I argue that non-epistemic values are necessarily embedded in the measure of evidential strength of contrastive evidence. When evidence is contrastive, evidence is stronger the more it favours a hypothesis over a set of plausible, mutually exclusive alternative hypotheses. In such a contrastive epistemic setting, evidence has an effect not only on a particular hypothesis, but on the whole probability distribution over the set of alternative hypotheses. A natural way of analysing the incremental impact of new evidence on a set (...) of alternative hypotheses is in terms of uncertainty or ‘entropy’ reduction. There is no unique single measure of uncertainty/entropy, however, and, consequently, no single unique measure of uncertainty reduction. Finally, I argue that the ‘right’ measure of entropy reduction, and hence of evidential strength, depends on the pragmatic context and non-epistemic values. (shrink)

In this paper I propose a hypothetical model of mental causation that I call semantic pruning and which could be defined as the causal influence of contents and meanings on the spatial configuration of the network of synapses of an individual. I will be guided by two central principles: 1) the causal influence of the mental occurs by virtue of external semantic constraints and consists in the selective activation of certain physical powers, 2) when the selective activation is continual, it (...) triggers a process of synaptic pruning in the neural and neuromuscular network. (shrink)

In this article we show that, even though the classification and diagnosis of Psychiatric Disorders are performed according to essentialist terms, the psychiatric diagnoses currently employed, do not actually meet these criteria. Diagnosis is performed operationally. In this paper, we suggest a change of perspective. We reject essentialism relating to PDs and argue for the Homeostatic Property Cluster model, which allows a greater insight into the ontology of PDs than the operational perspective. More specifically, we argue that the HPC model (...) allows for a synthesis of continuous and discrete methods of understanding the boundaries between PDs. Finally, we specify in a more general manner, the kind of ontology we deal with when adopting the HPC model, arguing that this model can be viewed as a mirror device, reflecting the ontological features of PDs. (shrink)

Executive functioning has been said to bear on a range of traditional philosophical topics, such as consciousness, thought, and action. Surprisingly, philosophers have not much engaged with the scientific literature on executive functioning. This lack of engagement may be due to several influential criticisms of that literature by Daniel Dennett, Alan Allport, and others. In this paper I argue that more recent research on executive functioning shows that these criticisms are no longer valid. The paper clears the way to a (...) more fruitful philosophical engagement with findings on the central executive system. (shrink)

Skilled action typically requires that individuals guide their activities toward some goal. In skilled action, individuals do so excellently. We do not understand well what this capacity to guide consists in. In this paper I provide a case study of how individuals shift visual attention. Their capacity to guide visual attention toward some goal (partly) consists in an empirically discovered sub-system – the executive system. I argue that we can explain how individuals guide by appealing to the operation of this (...) sub-system. Understanding skill and skilled action thus requires appreciating the role of the executive system. (shrink)

What kinds of norms constrain mechanistic discovery and explanation? In the mechanistic literature, the norms for good explanations are directly derived from answers to the metaphysical question of what explanations are. Prominent mechanistic accounts thus emphasize either ontic or epistemic norms. Still, mechanistic philosophers on both sides agree that there is no sharp distinction between the processes of discovery and explanation. Thus, it seems reasonable to expect that ontic and epistemic accounts of explanation will be accompanied by ontic and epistemic (...) accounts of discovery, respectively. As we will show here, however, recent discovery accounts implicitly rely on both ontic and epistemic norms to characterize the discovery process. In this paper, we develop an account that makes explicit that, and how, ontic and epistemic norms work together throughout the discovery process. By describing mechanism discovery as a process of pattern recognition we demonstrate that scientists have to develop epistemic activities to distinguish a pattern from its background. Furthermore, they have to determine which epistemic activities successfully describe how the pattern is implemented by identifying the pattern’s components. Our approach reveals that ontic and epistemic norms are equally important in mechanism discovery. (shrink)

