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Most accounts of mechanism discovery have focused on mechanisms that perform the work required to produce a phenomenon. These mechanisms are often subject to regulation by control mechanisms. Using the example of the molecular motor dynein, this paper examines one process by which such control mechanisms are discovered: after identifying additional components required to produce the phenomenon but which do not contribute directly to the work of producing that phenomenon, researchers investigate both how these components act on the original mechanism and do so in response to measurements of conditions relevant to the operation of the controlled mechanism.
To successfully address large-scale public health threats such as the novel coronavirus outbreak, policymakers need to limit feelings of fear that threaten social order and political stability. We study how policy responses to an infectious disease affect mass fear using data from a survey experiment conducted on a representative sample of the adult population in the USA (N = 5,461). We find that fear is affected strongly by the final policy outcome, mildly by the severity of the initial outbreak, and minimally by policy response type and rapidity. These results hold across alternative measures of fear and various subgroups of individuals regardless of their level of exposure to coronavirus, knowledge of the virus, and several other theoretically relevant characteristics. Remarkably, despite accumulating evidence of intense partisan conflict over pandemic-related attitudes and behaviors, we show that effective government policy reduces fear among Democrats, Republicans, and Independents alike.
This Element provides a comprehensive introduction to philosophy of neuroscience. It covers such topics as how neuroscientists procure knowledge, including not just research techniques but the use of various model organisms. It presents examples of knowledge acquired in neuroscience that are then employed to discuss more philosophical topics such as the nature of explanations developed in neuroscience, the different conception of levels employed in discussions of neuroscience, and the invocation of representations in neuroscience explanations. The text emphasizes the importance of brain processes beyond those in the neocortex and then explores what makes processing in neocortex different. It consider the view that the nervous system consists of control mechanisms and considers arguments for hierarchical vs. heterarchical organization of control mechanisms. It concludes by considering implications of findings in neuroscience for how humans conceive of themselves and practices such as embracing norms.
This article explores the use of model organisms in studying the cognitive phenomenon of decision-making. Drawing on the framework of biological control to develop a skeletal conception of decision-making, we show that two core features of decision-making mechanisms can be identified by studying model organisms, such as E. coli, jellyfish, C. elegans, lamprey, and so on. First, decision mechanisms are distributed and heterarchically structured. Second, they depend heavily on chemical information processing, such as that involving neuromodulators. We end by discussing the implications for studying distinctively human decision-making.
Two concepts figure prominently in Schaffner’s discussion of LeDoux’s account of fear and anxiety: level and self. With respect to level, I differentiate two conceptions of levels invoked in his account: levels of hypotheses (high-level and intermediate-level instantiations) and mechanistic levels involving components within mechanisms. Both are important for Schaffner’s purposes, but they operate differently and should be distinguished. Schaffner’s account of self focuses on personality, but I suggest that more relevant is how one represents oneself, including one’s personality. I develop Neisser’s account of five types of knowledge one might have about oneself and argue that Neisser’s conceptual self is of the most use in understanding LeDoux’s account of fear. I conclude by suggesting how the representation of oneself may be developed differently at different levels of theorizing about oneself.
This chapter offers a framework for understanding mechanistic explanations of psychiatric disorders in terms of altered activities in a heterarchical network of control mechanisms. This differs both from approaches that seek to characterize the mechanism responsible for producing the disease state and those that attribute the disease state to broken mechanisms. Control mechanisms operate on soft constraints in other mechanisms and thereby alter their operation. Although often viewed as hierarchical, the brain is organized as a heterarchical network, with many control mechanisms operating on the same controlled mechanisms and no chief executive. This poses challenges for attempts to understand the ramifications of altered functioning of components of the network. Using a recent example of research showing the effects of modifying the activity of proteins within the circadian clock on depression-like behavior in mice, this chapter illustrates how progress might be made as well as the challenges faced in explaining psychiatric disorders.
Research on diseases such as cancer reveals that primary mechanisms, which have been the focus of study by the new mechanists in philosophy of science, are often subject to control by other mechanisms. Cancer cells employ the same primary mechanisms as healthy cells but control them differently. I use cancer research to highlight just how widespread control is in individual cells. To provide a framework for understanding control, I reconceptualize mechanisms as imposing constraints on flows of free energy, with control mechanisms operating on flexible constraints in primary mechanisms. I argue that control mechanisms themselves often form complex, integrated networks.
