Philosophers of neuroscience have been employing scientific explanation as an epistemic value to evaluate neuroscientific models for the past twenty years. Consequently, they have developed mechanistic and non-mechanistic accounts of neuroscientific explanation. These two types of accounts explicate how to use a specific kind of explanatory value to evaluate the epistemic value of neuroscientific models. This paper presents a case study involving the canonical models from mathematical and (...) computational neuroscience. This case study will show that the above mechanistic and non-mechanistic framework overly focuses on analyzing neuroscientific models as objects with representational contents. As a consequence, it pays less attention to the process of modeling and the epistemic attitudes of modelers; moreover, it can miss some important epistemic values used by modelers. By reconstructing their modeling process, I will identify the relevant modelers' epistemic attitudes and argue that these modelers use different kinds of epistemic values to evaluate the same type of canonical models. Furthermore, among them, one epistemic value is not captured by the relevant mechanistic and non-mechanistic accounts. I develop a processual framework that centers on the modeling process and modelers' epistemic attitudes. This process framework is better because it it accommodates both the mechanistic and non-mechanistic accounts of neuroscientific explanation regarding the canonical model in addition to capturing what both accounts leave out. (shrink)

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction (...) with their styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system (...) in terms of predictive coding. (shrink)

Some how-possibly explanations have epistemic value because they are epistemically possible; we cannot rule out their truth. One paradoxical implication of that proposal is that epistemic value may be obtained from mere ignorance. For the less we know, then the more is epistemically possible. This chapter examines a particular class of problematic epistemically possible how-possibly explanations, viz. *epistemically opaque* how-possibly explanations. Those are how-possibly explanations justified by an epistemically opaque process. How could epistemically opaque how-possibly (...) explanations have epistemic value if they result from a process about which we lack knowledge or understanding? This chapter proposes three different strategies to salvage epistemic value from epistemic opacity, namely salvaging value from 1) functional transparency, 2) modal operator interpretation, and 3) pursuitworthiness. It illustrates using cases from deep neural network modeling. (shrink)

The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by a specific (...) theoretical goal associated with the need to indicate those conditions that should be met by the non-trivial theory of normative mechanisms and the specific models proposed by PP supporters. In this work, I use classical philosophical methods, such as conceptual analysis and critical reflection. I also analyze selected studies in the field of cognitive science, cognitive psychology, neurology, information theory and biology in terms of the methodology, argumentation and language used, in accordance with their theoretical importance for the issues discussed in this study. In this sense, the research presented here is interdisciplinary. My research framework is informed by the mechanistic model of explanation, which defines the necessary and sufficient conditions for explaining a given phenomenon. The research methods I chose are therefore related to the problems that I intend to solve. In the introductory chapter, “The concept of predictive processing”, I discuss the nature of PP as well as its main assumptions and theses. I also highlight the key concepts and distinctions for this research framework. Many authors argue that PP is a contemporary version of Kantianism and is exposed to objections similar to those made against the approach of Immanuel Kant. I discuss this thesis and show that it is only in a very general sense that the PP framework is neo-Kantian. Here we are not dealing with transcendental deduction nor with the application of transcendental arguments. I argue that PP is based on reverse engineering and abduction inferences. In the second part of this chapter, I respond to the objection formulated by Dan Zahavi, who directly accuses this research framework of anti-realistic consequences. I demonstrate that the position of internalism, present in the so-called conservative PP, does not imply anti-realism, and that, due to the explanatory role played in it by structural representations directed at real patterns, it is justified to claim that PP is realistic. In this way, I show that PP is a non-trivial research framework, having its subject, specific methods and its own epistemic status. Finally, I discuss positions classified as the socalled radical PP. In the chapter “Predictive processing as a Bayesian explanatory model” I justify the thesis according to which PP offers Bayesian modeling. Many researchers claim that the brain is an implemented statistical probabilistic network that is an approximation of the Bayesian 7 rule. In practice, this means that all cognitive processes are to apply Bayes' rule and can be described in terms of probability distributions. Such a solution arouses objections among many researchers and is the subject of wide criticism. The purpose of this chapter is to justify the thesis that Bayesian PP is a non-trivial research framework. For this purpose, I argue that it explains certain phenomena not only at the computational level described by David Marr, but also at the level of algorithms and implementation. Later in this chapter I demonstrate that PP is normative modeling. Proponents of the use of Bayesian models in psychology or decision theory argue that they are normative because they allow the formulation of formal rules of action that show what needs to be done to make a given action optimal. Critics of this approach emphasize that such thinking about the normativity of Bayesian modeling is unjustified and that science should shift from prescriptive to descriptive positions. In a polemic with Shira Elqayam and Jonathan Evans (2011), I show that the division they propose into prescriptivism and Bayesian descriptivism is apparent, because, as I argue, there are two forms of prescriptivism, i.e. the weak and the strong. I argue that the weak version is epistemic and can lead to anti-realism, while the strong version is ontic and allows one to justify realism in relation to Bayesian models. I argue that a weak version of prescriptivism is valid for PP. It allows us to adopt anti-realism in relation to PP. In practice, this means that you can explain phenomena using Bayes' rule. This does not, however, imply that they are Bayesian in nature. However, the full justification of realism in relation to the Bayesian PP presupposes the adoption of strong prescriptivism. This position assumes that phenomena are explained by Bayesian rule because they are Bayesian as such. If they are Bayesian in nature, then they should be explained using Bayesian modeling. This thesis will be substantiated in the chapters “Normative functions and mechanisms in the context of predictive processing” and “Normative mechanisms and actions in predictive processing”. In the chapter “The Free Energy Principle in predictive processing”, I discuss the Free Energy Principle (hereinafter FEP) formulated by Karl Friston and some of its implications. According to this principle, all biological systems (defined in terms of Markov blankets) minimize the free energy of their internal states in order to maintain homeostasis. Some researchers believe that PP is a special case of applying this principle to cognition, and that predictive mechanisms are homeostatic mechanisms that minimize free energy. The discussion of FEP is important due to the fact that some authors consider it to be important for explanatory purposes and normative. If this is the case, then FEP turns out to be crucial in explaining normative predictive mechanisms and, in general, any normative biological mechanisms. To define the explanatory possibilities of this principle, I refer to the discussion of its supporters on the issue they define as the problem of continuity and discontinuity between life and mind. A critical analysis of this discussion and the additional arguments I have formulated have allowed me to revise the explanatory ambitions of FEP. I also reject the belief that this principle is necessary to explain the nature of predictive mechanisms. I argue that the principle formulated and defended by Friston is an important research heuristic for PP analysis. 8 In the chapter “Normative functions and mechanisms in predictive processing”, I start my analyzes by formulating an answer to the question about the normative nature of homeostatic mechanisms. I demonstrate that predictive mechanisms are not homeostatic. I defend the view that a full explanation of normative mechanisms presupposes an explanation of normative functions. I discuss the most important proposals for understanding the normativity of a function, both from a systemic and teleosemantic perspective. I conclude that the non-trivial concept of a function must meet two requirements which I define as explanatory and normative. I show that none of the theories I have invoked satisfactorily meets both of these requirements. Instead, I propose a model of normativity based on Bickhard's account, but supplemented by a mechanistic perspective. I argue that a function is normative when: (1) it allows one to explain the dysfunction of a given mechanism; (2) it contributes to the maintenance of the organism's stability by shaping and limiting possible relations, processes and behaviors of a given system; and when (3) (according to the representational and predictive functions) it enables explaining the attribution of logical values of certain representations / predictions. In such an approach, a mechanism is normative when it performs certain normative functions and when it is constitutive for a specific action or behavior, despite the fact that for some reason it cannot realize it either currently or in the long-term. Such an understanding of the normativity of mechanisms presupposes the acceptance of the epistemic hypothesis. I argue that this hypothesis is not cognitively satisfactory, and therefore the ontic hypothesis should be justified, which is directly related to adopting the position of ontic prescriptivism. For this reason, referring to the mechanistic theory of scientific explanations, I formulate an ontical interpretation of the concept of a normative mechanism. According to this approach, a mechanism or a function is normative when they perform such and such causal roles in explaining certain actions and behaviors. With regard to the normative properties of predictive mechanisms and functions, this means that they are the causes of specific actions an organism carries out in the environment. In this way, I justify the necessity of accepting the ontic hypothesis and rejecting the epistemic hypothesis. The fifth chapter, “Normative mechanisms and actions in predictive processing”, is devoted to the dark room problem and the related exploration-exploitation trade-off. A dark room is the state that an agent could be in if it minimized the sum of all potential prediction errors. I demonstrate that, in accordance with the basic assumption of PP about the need for continuous and long-term minimization of prediction errors, such a state should be desirable for the agent. Is it really so? Many authors believe it is not. I argue that the test of the value of PP is the possibility of a non-trivial solution of this problem, which can be reduced to the choice between active and uncertainty-increasing exploration and safe and easily predictable exploitation. I show that the solution proposed by PP supporters present in the literature does not enable a fully satisfactory explanation of this dilemma. Then I defend the position according to which the full explanation of the normative mechanisms, and, subsequently, the solution to the dilemma of exploration and exploitation, involves reference to the existence of constraints present in the environment. The constraints 9 include elements of the environment that make a given mechanism not only causal but also normative. They are therefore key to explaining the predictive mechanisms. They do not only play the role of the context in which the mechanism is implemented, but, above all, are its constitutive component. I argue that the full explanation of the role of constraints in normative predictive mechanisms presupposes the integration of individual models of specific cognitive phenomena, because only the mechanistic integration of PP with other models allows for a non-trivial explanation of the nature of normative predictive mechanisms that would have a strong explanatory value. The explanatory monism present in many approaches to PP makes it impossible to solve the problem of the dark room. Later in this chapter, I argue that the Bayesian PP is normative not because it enables the formulation of such and such rules of action, but because the predictive mechanisms themselves are normative. They are normative because they condition the choice of such and such actions by agents. In this way, I justify the hypothesis that normative mechanisms make it possible to explain the phenomenon of agent motivation, which is crucial for solving the dark room problem. In the last part of the chapter, I formulate the hypothesis of distributed normativity, which assumes that the normative nature of certain mechanisms, functions or objects is determined by the relations into which these mechanisms, functions or objects enter. This means that what is normative (in the primary sense) is the relational structure that constitutes the normativity of specific items included in it. I suggest that this hypothesis opens up many areas of research and makes it possible to rethink many problems. In the “Conclusion”, I summarize the results of my research and indicate further research perspectives. (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)