Philosophers of psychology debate, among other things, which psychological models, if any, are (or provide) mechanistic explanations. This should seem a little strange given that there is rough consensus on the following two claims: 1) a mechanism is an organized collection of entities and activities that produces, underlies, or maintains a phenomenon, and 2) a mechanistic explanation describes, represents, or provides information about the mechanism producing, underlying, or maintaining the phenomenon to be explained (i.e. the explanandum phenomenon) (Bechtel and Abrahamsen (...) 2005; Craver 2007). If there is a rough consensus on what mechanisms are and that mechanistic explanations describe, represent, or provide information about them, then how is there no consensus on which psychological models are (or provide) mechanistic explanations? Surely the psychological models that are mechanistic explanations are the models that describe, represent, or provide information about mechanisms. That is true, of course; the trouble arises when determining what exactly that involves. Philosophical disagreement over which psychological models are mechanistic explanations is often disagreement about what it means to describe, represent, or provide information about a mechanism, among other things (Hochstein 2016; Levy 2013). In addition, one's position in this debate depends on a host of other seemingly arcane metaphysical issues, such as the nature of mechanisms, computational and functional properties (Piccinini 2016), and realization (Piccinini and Maley 2014), as well as the relation between models, methodologies, and explanations (Craver 2014; Levy 2013; Zednik 2015). Although I inevitably advocate a position, my primary aim in this chapter is to spell out all these relationships and canvas the positions that have been taken (or could be taken) with respect to mechanistic explanation in psychology, using dynamical systems models and cognitive models (or functional analyses) as examples. (shrink)

Despite their importance in the history of philosophy and in particular in the work of Aristotle and Kant, mental capacities have been neglected in recent philosophical work. By contrast, the notion of a capacity is deeply entrenched in psychology and the brain sciences. Driven by the idea that a cognitive system has the capacity it does in virtue of its internal components and their organization, it is standard to appeal to capacities in cognitive psychology. The main benefit of invoking capacities (...) in an account of the mind is that it allows for an elegant counterfactual analysis of mental states: it allows us to analyze mental states on three distinct yet interrelated levels. A first level of analysis pertains to the function of mental capacities. A second level of analysis pertains to the mental capacities employed, irrespective of the context in which they are employed. A third level of analysis pertains to the mental capacities employed, taking into account the context in which they are employed. This paper develops an account of perceptual capacities. This account involves an analysis of their function, their individuation and possession conditions, the relation between perceptual capacities and their employment, as well as their informational and neural base conditions. (shrink)

The levels of selection debate is generally taken to be a debate about how natural selection can occur at the various levels of biological organization. In this paper, we argue that questions about levels of selection should be analyzed separately from questions about levels of organization. In the deflationary proposal we defend, all that is necessary for multilevel selection is that there are cases in which particles are nested in collectives, and that both the collectives and the particles that compose (...) them each separately undergo natural selection. We argue that adopting this deflationary account helps to disentangle the levels of selection and the levels of organization, and thereby contributes to advancing the levels of selection debate. (shrink)

A common kind of explanation in cognitive neuroscience might be called functiontheoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it reveals (...) the causal structure of the mechanism underlying the capacity. If they are right, then a cognitive model that resists a transparent mapping to known neural mechanisms fails to be explanatory. I argue that a functiontheoretic characterization of a cognitive capacity can be genuinely explanatory even absent an account of how the capacity is realized in neural hardware. (shrink)

There are three main approaches to scientific explanation in the philosophical literature. The unification approach claims that science explains by fitting the particular facts and events within a general theoretical framework. The mechanistic approach claims that science explains by identifying mechanisms. According to the manipulationist approach an explanation ought to be such that it can be used to answer a “what-if-things-had-been-different question.” The article examines whether these three approaches are compatible or not in the case of the social sciences, and (...) it concludes by defending explanatory pluralism. (shrink)

Piccinini and Craver (Synthese 183:283–311, 2011) argue for the surprising view that psychological explanation, properly understood, is a species of mechanistic explanation. This contrasts with the ‘received view’ (due, primarily, to Cummins and Fodor) which maintains a sharp distinction between psychological explanation and mechanistic explanation. The former is typically construed as functional analysis, the analysis of some psychological capacity into an organized series of subcapacities without specifying any of the structural features that underlie the explanandum capacity. The latter idea, of (...) course, sees explanation as a matter of describing structures that maintain (or produce) the explanandum capacity. In this paper, I defend the received view by criticizing Piccinini and Craver’s argument for the claim that psychological explanation is not distinct from mechanistic explanation, and by showing how psychological explanations can possess explanatory force even when nothing is known about the underlying neurological details. I conclude with a few brief criticisms about the enterprise of mechanistic explanation in general. (shrink)

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...) shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter? (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

Ian Hacking instigated a revolution in 20th century philosophy of science by putting experiments (“interventions”) at the top of a philosophical agenda that historically had focused nearly exclusively on representations (“theories”). In this paper, I focus on a set of conceptual tools Hacking (1992) put forward to understand how laboratory sciences become stable and to explain what such stability meant for the prospects of unity of science and kind discovery in experimental science. I first use Hacking’s tools to understand sources (...) of instability and disunity in rodent behavioral neuroscience. I then use them to understand recent grass-roots collaborative open science initiatives aimed at establishing stability in this research area and tease out some implications for unity of science and kind creation and discovery in cognitive neuroscience. -/- . (shrink)

In this chapter, we discuss a selection of current views of the neural correlates of consciousness (NCC). We focus on the different predictions they make, in particular with respect to the role of prefrontal cortex (PFC) during visual experiences, which is an area of critical interest and some source of contention. Our discussion of these views focuses on the level of functional anatomy, rather than at the neuronal circuitry level. We take this approach because we currently understand more about experimental (...) evidence at this coarse level and because these results are appropriate for arbitrating between current theoretical frameworks. We discuss the Two-Visual-Systems Hypothesis (Milner & Goodale 1995; 2006), Local Recurrency (Lamme 2010; Lamme 2006), Higher Order (Lau 2008; Lau & Rosenthal 2011) and Global Workspace theories (Baars 1997; Baars 2005; Dehaene & Naccache 2001; Dehaene 2014). Despite the apparent stark differences between conscious and unconscious perceptual processing, available evidence suggests that their neural substrates must be largely shared. This indicates that the difference in neural activity between conscious and unconscious perceptual processing is likely to be subtle and highly specialized. We argue that current experimental evidence about the involvement of specific activity in prefrontal cortex supports the higher order neural theory of consciousness. In consequence, imaging techniques that focus only on marked differences between conscious and unconscious level of activity are likely to be insensitive to the relevant neural activity patterns that underlie conscious experiences. Finally, it follows from the evidence we discuss that the functional advantages of conscious over unconscious perceptual processing may be more limited than commonly thought. (shrink)

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli, and the origin of eukaryotic cells through endosymbiosis.

Some recent accounts of constitutive relevance have identified mechanism components with entities that are causal intermediaries between the input and output of a mechanism. I argue that on such accounts there is no distinctive inter-level form of mechanistic explanation and that this highlights an absence in the literature of a compelling argument that there are such explanations. Nevertheless, the entities that these accounts call ‘components’ do play an explanatory role. Studying causal intermediaries linking variables Xand Y provides knowledge of the (...) counterfactual conditions under which X will continue to bring about Y. This explanatory role does not depend on whether intermediate variables count as components. The question of whether there are distinctively mechanistic explanations remains open. (shrink)

Levels of organization and their use in science have received increased philosophical attention of late, including challenges to the well-foundedness or widespread usefulness of levels concepts. One kind of response to these challenges has been to advocate a more precise and specific levels concept that is coherent and useful. Another kind of response has been to argue that the levels concept should be taken as a heuristic, to embrace its ambiguity and the possibility of exceptions as acceptable consequences of its (...) usefulness. In this chapter, I suggest that each of these strategies faces its own attendant downsides, and that pursuit of both strategies (by different thinkers) compounds the difficulties. That both kinds of approaches are advocated is, I think, illustrative of the problems plaguing the concept of levels of organization. I end by suggesting that the invocation of levels may mislead scientific and philosophical investigations more than it informs them, so our use of the levels concept should be updated accordingly. (shrink)

