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William Bechtel - One of the best experts on this subject based on the ideXlab platform.
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Towards Mechanism 2.0: Expanding the Scope of Mechanistic Explanation
2016Co-Authors: Arnon Levy, William BechtelAbstract:Accounts of Mechanistic Explanation, especially as applied to biology and sometimes going under the heading of “new mechanism,” provided an attractive alternative to nomological accounts that preceded them. These accounts were motivated by selected examples, drawn primarily from cell and molecular biology and neuroscience. However, the range of examples that scientists take to be Mechanistic Explanations is far broader. We focus on examples that differ from those traditionally recruited by Mechanists. Our contention is that attention to additional examples will lead to a richer conception of Mechanistic Explanation, prompting a shift from what we refer to as Mechanism 1.0 to Mechanism 2.0.
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Can Mechanistic Explanation be reconciled with scale-free constitution and dynamics?
Studies in history and philosophy of biological and biomedical sciences, 2015Co-Authors: William BechtelAbstract:This paper considers two objections to Explanations that appeal to mechanisms to explain biological phenomena. Marom argues that the time-scale on which many phenomena occur is scale-free. There is also reason to suspect that the network of interacting entities is scale-free. The result is that mechanisms do not have well-delineated boundaries in nature. I argue that bounded mechanisms should be viewed as entities scientists posit in advancing scientific hypotheses. In positing such entities, scientists idealize. Such idealizations can be highly productive in developing and improving scientific Explanations even if the hypothesized mechanisms never precisely correspond to bounded entities in nature. Mechanistic Explanations can be reconciled with scale-free constitution and dynamics even if mechanisms as bounded entities don't exist.
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Addressing the Vitalist’s Challenge to Mechanistic Science: Dynamic Mechanistic Explanation
Vitalism and the Scientific Image in Post-Enlightenment Life Science 1800-2010, 2013Co-Authors: William BechtelAbstract:Vitalists, especially in the nineteenth century, correctly objected that mechanists’ Explanations in biology lacked the resources to explain important features of biological phenomena. As some mechanists, especially Claude Bernard, recognized, the key to addressing these objections was to incorporate in Mechanistic Explanations the contribution of organization found in living systems. In particular, it is necessary to understand how non-sequential organization (combined with nonlinear operations) enables mechanisms to exhibit the sort of complex behavior, including endogenously generated behavior, exhibited by living organisms. Non-sequential organization poses a serious problem for human understanding, which characterizes the functioning of mechanisms qualitatively in a step-by-step manner. To understand the effects of non-sequential organization between nonlinear operations requires developing mathematical equations to represent the operations and computational simulations using these equations to determine how various components of the mechanism change depending on their own state and those of other components of the mechanism. Further, analyzing the results of these simulations requires appropriate representations such as the phase-space representations employed in dynamical systems theory. Fortunately, Mechanistic science can be coupled with dynamical modeling to yield dynamic Mechanistic Explanations such as those being proposed in systems biology. These hold the promise of explaining the features of biological phenomena on which the vitalists appropriately focused attention.
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addressing the vitalist s challenge to Mechanistic science dynamic Mechanistic Explanation
2013Co-Authors: William BechtelAbstract:Vitalists, especially in the nineteenth century, correctly objected that mechanists’ Explanations in biology lacked the resources to explain important features of biological phenomena. As some mechanists, especially Claude Bernard, recognized, the key to addressing these objections was to incorporate in Mechanistic Explanations the contribution of organization found in living systems. In particular, it is necessary to understand how non-sequential organization (combined with nonlinear operations) enables mechanisms to exhibit the sort of complex behavior, including endogenously generated behavior, exhibited by living organisms. Non-sequential organization poses a serious problem for human understanding, which characterizes the functioning of mechanisms qualitatively in a step-by-step manner. To understand the effects of non-sequential organization between nonlinear operations requires developing mathematical equations to represent the operations and computational simulations using these equations to determine how various components of the mechanism change depending on their own state and those of other components of the mechanism. Further, analyzing the results of these simulations requires appropriate representations such as the phase-space representations employed in dynamical systems theory. Fortunately, Mechanistic science can be coupled with dynamical modeling to yield dynamic Mechanistic Explanations such as those being proposed in systems biology. These hold the promise of explaining the features of biological phenomena on which the vitalists appropriately focused attention.
