Modeling Life as Cognitive Info-Computation

This article presents a naturalist approach to cognition understood as a network of info-computational, autopoietic processes in living systems. It provides a conceptual framework for the unified view of cognition as evolved from the simplest to the most complex organisms, based on new empirical and theoretical results. It addresses three fundamental questions: what cognition is, how cognition works and what cognition does at different levels of complexity of living organisms. By explicating the info-computational character of cognition, its evolution, agent-dependency and generative mechanisms we can better understand its life-sustaining and life-propagating role. The info-computational approach contributes to rethinking cognition as a process of natural computation in living beings that can be applied for cognitive computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201

Robot Consciousness: Physics and Metaphysics Here and Abroad

Interest has been renewed in the study of consciousness, both theoretical and applied, following developments in 20th and early 21st-century logic, metamathematics, computer science, and the brain sciences. In this evolving narrative, I explore several theoretical questions about the types of artificial intelligence and offer several conjectures about how they affect possible future developments in this exceptionally transformative field of research. I also address the practical significance of the advances in artificial intelligence in view of the cautions issued by prominent scientists, politicians, and ethicists about the possible dangers of such sufficiently advanced general intelligence, including by implication the search for extraterrestrial intelligence

The evolution of rhythmic cognition: New perspectives and technologies in comparative research

Music is a pervasive phenomenon in human culture, and musical rhythm is virtually present in all musical traditions. Research on the evolution and cognitive underpinnings of rhythm can benefit from a number of approaches. We outline key concepts and definitions, allowing fine-grained analysis of rhythmic cognition in experimental studies. We advocate comparative animal research as a useful approach to answer questions about human music cognition and review experimental evidence from different species. Finally, we suggest future directions for research on the cognitive basis of rhythm. Apart from research in semi-natural setups, possibly allowed by “drum set for chimpanzees” prototypes presented here for the first time, mathematical modeling and systematic use of circular statistics may allow promising advances

a variational approach to niche construction

In evolutionary biology, niche construction is sometimes described as a genuine evolutionary process whereby organisms, through their activities and regulatory mechanisms, modify their environment such as to steer their own evolutionary trajectory, and that of other species. There is ongoing debate, however, on the extent to which niche construction ought to be considered a bona fide evolutionary force, on a par with natural selection. Recent formulations of the variational free-energy principle as applied to the life sciences describe the properties of living systems, and their selection in evolution, in terms of variational inference. We argue that niche construction can be described using a variational approach. We propose new arguments to support the niche construction perspective, and to extend the variational approach to niche construction to current perspectives in various scientific fields

A taxonomy of podcasts and its application to higher education

In this paper we address the uses of podcasts in higher education and we propose a taxonomy for podcasts. We describe results obtained within a study that is being conducted at the University of Minho, in Portugal, focusing on the use of podcasts and their implications towards learning in higher education. The project involves 6 lecturers from different scientific domains – Education, Humanities, Social Sciences, Engineering and Biology. These lecturers created 84 podcasts in order to support their undergraduate and master courses during the 1st and 2nd semesters of 2007/ 2008 and the 1st semester of 2008/ 2009. A total of 479 students - 372 undergraduate and 107 master students - were enrolled in 20 courses. Some students were not only podcasts listeners but they also had the challenge and the opportunity to create their own podcasts (34 episodes). Podcasts were classified in different types (Informative, Feedback, Guidelines and Authentic materials), styles (formal or informal), length (short, moderate or long), purpose and medium (audio or video), according to a taxonomy proposed by the authors. The majority of podcasts was Informative (76), followed by podcasts with Feedback (30), Guidelines (9) and Authentic materials (3). Most podcasts were short (102), mainly in informal style and only 21 were vodcasts. Students´ reactions about podcasts implementation in higher education revealed their acceptance of this new tool and their receptiveness to podcasting in other courses. The majority of students found podcasts a positive resource in learning, although they did not explore one of the main advantages of this technology – portability. Lecturers also found podcasting a useful resource for learning and recognized its great potential as a pedagogical tool but stressed that it is too time consuming

Marginal likelihoods in phylogenetics: a review of methods and applications

By providing a framework of accounting for the shared ancestry inherent to all life, phylogenetics is becoming the statistical foundation of biology. The importance of model choice continues to grow as phylogenetic models continue to increase in complexity to better capture micro and macroevolutionary processes. In a Bayesian framework, the marginal likelihood is how data update our prior beliefs about models, which gives us an intuitive measure of comparing model fit that is grounded in probability theory. Given the rapid increase in the number and complexity of phylogenetic models, methods for approximating marginal likelihoods are increasingly important. Here we try to provide an intuitive description of marginal likelihoods and why they are important in Bayesian model testing. We also categorize and review methods for estimating marginal likelihoods of phylogenetic models, highlighting several recent methods that provide well-behaved estimates. Furthermore, we review some empirical studies that demonstrate how marginal likelihoods can be used to learn about models of evolution from biological data. We discuss promising alternatives that can complement marginal likelihoods for Bayesian model choice, including posterior-predictive methods. Using simulations, we find one alternative method based on approximate-Bayesian computation (ABC) to be biased. We conclude by discussing the challenges of Bayesian model choice and future directions that promise to improve the approximation of marginal likelihoods and Bayesian phylogenetics as a whole.Comment: 33 pages, 3 figure

Learning Strategies in Coopetitive Environments

The objective of this chapter is to explore the learning strategies that can be deployed by firms in coopetitive configurations with no other choice than deploying an “adverse learning” mechanism to reach their customers through cooperation with their competitors. After exploring the mechanisms of asymmetric learning in a first section, the chapter adopts an ecological perspective (Hawley, 1950) in drawing parallels between animal organization and groups of firms in gaining a strategic advantage through asymmetric learning.coopetition; Learning Behavior; Learning Strategy.

Nature as a Network of Morphological Infocomputational Processes for Cognitive Agents

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted