The European Legacy, Volume 17, Issue 4, Page 558-559, July 2012.

Nicolai Hartmann was one of the most prolific and original, yet sober, clear and rigorous, 20th century German philosophers. Hartmann was brought up as a Neo-Kantian, but soon turned his back on Kantianism to become one of the most important proponents of ontological realism. He developed what he calls the “new ontology”, on which relies a systematic opus dealing with all the main areas of philosophy. His work had major influences both in philosophy and in various scientific disciplines. The contributions (...) collected in this volume from an international group of Hartmann scholars and philosophers explore subjects such as Hartmann's philosophical development from Neo-Kantianism to ontological realism, the difference between the way he and Heidegger overcame Neo-Kantianism, his Platonism concerning eternal objects and his interpretation of Plato, his Aristotelianism, his theoretical relation to Wolff's ontology and Meinong's theory of objects, his treatment and use of the aporematic method, his metaphysics, his ethics and theory of values, his philosophy of mind, his philosophy of mathematics, as well as the influence he had on 20th century philosophical anthropology and biology. (shrink)

In this paper, I discuss the similarity between Wittgenstein’s use of thought experiments and Relativity Theory. I begin with introducing Wittgenstein’s idea of “thought experiments” and a tentative classification of different kinds of thought experiments in Wittgenstein’s work. Then, after presenting a short recap of some remarks on the analogy between Wittgenstein’s point of view and Einstein’s, I suggest three analogies between the status of Wittgenstein’s mental experiments and Relativity theory: the topics of time dilation, the search for invariants, and (...) the role of measuring tools in Special Relativity. This last point will help to better define Wittgenstein’s idea of description as the core of his philosophical enterprise. (shrink)

Chaque fois que nous "donnons forme" à quelque chose, cette représentation nous figure, par signes, l'objet évoqué, mais nous confirme également qu'il ne s'agit que d'un simulacre. D'où le besoin, à chaque époque, de créer du vivant pour pallier cette déception : tel est le sens du mythe de Pygmalion. Ce livre s'organise donc autour de deux pôles : d'un côté la nécessité de figurer, et de figurer l'acte même de la perception (voir le chapitre "Un oeil immense artificiel") ; (...) de l'autre, l'impératif de dépasser le signe. Il s'agit d'un défi radical, qui concerne l'avenir même des arts. C'est la raison de ce titre, A vif, qui témoigne d'un "coup d'oeil" : un regard et une incision pratiquée à vif dans l'objet, un désir de définitif qui ne caresse plus la forme, mais qui pénètre, met à nu, éviscère dans les paroles et dans les actes. Dedans et derrière l'oeil, comme dans l'un des plus impassibles traités d'anatomie du Cinquecento. Aujourd'hui, est-il encore vif notre oeil qui cherche le vivant? N'est-il pas lui-même défiguré par l'informe qui s'est logé en nous? Où est la pupilla viva qui illumine le Paradis et qui, de Dante à Baudelaire, a animé, de longs siècles durant, tant de quête mentale? Il faut remonter à ces origines, parcourir les espaces séculaires et sidéraux dans lesquels le cosmos était ordre, beauté, compréhension ; où, justement, il était nécessaire de "captiver les yeux pour avoir l'âme". Cet essai se voudrait une parabole déployée au fil de l'ardente soif humaine d'"exorbiter", de brûler la distance du point de vue, pour accéder, maintenant et à jamais, au vivant. (shrink)

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...) philosophy of science, this framework is modeled after accounts of explanation in cognitive science. The framework distinguishes between the explanation-seeking questions that are likely to be asked by different stakeholders, and specifies the general ways in which these questions should be answered so as to allow these stakeholders to perform their roles in the Machine Learning ecosystem. By applying the normative framework to recently developed techniques such as input heatmapping, feature-detector visualization, and diagnostic classification, it is possible to determine whether and to what extent techniques from Explainable Artificial Intelligence can be used to render opaque computing systems transparent and, thus, whether they can be used to solve the Black Box Problem. (shrink)

The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations, and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, contemporary dynamicist (...) research reveals the need for a more sophisticated account of mechanistic explanation. (shrink)

Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc analytic techniques from Explainable AI can be used to refine target phenomena in medical science, to identify starting points for future (...) investigations of causal relationships, and to generate possible explanations of target phenomena in cognitive science. In this way, this paper describes how Explainable AI—over and above machine learning itself—contributes to the efficiency and scope of data-driven scientific research. (shrink)

