How can new drug lead suggestions beinferred from neurophysiological models? This paperaddresses this question based on a case study ofresearch into Parkinson''s disease at the GroningenUniversity Department of Pharmacy. It is argued thatneurophysiological box-and-arrow models can beunderstood as qualitative differential equationmodels. An inference task is defined to helpunderstand and possibly aid the discovery andexplanation of new drug lead suggestions.

Following Mayr (1961) evolutionary biologists often maintain that the hallmark of biology is its evolutionary perspective. In this view, biologists distinguish themselves from other natural scientists by their emphasis on why-questions. Why-questions are legitimate in biology but not in other natural sciences because of the selective character of the process by means of which living objects acquire their characteristics. For that reason, why-questions should be answered in terms of natural selection. Functional biology is seen as a reductionist science that applies (...) physics and chemistry to answer how-questions but lacks a biological point of view of its own. In this paper I dispute this image of functional biology. A close look at the kinds of issues studied in biology and at the way in which these issues are studied shows that functional biology employs a distinctive biological perspective that is not rooted in selection. This functional perspective is characterized by its concern with the requirements of the life-state and the way in which these are met. (shrink)

Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. I maintain that these differences lead to important consequences when we analyze computational AI programs, such as (...) AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by _locked_ abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning, because they are limited in what I call eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead _unlocked_. (shrink)

Part I Introduction The specific problem adressed in this thesis is: what is the rational use of theory and experiment in the process of scientific discovery, in theory and in the practice of drug research for Parkinson’s disease? The thesis aims to answer the following specific questions: what is: 1) the structure of a theory?; 2) the process of scientific reasoning?; 3) the route between theory and experiment? In the first part I further discuss issues about rationality in science as (...) introduction to part II, and I present an overview of my case-study of neuropharmacology, for which I interviewed researchers from the Groningen Pharmacy Department, as an introduction to part III. Part II Discovery In this part I discuss three theoretical models of scientific discovery according to studies in the fields of Logic, Cognition, and Computation. In those fields the structure of a theory is respectively explicated as: a set of sentences; a set of associated memory chunks; and as a computer program that can generate the observed data. Rationality in discovery is characterized by: finding axioms that imply observation sentences; heuristic search for a hypothesis, as part of problem solving, by applying memory chunks and production rules that represent skill; and finding the shortest program that generates the data, respectively. I further argue that reasoning in discovery includes logical fallacies, which are neccesary to introduce new hypotheses. I also argue that, while human subjects often make errors in hypothesis evaluation tasks from a logical perspective, these evaluations are rational given a probabilistic interpretation. Part III Neuropharmacology In this last part I discusses my case-study and a model of discovery in a practice of drug research for Parkinson’s disease. I discuss the dopamine theory of Parkinson’s disease and model its structure as a qualitative differential equation. Then I discuss the use and reasons for particular experiments to both test a drug and explore the function of the brain. I describe different kinds of problems in drug research leading to a discovery. Based on that description I distinguish three kinds of reasoning tasks in discovery, inference to: the best explanation, the best prediction and the best intervention. I further demonstrate how a part of reasoning in neuropharmacology can be computationally modeled as qualitative reasoning, and aided by a computer supported discovery system. (shrink)