We characterize access to empirical objects in biology from a theoretical perspective. Unlike objects in current physical theories, biological objects are the result of a history and their variations continue to generate a history. This property is the starting point of our concept of measurement. We argue that biological measurement is relative to a natural history which is shared by the different objects subjected to the measurement and is more or less constrained by biologists. We call symmetrization the (...) theoretical and often concrete operation which leads to considering biological objects as equivalent in a measurement. Last, we use our notion of measurement to analyze research strategies. Some strategies aim to bring biology closer to the epistemology of physical theories, by studying objects as similar as possible, while others build on biological diversity. (shrink)

Although molecular biology, genetics, and related special disciplines represent a large amount of empirical data, a practical method for the evaluation and overview of current knowledge is far from being realized. The main concepts and narratives in these fields have remained nearly the same for decades and the more recent empirical data concerning the role of noncoding RNAs and persistent viruses and their defectives do not fit into this scenario. A more innovative approach such as applied biocommunication theory could translate (...) empirical data into a coherent perspective on the functions within and between biological organisms and arguably lead to a sustainable integrative biology. (shrink)

Robert Henman’s article, Can brain scanning and imaging techniques contribute to a theory of thinking? came to my attention recently. My own work over the years has included applications of mathematics in the biological sciences, collaboration in experimental work in biochemistry, as well as work in the philosophy of the biological sciences. Henman suggests something that, at present, would be out of the ordinary.

This paper offers a practical argument for metaphysical emergence. The main message is that the growing reliance on so-called irrational scientific methods provides evidence that objects of science are indecomposable and as such, are better described by metaphysical emergence as opposed to the prevalent reductionistic metaphysics. I show that a potential counterargument that science will eventually reduce everything to physics has little weight given where science is heading with its current methodological trend. I substantiate my arguments by detailed examples (...) from biological engineering, but the conclusions are extendable beyond that discipline. (shrink)

In his famous article “The Unreasonable Effectiveness of Mathematics in the Natural Sciences” Eugen Wigner argues for a unique tie between mathematics and physics, invoking even religious language: “The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift which we neither understand nor deserve”. The possible existence of such a unique match between mathematics and physics has been extensively discussed by philosophers and historians of mathematics. Whatever the merits (...) of this claim are, a further question can be posed with regard to mathematization in science more generally: What happens when we leave the area of theories and laws of physics and move over to the realm of mathematical modeling in interdisciplinary contexts? Namely, in modeling the phenomena specific to biology or economics, for instance, scientists often use methods that have their origin in physics. How is this kind of mathematical modeling justified? (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific (...) research, yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

Background: how mind functions is subject to continuing scientific discussion. A simplistic approach says that, since no convincing way has been found to model subjective experience, mind cannot exist. A second holds that, since mind cannot be described by classical physics, it must be described by quantum physics. Another perspective concerns mind's hypothesized ability to interact with the world of quanta: it should be responsible for reduction of quantum wave packets; physics producing 'Objective Reduction' is postulated to form the basis (...) for mind-matter interactions. This presentation describes results derived from a new approach to these problems. It is based on well-established biology involving physics not previously applied to the fields of mind, or consciousness studies, that of critical feedback instability. -/- Methods: 'self-organized criticality' in complexity biology places system loci of control at critical instabilities, physical properties of which, including information properties, are presented. Their elucidation shows that they can model hitherto unexplained properties of experience. -/- Results: All results depend on physical properties of critical instabilities. First, at least one feed-back or feed-forward loop must have feedback gain, g = 1: information flows round the loop impress perfect images of system states back on themselves: they represent processes of perfect self-observation. This annihilates system quanta: system excitations are instability fluctuations, which cannot be quantized. Major results follow: -/- 1. Information vectors representing criticality states must include at least one attached information loop denoting self-observation. -/- 2. Such loop structures are attributed a function, 'registering the state's own existence', explaining -/- a. Subjective 'awareness of one's own presence' -/- b. How content-free states of awareness can be remembered (Jon Shear) -/- c. Subjective experience of time duration (Immanuel Kant) -/- d. The 'witness' property of experience – often mentioned by athletes 'in the zone' -/- e. The natural association between consciousness and intelligence -/- This novel, physically and biologically sound approach seems to satisfactorily model subjectivity. -/- Further significant results follow: -/- 1. Registration of external information in excited states of systems at criticality reduces external wave-packets: the new model exhibits 'Objective Reduction' of wave packets. -/- 2. High internal coherence (postulated by Domash & Penrose) leading to a. Non-separable information vector bundles. b. Non-reductive states (Chalmers's criterion for experience). -/- 3. Information that is: a. encoded in coherence negentropy; b. non-digitizable, and therefore c. computationally without digital equivalent (posited by Penrose). -/- Discussion and Conclusions: instability physics implies anharmonic motion, preventing excitation quantization, and totally different from the quantum physics of simple harmonic motion at stability. Instability excitations are different from anything hitherto conceived in information science. They can model aspects of mind never previously treated, including genuine subjectivity, objective reduction of wave-packets, and inter alia all properties given above. (shrink)

Biological conservation practices and approaches take many forms. Conservation projects do not only differ in their aims and methods, but also concerning their conceptual and normative background assumptions and their underlying motivations and objectives. We draw on philosophical distinctions from the ethics of conservation to explain variances of different positions on conservation projects along six dimensions: (1) conservation ideals, (2) intervention intuitions, (3) the moral considerability of nonhuman beings, (4) environmental values, (5) views on nature and (6) human (...) roles in nature. The result is a map of the moral landscape of biological conservation, on which these six dimensions are layered. This map functions as a heuristic tool to understand conceptual and normative foundations of specific conservation projects, which we will illustrate with four paradigmatic examples: the Pisavaara Strict Nature Reserve, Predator Free New Zealand, the Oostvaardersplassen Nature Reserve and the Great Green Wall Project. With this map as a heuristic tool, we aim to conceptually illuminate disagreement and clarify misunderstandings between representatives of different environmental protection strategies and to show that the same project can be supported (or criticised) on different grounds. (shrink)

