Our brains make up a series of signs and are engaged in making or manifesting or reacting to a series of signs: through this semiotic activity they are at the same time engaged in “being minds” and so in thinking intelligently. An important effect of this semiotic activity of brains is a continuous process of “externalization of the mind” that exhibits a new cognitive perspective on the mechanisms underling the semiotic emergence of abductive processes of meaning formation. To illustrate this (...) process I will take advantage of the analysis of some aspects of the cognitive interplay between internal and external representations. I consider this interplay critical in analyzing the relation between meaningful semiotic internal resources and devices and their dynamical interactions with the externalized semiotic materiality suitably stocked in the environment. Hence, minds are material, “extended” and artificial in themselves. A considerable part of human abductive thinking is occurring through an activity consisting in a kind of reification in the external environment and a subsequent re-projection and reinterpretation through new configurations of neural networks and chemical processes. I also illustrate how this activity takes advantage of hybrid representations and how it can nicely account for various processes of creative and selective abduction, bringing up the question of how multimodal aspects involving a full range of sensory modalities are important in hypothetical reasoning. (shrink)

Modelers are tackling ever more complex systems with the aid of computation. Model-based inferences can play a key role in their ability to handle complexity and produce reliable or informative models. We study here the role of model-based inference in the modern field of computational systems biology. We illustrate how these inferences operate and analyze the material and theoretical bases or conditions underlying their effectiveness. Our investigation reiterates the significance and centrality of model-based reasoning in day-to-day problem-solving practices, and the (...) role that debugging processes of partial or incomplete models can play in scientific inference and scientific discovery, particularly with respect to complex systems. We present several deeper implications such an analysis has for philosophy of science regarding the role of models in scientific practice. (shrink)