Reduction and reductionism have been central philosophical topics in analytic philosophy of science for more than six decades. Together they encompass a diversity of issues from metaphysics and epistemology. This article provides an introduction to the topic that illuminates how contemporary epistemological discussions took their shape historically and limns the contours of concrete cases of reduction in specific natural sciences. The unity of science and the impulse to accomplish compositional reduction in accord with a layer-cake vision of the sciences, the (...) seminal contributions of Ernest Nagel on theory reduction and how they strongly conditioned subsequent philosophical discussions, and the detailed issues pertaining to different accounts of reduction that arise in both physical and biological science (e.g., limit-case and part-whole reduction in physics, the difference-making principle in genetics, and mechanisms in molecular biology) are explored. The conclusion argues that the epistemological heterogeneity and patchwork organization of the natural sciences encourages a pluralist stance about reduction. (shrink)

The “Human Brain Project” (HBP) is a large-scale European neuroscience and information communication technology (ICT) project that has been a matter of heated controversy since its inception. With its aim to simulate the entire human brain with the help of supercomputing technologies, the HBP plans to fundamentally change neuroscientific research practice, medical diagnosis, and eventually the use of computers itself. Its controversial nature and its potential impacts render the HBP a subject of crucial importance for critical studies of science and (...) society. In this paper, we provide a critical exploratory analysis of the potential mid- to long-term impacts the HBP and its ICT infrastructure could be expected to have, provided its agenda will indeed be implemented and executed to a substantive degree. We analyse how the HBP aspires to change current neuroscientific practice, what impact its novel infrastructures could have on research culture, medical practice and the use of ICT, and how, given a certain degree of successful execution of the project’s aims, potential clinical and methodological applications could even transform society beyond scientific practice. Furthermore, we sketch the possibility that research such as that projected by the HBP may eventually transform our everyday world, even beyond the scope of the HBP’s explicit agenda, and beyond the isolated ‘application’ of some novel technological device. Finally, we point towards trajectories for further philosophical, historical and sociological research on the HBP that our exploratory analysis might help to inspire. Our analysis will yield important insights regardless of the actual success of the HBP. What we drive at, for the most part, is the broader dynamics of scientific and technological development of which the HBP agenda is merely one particularly striking exemplification. (shrink)

Evo-Devo exhibits a plurality of scientific “cultures” of practice and theory. When are the cultures acting—individually or collectively—in ways that actually move research forward, empirically, theoretically, and ethically? When do they become imperialistic, in the sense of excluding and subordinating other cultures? This chapter identifies six cultures – three /styles/ (mathematical modeling, mechanism, and history) and three /paradigms/ (adaptationism, structuralism, and cladism). The key assumptions standing behind, under, or within each of these cultures are explored. Characterizing the internal structure of (...) the cultures is necessary for understanding how they collaborate or compete, and how they are fragmented or integrated, in the rich interdisciplinary /trading zone/ (Galison 1997) of Evo-Devo. Evo-Devo is an important example of how science can progress through a radical plurality of perspectives and cultures. (shrink)

The idea that causation involves the production of changes due to the exertion of influence of something on something else—the core idea of causal realism—used to be the default view. Today this idea is at the heart of (i) transmission/causal process accounts, (ii) mechanistic accounts, and (iii) powers-based accounts. However, as I have previously argued (Ingthorsson 2021) the above-mentioned approaches are based—to varying degree—on the very problematic assumption that causal influence is essentially unidirectional; that it passes from whatever is the (...) cause to the effect. As first pointed out be Mario Bunge (1959), the idea that causal influence is unidirectional is incompatible with what is today considered a scientific fact, notably that all interactions are perfectly reciprocal; whenever any object exerts an influence on any other object, the latter simultaneously exerts the same kind of influence, of the same magnitude, on the former. Drawing on Bunge’s result, I have developed a fourth approach to efficient causation that accepts the reciprocity of interactions (Ingthorsson 2002 & 2021). According to this view a cause is not the action of something on something else that receives the influence, but the reciprocal action between powerful particulars which results in a modification of both sides, producing a new state of affairs. In this paper, I present the core ideas of the four different approaches to causal efficacy, what I think is problematic with the first three and finally attempt to compare how well the four approaches fare in explaining two often discussed types of causal interactions: (i) collisions between billiard balls, and (ii) how water dissolves salt. I argue that only reciprocal actions between powerful particulars can plausibly claim to fully account for these phenomena, in a manner compatible with the theories and findings of the natural sciences. (shrink)

This book aims to show that recent developments in neuroscience permit a defense of free will. Through language, human beings can escape strict biological determinism.

Se analiza el debate entre Eleonore Stump y Antonio Damasio a propósito de dos posibles estrategias a la hora de superar el “error” neurocientífico del dualismo cartesiano respecto de la correlación entre mente y cerebro, a saber: o bien se corrige el “error” neurocientífico de Descartes desde un modelo híbrido de tipo monista como el de Spinoza que ahora también se utiliza para justificar un materialismo eliminativo aún más radicalizado, a pesar de seguir manteniendo de un modo meramente heurístico las (...) diferencias entre mente y cerebro, al modo propuesto por Antonio Damasio; o bien se recurre a un modelo dualista vitalista de integración jerarquizada interactiva entre la mente y el cerebro, al modo propuesto por Aquinas, y aún antes Aristóteles, con una indudable ventaja. En este segundo caso sería posible justificar la existencia de diversos niveles de autorregulación deliberativa entre mente y cerebro, sin tener que restringir de un modo apriorista los márgenes de libertad y espontaneidad a un único nivel autoinmune básico de tipo determinista, salvo que se hiciera con fines meramente heurísticos, al modo ahora también señalado por Eleonore Stump. Por su parte la comunicación analiza las consecuencias que cada una de estas propuestas pueden llegar a tener para la justificación de los diversos tipos de autorregulación que hoy día se asignan a la neuroética, neuropolítica y neuroeconomía. (shrink)

Technology could be used to improve morality but it could do so in different ways. Some technologies could augment and enhance moral behaviour externally by using external cues and signals to push and pull us towards morally appropriate behaviours. Other technologies could enhance moral behaviour internally by directly altering the way in which the brain captures and processes morally salient information or initiates moral action. The question is whether there is any reason to prefer one method over the other? In (...) this article, I argue that there is. Specifically, I argue that internal moral enhancement is likely to be preferable to external moral enhancement, when it comes to the legitimacy of political decision-making processes. In fact, I go further than this and argue that the increasingly dominant forms of external moral enhancement may already be posing a significant threat to political legitimacy, one that we should try to address. Consequently, research and development of internal moral enhancements should be prioritised as a political project. (shrink)

Thinking about illness causation has a long and rich history in medicine. After a hiatus in the 1990s, the last one-and-a-half decades have seen a surge of publications on causality in the biomedical sciences. Interestingly, this surge is visible not only in the medical, epidemiological, bioinformatics, and public health literatures, but also among philosophical publications. In this essay, I review and discuss one most recent addition to the literature, "Causality: Philosophical Theory Meets Scientific Practice" written by philosophers Phyllis Illari and (...) Federica Russo about causality in the sciences, and particularly about the health sciences. (shrink)

I challenge the dominant understanding of what it means to say two thoughts are associated. The two views that dominate the current literature treat association as a kind of mechanism that drives sequences of thought. The first, which I call reductive associationism, treats association as a kind of neural mechanism. The second treats association as a feature of the kind of psychological mechanism associative processing. Both of these views are inadequate. I argue that association should instead be seen as a (...) highly abstract filler term, standing in for causal relations between representational states in a system. Associations, so viewed, could be implemented by many different mechanisms. I outline the role that this view gives associative models as part of a top-down characterization of psychological processes of any kind and of any complexity. (shrink)

. Philosophers and scientists often cite ontic factors when explaining the methods and success of scientific inquiry. That is, the adoption of a method or approach is explained in reference to the kind of system in which the scientist is interested: these are explanations of why scientists do what they do, that appeal to properties of their target systems. We present a framework for understanding such “Opticks to his Principia. Newton’s optical work is largely experiment-driven, while the Principia is primarily (...) mathematical, so usually, each work is taken to exemplify a different kind of science. However, Newton himself often presented them in terms of a largely consistent method. We use our framework to articulate an original and plausible position: that the differences between the Opticks and the Principia are due to the kinds of systems targeted. That is, we provide an ontic-driven explanation of methodological differences. We suspect that ontic factors should have a more prominent role in historical explanations of scientific method and development. (shrink)

Our epistemic access to the past is infamously patchy: historical information degrades and disappears and bygone eras are often beyond the reach of repeatable experiments. However, historical scientists have been remarkably successful at uncovering and explaining the past. I argue that part of this success is explained by the exploitation of dependencies between historical events, entities, and processes. For instance, if sauropod dinosaurs were hot blooded, they must have been gluttons; the high-energy demands of endothermy restrict sauropod grazing strategies. Understanding (...) such dependencies extends our reach into the past in spite of incomplete data. In addition, this serves as a counterexample to two accounts of method in the historical sciences. By one, historical science proceeds by identifying ‘smoking guns’: traces that discriminate between live hypotheses. By the other, historical hypotheses are supported by consilience: the convergence of independent lines of evidence. However, testing for ‘coherency’ between past hypotheses also plays a critical role in historical confirmation. Just as historical scientists exploit dependencies between past entities and present entities to infer what the past was like, they also exploit dependencies between past entities themselves. I do not suggest that archetypical historical science proceeds in this manner. Rather, the lesson I draw is that historical methodology cannot be characterized as archetypically relying on one method or another. Historical science is at base opportunistic, and is resistant to unitary analyses. 1 Introduction2 Snowballs and Explosions: The Basic Idea3 Were Sauropods Endothermic?4 Dependent Entities and Interdependent Explanations5 Smoking Guns and Consilience. (shrink)

This paper uses the economic crisis of 2008 as a case study to examine the explanatory validity of collective mental representations. Distinguished economists such as Paul Krugman and Joseph Stiglitz attribute collective beliefs, desires, intentions, and emotions to organizations such as banks and governments. I argue that the most plausible interpretation of these attributions is that they are metaphorical pointers to a complex of multilevel social, psychological, and neural mechanisms. This interpretation also applies to collective knowledge in science: scientific communities (...) do not literally have collective representations, but social mechanisms do make important contributions to scientific knowledge. (shrink)

In a recent issue of The Monist, Alisa Bokulich argues that those who embrace an ontic conception of scientific explanation are committed to rejecting an explanatory role for idealized, i.e., deliberately false, models. Her argument is based on an inaccurate characterization of the ontic view. Indeed, her positive view of idealization embraces rather than opposes the ontic conception. Because Bokulich is not alone in this misunderstanding, an effort to diagnose and correct it might prevent scholars from talking past one another (...) and, hopefully, nudge future discussions down more inviting paths. (shrink)

Life-science phenomena are often explained by specifying the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim and to provide us with a better understanding of what mechanisms are and how they explain. Although there is disagreement among current mechanists on various issues, they share a common core position and a seeming commitment to some form of scientific realism. But is such a commitment necessary? Is it the best way to go about mechanistic (...) explanation? In this article, we propose an alternative antirealist account that also fits explanatory practice in the life sciences. We pay special attention to mechanistic models, i.e. scientific models that involve a mechanism, and to the role of coherence considerations in building such models. To illustrate our points, we consider the mechanism for the action potential. 1 Introduction2 Some Core Features of Mechanistic Explanation3 Scientific Realism and Mechanistic Explanation4 Antirealist Mechanistic Explanation: The Case of the Action Potential5 Some Outstanding Issues for the Antirealist Mechanist6 Two Problems for the Realist Mechanist7 Conclusions. (shrink)

There continues to be significant confusion about the goals, scope, and nature of modelling practice in neuroeconomics. This article aims to dispel some such confusion by using one of the most recent critiques of neuroeconomic modelling as a foil. The article argues for two claims. First, currently, for at least some economic model of choice behaviour, the benefits derivable from neurally informing an economic model do not involve special tractability costs. Second, modelling in neuroeconomics is best understood within Marr’s three-level (...) of analysis framework and in light of a co-evolutionary research ideology. The first claim is established by elucidating the relationship between the tractability of a model, its descriptive accuracy, and its number of variables. The second claim relies on an explanation of what it can take to neurally inform an economic model of choice behaviour. 1 Introduction2 Neurally Informed Models of Choice: A Case Study2.1 A case study on risk-sensitive choice2.1.1 Target and modelling framework2.1.2 Research question and hypotheses2.1.3 Competitive models of risk-sensitive behaviour2.1.4 Model-based fMRI: from economics to brains and back2.2 Neurally informed modelling3 Tractability: When Does Size Matter?4 Neural Integration and the Co-evolutionary Research Ideology5 Conclusion. (shrink)

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) From the use of Bayesian (...) models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms. (shrink)

Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in (...) explicit protocols for evaluating evidence. Next we provide case studies which exemplify the ways in which evidence of mechanisms complements evidence of correlation in practice. Finally, we put forward some general considerations as to how the two sorts of evidence can be more closely integrated by EBM. (shrink)

In 1895 in the Project for a Scientific Psychology Freud tried to integrate psychology and neurology in order to develop a neuroscientific psychology. Since 1880 Freud made no distinction between psychology and physiology. His papers from the end of the 1880s to1890 were very clear on this scientific overlap: as with many of its contemporaries, Freud thought about psychology essentially as the physiology of the brain. Years later he had to surrender, realizing a technological delay, not capable to pursue its (...) ambitious aim, and until that moment psychoanalysis would have to use its more suitable clinical method. Also, he seemed skeptical about the phrenology drift, typical of that time, in which any psychological function needed to be located in its neuroanatomical area. He could not see the progresses of neurosciences and its fruitful dialogue with psychoanalysis, occurred also thanks to the improvements in the field of neuroimaging, which has made possible a remarkable advance in the knowledge of the mind-brain system and a better observation of the psychoanalytical theories. After years of investigations, deriving from research and clinical work of the last century, the discovery of neural networks, together with the free energy principle, we are observing under a new light the psychodynamic neuroscience in its exploration of the mind-brain system. In this narrative review we summarize the important developments of the psychodynamic neuroscience, with particular regard to the free energy principle, the resting state networks, especially the Default Mode Network in its link with the Self; finally we suggest a discussion by approaching the concept of Alpha Function, proposed by the psychoanalyst Wilfred Ruprecht Bion, trying to speculate about the connection with neuroscience. (shrink)

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in (...) computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology. (shrink)

This paper addresses concerns raised recently by Datteri (Biol Philos 24:301–324, 2009) and Craver (Philos Sci 77(5):840–851, 2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri’s no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and systems (...) neuroscience which also elicit neural plasticity. Furthermore, I propose that Datteri and Craver’s arguments concerning the limitations of prosthetic modelling in basic neuroscience, as opposed to neuroengineering, rests on too narrow a view of the ways models in neuroscience should be evaluated, and that a more pluralist approach is needed. I distinguish organisational validity of models from mechanistic validity. I argue that while prosthetic models may be deficient in the latter of these explanatory virtues because of neuroplasticity, they excel in the former since organisational validity tracks the extent to which a model captures coding principles that are invariant with plasticity. Changing the brain, I conclude, is one viable route towards explaining the brain. (shrink)

How has multiplicity superseded impossibility in philosophical challenges to artificial consciousness? I assess a trajectory in recent debates on artificial consciousness, in which metaphysical and explanatory challenges to the possibility of building conscious machines lead to epistemological concerns about the multiplicity underlying ‘what it is like’ to be a conscious creature or be in a conscious state. First, I analyse earlier challenges which claim that phenomenal consciousness cannot arise, or cannot be built, in machines. These are based on Block’s Chinese (...) Nation and Chalmers’ Hard Problem. To defuse such challenges, theorists of artificial consciousness can appeal to empirical methods and models of explanation. Second, I explain why this naturalistic approach produces an epistemological puzzle on the role of biological properties in phenomenal consciousness. Neither behavioural tests nor theoretical inferences seem to settle whether our machines are conscious. Third, I evaluate whether the new challenge can be managed through a more fine-grained taxonomy of conscious states. This strategy is supported by the development of similar taxonomies for biological species and animal consciousness. Although it makes sense of some current models of artificial consciousness, it raises questions about their subjective and moral significance. (shrink)

It is commonly assumed that the concept of mechanism is a keytool for the scientific understanding of observable phenomena. However, there is no single definition of mechanism in the current philosophy of science. In fact, philosophers have developed several characterizations of what seemed to be a clear intuitive concept for scientists. In this paper, I will analyze these philosophical conceptions of mechanism, highlighting their problematic aspects and proposing a new mechanistic approach based on the idea that the pertinent levels of (...) organization for a mechanistic explanation can be identified with intervals of quasi-decompositionality. I argue that this approach allows us to consider that activities are directly derived from the entities’ structure. Consequently, a mechanistic explanation implies an arbitrary but not capricious choice of an organizational level. According to this approach, inter-level causation is merely apparent and there is no place for emergent properties. (shrink)

Questions about phenomenology’s role in non-philosophical disciplines gained renewed attention. While we claim that phenomenology makes indispensable, unique contributions to different domains of scientific practice such as concept formation, experimental design, and data collection, we also contend that when it comes to explanation, phenomenological approaches face a dilemma. Either phenomenological attempts to explain conscious phenomena do not satisfy a central constraint on explanations, i.e. the asymmetry between explanans and explanandum, or they satisfy this explanatory asymmetry only by largely merging with (...) non-phenomenological explanation types. The consequence of this dilemma is that insofar as phenomenological approaches are explanatory, they do not provide an own type of explanation. We substantiate our two claims by offering three case studies of phenomenologically inspired experiments in cognitive science. Each case study points out a specific phenomenological contribution to experimental practice while also illustrating how phenomenological approaches face the explanatory dilemma we outline. (shrink)

‘Possibilist Explanation’ is a promising account of scientific explanation which avoids the familiar problems of “how-possibly explanations”. It explains an event by showing how-actually it was epistemically possible, instead of why it was epistemically necessary. Its explanandum is the epistemic possibility of an actual event previously considered epistemically impossible. To define PE, two new concepts are introduced: ‘permissive condition’ and ‘possibilist law’. A permissive condition for an event is something that does not entail the event itself, but a necessary condition (...) for it. A ‘Possibilist Law’ is a kind of scientific law that predicts that in the absence of a necessary condition N, the event E is not possible. Both PE and PL are legitimate and neglected parts of scientific knowledge and are especially suitable for human sciences. (shrink)

Abstract‘Possibilist Explanation’ is a promising account of scientific explanation which avoids the familiar problems of “how-possibly explanations”. It explains an event by showing how-actually it was epistemically possible, instead of why it was epistemically necessary. Its explanandum is the epistemic possibility of an actual event previously considered epistemically impossible. To define PE, two new concepts are introduced: ‘permissive condition’ and ‘possibilist law’. A permissive condition for an event is something that does not entail the event itself, but a necessary condition (...) for it. A ‘Possibilist Law’ is a kind of scientific law that predicts that in the absence of a necessary condition N, the event E is not possible. Both PE and PL are legitimate and neglected parts of scientific knowledge and are especially suitable for human sciences. (shrink)

The Recursive Bayesian Net formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how (...) a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell's mechanism for apoptosis. (shrink)