There are two fundamentally distinct kinds of biological theorizing. "Formal biology" focuses on the relations, captured in formal laws, among mathematically abstracted properties of abstract objects. Population genetics and theoretical mathematical ecology, which are cases of formal biology, thus share methods and goals with theoretical physics. "Compositional biology," on the other hand, is concerned with articulating the concrete structure, mechanisms, and function, through developmental and evolutionary time, of material parts and wholes. Molecular genetics, biochemistry, developmental biology, and physiology, (...) which are examples of compositional biology, are in serious need of philosophical attention. For example, the very concept of a "part" is understudied in both philosophy of biology and philosophy of science. ;My dissertation is an attempt to clarify the distinction between formal biology and compositional biology and, in so doing, provide a clear philosophical analysis, with case studies, of compositional biology. Given the social, economic, and medical importance of compositional biology, understanding it is urgent. For my investigation, I draw on the philosophical fields of metaphysics and epistemology, as well as philosophy of biology and philosophy of science. I suggest new ways of thinking about some classic philosophy of science issues, such as modeling, laws of nature, abstraction, explanation, and confirmation. I hint at the relevance of my study of two kinds of biological theorizing to debates concerning the disunity of science. (shrink)

The perception of reality by biosystems is based on different, and in certain respects more effective, principles than those utilized by the more formal procedures of science. As a result, what appears as random pattern to the scientific method can be meaningful pattern to a living organism. The existence of this complementary perception of reality makes possible in principle effective use by organisms of the direct interconnections between spatially separated objects shown to exist in the work of J. S. Bell.

Darwin’s treatment of morality in The Descent of Man has generated a wide variety of responses among moral philosophers. Among these is the dismissal of evolution as irrelevant to ethics by Darwin’s contemporary Henry Sidgwick; the last, and arguably the greatest, of the Nineteenth Century British Utilitarians. This paper offers a re-examination of Sidgwick’s response to evolutionary considerations as irrelevant to ethics and the absence of any engagement with Darwin’s work in Sidgwick’s main ethical treatise, The Methods of Ethics (...) . This assessment of Sidgwick’s response to Darwin’s work is shown to have significance for a number of ongoing controversies in contemporary metaethics. (shrink)

The problem with reductionism in biology is not the reduction, but the implicit attitude of determinism that usually accompanies it. Methodological reductionism is supported by deterministic beliefs, but making such a connection is problematic when it is based on an idea of determinism as fixed predictability. Conflating determinism with predictability gives rise to inaccurate models that overlook the dynamic complexity of our world, as well as ignore our epistemic limitations when we try to model it. Furthermore, the assumption of a (...) strictly deterministic framework is unnecessarily hindering to biology. By removing the dogma of determinism, biological methods, including reductive methods, can be expanded to include stochastic models and probabilistic interpretations. Thus, the dogma of reductionism can be saved once its ties with determinism are severed. In this paper, I analyze two problems that have faced molecular biology for the last 50 years—protein folding and cancer. Both cases demonstrate the long influence of reductionism and determinism on molecular biology, as well as how abandoning determinism has opened the door to more probabilistic and unconstrained reductive methods in biology. (shrink)

In the light of the results obtained during the last two decades in analysis of signals by time series, it has become evident that the tools of non linear dynamics have their elective role of application in biological, and, in particular, in neuro-physiological and psycho-physiological studies. The basic concept in non linear analysis of experimental time series is that one of recurrence whose conceptual counterpart is represented from variedness and variability that are the foundations of complexity in dynamic processes. (...) Thus, the recurrence plots and the Recurrence Quantification Analysis (RQA) are discussed. It is shown that RQA represents the most general and correct methodology in investigation of experimental time series. By it we arrive to inspect the inner structure of the time series connected to the signals under investigation. Linked to RQA we prospect also the method CZF, recently introduced by us. It is able to account for a true estimation of variability of signals in time as well as in frequency domain. And, consequently, it may be used in conjunction with classical Fourier analysis, accounting however that it is inappropriate in analysis of non linear and non stationary experimental time series. The use of CZF method in fractal analysis is also considered in addition to standard index as Hurst exponent. A large field of possible applications in neurological as well as in psycho-physiological studies is given. Also, there are given examples of other and (possibly linked) applications as example the analysis of beat-to-beat fluctuations of human heartbeat intervals that is sovereign in psycho-physiological studies. We give applications on some different planes to evidence the particular sensitivity of such methods. We reach the objective to show that the previously exposed methods are also able to predict in advance the advent of ventricular tachycardia and/or of ventricular fibrillation. The RQA analysis gives good results. The CZF method gives the most excellent results showing that it is able to give very significant indexes of prediction. We also apply such methods in investigation of state anxiety, and proposing in detail a quantum like model of such phenomenological status of the mind. (shrink)

What does it look like when a group of scientists set out to re-envision an entire field of biology in symbolic and formal terms? I analyze the founding and articulation of Numerical Taxonomy between 1950 and 1970, the period when it set out a radical new approach to classification and founded a tradition of mathematics in systematic biology. I argue that introducing mathematics in a comprehensive way also requires re-organizing the daily work of scientists in the field. Numerical taxonomists sought (...) to establish a mathematical method for classification that was universal to every type of organism, and I argue this intrinsically implicated them in a qualitative re-organization of the work of all systematists. I also discuss how Numerical Taxonomy’s re-organization of practice became entrenched across systematic biology even as opposing schools produced their own competing mathematical methods. In this way, the structure of the work process became more fundamental than the methodological theories that motivated it. (shrink)

Who is a child’s father? Is it the man who raised her, or the one whose genes she carries—or both? We look at the view that men who have raised children they falsely believed to be ‘their own’ have been victims of a form of fraud or are ‘false fathers’. We consider the question of who has been harmed in such cases, and in what the harm consists. We use conceptual analysis, a philosophical method of investigating the use of a (...) concept and the logical implications of its various interpretations. We devise and discuss a number of possible scenarios in which a couple (arguably) become the parents of a child. We use these scenarios to illustrate the tenuousness of the claim that we can simply rely on biology to clarify parent-child relationships. We also discuss some of the underpinnings and implications of the language in which the debate on ‘paternity fraud’ has been framed: ‘duped’ or ‘false’ fathers and ‘cuckoo children’. (shrink)

Research in ecology and evolutionary biology (evo-eco) often tries to emulate the “hard” sciences such as physics and chemistry, but to many of its practitioners feels more like the “soft” sciences of psychology and sociology. I argue that this schizophrenic attitude is the result of lack of appreciation of the full consequences of the peculiarity of the evo-eco sciences as lying in between a-historical disciplines such as physics and completely historical ones as like paleontology. Furthermore, evo-eco researchers have gotten stuck (...) on mathematically appealing but philosophi- cally simplistic concepts such as null hypotheses and p-values defined according to the frequentist approach in statistics, with the consequence of having been unable to fully embrace the complexity and subtlety of the problems with which ecologists and evolutionary biologists deal with. I review and discuss some literature in ecology, philosophy of science and psychology to show that a more critical methodological attitude can be liberating for the evo-eco scientist and can lead to a more fecund and enjoyable practice of ecology and evolutionary biology. With this aim, I briefly cover concepts such as the method of multiple hypotheses, Bayesian analysis, and strong inference. (shrink)

Cattle farming is a traditional agricultural system that contributes to the rural economic, social, and cultural values of the communities. Cattle as common with other livestock, are affected by many diseases that cause mortality and economic losses. In many rural households, the use of plants and associated knowledge are popular for managing cattle diseases, especially in areas experiencing challenges with conventional veterinary medicine. Evidence on the documentation of indigenous knowledge and biological evaluation of plants used against cattle diseases remain (...) understudied and fragmented. The aim of the review is to collate and analyze the ethnoveterinary knowledge and biological evaluation of plants used against cattle diseases in South Africa. Different scientific databases were systematically explored to extract data from 37 eligible studies. A total of 310 medicinal plants from 81 families were used to treat 10 categories of cattle diseases across seven (7) provinces in South Africa. Leguminosae (Fabaceae), Compositae (Asteraceae), Asparagaceae, and Xanthorrhoeaceae were the most frequently used plant families. Common plant parts used were leaves and roots. Twenty-seven (27) combination remedies involving 2–6 plants were identified as treatment regimes against cattle diseases. Common preparation methods were infusion and decoction while the administration mode was predominantly unspecified (52%) while oral and topical contributed 26 and 22%, respectively. In terms of diseases, the most treated ones were general systems infection, reproduction disorders, and gastrointestinal problems. Currently, an estimated 21% of the 310 plants have been evaluated for diverse biological activities using relevant bioassays related to cattle diseases. Antibacterial activity remained the most studied biological activity. Evidence from the review revealed the significance of ethnoveterinary medicine against cattle diseases especially in rural areas of South Africa. Nevertheless, the use of plants for cattle diseases among other ethnic groups, particularly in the Northern Cape and Western Cape, remains understudied. (shrink)

Phylogeography, a relatively new subdicipline of evolutionary biology that attempts to unify the fields of phylogenetics and population biology in an explicit geographical context, has hosted in recent years a highly polarized debate related to the purported benefits and limitations that qualitative versus quantitative methods might contribute or impose on inferential processes in evolutionary biology. Here we present a friendly, non-technical introduction to the conflicting methods underlying the controversy, and exemplify it with a balanced selection of quotes from (...) the primary biological literature, to invite the philosophy of biology community to pay attention to the elements that have played a primary role in its presumed resolution. We also present the basic features of our own metascientific take on the debate, and point out—as a preliminary step in preparation for upcoming, more detailed treatments—the importance that appeals to authority in fields external to phylogeography per se have played in the current status of this highly visible evolutionary biology dispute. (shrink)

Before the Darwinian revolution species were thought to be universals. Since then, numerous attempts have been made to propose new definitions. The twentieth-century German philosopher Nicolai Hartmann defined 'species' as an individual system of processes and a process of life of a higher-order. To provide a clear understanding of Hartmann's conception of species, I first present his method of definition. Then I look at Hartmann's Philosophie der Natur (1950) to present his concepts of "organism" and "species." And I end the (...) paper by pointing out two possible systematic inconsistencies. (shrink)

One potentially extremely fruitful use of the tools of corpus analysis in the philosophy of science is to help us understand disputed terrains within the sciences that we study. For philosophers of biology, for instance, few controversies are as heated as those over the concepts we use in taxonomy to classify the living world, with the definition of ‘species’ perhaps most fundamental among them. As many understandings of biodiversity, in turn, involve counting the number of species present in a given (...) area, these taxonomic concepts thus become crucially implicated in our efforts in conservation biology and our response to climate change. In this chapter, we present a corpus of taxonomic journal papers, and illustrate how it might be used to make progress in the history and philosophy of taxonomy. What parts of the biological world do taxonomists most often study? Are these areas of focus related to other methodological commitments, or perhaps to their underlying conceptual disagreement? Are species that are more important to conservation, or economic concerns, or more important to local cultures, more or less likely to be the target of biological study? Corpus-based methods, we argue, are uniquely powerful for approaching questions such as these, and we hope that the case we present can serve as a fruitful example for others considering their implementation. (shrink)