To date, no definition of life has been unequivocally accepted by the scientific community. In frustration, some authors advocate alternatives to standard definitions. These include using a list of characteristic features, focusing on life’s effects, or categorizing biospheres rather than life itself; treating life as a fuzzy category, a process or a cluster of contingent properties; or advocating a ‘wait-and-see’ approach until other examples of life are created or discovered. But these skeptical, operational, and pluralistic approaches have intensified the debate, (...) rather than settled it. Given the failure of even these approaches, we advocate a new strategy. In this paper, we reverse the usual line of reasoning and argue that the “life problem” arises from thinking incorrectly about the nature of life. Scientists most often conceptualize life as a class or kind, with earthly life as a single instance of it. Instead, we advocate thinking about Earth’s Life as an individual, in the way that species are now thought to be. In this view, Life is a monophyletic clade that originated with a last universal common ancestor, and includes all its descendants. We can continue to use the category ‘life’ pragmatically to refer to similarities between various phenomena and Life. But the relevant similarities are a matter of interest and preference, not a matter of fact. The search for other life in the Universe, then, is merely a search for entities that resemble parts of Life in whatever sense astrobiologists find most appealing. This does not mean that the search for evolved complexity elsewhere in the universe or its creation in the lab are futile endeavors, but that debates over whether they count as ‘life’ are. Ironically, finally abandoning the concept ‘life’ may make our searches for evolved complexity more fruitful. We explain why. (shrink)

"Epistola a Memeceo, Maximas capitales, Sentencias vaticanas, fragmentos y testimonios; texto griego y traduccion": p. [87]-161.

This paper discusses the varieties of capitalism in transitional societies in Latin America and Central / Eastern Europe. The intended purpose of these transitions from semi-closed import-substituting economies in the first case and state socialist ones in the second was to institutionalize open-market economies. Twenty or thirty years later, there is a variety of types of capitalism in these countries, which I classify into three: open-market, neo-mercantilist, and anemic. The question for sociology is whether these quite different variants represent temporary (...) stages or distortions in the same process of transition or whether, on the contrary, they may institutionalize as discrete forms of peripheral capitalism. Neither standard “legacy” argu­ments nor institutionalist theories offer satisfactory answers to this question. The multiple modernities approach, on the other hand, is more appropriate as a theoretical perspective, but it has not produced yet specific propositions applicable to this question. My paper makes two claims. First, the successful transfer of institutions depends on the congruence between these institutions and the broader institutional framework of the recipient economies, a point not developed by institutionalist theories. I offer a hypothesis in this regard: Two critical nodes of congruence are the regulatory and extractive capacity of the state and the strength of civil society. Second, market capitalism (as liberal democracy as well) is a complex institution, and some of its components “travel” more easily across societies and institutional frameworks, and therefore are easier to institutionalize. This is the source of the hybrid variants. (shrink)

In this paper I respond to Marcus William Hunt’s argument that vegan parents have pro tanto reasons for not raising their children on a vegan diet because such a diet is potentially harmful to children’s physical and social well-being. In my rebuttal, first I show that in practice all vegan diets, with the exception of wacky diets, are beneficial to children’s well-being ; and that all animal-based diets are potentially unhealthful. Second, I show that vegan children are no more socially (...) outcast than any other group. In other words, veganism does not harm the lives of children. Having considered several studies, I show that the moral reasons that vegan parents may have for raising their children on a vegan diet significantly outweigh the reasons for raising their children on an animal-based diet. Thus, I conclude that parents have a moral obligation to raise their children on a vegan diet. (shrink)

Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...) are often deployed unsystematically, Bayesian reverse-engineering avoids several important worries that have been raised about the explanatory credentials of Bayesian cognitive science: the worry that the lower levels of analysis are being ignored altogether; the challenge that the mathematical models being developed are unfalsifiable; and the charge that the terms ‘optimal’ and ‘rational’ have lost their customary normative force. But while Bayesian reverse-engineering is therefore a viable and productive research strategy, it is also no fool-proof recipe for explanatory success. (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)

In a context of pervasive digitalization of the social world, both before and during the COVID-19 pandemic, the field of education has undergone major changes with the development of digital practices and settings. However, the physical presence of the subjects and the body remain something primordial and irreplaceable in traditional educational processes. Thus, it is often assumed that virtuality is opposed to the corporeal reality of the subjects involved in teaching, learning and studying. In this paper we aim to critically (...) challenge this assumption by addressing the phenomenon of virtuality in a more original sense: as a fundamental dimension of corporeality itself. Drawing on Merleau-Ponty’s phenomenological insights, we argue that the experience of the virtual is inherently embodied and fully real. We understand corporeality and virtuality as entangled and multidimensional phenomena. At the same time, this leads us to rethink the digital as only one possible medium in which virtuality can appear as inherent to embodied experience. We develop our phenomenological approach in three instances. First, we question and bracket two common assumptions about corporeality. From our critical perspective, assumptions about a disembodied digitality and a devirtualized corporeality represent extremes within which educational discourses currently move. Second, we address the inherent virtuality of embodied experience. We outline the heuristic concept of the virtual body to describe corporeality and virtuality as entangled and multidimensional phenomena that encompass extension, intercorporeality and intermediality. Finally, we make some remarks on the relationships of corporeality and virtuality considering the challenges of digital education. (shrink)

This chapter subsumes David Marr’s levels of analysis account of explanation in cognitive science under the framework of mechanistic explanation: Answering the questions that define each one of Marr’s three levels is tantamount to describing the component parts and operations of mechanisms, as well as their organization, behavior, and environmental context. By explicating these questions and showing how they are answered in several different cognitive science research programs, this chapter resolves some of the ambiguities that remain in Marr’s account, and (...) shows that many different areas and traditions of cognitive scientific research can be unified under the mechanistic framework. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, size (...) of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (shrink)

Previous research has demonstrated that ratings of the perceived pleasantness and quality of odors can be modulated by auditory stimuli presented at around the same time. Here, we extend these results by assessing whether the hedonic congruence between odor and sound stimuli can modulate the perception of odor intensity, pleasantness, and quality in untrained participants. Unexpectedly, our results reveal that broadband white noise, which was rated as unpleasant in a follow-up experiment, actually had a more pronounced effect on participants’ odor (...) ratings than either the consonant or dissonant musical selections. In particular, participants rated the six smells used as being less pleasant and less sweet when they happened to be listening to white noise, as compared to any one of the other music conditions. What is more, these results also add evidence to support the existence of a close relationship between an odor’s hedonic character and the perception of odor quality. So, for example, independent of the sound condition, pleasant odors were rated as sweeter, less dry, and brighter than the unpleasant odors. These results are discussed in terms of their implications for the understanding of crossmodal correspondences between olfactory and auditory stimuli. (shrink)

This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a longstanding research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal (...) of network neuroscience, two concerns are addressed. In response to the concern that functional network models are non-explanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general. (shrink)

This article discusses the relation between empirical and normative approaches in bioethics. The issue of dwarf tossing, while admittedly unusual, is chosen as a point of departure because it challenges the reader to look with fresh eyes upon several central bioethical themes, including human dignity, autonomy, and the protection of vulnerable people. After an overview of current approaches to the integration of empirical and normative ethics, we consider five ways that the empirical and normative can be brought together to speak (...) to the problem of dwarf tossing: prescriptive applied ethics, theoretical ethics, critical applied ethics, particularist ethics and integrated empirical ethics. We defend a position of critical applied ethics that allows for a two-way relation between empirical and normative theories. Against efforts fully to integrate the normative and the empirical into one synthesis, we propose that the two should stand in tension and relation to one another. The approach we endorse acknowledges that a social practice can and should be judged both by the gathering of empirical data and by normative ethics. Critical applied ethics uses a five stage process that includes: (a) determination of the problem, (b) description of the problem, (c) empirical study of effects and alternatives, (d) normative weighing and (e) evaluation of the effects of a decision. In each stage, we explore the perspective from both the empirical (sociological) and the normative ethical point of view. We conclude by applying our five-stage critical applied ethics to the example of dwarf tossing. (shrink)

Whereas most branches of neuroscience are thought to provide mechanistic explanations, systems neuroscience is not. Two reasons are traditionally cited in support of this conclusion. First, systems neuroscientists rarely, if ever, rely on the dual strategies of decomposition and localization. Second, they typically emphasize organizational properties over the properties of individual components. In this paper, I argue that neither reason is conclusive: researchers might rely on alternative strategies for mechanism discovery, and focusing on organization is often appropriate and consistent with (...) the norms of mechanistic explanation. Thus, many explanations in systems neuroscience can also be viewed as mechanistic explanations. (shrink)