Mechanistic models in molecular systems biology are generally mathematical models of the action of networks of biochemical reactions, involving metabolism, signal transduction, and/or gene expression. They can be either simulated numerically or analyzed analytically. Systems biology integrates quantitative molecular data acquisition with mathematical models to design new experiments, discriminate between alternative mechanisms and explain the molecular basis of cellular properties. At the heart of this approach are mechanistic models of molecular networks. We focus on the articulation and development (...) of mechanistic models, identifying five constraints which guide the articulation of models in molecular systems biology. These constraints are not independent of one another, with the result that modeling becomes an iterative process. We illustrate the use of these constraints in the modeling of the mechanism for bistability in the lac operon. (shrink)

The idea at the core of the New Mechanical account of explanation can be summarized in the claim that explaining means showing ‘how things work’. This simple motto hints at three basic features of Mechanistic Explanation (ME): ME is an explanation-how, that implies the description of the processes underlying the phenomenon to be explained and of the entities that engage in such processes. These three elements trace a fundamental contrast with the view inherited from Hume and (...) later from strict logical empiricism (see Creath 2017), focused on epistemic and formal features of science and according to which issues concerning the kind of entities and processes that lie within a theory’s domain are extraneous to science and belong instead to ontology or metaphysics. Philosophers belonging to the new mechanical philosophy believe that the received view of scientific explanation (Hempel 2001), pivoting on the notion of law of nature, overshadows this insight. Since its origin in the 17th century, mechanical philosophy aimed to explain natural phenomena by reducing them to mechanisms. Traditional attempts to define the concept of mechanism involved the identification of a limited set of fundamental elements as, for instance, contact action, action at a distance, inertial motion (see e.g. Hesse 2005), and, more recently, transmission of a mark, or of a conserved quantity (see Frisch, this volume). The new mechanical philosophy rejects this austere characterization of mechanisms and mechanistic explanation and aim at providing a novel, philosophically rigorous explication of the concept of mechanism and of its role in scientific explanation and practice. ME has been adopted with profit in philosophy of special sciences (for instance in biomedical sciences, e.g. in the explanation of chemical transmission at synapses ((Machamer, Darden and Craver 2000), MDC henceforth); but also in social sciences, e.g. the three kinds of social mechanisms in Coleman’s analysis of Max Weber’s account of the role of the Protestant ethic in the growth of capitalism (Hedström and Swedberg 1998)), where exceptionless regularities are rarely ever found. In physics, it is generally possible to formulate explanations in law-based form, with the result that the plurality of explanatory forms might be overlooked. This should not come as a surprise, given that physics was the main inspiration for logical empiricists, and, in particular, Newtonian physics was a template for Hempel’s formulation of the covering law model. However, this situation is unfortunate, since, we will argue, knowing how things work is often part of the explanation of physical phenomena. In this chapter, we provide an introduction to the basic features of ME, with specific focus on its application to physics (section 1). The main part of the chapter is devoted to the defence of two theses: on the one hand, some domains of physics are not compatible with mechanistic reasoning and explanation (section 2); on the other hand, a comprehensive account of explanation in physics can’t dispense with ME (section 3). (shrink)