Synthetic biology is described as a new field of biotechnology that models itself on engineering sciences. However, this view of synthetic biology as an engineering field has received criticism, and both biologists and philosophers have argued for a more nuanced and heterogeneous understanding of the field. This paper elaborates the heterogeneity of synthetic biology by clarifying the role of design and the variability of design methodologies in synthetic biology. I focus on two prominent design methodologies: rational design and directed evolution. (...) Rational design resembles the design methodology of traditional engineering sciences. However, it is often replaced and complemented by the more biologically-inspired method of directed evolution, which models itself on natural evolution. These two approaches take philosophically different stances to the design of biological systems. Rational design aims to make biological systems more machine-like, whereas directed evolution utilizes variation and emergent features of living systems. I provide an analysis of the methodological basis of these design approaches, and highlight important methodological differences between them. By analyzing the respective benefits and limitations of these approaches, I argue against the engineering-dominated conception of synthetic biology and its “methodological monism”, where the rational design approach is taken as the default design methodology. Alternative design methodologies, like directed evolution, should be considered as complementary, not competitive, to rational design. (shrink)

I describe two traditions of philosophical accounts of evidence: one characterizes the notion in terms of signs of success, the other characterizes the notion in terms of conditions of success. The best examples of the former rely on the probability calculus, and have the virtues of generality and theoretical simplicity. The best examples of the latter describe the features of evidence which scientists appeal to in practice, which include general features of methods, such as quality and relevance, and general (...) features of evidence, such as patterns in data, concordance with other evidence, and believability of the evidence. Two infamous episodes from biomedical research help to illustrate these features. Philosophical characterization of these latter features—conditions of success—has the virtue of potential relevance to, and descriptive accuracy of, practices of experimental scientists. (shrink)

Throughout the biological and biomedical sciences there is a growing need for, prescriptive ‘minimum information’ (MI) checklists specifying the key information to include when reporting experimental results are beginning to find favor with experimentalists, analysts, publishers and funders alike. Such checklists aim to ensure that methods, data, analyses and results are described to a level sufficient to support the unambiguous interpretation, sophisticated search, reanalysis and experimental corroboration and reuse of data sets, facilitating the extraction of maximum value from (...) data sets them. However, such ‘minimum information’ MI checklists are usually developed independently by groups working within representatives of particular biologically- or technologically-delineated domains. Consequently, an overview of the full range of checklists can be difficult to establish without intensive searching, and even tracking thetheir individual evolution of single checklists may be a non-trivial exercise. Checklists are also inevitably partially redundant when measured one against another, and where they overlap is far from straightforward. Furthermore, conflicts in scope and arbitrary decisions on wording and sub-structuring make integration difficult. This presents inhibit their use in combination. Overall, these issues present significant difficulties for the users of checklists, especially those in areas such as systems biology, who routinely combine information from multiple biological domains and technology platforms. To address all of the above, we present MIBBI (Minimum Information for Biological and Biomedical Investigations); a web-based communal resource for such checklists, designed to act as a ‘one-stop shop’ for those exploring the range of extant checklist projects, and to foster collaborative, integrative development and ultimately promote gradual integration of checklists. (shrink)

Synthetic biology offers a powerful method to design and construct biological devices for human purposes. Two prominent design methodologies are currently used. Rational design adapts the design methodology of traditional engineering sciences, such as mechanical engineering. Directed evolution, in contrast, models its design principles after natural evolution, as it attempts to design and improve systems by guiding them to evolve in a certain direction. Previous work has argued that the primary difference between these two is the way they treat (...) variation: rational design attempts to suppress it, whilst direct evolution utilizes variation. I argue that this contrast is too simplistic, as it fails to distinguish different types of variation and different phases of design in synthetic biology. I outline three types of variation and show how they influence the construction of synthetic biological systems during the design process. Viewing the two design approaches with these more fine-grained distinctions provides a better understanding of the methodological differences and respective benefits of rational design and directed evolution, and clarifies the constraints and choices that the different design approaches must deal with. (shrink)

The key assumption behind evolutionary epistemology is that animals are active learners or ‘knowers’. In the present study, I updated the concept of natural learning, developed by Henry Plotkin and John Odling-Smee, by expanding it from the animal-only territory to the biosphere-as-a-whole territory. In the new interpretation of natural learning the concept of biological information, guided by Peter Corning’s concept of “control information”, becomes the ‘glue’ holding the organism–environment interactions together. The control information guides biological systems, from bacteria (...) to ecosystems, in the process of natural learning executed by the universal algorithm. This algorithm, summarized by the acronym IGPT (information-gain-process-translate) incorporates natural cognitive methods including sensing/perception, memory, communication, and decision-making. Finally, the biosphere becomes the distributed network of communicative interactions between biological systems termed the interactome. The concept of interactome is based on Gregory Bateson’s natural epistemology known as the “ecology of mind”. Mimicking Bateson’s approach, the interactome may also be designated “physiology of mind”—the principle behind regulating the biosphere homeostasis. (shrink)

The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More (...) specifically, I argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs. (shrink)

