Isabella Sarto-Jackson

There is a conceptual crisis in the biomedical sciences that is particularly salient in psychopathology research. Underlying the crisis is a controversy that pertains to the current medical model of disease that largely draws from causal-mechanistic explanations. The bedrock of this model is the analysis of biological part-dysfunctions that aims at unequivocally defining a pathological condition and demarcating it from its neighboring entities. This endeavor has led to a quest for physiological, biochemical, and genetic signatures. Yet, so far there is little evidence for reliable biomarkers for any mental disorder. The contemporary biomedical paradigm largely ignores historical, dynamic, and system-level aspects—a view that has contributed much to the conceptual disjunction of the patient as a person from his/her disease. Notwithstanding the impressive progress in the biomedical sciences, increasingly more critics question whether the constituting framework is sufficient to convey a comprehensive understanding of illnesses, especially mental illnesses. Thus, the medical model urgently requires an update. But rather than revamping it by methodological advancements, it will be necessary to critically review its philosophical roots. The most problematic issues that require reworking are: the preponderance of the biostatistical theory; the undue decoupling of physiological from evolutionary explanations of function; the clinging to the Modern Synthesis ; and the neglect of dynamic and system-level properties. The proposed overhaul requires the heeding of historical explanations that draw from the Extended Evolutionary Synthesis as well as systems biology approaches for tackling multilevel and dynamic phenomena of complex systems.

There is a conceptual crisis in the biomedical sciences that is particularly salient in psychopathology research. Underlying the crisis is a controversy that pertains to the current medical model of disease that largely draws from causal-mechanistic explanations. The bedrock of this model is the analysis of biological part-dysfunctions that aims at unequivocally defining a pathological condition and demarcating it from its neighboring entities. This endeavor has led to a quest for physiological, biochemical, and genetic signatures. Yet, so far there is little evidence for reliable biomarkers for any mental disorder. The contemporary biomedical paradigm largely ignores historical, dynamic, and system-level aspects—a view that has contributed much to the conceptual disjunction of the patient as a person from his/her disease. Notwithstanding the impressive progress in the biomedical sciences, increasingly more critics question whether the constituting framework is sufficient to convey a comprehensive understanding of illnesses, especially mental illnesses. Thus, the medical model urgently requires an update. But rather than revamping it by methodological advancements, it will be necessary to critically review its philosophical roots. The most problematic issues that require reworking are: the preponderance of the biostatistical theory; the undue decoupling of physiological from evolutionary explanations of function; the clinging to the Modern Synthesis ; and the neglect of dynamic and system-level properties. The proposed overhaul requires the heeding of historical explanations that draw from the Extended Evolutionary Synthesis as well as systems biology approaches for tackling multilevel and dynamic phenomena of complex systems.

Our primary goal in this article is to discuss the cross-talk between biological and cultural factors that become manifested in the individual brain development, neural wiring, neurochemical homeostasis, and behavior. We will show that behavioral propensities are the product of both cultural and biological factors and an understanding of these interactive processes can provide deep insights into why people behave the way they do. This interdisciplinary perspective is offered in an effort to generate dialog and empirical work among scholars interested in merging aspects of anthropology and neuroscience, and anticipates that biological and cultural anthropology converge. We discuss new theoretical developments, hypothesis-testing strategies, and cross-disciplinary methods of observation and data collection. We believe that the exigency of integrating anthropology and the neurosciences is indisputable and anthropology's role in an emerging interdisciplinary science of human behavior will be critical because its focus is, and has always been, on human biological and cultural systems.