Prominent research programs dealing with the nature and mechanisms of interpersonal social coordination have emerged in cognitive science, developmental psychology and evolutionary anthropology. I argue that the mechanistic approach to explanation in contemporary philosophy of science can facilitate interdisciplinary integration and division of labor between these different disciplinary research programs. By distinguishing phenomenal models from mechanistic models and structural decomposition from functional decomposition in the process of mechanism discovery, I argue that behavioral and cognitive scientists can (...) make interlocking contributions to families of mechanism models representing a) what central forms of interpersonal social coordination consist in, b) what types of agents engage in such forms of coordination, c) what types of cognitive mechanisms give rise to such forms of coordination, d) how the operation of such mechanisms is modulated by the natural and social environments that the agents populate. Thus mechanistic philosophy of science can play a heuristic role in guiding the research strategies of scientists in different scientific disciplines in a manner that is complementary to the research strategies of scientists in other disciplines and thereby efficient from the point of view of the division of epistemic labor. (shrink)

We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and (...) the transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call “epistemic competition” that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe—contrary to many other accounts of scientific change—that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape. (shrink)

One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance (...) and linking the model to the phenomenon, (ii) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem. (shrink)

The new mechanistic philosophy seeks to identify and explain the mechanisms of various phenomena, including their overall organization and the interactions between the individualized components. This paper argues that among the phenomena that can be approached within the new mechanistic framework are near-death experiences, which can be included within the vast range of experiences that are grouped under the category of religious experience. Such experiences involve a complex set of cognitive, affective, and behavioural processes. Since studying such experiences (...) is far from methodologically simple, we try to show the feasibility of applying the mechanistic explanation to near-death experiences. While some scholars (such as Egil Asprem and Ann Taves) argue that mechanistic explanation can shed new light on the explanation of religion, we instead emphasize neglected limits of such an explanation, as well as its epistemic-methodological advantages in comparison with alternative explanatory models, especially the models proposed by Michael Marsh, Frederick S. Barrett and Roland R. Griffiths. (shrink)

Although it has been argued that mechanistic explanation is compatible with abstraction, there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to (...) legitimize mechanistic abstract models, and also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation. Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways. (shrink)

Deep learning (DL) is a statistical technique for pattern classification through which AI researchers train artificial neural networks containing multiple layers that process massive amounts of data. I present a three-level account of explanation that can be reasonably expected from DL models in cognitive neuroscience and that illustrates the explanatory dynamics within a future-biased research program (Feest Philosophy of Science 84:1165–1176, 2017 ; Doerig et al. Nature Reviews: Neuroscience 24:431–450, 2023 ). By relying on the mechanistic framework (...) (Craver Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Clarendon, 2007 ; Stinson Eppuor si muove: Doing history and philosophy of Science with Peter Machamer. Springer, 2017, The Routledge Handbook of the computational mind. Routledge, 2018 ), I develop an account that corresponds to the stages of mechanism discovery, i.e., our shifting epistemic position and epistemic goals, and propose a representative model for each level. Generic, theoretical DL models at _Level 1_ address the general features of a cognitive phenomenon through exploration and provide us with how-possibly explanations. On the other hand, DL models at _Level 2_ either identify the interaction between the features or represent the epistemic stage when the researcher is still unsure if the modeled features are crucial or arbitrary. These models should provide us with how-plausibly explanations. Finally, specific DL models of specific brain areas, i.e., ersatz models filled with relevant cognitive and neuroscientific details, are at _Level 3_. At this level, a researcher can advance how-actually explanations of cognitive phenomena. The main strength of this account is that it elucidates both _global explanatory dynamics_ and _local explanatory dynamics_ (cf. Dresow Synthese 199:10441–10474, 2021 ). The former occurs when the transition between levels happens in accordance with the process of obtaining more details about a particular cognitive phenomenon through multiple DL models. The latter, meanwhile, involves cases in which the transition between levels takes place within a single DL model by elucidating internal mechanisms (e.g., using Explainable AI techniques for rendering models more transparent). (shrink)

This paper addresses a fundamental line of research in neuroscience: the identification of a putative neural processing core of the cerebral cortex, often claimed to be “canonical”. This “canonical” core would be shared by the entire cortex, and would explain why it is so powerful and diversified in tasks and functions, yet so uniform in architecture. The purpose of this paper is to analyze the search for canonical explanations over the past 40 years, discussing the theoretical frameworks (...) informing this research. It will highlight a bias that, in my opinion, has limited the success of this research project, that of overlooking the dimension of cortical development. The earliest explanation of the cerebral cortex as canonical was attempted by David Marr, deriving putative cortical circuits from general mathematical laws, loosely following a deductive-nomological account. Although Marr’s theory turned out to be incorrect, one of its merits was to have put the issue of cortical circuit development at the top of his agenda. This aspect has been largely neglected in much of the research on canonical models that has followed. Models proposed in the 1980s were conceived as mechanistic. They identified a small number of components that interacted as a basic circuit, with each component defined as a function. More recent models have been presented as idealized canonical computations, distinct from mechanistic explanations, due to the lack of identifiable cortical components. Currently, the entire enterprise of coming up with a single canonical explanation has been criticized as being misguided, and the premise of the uniformity of the cortex has been strongly challenged. This debate is analyzed here. The legacy of the canonical circuit concept is reflected in both positive and negative ways in recent large-scale brain projects, such as the Human Brain Project. One positive aspect is that these projects might achieve the aim of producing detailed simulations of cortical electrical activity, a negative one regards whether they will be able to find ways of simulating how circuits actually develop. (shrink)