The INBIOSA project brings together a group of experts across many disciplines who believe that science requires a revolutionary transformative step in order to address many of the vexing challenges presented by the world. It is INBIOSA’s purpose to enable the focused collaboration of an interdisciplinary community of original thinkers. This paper sets out the case for support for this effort. The focus of the transformative research program proposal is biology-centric. We admit that biology to date has been more fact-oriented (...) and less theoretical than physics. However, the key leverageable idea is that careful extension of the science of living systems can be more effectively applied to some of our most vexing modern problems than the prevailing scheme, derived from abstractions in physics. While these have some universal application and demonstrate computational advantages, they are not theoretically mandated for the living. A new set of mathematical abstractions derived from biology can now be similarly extended. This is made possible by leveraging new formal tools to understand abstraction and enable computability. [The latter has a much expanded meaning in our context from the one known and used in computer science and biology today, that is "by rote algorithmic means", since it is not known if a living system is computable in this sense (Mossio et al., 2009).] Two major challenges constitute the effort. The first challenge is to design an original general system of abstractions within the biological domain. The initial issue is descriptive leading to the explanatory. There has not yet been a serious formal examination of the abstractions of the biological domain. What is used today is an amalgam; much is inherited from physics (via the bridging abstractions of chemistry) and there are many new abstractions from advances in mathematics (incentivized by the need for more capable computational analyses). Interspersed are abstractions, concepts and underlying assumptions “native” to biology and distinct from the mechanical language of physics and computation as we know them. A pressing agenda should be to single out the most concrete and at the same time the most fundamental process-units in biology and to recruit them into the descriptive domain. Therefore, the first challenge is to build a coherent formal system of abstractions and operations that is truly native to living systems. Nothing will be thrown away, but many common methods will be philosophically recast, just as in physics relativity subsumed and reinterpreted Newtonian mechanics. -/- This step is required because we need a comprehensible, formal system to apply in many domains. Emphasis should be placed on the distinction between multi-perspective analysis and synthesis and on what could be the basic terms or tools needed. The second challenge is relatively simple: the actual application of this set of biology-centric ways and means to cross-disciplinary problems. In its early stages, this will seem to be a “new science”. This White Paper sets out the case of continuing support of Information and Communication Technology (ICT) for transformative research in biology and information processing centered on paradigm changes in the epistemological, ontological, mathematical and computational bases of the science of living systems. Today, curiously, living systems cannot be said to be anything more than dissipative structures organized internally by genetic information. There is not anything substantially different from abiotic systems other than the empirical nature of their robustness. We believe that there are other new and unique properties and patterns comprehensible at this bio-logical level. The report lays out a fundamental set of approaches to articulate these properties and patterns, and is composed as follows. -/- Sections 1 through 4 (preamble, introduction, motivation and major biomathematical problems) are incipient. Section 5 describes the issues affecting Integral Biomathics and Section 6 -- the aspects of the Grand Challenge we face with this project. Section 7 contemplates the effort to formalize a General Theory of Living Systems (GTLS) from what we have today. The goal is to have a formal system, equivalent to that which exists in the physics community. Here we define how to perceive the role of time in biology. Section 8 describes the initial efforts to apply this general theory of living systems in many domains, with special emphasis on crossdisciplinary problems and multiple domains spanning both “hard” and “soft” sciences. The expected result is a coherent collection of integrated mathematical techniques. Section 9 discusses the first two test cases, project proposals, of our approach. They are designed to demonstrate the ability of our approach to address “wicked problems” which span across physics, chemistry, biology, societies and societal dynamics. The solutions require integrated measurable results at multiple levels known as “grand challenges” to existing methods. Finally, Section 10 adheres to an appeal for action, advocating the necessity for further long-term support of the INBIOSA program. -/- The report is concluded with preliminary non-exclusive list of challenging research themes to address, as well as required administrative actions. The efforts described in the ten sections of this White Paper will proceed concurrently. Collectively, they describe a program that can be managed and measured as it progresses. (shrink)

The guide explains the basic concepts of natural, social and evolutionary components and methods of risk management and control of modern biotechnologies stemming from the general theory of human evolution. A a transdisciplinary approach is a feature of the presentation of the material The risk is considered from the point of view of evolutionary anthropology, as a basic element of a stable evolutionary strategy of our biological species and the basis of a combination of natural-scientific and humanitarian forms (...) of presentation and interpretation of the material. CONTENT BIOSAFETY AND BIOETHICS IN BIOTECHNOLOGY (Introduction). Part 1. BIOTECHNOLOGY AS A FACTOR OF EVOLUTION AND A SOURCE OF TECHNOGENIC RISK. Chapter 1. GENERAL THEORY OF TECHNOLOGICAL RISK. Chapter 2. RISK AS AN ATTRIBUTE OF STABLE HUMAN EVOLUTIONARY STRATEGY. Chapter 3. BIOTECHNOLOGY AS A SOURCE OF BIORISKS Chapter 4. COMPONENTS OF THE "BIOLOGICAL FACTOR" OF RISK. Chapter 5. BIOLOGICAL WEAPONS and BIOTERRORISM. Part 2. BIOLOGICAL, SOCIAL AND EVOLUTIONARY COMPONENTS OF THE RISK OF MODERN BIOTECHNOLOGIES Chapter 6. RISK MANAGEMENT DURING BIOTECHNOLOGICAL RESEARCH.. Chapter 7. BIOTECHNOLOGY AS A SOURCE OF SOCIAL INSTABILITY. Chapter 8. BIOTECHNOLOGY AS A SOURCE OF CIVILIZATIONAL RISK. -/- . (shrink)

Modes of teaching and learning have had to rapidly shift amid the COVID-19 pandemic. As an emergency response, students from Philippine public schools were provided learning modules based on a minimized list of essential learning competencies in Biology. Using a cross-sectional survey method, we investigated students’ perceptions of the Biology self-learning modules (BSLM) that were designed in print and digitized formats according to a constructivist learning approach. Senior high school STEM students from grades 11 (n = 117) and 12 (n (...) = 104) participated in a survey using a 3-point Likert-scale questionnaire uploaded online through Google Forms. The survey results indicate that majority of the students perceived the modules positively, suggesting that aspects of the modules that were salient to students corresponded to essential elements of constructivist pedagogies. However, during interviews, students reported several difficulties in learning with BSLM as it was constrained by, to name a few, the use of unfamiliar words, lack of access to supporting resources, slow internet connection, and time constraints. To address these problems, teachers reported that they gave deadline extensions, complemented modules with other channels of support, and used online and offline platforms for reaching out to students to answer their queries and plan out their schedule for the week. The findings across the data sources point to the complex demands of emergency distance education that teachers, as curriculum designers and enactors, need to bear in mind in order to craft productive pedagogies, constructivist or otherwise, during this unprecedented time. (shrink)