In many fields in the life sciences investigators refer to downward or top-down causal effects. Craver and I defended the view that such cases should be understood in terms of a constitution relation between levels in a mechanism and intralevel causal relations (occurring at any level). We did not, however, specify when entities constitute a higher-level mechanism. In this article I appeal to graph-theoretic representations of networks, now widely employed in systems biology and neuroscience, and associate mechanisms with modules that exhibit high clustering. As a result of interconnections within clusters, mechanisms often exhibit complex dynamic behaviors that constrain how individual components respond to external inputs, a central feature of top-down causation.
Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. To explore this role, we examine diagrammatic formats that have been devised by biologists to (a) identify and illuminate phenomena involving circadian rhythms and (b) develop and modify mechanistic explanations of these phenomena.
Proponents of mechanistic explanation all acknowledge the importance of organization. But they have also tended to emphasize specificity with respect to parts and operations in mechanisms. We argue that in understanding one important mode of organization—patterns of causal connectivity—a successful explanatory strategy abstracts from the specifics of the mechanism and invokes tools such as those of graph theory to explain how mechanisms with a particular mode of connectivity will behave. We discuss the connection between organization, abstraction, and mechanistic explanation and illustrate our claims by looking at an example from recent research on so-called network motifs.
This chapter provides empirical and theoretical understanding of cognition. Today localizationism dominates neuroscience, ranging from single cell recording to functional magnetic resonance imaging (FMRI), while anti-localizationism has a new home in dynamical systems modeling. Cognitive science encompasses both. It is sometimes said that the cognitive revolution stemmed from seizing on a new technology, the digital computer, as a metaphor for the mind. Artificial neural network represents a counterpoint to discrete computation. Symbolic architectures share a commitment to representations whose elements are symbols and operations on those representations that typically involve moving, copying, deleting, comparing, or replacing symbols. The chapter highlights just two trends: the expansion of inquiry down into the brain (cognitive neuroscience) and out into the body and world (embedded and extended cognition). The expansion outward has been more diverse, but the transitional figure clearly is James J. Gibson.
This chapter focuses on the knowledge of the references of perceptual demonstratives: terms like 'this' and 'that' used to refer to currently perceived objects, such as a tree or a person. It has often been remarked, as a basic problem in theory of meaning, that the only credible accounts of meaning are truth conditional, but that it is hard to understand how the functional organization of a subject could constitute their grasp of the truth conditions of the statements they make and the thoughts they have. Functionalism stops short, with a mere characterization of the transitions from content to content one does engage in. In perception we are confronted with the references of perceptual-demonstrative terms, and to that extent we can be said to perceive the intended model for demonstrative discourse. There is an epistemic role for consciousness, for sensory awareness in particular, in our grasp of meaning.
Psychologists and neuroscientists began to build bridges and linked their inquiries together. Both philosophers and scientists employ the term reduction in characterizing relations between the results of higher-level and basic-level inquiries that are supposedly jeopardized by multiple realization. This chapter describes an understanding of reduction provided by the framework of mechanistic explanation that fits with the pursuit's scientists label reductionistic. There are differences between the mechanisms in different species that result in what are treated as the same phenomena. The chapter takes up this issue directly and discusses that the same standards of typing are applied to phenomena as to realizations. It considers what happens when one uses a coarser grain to type neural phenomena. The chapter presents the research on circadian rhythms as an exemplar as this is a field in which the issues concerning multiple realization, conservation of mechanism, and identity.
This article argues that the basic account of mechanism and mechanistic explanation, involving sequential execution of qualitatively characterized operations, is itself insufficient to explain biological phenomena such as the capacity of living organisms to maintain themselves as systems distinct from their environment. This capacity depends on cyclic organization, including positive and negative feedback loops, which can generate complex dynamics. Understanding cyclically organized mechanisms with complex dynamics requires coordinating research directed at decomposing mechanisms into parts (entities) and operations (activities) with research using computational models to recompose mechanisms and determine their dynamic behavior. This coordinated endeavor yields dynamic mechanistic explanations.