Re-Engineering Philosophy for Limited Beings is about new approaches to many of the big topics in philosophy of science today, but with a very different take. To begin with, we are urged to reject the received Cartesian-Laplacean myths: Descartes’ certainty and Laplace’s computational omniscience. Instead, Wimsatt re-engineers a philosophy for human beings with all their cognitive limitations. His approaches find their starting point in the actual practices of scientists themselves, which he strongly identifies with engineering practices as the source of researchers’ solutions for dealing with a complex world. He aims to construct an understanding of scientific methodology around the central role of reduction. But he dismisses eliminative reductionism in favor of a heuristic-based realist view. Wimsatt’s world is a complex one, and this means that science needs to do away with all the absolute and simple answers, because they do not reflect the world we are living in. A complex world requires the mindset and tinkering of an engineer to uncover its reality. The appropriate response must be heuristics all the way down as we constantly seek out reliable inferences on often shifting ground. To this end, we aim for models and theories that are robust, just as engineers aim to build robust machines. And although errors occur and approaches are fallible, they allow us to continually adapt the heuristics applied and sharpen our perceptions so as to develop more refined tools for investigating and understanding the world.

Do sensory measurements deserve the label of “measurement”? We argue that they do. They fit with an epistemological view of measurement held in current philosophy of science, and they face the same kinds of epistemological challenges as physical measurements do: the problem of coordination and the problem of standardization. These problems are addressed through the process of “epistemic iteration,” for all measurements. We also argue for distinguishing the problem of standardization from the problem of coordination. To exemplify our claims, we draw on olfactory performance tests, especially studies linking olfactory decline to neurodegenerative disorders

Any discipline of human knowledge is characterized by three fundamental elements: the complexity of its content, the method used for its elaboration, and the language used for its expression. This article argues that any method for making knowledge is a particular combination of three main components that we can call science, art, and revelation. The right combination depends on the complexity of the slice of reality that we wish to understand in each case. Is there a relationship between the quantity and quality of a particular piece of knowledge and the quantity and quality of its eventual audience? Such a relationship serves, I believe, to avoid certain old misunderstandings

Biological sciences have strived to adopt the conceptual framework of physics and have become increasingly quantitatively oriented, aiming to refute the assertion that biology appears unquantifiable, unpredictable, and messy. But despite all effort, biology is characterized by a paucity of quantitative statements with universal applications. Nonetheless, many biological disciplines—most notably molecular biology—have experienced an ascendancy over the last 50 years. The underlying core concepts and ideas permeate and inform many neighboring disciplines. This surprising success is probably not so much attributable to mathematical and statistical approaches in molecular biology, but rather to the preponderance of qualitative approaches, especially visualization. Visualizations can be afforded by quantitative research, but usually they rely on both, quantitative and qualitative research. I claim the following three features to be responsible for the unceasing zeal for using visualizations: visual representations facilitate reasoning, images can be cognitively processed in a “fast” manner, and abstractions of visual representations prompt conceptual advances. In summary, visualizations have largely contributed to the success of molecular biology by conveying its concepts to other disciplines, and at the same time, eclipsing mere quantitative approaches. However, visualizations also bear the risk of misinterpretation when traversing neighboring disciplines and even more so when pervading nonscientific domains

Without doubt, good numbers that characterize sharply and completely the phenomena being studied, and precise explanation of these phenomena that can be expressed mathematically, are tremendous advantages for a field of science. But not all fields of science are lucky enough to be able to achieve these features. And when they are not, nonetheless to force the phenomena studied to be characterized largely with numbers and the causal mechanisms to be described mathematically can court seriously limiting and distorting the field of study. Research by economists on long-run economic development is an example of a field of research where this has happened. But unfortunately the phenomena are widespread throughout science