Perhaps the most familiar understanding of “naturalism” derives from Quine, understanding it as a continuity of empirical theories of the world as described through the scientific method. So, it might be surprising that one of the most important naturalistic moral realists, Philippa Foot, rejects standard evolutionary biology in her justly lauded _Natural Goodness_. One of her main reasons for this is the true claim that humans can flourish (eudaimonia) without reproducing, which she claims cannot be squared with evolutionary theory and (...) biology more generally. The present argument concludes that Foot was wrong to reject evolutionary theory as the empirical foundation of naturalized eudaimonist moral realism. This is based on contemporary discussion of _biological function_ and _evolutionary fitness_, from which a definition of “eudaimonia” is constructed. This gives eudaimonist moral realism an empirically respectable foundation. (shrink)

In this paper I take evolutionary biology as an example to reflect on the role of philosophy and on the transformations that philosophy is constantly stimulated to do in its own approach when dealing with science. I consider that some intellectual movements within evolutionary biology (more specifically, the various calls for 'synthesis') express metascientific views, i.e., claims about 'what it is to do research' in evolutionary biology at different times. In the construction of metascientific views I see a fundamental role (...) to be played by philosophy, and, at the same time, a need to complement the philosophical methods with many more methods coming from other sciences. What leads philosophy out of itself is its own attention to scientific practice. My humble methodological suggestions are, at this stage, only meant to help us imagine metascientific views that are built with a more scientific, interdisciplinary approach, in order to attenuate partiality, subjectivity and impressionism in describing the scientific community. And yet, we should not be naïve and imbued with the myth of 'datadriven' research, especially in this field: other complex issues about metascientific views call for a serious, constant philosophical reflection on scientific practice. (shrink)

Stapp and others have proposed that reality involves a fundamental life process, or creative process. It is shown how this process description may be unified with the description that derives from quantum physics. The methods of the quantum physicist and of the biological sciences are seen to be two alternative approaches to the understanding of nature, involving two distinct modes of description which can usefully supplement each other, and neither on its own contains the full story. The unified (...) view explains the major features of quantum mechanics and suggests that biological systems may function more effectively than would be expected on the basis of quantum mechanics alone. (shrink)

This article identifies empirical, conceptual and normative avenues to advance the speciesism debate. First, I highlight the application of Evolutionary Debunking Arguments (EDAs) as one such avenue: especially where (anti-)speciesist positions heavily rely on appeals to moral intuition, and EDAs have potential to move the debate forward. Second, an avenue for conceptual progress is the delineation of speciesism from other views in its vicinity, specifically from the view that biological differences between species are sometimes morally relevant (‘species-relativism’). Third, if (...) we adopt Singer’s definition of speciesism, then a limitation of the current debate is that it is not obvious whether the core ethical principle that underlies anti-speciesist positions—the Principle of Equal Consideration of Interests—is widely applicable. Arguably, the interests of animals are often too dissimilar to establish what equal consideration amounts to. I underscore the need for integrating philosophical and empirical research, to come to terms with the extent to which the interests of members of different species are alike, and with the question of whether any dissimilarities might be morally relevant. (shrink)

Aristotle was the first thinker to articulate a taxonomy of scientific knowledge, which he set out in Posterior Analytics. Furthermore, the “special sciences”, i.e., biology, zoology and the natural sciences in general, originated with Aristotle. A classical question is whether the mathematical axiomatic method proposed by Aristotle in the Analytics is independent of the special sciences. If so, Aristotle would have been unable to match the natural sciences with the scientific patterns he established in the Analytics. In this paper, I (...) reject this pessimistic approach towards the scientific value of natural sciences. I believe that there are traces of biology in the Analytics as well as traces of the Analytics’ theory in zoological treatises. Moreover, for a lack of chronological clarity, I think it’s better to unify Aristotle’s model of scientific research, which includes Analytics and the natural sciences together. (shrink)

We present a technique for the analysis of pattern formation by a class of models for the formation of ocular dominance stripes in the striate cortex of some mammals. The method, which employs the adiabatic approximation to derive a set of ordinary differential equations for patterning modes, has been successfully applied to reaction-diffusion models for striped patterns [1]. Models of ocular dominance stripes have been studied [2,3] by computation, or by linearization of the model equations. These techniques do not provide (...) a rationale for the origin of the stripes. We show here that stripe formation is a non-linear property of the models. Our analysis indicates that stripe selection is closely linked to a property in the dynamics of the models which arises from a symmetry between ipsilateral and contralateral synapses to the visual cortex of a given hemisphere. (shrink)

A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here (...) is how cutting-edge bioengineering scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Nersessian pushes the boundaries of the philosophy of science and cognitive science into areas not ventured before. She recounts a decades-long, wide-ranging, and richly detailed investigation of the innovative interdisciplinary modeling practices of bioengineering researchers in four university laboratories. She argues and demonstrates that the methods of cognitive ethnography and qualitative data analysis, placed in the framework of distributed cognition, provide the tools for a philosophical analysis of how scientific discoveries arise from complex systems in which the cognitive, social, material, and cultural dimensions of problem-solving are integrated into the epistemic practices of scientists. Specifically, she looks at how interdisciplinary environments shape problem-solving. Although Nersessian’s case material is drawn from the bioengineering sciences, her analytic framework and methodological approach are directly applicable to scientific research in a broader, more general sense, as well. (shrink)