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Diagramming Phenomena for Mechanistic Explanation - eScholarship
2012Co-Authors: William Bechtel, Adele AbrahamsenAbstract:Diagramming Phenomena for Mechanistic Explanation William Bechtel (bechtel@ucsd.edu) Department of Philosophy, University of California, San Diego La Jolla, CA 92014 USA Adele Abrahamsen (aabrahamsen@ucsd.edu) Center for Research in Language, University of California, San Diego La Jolla, CA 92014 USA Abstract thoughts or sentences. The idea of Mechanistic Explanation quickly took root in biology. Although resisted by vitalists, who contended that something beyond physical processes was required for the functions of life, other early investiga- tors of physiological phenomena embraced Mechanistic ex- planations. As their inquiries progressed, researchers ex- panded the range of operations involved in biological mech- anisms beyond Cartesian physical contact. Newtonian forc- es, chemical bonding, and electrical conductance were among the operations used in explaining such phenomena as metabolism, nerve transmission, and heredity. Mechanistic Explanation was largely overlooked by 20 th century philosophers of science, who drew from physics the idea that scientists explain phenomena by deriving them from laws (Hempel, 1965). More recently, philosophers focusing on the life sciences have moved the spotlight once again to Mechanistic Explanation (Bechtel & Richardson, 1993/2010; Bechtel & Abrahamsen, 2005; Machamer, Darden, & Craver, 2000). Typically, life scientists treat the system that generates a phenomenon as a mechanism. They decompose it into parts and operations and then recompose it (conceptually, physically, or mathematically) to arrive at an account of how the coordinated performance of these operations could indeed generate the phenomenon. Although one may try to describe linguistically the parts and operations of a mechanism and how they interact, often telling a narrative about how each part in succession per- forms its operation, diagrams generally provide a more use- ful representational format for conceptualizing and reason- ing about a mechanism. Parts may be represented by labels, symbols, or abstract shapes, and the operations by which they interact represented by arrows. Diagrams can illustrate the structural and functional relations between many com- ponents and allow viewers to direct their attention succes- sively to different activities that may be occurring concur- rently in the mechanism. The initial step in Mechanistic research, though, is deline- ation of the phenomenon to be explained, and that is where we begin our inquiry into diagrams. Linguistic descriptions of phenomena have been the focus in many philosophical accounts of Mechanistic Explanation (e.g., “proteins are syn- thesized by constructing strings of amino acids in the order specified in a sequence of DNA”). However, scientists typi- cally work with much more specific accounts of phenome- na, often incorporating numerical values determined in their research. Frequently the numerical data relied upon in char- As part of an inquiry into how diagrams figure in scientific practice, we examine diagrams that represent phenomena in- volving circadian rhythms. Different diagrammatic formats are developed and revised over time to best represent different phenomena for which Explanations will be sought. Some dia- grams are less transparent than others, so learning is often re- quired in order to see the information conveyed. Keywords: Diagrams; Graphs, Mechanistic Explanation, Vis- ual representation; Circadian rhythms. Introduction The notion of representation covers a lot of territory in cog- nitive science, encompassing both internal and external en- codings of information and a variety of formats. Cognitive scientists have long focused on language-like internal repre- sentations, with some dispute over possible ways they might be supplemented by analog formats. Recent years have brought increased attention to external representations and especially to those incorporating analog formats—that is, diagrams. For example, Hegarty (2004) has shown how in- dividuals perform simulations with diagrams in solving problems, and Cheng (2011) has explored how alternative diagramming techniques can foster learning. However, ex- cept for the pioneering analysis by Nessessian (2008) of the role of diagrams in Maxwell’s discoveries, there has been little investigation of the use of diagrams in science. Almost all scientific papers include diagrams, and readers often focus on these as they navigate a paper. They are well suited not only for displaying instruments, techniques, multistep procedures, and results but also scientific reasoning. Most generally this involves the construction, evaluation and revi- sion of hypotheses but our particular interest is in sciences pursing Mechanistic Explanations, notably the life sciences. The project of explaining a phenomenon by identifying and understanding the mechanism responsible for it has roots in the scientific revolution beginning in the 16th cen- tury. Descartes posited that phenomena such as magnetic attraction are generated by the coordinated activities of con- stituent parts (in his case, hypothesized corpuscles). He ap- plied the idea of contact action between particles to explain not just physical phenomena, but nearly all phenomena ex- hibited by living organisms. The only exceptions were rea- soning and language use, which he attributed to an immate- rial mind because he could not conceive of a mechanism capable of constructing novel, semantically appropriate
Mark D. Fricker - One of the best experts on this subject based on the ideXlab platform.
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A Mechanistic Explanation of the transition to simple multicellularity in fungi.
