanism of apoptosis, was first identified and characterized. We will use the example of cytological and biochemical theoretical descriptions of the mechanism of apoptosis to draw lessons about the meaning of the concept of mechanism in biomedical contexts and to contrast our preferred account of mechanism with some prominent accounts within the philosophical literature. The main outcome of our discussion will be that commitment to mechanism is first and foremost a methodological stance.

Criteria for empirical theories of consciousness should focus on the explanatory power of mechanisms, not on functional equivalence

Doerig and colleagues put forward the notion that we need hard and theory-neutral criteria by which to arbitrate between empirical (mechanistic) theories of consciousness. However, most of the criteria that they propose are not theory neutral because they focus on functional equivalence between systems. Because empirical theories of consciousness are mechanistic rather than functionalist, we think these criteria are not helpful when arbitrating between them.

Scientific Practice in Modeling Diseases: Stances from Cancer Research and Neuropsychiatry

In the last few decades, philosophy of science has increasingly focused on multilevel models and causal mechanistic explanations to account for complex biological phenomena. On the one hand, biological and biomedical works make extensive use of mechanistic concepts; on the other hand, philosophers have analyzed an increasing range of examples taken from different domains in the life sciences to test-support or criticize-the adequacy of mechanistic accounts. The article highlights some challenges in the elaboration of mechanistic explanations with a focus on cancer research and neuropsychiatry. It jointly considers fields, which are usually dealt with separately, and keeps a close eye on scientific practice. The article has a twofold aim. First, it shows that identification of the explananda is a key issue when looking at dynamic processes and their implications in medical research and clinical practice. Second, it discusses the relevance of organizational accounts of mechanisms, and questions whether thorough self-sustaining mechanistic explanations can actually be provided when addressing cancer and psychiatric diseases. While acknowledging the merits of the wide ongoing debate on mechanistic models, the article challenges the mechanistic approach to explanation by discussing, in particular, explanatory and conceptual terms in the light of stances from medical cases.

Context-sensitive computational mechanistic explanation in cognitive neuroscience

Mainstream cognitive neuroscience aims to build mechanistic explanations of behavior by mapping abilities described at the organismal level via the subpersonal level of computation onto specific brain networks. We provide an integrative review of these commitments and their mismatch with empirical research findings. Context-dependent neural tuning, neural reuse, degeneracy, plasticity, functional recovery, and the neural correlates of enculturated skills each show that there is a lack of stable mappings between organismal, computational, and neural levels of analysis. We furthermore highlight recent research suggesting that task context at the organismal level determines the dynamic parcellation of functional components at the neural level. Such instability prevents the establishment of specific computational descriptions of neural function, which remains a central goal of many brain mappers - including those who are sympathetic to the notion of many-to-many mappings between organismal and neural levels. This between-level instability presents a deep epistemological challenge and requires a reorientation of methodological and theoretical commitments within cognitive neuroscience. We demonstrate the need for change to brain mapping efforts in the face of instability if cognitive neuroscience is to maintain its central goal of constructing computational mechanistic explanations of behavior; we show that such explanations must be contextual at all levels.

Clarifying the relation between mechanistic explanations and reductionism

The topic of mechanistic explanation in neuroscience has been a subject of recent discussion. There is a lot of interest in understanding what these explanations involve. Furthermore, there is disagreement about whether neurological mechanisms themselves should be viewed as reductionist in nature. In this paper I will explain how these two issues are related. I will, first, describe how mechanisms support a form of antireductionism. This is because the mechanisms that exist should be seen as involving part-whole relations, where the behavior of a whole is more than the sum of its parts. After this, I will consider mechanistic explanations and how they can be understood. While some people think the explanations concern existing entities in the world, I will argue that we can understand the explanations by viewing them in terms of arguments. Despite the fact that it is possible to understand mechanistic explanations in this manner, the antireductionist point remains.

"How Many Individuals Consider Themselves to Be Cell Biologists but Are Informed by the Journal That Their Work Is Not Cell Biology"

What can we gain from co-analyzing experimental cultures, regionalization, and disciplinary phenomena of late twentieth century life sciences under our historiographic looking glass? This essay investigates the potential of such a strategy for the case of cell biology after 1960. By merging perspectives from historical epistemology inspired by the work of Hans-Jörg Rheinberger with a focus on boundary work in the realm of scientific publishing, community building, and disciplinary norms, a set of understudied scientific practices is exposed. These practices, historically subsumed under the label descriptive, have been as central in cell biology as hypothesis-driven research aiming at mechanistic explanations of cellular function. Against the background of an increasing molecular-mechanistic imperative in cell biology since the late 1960s, knowledge from descriptive practices was often judged as having low value but was nonetheless frequently cited and considered essential. Investigating the underlying epistemic practices and their interactions with disciplinary gatekeeping phenomena (as policed by journals and learned societies) provides historiographic access to the plurality of experimental cultures of cell biology, scattered into many interdisciplinary research fields-with some of them only partially engaged with mechanistic questions.

Bridges and Mechanisms: Integrating Systems Science Thinking into Implementation Research

We present a detailed argument for how to integrate, or bridge, systems science thinking and methods with implementation science. We start by showing how fundamental systems science principles of structure, dynamics, information, and utility are relevant for implementation science. Then we examine the need for implementation science to develop and apply richer theories of complex systems. This can be accomplished by emphasizing a causal mechanisms approach. Identifying causal mechanisms focuses on the "cogs and gears" of public health, clinical, and organizational interventions. A mechanisms approach focuses on how a specific strategy will produce the implementation outcome. We show how connecting systems science to implementation science opens new opportunities for examining and addressing social determinants of health and conducting equitable and ethical implementation research. Finally, we present case studies illustrating successful applications of systems science within implementation science in community health policy, tobacco control, health care access, and breast cancer screening.

Beyond embodiment: Rethinking the integration of cognitive neuroscience and mechanistic explanations

This commentary critiques Mougenot and Matheson's proposal to integrate embodied cognition with mechanistic explanations in cognitive neuroscience. We suggest more promising directions for embodied cognitive neuroscience, focusing on neuroethological research and evolutionary studies of nervous systems. These approaches, compatible with wide mechanistic explanations, offer a robust path forward by examining central nervous system function within whole organisms in their environments.