[penultimate draft; prepared for publication in Aristotle’s Parts of Animals: A Critical Guide, ed. Sophia Connell – please cite final version] -/- Parts of Animals II.10 makes a new beginning in Aristotle’s study of animals. In it, Aristotle proposes to “now speak as if we are once more at an origin, beginning first with those things that are primary” (655b28-9). This is the start of his account of the non-uniform parts of blooded animals: parts such as eyes, noses, mouths, etc., (...) as opposed to uniform parts like blood and flesh. PA II.10 proposes a new strategy for studying these parts: “one ought to speak about the human kind first” (656a10). Beginning “first” with the “primary” things thus amounts to beginning with humans (655b28-9). -/- Why does Aristotle think this strategy is appropriate for his project? One answer is that it reflects a fundamental anthropocentrism. Lloyd, for instance, has raised the possibility that the basic assumptions that guide Aristotle’s biology may to some extent reflect the anthropocentrism of his culture, where the relation of humans to animals was “a preoccupation of popular beliefs”. In this paper, I develop an interpretation that both builds on and challenges this suggestion. I do so by investigating how Aristotle thinks this strategy works in theory and in practice: his justifications for adopting it and its interaction with his scientific commitments. I argue that Aristotle adopts it in part because he thinks that humans are such that his scientific concepts apply to them in a particularly clear way. (This too is a form of anthropocentrism: one that might be resisted by arguing that humans are not the best illustrations of these concepts—that Aristotle ought to look elsewhere for models with the features he desires.) -/- More specifically, Aristotle holds that starting with humans helps establish the causal explanations of the parts of other animals, particularly when they are recalcitrant. What makes humans suitable for this role is a special teleological relation between their parts and the ends they serve: in humans, he supposes, this connection is particularly tight (in a way I explain below). Consequently, Aristotle thinks he can use humans to illuminate which sorts of features tend to be for the sake of which ends, and then extend the results of this inquiry to the parts of the other animals. (shrink)

While seeking novel food sources to feed the increasing population of the globe, several alternatives have been discussed, including algae, fungi or in vitro meat. The increasingly propagated usage of farmed insects for human nutrition raises issues regarding food safety, consumer information and animal protection. In line with law, insects like any other animals must not be reared or manipulated in a way that inflicts unnecessary pain, distress or harm on them. Currently, there is a great need for research in (...) the area of insect welfare, especially regarding species-specific needs, health, farming systems and humane methods of killing. Recent results from neurophysiological, neuroanatomical and behavioral sciences prompt caution when denying consciousness and therefore the likelihood of presence of pain and suffering or something closely related to it to insects. From an animal protection point of view, these issues should be satisfyingly solved before propagating and establishing intensive husbandry systems for insects as a new type of mini-livestock factory farming. (shrink)

Melinda Fagan’s book on the philosophy of stem cell biology is a superb discussion of this exciting field of contemporary science, and the first book-length philosophical treatment of the subject. It contains a detailed and insightful examination of stem cell science, its structure, methods, and challenges.The book does not require any previous knowledge of stem cell biology—all the relevant scientific details and concepts, the central experimental procedures and results, as well as the historical development of the field, are presented (...) in as clear and accessible a way as possible. The presentation of the science starts in Chapter 1, with a general characterisation of stem cell biology, and continues throughout the book, with every chapter deepening and complicating the initial picture. Fagan’s lucid style makes even the more technical and detailed discussions of the research on human embryonic stem cells and on blood stem cells easy to follow.After the first in .. (shrink)

Many believe that a suitably programmed computer could act for its own goals and experience feelings. I challenge this view and argue that agency, mental causation and qualia are all founded in the unique, homeostatic nature of living matter. The theory was formulated for coherence with the concept of an agent, neuroscientific data and laws of physics. By this method, I infer that a successful action is homeostatic for its agent and can be caused by a feeling - which does (...) not motivate as a force, but as a control signal. From brain research and the locality principle of physics, I surmise that qualia are a fundamental, biological form of energy generated in specialized neurons. Subjectivity is explained as thermodynamically necessary on the supposition that, by converting action potentials to feelings, the neural cells avert damage from the electrochemical pulses. In exchange for this entropic benefit, phenomenal energy is spent as and where it is produced - which precludes the objective observation of qualia. (shrink)

As the world’s population grows and urbanization continues, the global waste crisis is becoming more severe, especially in developing countries. Without proper waste management, they may encounter various environmental and health risks. Biological technologies are regarded as promising waste management and recycling approaches in developing countries due to their cost-effectiveness and capability to handle diverse waste categories. One prominent technology in this aspect is the vermicomposting of organic waste utilizing the black soldier fly larvae. Nevertheless, significant financial resources are (...) still necessary to advance and expand biological waste treatment, which requires the participation of businesses. By examining waste management companies listed on the Vietnamese stock market, we highlight two challenges that hinder the adoption and scaling of the waste treatment system using black soldier flies and potentially other biological technologies in Vietnam. Specifically, the business regards the environmental services they offer primarily as a means to generate profit rather than as a genuine commitment to environmental preservation, and the values of companies operating in the environmental sector, particularly in waste management, have been significantly undervalued by the Vietnamese investing public. Therefore, we advocate for societal shifts toward the eco-surplus culture and apply the semiconducting principle of monetary and environmental values exchange to address the problem. (shrink)

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent (...) introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes. (shrink)

Reports of patients with schizophrenia show a fragmented and anomalous subjective experience. This pathological subjective experience, we suggest, can be related to the fact that disembodiment inhibits the possibility of intersubjective experience, and more importantly of common sense. In this paper, we ask how to investigate the anomalous experience both from qualitative and quantitative viewpoints. To our knowledge, few studies have focused on a clinical combination of both first- phenomenological assessment and third-person biological methods, especially for Schizophrenia, or (...) ASD therapeutics and diagnosis. We will thus attempt to bring forward a second-person scientific design, accounting for both the first-person subjective experiential aspects, and respective third-person neurobiological correlates of embodied aesthetics in anomalous experience. From this proposal, we further explore the consequences to clinical and research practice. (shrink)

It is illegitimate to read any ontology about "race" off of biological theory or data. Indeed, the technical meaning of "genetic variation" is fluid, and there is no single theoretical agreed-upon criterion for defining and distinguishing populations (or groups or clusters) given a particular set of genetic variation data. Thus, by analyzing three formal senses of "genetic variation"—diversity, differentiation, and heterozygosity—we argue that the use of biological theory for making epistemic claims about "race" can only seem plausible when (...) it relies on the user’s own assumptions about race; the move from biological measures to claims about “race” inevitably amounts to a pernicious reification. We also excavate assumptions in the history of the technical discourse over the concept of "race" (e.g., Livingstone's and Dobzhansky's 1962 exchange, Edwards' 2003 response to Lewontin 1972, as well as contemporary discussions of cladistic "race", and "races" as clusters). We show that claims about the existence (or non-existence) of "race" are underdetermined by biological facts, methods, and theories. Biological theory does not force the concept of "race" upon us; our social discourse, social ontology, and social expectations do. We become prisoners of our abstractions at our own hands, and at our own expense. (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 Cell Ontology (CL) is designed to provide a standardized representation of cell types for data annotation. Currently, the CL employs multiple is_a relations, defining cell types in terms of histological, functional, and lineage properties, and the majority of definitions are written with sufficient generality to hold across multiple species. This approach limits the CL’s utility for cross-species data integration. To address this problem, we developed a method for the ontological representation of cells and applied this method to develop a (...) dendritic cell ontology (DC-CL). DC-CL subtypes are delineated on the basis of surface protein expression, systematically including both species-general and species-specific types and optimizing DC-CL for the analysis of flow cytometry data. This approach brings benefits in the form of increased accuracy, support for reasoning, and interoperability with other ontology resources. 104. Barry Smith, “Toward a Realistic Science of Environments”, Ecological Psychology, 2009, 21 (2), April-June, 121-130. Abstract: The perceptual psychologist J. J. Gibson embraces a radically externalistic view of mind and action. We have, for Gibson, not a Cartesian mind or soul, with its interior theater of contents and the consequent problem of explaining how this mind or soul and its psychological environment can succeed in grasping physical objects external to itself. Rather, we have a perceiving, acting organism, whose perceptions and actions are always already tuned to the parts and moments, the things and surfaces, of its external environment. We describe how on this basis Gibson sought to develop a realist science of environments which will be ‘consistent with physics, mechanics, optics, acoustics, and chemistry’. (shrink)

When developing phylogenetic systematics, the entomologist Willi Hennig adopted elements from Nicolai Hartmann’s ontology. In this historical essay I take on the task of documenting this adoption. I argue that in order to build a metaphysical foundation for phylogenetic systematics, Hennig adopted from Hartmann four main metaphysical theses. These are (1) that what is real is what is temporal; (2) that the criterion of individuality is to have duration; (3) that species are supra-individuals; and (4) that there are levels of (...) reality, each of which may be subject to different kinds of law. Reliance on Hartmann’s metaphysics allowed Hennig to ground some of the main theoretical principles of phylogenetic systematics, namely that the biological categories—from the semaphoront to the highest rank—have reality and individuality despite not being universals, and that they form a hierarchy of levels, each of which may require different kinds of explanation. Hartmann’s metaphysics thereby provided a philosophical justification for Hennig’s phylogenetic systematics, both as a theory and as a method of classification. (shrink)

Ontologies of living things are increasingly grounded on the concepts and practices of current life science. Biological development is a process, undergone by living things, which begins with a single cell and (in an important class of cases) ends with formation of a multicellular organism. The process of development is thus prima facie central for ideas about biological individuality and organismality. However, recent accounts of these concepts do not engage developmental biology. This paper aims to fill the gap, (...) proposing the lineage view of stem cells as an ontological framework for conceptualizing organismal development. This account is grounded on experimental practices of stem cell research, with emphasis on new techniques for generating biological organization in vitro. On the lineage view, a stem cell is the starting point of a cell lineage with a specific organismal source, time-interval of existence, and ‘tree topology’ of branch-points linking the stem to developmental termini. The concept of ‘enkapsis’ accommodates the cell-organism relation within the lineage view; this hierarchical notion is further explicated by considering the methods and results of stem cell experiments. Results of this examination include a (partial) characterization of stem cells’ developmental versatility, and the context-dependence of developmental processes involving stem cells. (shrink)

Support for the biological concept of race declined slowly but steadily during the second half of the twentieth century. However, debate about the validity of the race concept has recently been reignited. Genetic-clustering studies have shown that despite the small proportion of genetic variation separating continental populations, it is possible to assign some individuals to their continents of origin, based on genetic data alone. Race naturalists have interpreted these studies as empirically confirming the existence of human subspecies, and by (...) extension biological races. However, the new racial naturalism is not convincing. The continental clusters appealed to by race naturalists are arbitrary and superficial groupings, which should not be elevated to subspecies status. Moreover, the criteria applied to humans are not consistent with those used to define subspecies in nonhuman animals, and no rationale has been given for this differential treatment. (shrink)

This is the Guest Editor's Introduction to a volume of Philosophia Reformata that considers Jaap Klapwijk's book in which he seeks to discuss evolutionary biology in relation to Christian philosophy.