Nature communications, 2020Co-Authors: Luke L. M. Heaton, Nick S. Jones, Mark D. FrickerAbstract:Development of multicellularity was one of the major transitions in evolution and occurred independently multiple times in algae, plants, animals, and fungi. However recent comparative genome analyses suggest that fungi followed a different route to other eukaryotic lineages. To understand the driving forces behind the transition from unicellular fungi to hyphal forms of growth, we develop a comparative model of osmotrophic resource acquisition. This predicts that whenever the local resource is immobile, hard-to-digest, and nutrient poor, hyphal osmotrophs outcompete motile or autolytic unicellular osmotrophs. This hyphal advantage arises because transporting nutrients via a contiguous cytoplasm enables continued exploitation of remaining resources after local depletion of essential nutrients, and more efficient use of costly exoenzymes. The model provides a Mechanistic Explanation for the origins of multicellular hyphal organisms, and explains why fungi, rather than unicellular bacteria, evolved to dominate decay of recalcitrant, nutrient poor substrates such as leaf litter or wood. Multicellularity is one of the major transitions in evolution. Here, authors use a model to show that compared to unicellular bacteria, multicellular fungi can more rapidly colonise immobile, nutrient poor resources because exoenzymes provide greater or longer lasting benefits to mycelial organisms.
Adele Abrahamsen - One of the best experts on this subject based on the ideXlab platform.
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Diagramming Phenomena for Mechanistic Explanation - eScholarship
2012Co-Authors: William Bechtel, Adele AbrahamsenAbstract:Diagramming Phenomena for Mechanistic Explanation William Bechtel (bechtel@ucsd.edu) Department of Philosophy, University of California, San Diego La Jolla, CA 92014 USA Adele Abrahamsen (aabrahamsen@ucsd.edu) Center for Research in Language, University of California, San Diego La Jolla, CA 92014 USA Abstract thoughts or sentences. The idea of Mechanistic Explanation quickly took root in biology. Although resisted by vitalists, who contended that something beyond physical processes was required for the functions of life, other early investiga- tors of physiological phenomena embraced Mechanistic ex- planations. As their inquiries progressed, researchers ex- panded the range of operations involved in biological mech- anisms beyond Cartesian physical contact. Newtonian forc- es, chemical bonding, and electrical conductance were among the operations used in explaining such phenomena as metabolism, nerve transmission, and heredity. Mechanistic Explanation was largely overlooked by 20 th century philosophers of science, who drew from physics the idea that scientists explain phenomena by deriving them from laws (Hempel, 1965). More recently, philosophers focusing on the life sciences have moved the spotlight once again to Mechanistic Explanation (Bechtel & Richardson, 1993/2010; Bechtel & Abrahamsen, 2005; Machamer, Darden, & Craver, 2000). Typically, life scientists treat the system that generates a phenomenon as a mechanism. They decompose it into parts and operations and then recompose it (conceptually, physically, or mathematically) to arrive at an account of how the coordinated performance of these operations could indeed generate the phenomenon. Although one may try to describe linguistically the parts and operations of a mechanism and how they interact, often telling a narrative about how each part in succession per- forms its operation, diagrams generally provide a more use- ful representational format for conceptualizing and reason- ing about a mechanism. Parts may be represented by labels, symbols, or abstract shapes, and the operations by which they interact represented by arrows. Diagrams can illustrate the structural and functional relations between many com- ponents and allow viewers to direct their attention succes- sively to different activities that may be occurring concur- rently in the mechanism. The initial step in Mechanistic research, though, is deline- ation of the phenomenon to be explained, and that is where we begin our inquiry into diagrams. Linguistic descriptions of phenomena have been the focus in many philosophical accounts of Mechanistic Explanation (e.g., “proteins are syn- thesized by constructing strings of amino acids in the order specified in a sequence of DNA”). However, scientists typi- cally work with much more specific accounts of phenome- na, often incorporating numerical values determined in their research. Frequently the numerical data relied upon in char- As part of an inquiry into how diagrams figure in scientific practice, we examine diagrams that represent phenomena in- volving circadian rhythms. Different diagrammatic formats are developed and revised over time to best represent different phenomena for which Explanations will be sought. Some dia- grams are less transparent than others, so learning is often re- quired in order to see the information conveyed. Keywords: Diagrams; Graphs, Mechanistic Explanation, Vis- ual representation; Circadian rhythms. Introduction The notion of representation covers a lot of territory in cog- nitive science, encompassing both internal and external en- codings of information and a variety of formats. Cognitive scientists have long focused on language-like internal repre- sentations, with some dispute over possible ways they might be supplemented by analog formats. Recent years have brought increased attention to external representations and especially to those incorporating analog formats—that is, diagrams. For example, Hegarty (2004) has shown how in- dividuals perform simulations with diagrams in solving problems, and Cheng (2011) has explored how alternative diagramming techniques can foster learning. However, ex- cept for the pioneering analysis by Nessessian (2008) of the role of diagrams in Maxwell’s discoveries, there has been little investigation of the use of diagrams in science. Almost all scientific papers include diagrams, and readers often focus on these as they navigate a paper. They are well suited not only for displaying instruments, techniques, multistep procedures, and results but also scientific reasoning. Most generally this involves the construction, evaluation and revi- sion of hypotheses but our particular interest is in sciences pursing Mechanistic Explanations, notably the life sciences. The project of explaining a phenomenon by identifying and understanding the mechanism responsible for it has roots in the scientific revolution beginning in the 16th cen- tury. Descartes posited that phenomena such as magnetic attraction are generated by the coordinated activities of con- stituent parts (in his case, hypothesized corpuscles). He ap- plied the idea of contact action between particles to explain not just physical phenomena, but nearly all phenomena ex- hibited by living organisms. The only exceptions were rea- soning and language use, which he attributed to an immate- rial mind because he could not conceive of a mechanism capable of constructing novel, semantically appropriate
