Despite recent major advances in the neuroscience underlying cognition, the processes of its emergence and evolution are far from being understood. In our view, current interrelated concepts of mind; knowledge; epistemology; perception; cognition and information fail to reflect the real dynamics of mental processes, their ontology and their logic. It has become routine to talk about information in relation to these processes, but there is no consensus about its most relevant qualitative and functional properties. We present a theory of human (...) cognition based on an ontology and epistemology of information and information processes originally proposed by Wu including an ontological doctrine of the different grades of information; and an informational epistemology based on a noegenesis of the doctrine of informational intermediaries that mediate between the cognitive subject and object. This theory is supported by the new, non-propositional logic proposed recently by Brenner. We demonstrate the utility of our approach for the reconceptualization of the virtual properties of reality and cognition. It is strongly anti-representationalist and can provide the basis for the integration of inputs from outside the brain into cognitive structures. For us, the philosophy of information is a metaphilosophy, implying major changes in both the content and methodology of standard philosophical disciplines. We suggest that this philosophy of information and our informational approach may help guide research in a number of current areas of cognitive science. (shrink)

In this paper, I examine the comparatively neglected intuition of production regarding causality. I begin by examining the weaknesses of current production accounts of causality. I then distinguish between giving a good production account of causality and a good account of production. I argue that an account of production is needed to make sense of vital practices in causal inference. Finally, I offer an information transmission account of production based on John Collier’s work that solves the primary weaknesses of current (...) production accounts: applicability and absences. (shrink)

Current research in molecular epidemiology uses biomarkers to model the different disease phases from environmental exposure, to early clinical changes, to development of disease. The hope is to get a better understanding of the causal impact of a number of pollutants and chemicals on several diseases, including cancer and allergies. In a recent paper Russo and Williamson address the question of what evidential elements enter the conceptualisation and modelling stages of this type of biomarkers research. Recent research in causality has (...) examined Ned Hall’s distinction between two concepts of causality: production and dependence. In another recent paper, Illari examined the relatively under-explored production approach to causality, arguing that at least one job of an account of causal production is to illuminate our inferential practices concerning causal linking. Illari argued that an informational account solves existing problems with traditional accounts. This paper follows up this previous work by investigating the nature of the causal links established in biomarkers research. We argue that traditional accounts of productive causality are unable to provide a sensible account of the nature of the causal link in biomarkers research, while an informational account is very promising. (shrink)

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at (...) this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. (shrink)