Thanks to genetic and biochemical advances on the molecular mechanism of circadian rhythms in Drosophila, theoretical models closely related to experimental observations can be considered for the regulatory mechanism of the circadian clock in this organism. Modeling is based on the autoregulatory negative feedback exerted by a complex between PER and TIM proteins on the expression of per and tim genes. The model predicts the occurrence of sustained circadian oscillations in continuous darkness. When incorporating light‐induced TIM degradation, the (...) model accounts for damping of oscillations in constant light, entrainment of the rhythm by light‐dark cycles of varying period or photoperiod, and phase shifting by light pulses. The model further provides a molecular dynamical explanation for the permanent or transient suppression of circadian rhythmicity triggered in a variety of organisms by a critical pulse of light. Finally, the model shows that to produce a robust rhythm the various clock genes must be expressed at the appropriate levels since sustained oscillations only occur in a precise range of parameter values. BioEssays 22:84–93, 2000. ©2000 John Wiley & Sons, Inc. (shrink)

The “reproductive suppression hypothesis” states that the strong desire of adolescent girls in our culture to control their weight may reflect the operation of an adaptive mechanism by which ancestral women controlled the timing of their sexual maturation and hence first reproduction, in response to cues about the probable success of reproduction in the current situation. We develop a model based on this hypothesis and explore its behavior and evolutionary and psychological implications across a range of parameter values. We (...) use the process of model development to identify assumptions implicit in the reproductive suppression hypothesis and variables that need to be measured in order to investigate it more fully. In addition, because costs were probably associated with weight control, an important part of this analysis is the specification of situations in which the benefits of adaptive adjustment of the maturation schedule could have outweighed the costs of achieving that adjustment by slowing adolescent weight gain. (shrink)

There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue (...) that individual models do not provide mechanistic explanations by themselves. Instead, individual models are always used to complement a huge body of background information and pre-existing models about the target system. With this in mind, I argue that mechanistic explanations are distributed across sets of different, and sometimes contradictory, scientific models. Each of these models contributes limited, but essential, information to the same mechanistic explanation, but none can be considered a mechanistic explanation in isolation of the others. (shrink)

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the (...) generation of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors. (shrink)

An increasing number of philosophers have promoted the idea that mechanism provides a fruitful framework for thinking about the explanatory contributions of computational approaches in cognitive neuroscience. For instance, Piccinini and Bahar :453–488, 2013) have recently argued that neural computation constitutes a sui generis category of physical computation which can play a genuine explanatory role in the context of investigating neural and cognitive processes. The core of their proposal is to conceive of computational explanations in cognitive neuroscience as a subspecies (...) of mechanistic explanations. This paper identifies several challenges facing their mechanistic account and sketches an alternative way of thinking about the epistemic roles of computational approaches used in the study of brain and cognition. Drawing on examples from both low-level and systems-level computational neuroscience, I argue that at least some computational explanations of neural and cognitive processes are partially independent from mechanistic constraints. (shrink)

This paper proposes an extended version of the interventionist account for causal inference in the practical context of biological mechanism research. This paper studies the details of biological mechanism researchers’ practices of assessing the evidential legitimacy of experimental data, arguing why quantity and variety are two important criteria for this assessment. Because of the nature of biological mechanism research, the epistemic values of these two criteria result from the independence both between the causation of data generation and the causation (...) in question and between different interventions, not techniques. The former independence ensures that the interventions in the causation in question are not affected by the causation that is responsible for data generation. The latter independence ensures the reliability of the final mechanisms not only in the empirical but also the formal aspects. This paper first explores how the researchers use quantity to check the effectiveness of interventions, where they at the same time determine the validity of the difference-making revealed by the results of interventions. Then, this paper draws a distinction between experimental interventions and experimental techniques, so that the reliability of mechanisms, as supported by the variety of evidence, can be safely ensured in the probabilistic sense. The latter process is where the researchers establish evidence of the mechanisms connecting the events of interest. By using case studies, this paper proposes to use ‘intervention’ as the fruitful connecting point of literature between evidence and mechanisms. (shrink)

Explanations implicitly end with something that makes sense, and begin with something that does not make sense. A statistical relationship, for example, a numerical fact, does not make sense; an explanation of this relationship adds something, such as causal information, which does make sense, and provides an endpoint for the sense-making process. Does social science differ from natural science in this respect? One difference is that in the natural sciences, models are what need ‘‘understanding.’’ In the social sciences, (...) matters are more complex. There are models, such as causal models, which need to be understood, but also depend on background knowledge that goes beyond the model and the correlations that make it up, which produces a regress. The background knowledge is knowledge of in-filling mechanisms, which are normally made up of elements that involve the direct understanding of the acting and believing subjects themselves. These models, and social science explanations generally, are satisfactory only when they end the regress in this kind of understanding or use direct understanding evidence to decide between alternative mechanism explanations. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working (...) memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

This thesis addresses two enduring issues in legal theory-- rationality and its association with rule of law values--by offering detailed models of two patterns of legal reasoning. One is reasoning by analogy. The other is the inference process that legal reasoners use when they defer epistemically to scientific experts in the course of reaching legal decisions. Discussions in both chapters reveal that the inference pattern known as "abduction" is a deeply important element of many legal inferences, including analogy and (...) epistemic deference to experts--something that has been largely unrecognized in legal theory. After presenting a detailed model of analogy, Chapter 1 argues that legal reasoning by analogy, like analogical argument in logic, mathematics, and the natural sciences, possesses a great deal of rational force when properly executed. Chapter 2 investigates the reasoning process by which non-expert legal reasoners defer epistemically to the judgments of scientists. It presents five main conclusions or explanations. Every practical syllogism actually consists of two distinct chains of inference, and what is usually considered in writings on practical reasoning to be the minor premise of a practical syllogism is actually the conclusion of a separate chain of argument. When a practical reasoner defers epistemically to a theoretical expert, this separate chain of argument has a special structure, which is modeled in Chapter 2. The non-expert legal reasoner must always make a prescriptive practical judgment about the standard of epistemic appraisal and level of epistemic confidence that the reasoner requires of scientific evidence before it may be relied upon in reaching a legal decision. Legal systems should be committed to an emergent rule of law ideal identified and referred to as "intellectual due process" which condemns epistemically arbitrary reasoning processes insofar as they play a role in legal decisionmaking. Since non-arbitrariness is a necessary condition of both epistemic and legal legitimacy, we have compelling reason to doubt that the non-arbitrariness condition can be met when non-experts defer epistemically to scientific experts. (shrink)

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. (shrink)

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at (...) a high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes. (shrink)

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among entities at (...) a high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes. (shrink)

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. (shrink)

The paper explores how, in economics and biology, theoretical models are used as explanatory devices. It focuses on a modelling strategy by which, instead of starting with an unexplained regularity in the world, the modeller begins by creating a credible model world. The model world exhibits a regularity, induced by a mechanism in that world. The modeller concludes that there may be a part of the real world in which a similar regularity occurs and that, were that the (...) case, the model would offer an explanation. Little concrete guidance is given about where such a regularity might be found. Three modelling exercises in evolutionary game theory—one from economics and two from biology—are used as case studies. Two of these (one from each discipline) exemplify ‘explanation in search of observation’. The third goes a step further, analysing a regularity in a model world and treating it as informative about the real world, but without saying anything about real phenomena. The paper argues that if the relation between the model and real worlds is understood in terms of similarity, and if modelling is understood as an ongoing discovery process rather than as the demonstration of empirical truths, there can be value in creating explanations before finding the regularities that are to be explained. (shrink)

Legal scholarship exploring the nature of evidence and the process of juridical proof has had a complex relationship with formal modeling. As evident in so many fields of knowledge, algorithmic approaches to evidence have the theoretical potential to increase the accuracy of fact finding, a tremendously important goal of the legal system. The hope that knowledge could be formalized within the evidentiary realm generated a spate of articles attempting to put probability theory to this purpose. This literature was (...) both insightful and frustrating. Much light was shed on the legal system, but it also quickly became evident that the tools of probability theory were in many ways ill-constructed for the task. Fundamental incompatibilities between the structure of legal decision making and the extant formal tools were identified, and it became evident that many of the purported explanations of legal phenomena were internally inconsistent. As a consequence, interest in this type of formal modeling declined, and attention was directed toward different kinds of explanations of the phenomena. Perhaps under the influence of a recent trend toward various types of formal modeling in legal scholarship, a recent burst of articles, rather than attempting to explain the macro structure of trials, which was the previous object of interest, attempts to quantify the probative value of various items of evidence in ways consistent with the formal features of various probability theories, and then to study decision making from that perspective. For example, the value of evidence is often purported to be its likelihood ratio, that is, the probability of discovering or receiving the evidence given a hypothesis (e.g., the defendant did it) divided by the probability of discovering or receiving the evidence given the negation of the hypothesis (the defendant didn't do it). Alternatively, the value of evidence is purported (more contextually) to be the information gain it provides, defined as the increase in probability it provides for a hypothesis above the probability of the hypothesis based on the other available evidence. Both conceptions then assume that all of the various probability assessments conform or ought to conform to the dictates of Bayes' theorem (that maintains consistency among such assessments); empirical studies are then done testing the extent to which this is so and proposing how the law can increase the probability that it is so. The general criticisms of using Bayes' theorem as a formal model of juridical proof are well known and were integral to the last wave of interest in formal modeling of the evidentiary process. This paper thus for the most part puts aside that more general issue, and focuses specifically on mathematical modeling of the value of particular items of evidence. The paper demonstrates that formal modeling has only limited value in explaining the value of legal evidence, much more limited than those constructing and discussing the models assume, and thus that the conclusions they draw about the value of evidence are unwarranted. This is done through a discussion of four recent examples that attempt to quantify evidence relating to, respectively, carpet fibers, infidelity, DNA random-match evidence, and character evidence used to impeach a witness. This article thus makes two contributions. First, and most importantly, it is another demonstration of the complex relationship between algorithmic tools and legal decision making. Second, at a minimum it points out serious pitfalls for analytical or empirical studies of juridical proof. (shrink)

The value of optimality modeling has long been a source of contention amongst population biologists. Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation. Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus serves (...) as a case study, on the basis of which I suggest that there is a widely felt tension in science between explanatory independence and broad epistemic interdependence, and that this tension influences scientific methodology. (shrink)

We argue that non-epistemic values, including moral ones, play an important role in the construction and choice of models in science and engineering. Our main claim is that non-epistemic values are not only “secondary values” that become important just in case epistemic values leave some issues open. Our point is, on the contrary, that non-epistemic values are as important as epistemic ones when engineers seek to develop the best model of a process or (...) problem. The upshot is that models are neither value-free, nor depend exclusively on epistemic values or use non-epistemic values as tie-breakers. (shrink)

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality (...) assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. (shrink)

Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that (...) point of view, we must take into account the tradeoff between the representational/explanatory goals of generality and precision that govern the practice of model-building. In some modeling scenarios, it is rational to maximize explanatory generality at the expense of mechanistic precision. This tradeoff allows me to put forward a problem for the mechanist position. If mechanistic modeling endorses generality as a valuable goal, then Subordination should be rejected. If mechanists reject generality as a goal, then Integration is false. I suggest that mechanists should accept that functional analysis can offer acceptable explanations of cognitive phenomena. (shrink)

The cultural red king effect occurs when discriminatory bargaining practices emerge because of a disparity in learning speed between members of a minority and a majority. This effect has been shown to occur in some Nash Demand Game models and has been proposed as a tool for shedding light on the origins of sexist and racist discrimination in academic collaborations. This paper argues that none of the three main strategies used in the literature to support the epistemic value (...) of these models—structural similarity, empirical confirmation, and how-possibly explanations—provides strong support for this modeling practice in its present form. (shrink)

This book provides a practitioner's foundation for the process of explanatory model building, breaking down that process into five stages. Donald W. Katzner presents a concrete example with unquantified variable values to show how the five-stage procedure works. He describes what is involved in explanatory model building for those interested in this practice, while simultaneously providing a guide for those actually engaged in it. The combination of Katzner's focus on modeling and on mathematics, along with (...) his focus on the explanatory performance of modeling, promises to become an important contribution to the field. (shrink)

We explore the following two cognitive questions regarding crosslinguistic variation in lexical semantic systems: Why are some linguistic categories—that is, the associations between a term and a portion of the semantic space—harder to learn than others? How does learning a language‐specific set of lexical categories affect processing in that semantic domain? Using a computational word‐learner, and the domain of color as a testbed, we investigate these questions by modeling both child acquisition of color terms and adult behavior on a (...) non‐verbal color discrimination task. A further goal is to test an approach to lexical semantic representation based on the principle that the more languages label any two situations with the same word, the more conceptually similar those two situations are. We compare such a crosslinguistically based semantic space to one based on perceptual similarity. Our computational model suggests a mechanistic explanation for the interplay between term frequency and the semantic closeness of learned categories in developmental error patterns for color terms. Our model also indicates how linguistic relativity effects could arise from an acquisition mechanism that yields language‐specific topologies for the same semantic domain. Moreover, we find that the crosslinguistically inspired semantic space supports these results at least as well as—and in some aspects better than—the purely perceptual one, thus confirming our approach as a practical and principled method for lexical semantic representation in cognitive modeling. (shrink)

In this thesis I examine a number of topics that bear on explanation and understanding in molecular and cell biology, in order to shed new light on explanatory practice in those areas and to find novel angles from which to approach relevant philosophical debates. The topics I look at include mechanism, emergence, cellular complexity, and the informational role of the genome. I develop a perspective that stresses the intimacy of the relations between ontology and epistemology. Whether a phenomenon looks (...) mechanistic, or complex, or indeed emergent, is largely an epistemic matter, yet has an objective basis in features of the world. After reviewing several concepts of mechanism I consider the influential recent account of Machamer, Darden and Craver (MDC). That account makes interesting proposals concerning the relationship between mechanistic explanation and intelligibility, which are consistent with the results of the investigation I undertake into the science surrounding protein folding. In relation to a number of other issues pertaining to biological systems I conclude that the MDC account is insufficiently nuanced, however, leading me to outline an alternative approach to mechanism. This emphasizes the importance of structure—function relations and addresses issues raised by reflection on the nature of cellular complexity. These include the distinction between structure and process and the different possible bases on which system organization may be maintained. The account I give of emergence construes the phenomenon in terms of psychological deficit: phenomena are emergent when we lack the capacity to trace through and model their causal structures using our cognitive schemas. I conclude by developing these ideas into a preliminary and partial account of explanation and understanding. This aspires to cover the significant fraction of work in molecular and cell biology that correlates biological structures, processes and functions by visualizing phenomena and making them imaginable. (shrink)

The recent discussion on scientific representation has focused on models and their relationship to the real world. It has been assumed that models give us knowledge because they represent their supposed real target systems. However, here agreement among philosophers of science has tended to end as they have presented widely different views on how representation should be understood. I will argue that the traditional representational approach is too limiting as regards the epistemic value of modelling given the focus (...) on the relationship between a single model and its supposed target system, and the neglect of the actual representational means with which scientists construct models. I therefore suggest an alternative account of models as epistemic tools. This amounts to regarding them as concrete artefacts that are built by specific representational means and are constrained by their design in such a way that they facilitate the study of certain scientific questions, and learning from them by means of construction and manipulation. (shrink)

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical (...) models, models now come in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)

This article addresses the contributions of the literature on the new mechanistic philosophy of science for the scientific practice of model building in ecology. This is reflected in a one-to-one interdisciplinary collaboration between an ecologist and a philosopher of science during science-in-the-making. We argue that the identification, reconstruction and understanding of mechanisms is context-sensitive, and for this case study mechanistic modeling did not present a normative role but a heuristic one. We expect our study to provides (...) useful epistemic tools for the improvement of empirically-riven work in the debates about mechanistic explanation of ecological phenomena. (shrink)

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation have failed to address how a mathematical model could contribute (...) to such explanations. I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena, where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism’s ability to respond to perturbations. I offer general conclusions for philosophical accounts of explanation. (shrink)

We address the question of whether and to what extent explanatory and modelling strategies in systems biology are mechanistic. After showing how dynamic mathematical models are actually required for mechanistic explanations of complex systems, we caution readers against expecting all systems biology to be about mechanistic explanations. Instead, the aim may be to generate topological explanations that are not standardly mechanistic, or to arrive at design principles that explain system organization and behaviour in general, but not (...) specific mechanisms. These abstraction strategies serve various aims, including prediction and control, that are central to understanding the epistemic diversity of systems biology. (shrink)

Philosophy of Science in Practice (PoSiP) has the “practice of science” as its object of research. Notwithstanding, it does not possess yet any general or specific methodology in order to achieve its goal. Instead of sticking to one protocol, PoSiP takes advantage of a set of approaches from different fields. This thesis takes as a starting point a collaborative and interdisciplinary research between two Ph.D. students from distinct areas: ecology and philosophy. This collaboration showed how a scientist could benefit from (...) philosophy of science (in this case study the philosophical approach of. mechanistic explanation) to construct a model of his explanandum, by means of heuristics approach (heuristics as an instrument but also a methodological approach) and, also allowed philosophy of science take a closer look into the scientific practice to investigate how explanations are constructed and how scientific understanding is achieved (in this thesis, with a dialogue with the contextual theory of scientific understanding). As a result, it is asserted that (i) mechanistic explanation possess limitations but may work as epistemic instruments that mediate between theories, data, scientists, and models; (ii) explanation construction and scientific understanding deeply relies on intuition; (iii) scientific understanding is an instant, a moment, a temporary achievement, and its process may happen in degrees; (iv) philosophy of science, by means of heuristics process, may enhance scientists’ epistemic virtues, improving his academic skills, by means of self-evaluation. This research shows that interdisciplinarity and collaborative work can act, through heuristics, as a toolbox for PoSiP to achieve its goal of understanding how science is made. Despite its success, an analysis of this collaborative practice leads to some fundamental issues. First, philosophy of science in practice is a philosophy of past practice, in that the majority of examples used by mainstream PoSiP come from the final products of science. Second, is it philosophy of [science in practice] or philosophy of science [in practice]? How to practice philosophy of scientific practice and, how to practice interdisciplinarity in the philosophy of scientific practices simultaneously to its scientific activity? This research exposes the epistemic role heuristics and interdisciplinarity possess as methodological toolboxes for philosophy of science in practice. It is defended that other ways of constructing sciences would be through different dynamics such as collaborative networks and interdisciplinarity research contributing to the vision of Trading Zones from Peter Galison, in which bridges between specialized disciplines are created in order to exchange knowledge and information. (shrink)

The philosophy of mechanisms has developed rapidly during the last 30 years. As mechanisms-based explanations (MBEs) are often seen as an alternative to nomological, law-based explanations, MBEs could be relevant in IS. We begin by offering a short history of mechanistic philosophy and set out to clarify the contemporary landscape. We then suggest that mechanistic models provide an alternative to variance and process models in IS. Finally, we highlight how MBEs typically contain deliberate misrepresentations. Although MBEs have (...) recently been advocated as critical realist (CR) accounts in IS, idealizations (deliberate misrepresentations) seem to violate some fundamental tenets of CR and research method principles for CR. Idealizations in MBEs, therefore, may risk being regarded as flawed in IS. If it turns out that CR cannot account for idealizations, naturalism can, and it does so without extra-philosophical baggage. (shrink)

This paper discusses the relevance of models for cognitive science that integrate mechanistic and computational aspects. Its main hypothesis is that a model of a cognitive system is satisfactory and explanatory to the extent that it bridges phenomena at multiple mechanistic levels, such that at least several of these mechanistic levels are shown to implement computational processes. The relevant parts of the computation must be mapped onto distinguishable entities and activities of the mechanism. The ideal is (...) contrasted with two other accounts of modeling in cognitive science. The first has been presented by David Marr in combination with a distinction of “levels of computation”. The second builds on a hierarchy of “mechanistic levels” in the sense of Carl Craver. It is argued that neither of the two accounts secures satisfactory explanations of cognitive systems. The mechanistic-computational ideal can be thought of as resulting from a fusion of Marr’s and Craver’s ideals. It is defended as adequate and plausible in light of scientific practice, and certain metaphysical background assumptions are discussed. (shrink)

Recent extensions of mechanistic explanation into psychology suggest that cognitive models are only explanatory insofar as they map neatly onto, and serve as scaffolding for more detailed neural models. Filling in those neural details is what these accounts take the integration of cognitive psychology and neuroscience to mean, and they take this process to be seamless. Critics of this view have given up on cognitive models possibly explaining mechanistically in the course of arguing for cognitive models having (...) explanatory value independent of how well they align with neural mechanisms. We can have things both ways, however. The problem with seamless integration accounts is their seamlessness, not that they take cognitive models to be mechanistic. A non-componential view of mechanisms allows for cognitive and neural models that cross cut one another, and for cognitive models that don’t decompose into parts. I illustrate the inadequacy of seamless accounts of integration by contrasting how “filter” models of attention in psychology and of sodium channels in neuroscience developed; by questioning whether the mappings generated by neuroimaging subtraction studies achieve integration; and by reinterpreting the evidence for cognitive models of memory having been successfully integrated with neural models. I argue that the integrations we can realistically expect are more partial, patchy, and full of loose threads than the mosaic unity Craver describes. (shrink)

This thesis mounts an attack against accounts of perceptual justification that attempt to analyze it in terms of evidential justifiers, and has defended the view that perceptual justification should rather be analyzed in terms of non-evidential justification. What matters most to perceptual justification is not a specific sort of evidence, be it experiential evidence or factive evidence, what matters is that the perceptual process from sensory input to belief output is reliable. I argue for this conclusion in the following (...) way. Chapter 1: The Arguments from HallucinationChapter 1 presents a skeptical argument from hallucination that starts from a premise about the alleged introspective indistinguishability of perception and hallucination, and concludes that perceptual knowledge is impossible. Underlying this argument are certain assumptions about the nature of perceptual justification, and different responses to the argument portray different views of perceptual justification. Evidentialism and dogmatism both reject the premise that appearances do not provide evidence sufficient for perceptual knowledge, although they disagree about the precise nature of the appearances. Epistemological disjunctivism rejects the premise that introspectively indistinguishable experiences provide the same evidence, while upholding the idea that some sort of evidence, namely factive evidence, is necessary for perceptual justification and knowledge. Process reliabilism rejects the premise that evidence is necessary for perceptual knowledge on the grounds that even perceptual justification can be obtained without evidence, as long as the cognitive process producing the belief is reliable.Chapter 2: EvidentialismChapter 2 starts by introducing some motivations for experientialist views of perceptual justification, such as evidentialism and dogmatism, which hold that conscious experiences evidentially justify perceptual beliefs. These motivations have to do with their ability to accommodate the New Evil Demon Intuition that demon-deceived subjects have justified perceptual beliefs and the Blindsight Intuition that blindsighters do not have justified perceptual beliefs. However, experientialist views are also faced with a Sellarsian dilemma: either experience is construed as non-propositional, but then the proposed evidential relation is unclear, or experience is construed as propositional, but then it is ad hoc to hold that they can evidentially justify without being justified themselves.Chapter 2 continues with a discussion of evidentialism, which grasps the first horn of this dilemma. I argue that evidentialism cannot adequately explain why a non-propositional experience constitutes evidence for this rather than that belief, and also does not provide any account of how to determine which belief fits an evidence-set which partly consists out of non-propositional experiences. Although a reliabilist version of evidentialism can provide a better account of these matters, it suffers from counter-intuitive predictions about justification and a conception of evidence that is so liberal that it could even include blindsighters and clairvoyants as having such evidence.Chapter 3: DogmatismChapter 3 focuses on variants of dogmatism, a theory of perceptual justification that grasps the second horn of the Sellarsian dilemma by holding that a perceptual experience with the content that p is sufficient for immediate prima facie justification of the belief that p. I first argue that dogmatism should identify perceptual experiences with high-level conscious states to accommodate the problem of novice vs. expert identification and the problem of the speckled hen. I then argue that this version of dogmatism still fails to meet a certain explanatory challenge which I dub the `Distinctiveness Problem'. Dogmatism should explain what is so distinctive about perceptual experience that enables it, in contrast with desire and imagination, to evidentially justify the belief that p without, in contrast with belief, being justified itself.Phenomenalist answers to this problem fail because they either do not provide a distinctive property of perceptual experience, or else provide a property that is reducible to or possibly caused by beliefs. In both cases, dogmatism would have the result that intuitively unjustifying experiences, such as imaginations, nevertheless become capable of justifying beliefs. Moreover, phenomenalists still need some explanation of why phenomenology is epistemically relevant at all.A new variant of the problem of the speckled hen is an intuitive illustration of this point against dogmatists. If a perceptual experience that p is not reliably connected to the fact that p or the belief that p, then beliefs based on this experience simply will not be intuitively justified. One possible response to this problem incorporates reliability into the content of experience in the sense that an experience cannot have the content that p if it is not reliably connected to both the fact that p and the belief that p. However, this will lose the motivation from the New Evil Demon Scenario, and will make large concessions towards reliabilism.Chapter 4: Epistemological DisjunctivismGiven these problems for experientialist views of perceptual justification, chapter 4 focuses on a different evidential account of justification: epistemological disjunctivism. According to the Justified True Belief version of Epistemological Disjunctivism, the fact that I see that p is the factive and reflectively accessible evidence in virtue of which I know that p. I argue that JTBED faces formidable problems: first, it cannot adequately deal with the basis problem, second, it cannot accommodate the New Evil Demon Intuition, third,it faces the problem of hyper-intellectualization, and fourth, it has no good account of reflective access.Knowledge First Epistemological Disjunctivism, in contrast, holds that seeing that p just is a way of knowing that p. The fact that I see that p constitutes my justification for believing that p, even though it is not necessary for my knowledge that p. Although this view solves most of the above problems, I argue that KFED still faces a challenge of its own: its notion of justification is so strong that justification entails knowledge but is not itself entailed by it. This means that it too faces a hyper-intellectualization objection because it cannot accommodate animal justification, and that it too will struggle to accommodate the New Evil Demon Intuition.Nevertheless, there is something to the epistemological disjunctivist idea that higher-order capabilities are important to perceptual justification. Specifically, higher-order capabilities provide one with a way of accommodating the Blindsight Intuition without appealing to the notion of experiential evidence: blindsighters are epistemically worse off than normal human perceivers because they lack higher-order beliefs about how they are gaining their information. If one holds that these higher-order beliefs provide a special sort of justification for perceptual beliefs, then blindsighters lack this type of justification. Chapter 5: Process ReliabilismAfter displaying the problems of several evidential accounts of perceptual justification, chapter 5 introduces a non-evidential account: process reliabilism. The classic version of this view holds that the reliability of a belief-forming process is necessary and sufficient for the prima facie justification of a specific set of beliefs, namely, those that arise out of belief-independent processes. Two classic major problems for this view consist of counterexamples to both the necessity and sufficiency of reliability for justification. Although the Generality Problem raises some difficulties for reliabilism, they do not appear to be insurmountable.The second part of chapter 5 focuses on a variety of process reliabilism, inferentialist reliabilism, that is explicitly aimed at responding to clairvoyance cases. According to inferentialist reliabilism, one should distinguish between basic and non-basic beliefs, where basic beliefs are those that result out of the non-inferential operation of an inferentially opaque cognitive system that developed in a natural way. Reliability is only sufficient for the justification of basic beliefs, while non-basic beliefs also require conditional reliability and justification of the beliefs on which they are based.This distinction between basic and non-basic beliefs is meant to answer the challenge arising out of clairvoyance cases because these are all supposedly cases in which the resulting beliefs arenon-basic. However, I argue that the beliefs in these cases come out as non-basic primarily because of the etiological constraint on cognitive systems, a constraint that is unfortunately undermotivated. It seems that beliefs can be justified even if they are produced by a cognitive system that developed by accident, or by a system that was artificially designed to produce those beliefs. So even though the inferentialist reliabilist might have a point in distinguishing between basic and non-basic beliefs, it is still in need of a different response to clairvoyance cases. Chapter 6: Rejoinders for ReliabilismChapter 6 improves on the inferentialist reliabilist account of justification by presenting a different way of dealing with the clairvoyance cases and New Evil Demon Scenario. I start from the empirically plausible idea that we know that we are perceiving when we are perceiving, not because of the use of experiential evidence, but rather, because of an unconscious source-monitoring mechanism. I then argue that the existence of such a source-monitoring mechanism could be used to explain why clairvoyance cases actually have to do with defeat rather than absence of prima facie justification: beliefs that pop up in our head should be recognized as stemming from untrustworthy sources, and so we acquire defeating higher-order beliefs about the first-order beliefs. I also argue that this defeat should be able to occur even if the higher-order beliefs are themselves unjustified. This defeater-account of clairvoyance is preferable to several externalist alternatives. Making reliability relative to a specific world seems rather ad hoc, and further requirements on justification, such as evidence-based belief, cognitively integrated belief, or properly produced belief, all appear unnecessary and are susceptible to adapted versions of the clairvoyance case.I further argue for an alternative inferentialist reliablist response to the New Evil Demon Intuition which treats it as mistaken rather than correct. The New Evil Demon Intuition might arise because of a mistaken view of actual perceptual justification as having to do with experiential evidence. This mistaken view could be explained by the fact that experience is actually reliably connected to belief and is often cited in response to a knowledge-challenge. If this is a correct analysis of the New Evil Demon Intuition, then there is an element of question-begging involved when this scenario is still pressed against inferentialist reliabilists who do not adhere to the experientialist assumption it presupposes.With the inferentialist reliabilist account in hand,we are able to see that many of the premises of the epistemological argument from hallucination are problematic. Evidence does not appear to be required for perceptual knowledge, as perceptual justification does not even require such evidence. Moreover, if we accept a reliabilist model of introspection, then we are often able to know on the basis of introspection that we are perceiving rather than hallucinating. Once one gets clear about the alternatives, it becomes apparent that the argument thrives on internalist assumptions about justification and knowledge that one need not accept. (shrink)

We address the question of whether and to what extent explanatory and modelling strategies in systems biology are mechanistic. After showing how dynamic mathematical models are actually required for mechanistic explanations of complex systems, we caution readers against expecting all systems biology to be about mechanistic explanations. Instead, the aim may be to generate topological explanations that are not standardly mechanistic, or to arrive at design principles that explain system organization and behaviour in general, but not (...) specific mechanisms. These abstraction strategies serve various aims, including prediction and control, that are central to understanding the epistemic diversity of systems biology. (shrink)

Philosophers of science have recently focused on the scientific activity of modeling phenomena, and explicated several of its properties, as well as the activities embedded into it. A first approach to modeling has been elaborated in terms of representing a target system: yet other epistemic functions, such as producing data or detecting phenomena, are at least as relevant. Additional useful distinctions have emerged, such as the one between phenomenological and mechanistic models. In biological sciences, besides mathematical (...) models, models now come in three forms: in vivo, in vitro and in silico. Each has been investigated separately, and many specific problems they raised have been laid out. Another relevant distinction is disciplinary: do models differ in significant ways according to the discipline involved—medicine or biology, evolutionary biology or earth science? Focusing on either this threefold distinction or the disciplinary boundaries reveals that they might not be sufficient from a philosophical perspective. On the contrary, focusing on the interaction between these various kinds of models, some interesting forms of explanation come to the fore, as is exemplified by the papers included in this issue. On the other hand, a focus on the use of models, rather than on their content, shows that the distinction between biological and medical models is theoretically sound. (shrink)