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diagramming phenomena for Mechanistic Explanation
Cognitive Science, 2012Co-Authors: William Bechtel, Adele AbrahamsenAbstract:As part of an inquiry into how diagrams figure in scientific practice, we examine diagrams that represent phenomena involving circadian rhythms. Different diagrammatic formats are developed and revised over time to best represent different phenomena for which Explanations will be sought. Some diagrams are less transparent than others, so learning is often required in order to see the information conveyed.
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CogSci - Diagramming Phenomena for Mechanistic Explanation
Cognitive Science, 2012Co-Authors: William Bechtel, Adele AbrahamsenAbstract:As part of an inquiry into how diagrams figure in scientific practice, we examine diagrams that represent phenomena involving circadian rhythms. Different diagrammatic formats are developed and revised over time to best represent different phenomena for which Explanations will be sought. Some diagrams are less transparent than others, so learning is often required in order to see the information conveyed.
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Dynamic Mechanistic Explanation: computational modeling of circadian rhythms as an exemplar for cognitive science.
Studies in history and philosophy of science, 2010Co-Authors: William Bechtel, Adele AbrahamsenAbstract:We consider computational modeling in two fields: chronobiology and cognitive science. In circadian rhythm models, variables generally correspond to properties of parts and operations of the responsible mechanism. A computational model of this complex mechanism is grounded in empirical discoveries and contributes a more refined understanding of the dynamics of its behavior. In cognitive science, on the other hand, computational modelers typically advance de novo proposals for mechanisms to account for behavior. They offer indirect evidence that a proposed mechanism is adequate to produce particular behavioral data, but typically there is no direct empirical evidence for the hypothesized parts and operations. Models in these two fields differ in the extent of their empirical grounding, but they share the goal of achieving dynamic Mechanistic Explanation. That is, they augment a proposed Mechanistic Explanation with a computational model that enables exploration of the mechanism’s dynamics. Using exemplars from circadian rhythm research, we extract six specific contributions provided by computational models. We then examine cognitive science models to determine how well they make the same types of contributions. We suggest that the modeling approach used in circadian research may prove useful in cognitive science as researchers develop procedures for experimentally decomposing cognitive mechanisms into parts and operations and begin to understand their nonlinear interactions.
Yue Gu - One of the best experts on this subject based on the ideXlab platform.
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Mechanistic Explanation of time dependent cross phenomenon based on quorum sensing a case study of the mixture of sulfonamide and quorum sensing inhibitor to bioluminescence of aliivibrio fischeri
Science of The Total Environment, 2018Co-Authors: Yue GuAbstract:Abstract Cross-phenomenon in which the concentration-response curve (CRC) for a mixture crosses the CRC for the reference model has been identified in many studies, expressed as a heterogeneous pattern of joint toxic action. However, a Mechanistic Explanation of the cross-phenomenon has thus far been extremely insufficient. In this study, a time-dependent cross-phenomenon was observed, in which the cross-concentration range between the CRC for the mixture of sulfamethoxypyridazine (SMP) and ( Z -)-4-Bromo-5-(bromomethylene)-2(5H)-furanone (C30) to the bioluminescence of Aliivibrio fischeri ( A . fischeri ) and the CRC for independent action model with 95% confidence bands varied from low-concentration to higher-concentration regions in a timely manner expressed the joint toxic action of the mixture changing with an increase of both concentration and time. Through investigating the time-dependent hormetic effects of SMP and C30 (by measuring the expression of protein mRNA, simulating the bioluminescent reaction and analyzing the toxic action), the underlying mechanism was as follows: SMP and C30 acted on the quorum sensing (QS) system of A . fischeri , which induced low-concentration stimulatory effects and high-concentration inhibitory effects; in the low-concentration region, the stimulatory effects of SMP and C30 made the mixture produce a synergistic stimulation on the bioluminescence; thus, the joint toxic action exhibited antagonism. In the high-concentration region, the inhibitory effects of SMP and C30 in the mixture caused a double block in the loop circuit of the QS system; thus, the joint toxic action exhibited synergism. With the increase of time, these stimulatory and inhibitory effects of SMP and C30 were changed by the variation of the QS system at different growth phases, resulting in the time-dependent cross-phenomenon. This study proposes an induced mechanism for time-dependent cross-phenomenon based on QS, which may provide new insight into the Mechanistic investigation of time-dependent cross-phenomenon, benefitting the environmental risk assessment of mixtures.
Dingmar Van Eck - One of the best experts on this subject based on the ideXlab platform.
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Mechanistic Explanation cognitive systems demarcation and extended cognition
Studies in History and Philosophy of Science, 2016Co-Authors: Dingmar Van Eck, Huib Looren De JongAbstract:Abstract Approaches to the Internalism–Externalism controversy in the philosophy of mind often involve both (broadly) metaphysical and explanatory considerations. Whereas originally most emphasis seems to have been placed on metaphysical concerns, recently the Explanation angle is getting more attention. Explanatory considerations promise to offer more neutral grounds for cognitive systems demarcation than (broadly) metaphysical ones. However, it has been argued that Explanation-based approaches are incapable of determining the plausibility of internalist-based conceptions of cognition vis-a-vis externalist ones. On this perspective, improved metaphysics is the route along which to solve the Internalist–Externalist stalemate. In this paper we challenge this claim. Although we agree that Explanation-orientated approaches have indeed so far failed to deliver solid means for cognitive system demarcation, we elaborate a more promising Explanation-oriented framework to address this issue. We argue that the mutual manipulability account of constitutive relevance in mechanisms, extended with the criterion of ‘fat-handedness’, is capable of plausibly addressing the cognitive systems demarcation problem, and thus able to decide on the explanatory traction of Internalist vs. Externalist conceptions, on a case-by-case basis. Our analysis also highlights why some other recent Mechanistic takes on the problem of cognitive systems demarcation have been unsuccessful. We illustrate our claims with a case on gestures and learning.
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Mechanistic Explanation in engineering science
European Journal for Philosophy of Science, 2015Co-Authors: Dingmar Van EckAbstract:In this paper I apply the Mechanistic account of Explanation to engineering science. I discuss two ways in which this extension offers further development of the Mechanistic view. First, functional individuation of mechanisms in engineering science proceeds by means of two distinct sub types of role function, behavior function and effect function, rather than role function simpliciter. Second, it offers refined assessment of the explanatory power of Mechanistic Explanations. It is argued that in the context of malfunction Explanations of technical systems, two key desiderata for Mechanistic Explanations, ‘completeness and specificity’ and ‘abstraction’, pull in opposite directions. I elaborate a novel explanatory desideratum to accommodate this explanatory context, dubbed ‘local specificity and global abstraction’, and further argue that it also holds for Mechanistic Explanations of malfunctions in the biological domain. The overall result is empirically-informed understanding of Mechanistic Explanation in engineering science, thus contributing to the ongoing project of understanding Mechanistic Explanation in novel or relatively unexplored domains. I illustrate these claims in terms of reverse engineering and malfunction Explanations in engineering science.
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Reconciling Ontic and Epistemic Constraints on Mechanistic Explanation, Epistemically
Axiomathes, 2014Co-Authors: Dingmar Van EckAbstract:In this paper I address the current debate on ontic versus epistemic conceptualizations of Mechanistic Explanation in the mechanisms literature. Illari recently argued that good Explanations are subject to both ontic and epistemic constraints: they must describe mechanisms in the world (ontic aim) in such fashion that they provide understanding of their workings (epistemic aim). Elaborating upon Illari’s ‘integration’ account, I argue that causal role function discovery of mechanisms and their components is an epistemic prerequisite for achieving these two aims. This analysis extends Illari’s account in important ways, putting more pressure on ontic readings of Mechanistic Explanation and providing an answer to the question how ontic and epistemic constraints on Mechanistic Explanation are related. I argue these point in terms of cases on memory research drawn from neuroscience and research on extinct neurogenetic mechanisms from early nervous systems biology.