Evolutionary epistemology has experienced a continuous rise over the last decades. Important new theoretical considerations and novel empirical findings have been integrated into the existing framework. In this paper, I would like to suggest three lines of research that I believe will significantly contribute to further advance EE: ontogenetic considerations, key ideas from cognitive biology, and the framework of the Extended Evolutionary Synthesis. EE, in particular the program of the evolution of epistemological mechanisms, seeks to provide a phylogenetic account of the generation of cognitive processes underlying knowledge creation. Traditionally, EE and EEM have been oriented towards an account of evolutionary theory that mainly drew from the tenets of the Modern Synthesis. The Modern Synthesis largely dismisses ontogenetic processes and considers them irrelevant for evolutionary explanations. If anything, the role of development in evolution is believed to be that of a constraint. There is, however, ample evidence for a tight intertwinement of developmental and evolutionary processes. Organisms employ their cognitive apparatus to interact with the environment in order to achieve a fully functioning perceptual and cognitive nervous system. Also, ontogeny provides generative potentials to enable variations that natural selection can act upon. EEM’s agenda may, therefore, strongly benefit from bringing together ontogenetic and phylogenetic approaches. To grapple with this challenge, an alternative vision of the evolutionary theory termed Extended Evolutionary Synthesis could be used. This extended evolutionary theory explores relationships between the processes of individual development and phenotypic change during evolution and can provide a more suitable framework for EEM to draw from. In recent years, cognitive biology has gained momentum as an independent research field. Cognitive biology builds on the concepts of EEM and understands knowledge as a biogenic phenomenon. Its main objective is also the formulation of substantiated interrelations between cognition and evolution but it focuses on cognitive functionality at all levels of biological organization. It thus employs a “vertical” approach that encompasses nested hierarchies which span from single molecules, cells, and tissues to the organismal level, communities, and societies. In contrast to cognitive biology, EEM is here understood to adopt a “horizontal” approach that focuses on phylogenetic explanations of cognition and knowledge acquisition. Linking EEM with the key ideas of cognitive biology could make EEM’s research program stronger as it can more easily accommodate phylogenetic and ontogenetic questions within a hierarchical, multilevel perspective. This is of particular importance for a more comprehensive account of cognition since living systems are subject to context-dependent causal influences from different organizational levels. In addition to EEM, there is a second program of EE. This program has been labeled evolutionary epistemology of theories and understands the increase in human knowledge, such as scientific theories, as naturalistic accounts of evolution. Both, EEM and EET initially drew from the core concepts of the Modern Synthesis. Several scholars have severely criticized the analogies made between EET and the Neo-Darwinian key processes of evolution. In particular processes of random mutation, the rate of variation, natural selection as the unique driving force, and the adaptationist agenda are believed to reveal disanalogies. In contrast to the Modern Synthesis, the Extended Evolutionary Synthesis not only recognizes developmental processes but also ecological interactions and systems dynamics as well as social and cultural evolutionary reciprocity as important evolutionary processes. Concepts of the Extended Evolutionary Synthesis are therefore expected to be more fruitful for re-conceptualizing parallels between scientific theorizing and biological evolution.

Suspicion of “physics envy” surrounds the standard statistical toolbox used in the empirical sciences, from biology to psychology. Mainstream methods in these fields, various lines of criticism point out, often fall short of the basic requirements of measurement. Quantitative scales are applied to variables that can hardly be treated as measurable magnitudes, like preferences or happiness; hypotheses are tested by comparing data with conventional significance thresholds that hardly mention effect sizes. This article discusses what I call “shmeasurement.” To “shmeasure” is to fail to apply quantitative tools to quantitative questions. We “shmeasure” when we try to measure what cannot be measured, or, conversely, when we ask binary questions of continuous measurements. Following the critics of standard statistical tools, it is argued that our statistical toolbox is indeed less concerned with the measurement of magnitudes than we take it to be. This article adds, however, that measurement is not all there is to scientific activity. Most techniques of proof do not resemble measurement as much as voting—a practice that makes frequent use of numbers, figures, or measurements, yet is not chiefly concerned with assessing quantities. Measurement is only one among three functions of the scientific toolbox, the other two being collating observations and deciding which hypotheses to relinquish. I thus make a plea for “shmeasurement”: the mismeasure of things starts to make more sense once we take into account the nonquantitative side of scientific practice