Group Report: The Behavior of Natural and Artificial Systems: Solutions to Functional Demands (original) (raw)
Related papers
Title: Affectivity and the Mechanization of Living Organisms
There is a contemporary enthusiasm for the mechanistic conception of things. This enthusiasm is expressed across different domains by transhumanism, artificial intelligence, computation, mechanisms, industrialization, etc. Considering this enthusiasm, it might be assumed that an organic conception is either settled or outdated. However, it is far from being the case. When it comes to the human organism, the analogy or metaphor of a computational machine is often presupposed. In this case, what distinguishes a mechanical system from an organic system? Is cognition dependent or independent of the living world? Is there continuity or discontinuity between neurocognitive systems and living systems? My goal is to review the situation regarding the study of neurocognition in relation to the living world and to analyze the plausibility of affectivity as the feature of life. Therefore, my investigation covers the fields of neuroscience, cognitive science and philosophy of mind. First, I establish an overview of the study of neurocognition with its prominent approaches. Afterwards, I analyze affectivity which seems to be a relevant proposition in order to distinguish mechanical systems from organic systems. Through my investigation, I esteem being able to provide an enlightening portrait of the field of neurocognition in connection with life.
Affectivity and the Mechanization of Living Organisms
2021
There is a contemporary enthusiasm for the mechanistic conception of things. This enthusiasm is expressed across different domains by transhumanism, artificial intelligence, computation, mechanisms, industrialization, etc. Considering this enthusiasm, it might be assumed that an organic conception is either settled or outdated. However, it is far from being the case. When it comes to the human organism, the analogy or metaphor of a computational machine is often presupposed. In this case, what distinguishes a mechanical system from an organic system? Is cognition dependent or independent of the living world? Is there continuity or discontinuity between neurocognitive systems and living systems? My goal is to review the situation regarding the study of neurocognition in relation to the living world and to analyze the plausibility of affectivity as the feature of life. Therefore, my investigation covers the fields of neuroscience, cognitive science and philosophy of mind. First, I establish an overview of the study of neurocognition with its prominent approaches. Afterwards, I analyze affectivity which seems to be a relevant proposition in order to distinguish mechanical systems from organic systems. Through my investigation, I esteem being able to provide an enlightening portrait of the field of neurocognition in connection with life.
A biological perspective on autonomous agent design
Robotics and Autonomous Systems, 1990
The inability of current "classical" AI systems to handle unconstrained interaction with tbe real world has recently lead to a search for new control architectures for autonomous agents. We argue that simpler natural animals already exhibit most of the properties required by an autonomous agent, and suggest that designers of autonomous agents should draw directly upon the neural basis of behavior in these animals. The relevant behavioral and neurobiological literature is briefly reviewed. An artificial nervous system for controlling the behavior of a simulated insect is then developed. The design of this artificial insect is based in part upon specific behaviors and neural circuits from several natural animals. The insect exhibits a number of characteristics which are remarkably reminiscent of natural animal behavior.
Proposing a new focus for the study of natural and articial cognitive systems
2002
In the study of systems the function of the system is often a good hint to how it works. In the following paper I would like to suggest that in studying or modeling a cognitive system our pre -knowledge of their functions should be treated carefully. We should focus on the statistical distribution of the system's environment and the ways this distribution affects the behavior and development of the cognitive system. I will show an example of how such a focus changes the view of the immune system. I would also like to show how this new outlook on the study of cognitive systems could affect attempts at creating artificial cognitive system.
2011
Traditionally artificial intelligence has been focused on attempting to replicate the cognitive abilities of the human brain. Alternative approaches to artificial intelligence take inspiration from a wider range of biological processes such as evolution, networks of neurons and learning. In recent decades there has been an explosion of new artificial intelligence methods inspired by even more biological processes, such as the immune system, colonies of ants, physical embodiment, development, coevolution, self-organization, and behavioral autonomy, to mention just a few.
Proposing a new focus for the study of natural and artificial cognitive systems
2002
In the study of systems the function of the system is often a good hint to how it works. In the following paper I would like to suggest that in studying or modeling a cognitive system our pre -knowledge of their functions should be treated carefully. We should focus on the statistical distribution of the system's environment and the ways this distribution affects the behavior and development of the cognitive system. I will show an example of how such a focus changes the view of the immune system. I would also like to show how this new outlook on the study of cognitive systems could affect attempts at creating artificial cognitive system.
Just how intelligent are human beings? Are we capable of comprehending the extent of animal – and human – intellect? What does it mean to be intelligent? Does it require agency and autonomy? Do animals – and humans – possess 'free will'? And do intelligence and free will lead to social progress? Classic debates on the issue of intelligence and free will are rapidly evolving with new and fascinating findings from the frontiers of neuroscience. This course will cover the science and philosophy of animal learning and cognition by looking at how the brain transduces environmental energy into electrochemical signals and reorganizes itself in response to its environment producing intelligent behaviour. We will begin with Franz de Wall's exploration of evolutionary changes in animals that lead to intelligence, including learning, reasoning, problem solving, planning, empathy, and even a sense of justice. Next we will reconsider current knowledge of the molecular mechanisms underlying cognition and arguments by Raymond Tallis that they are inadequate in explaining animal consciousness. Finally, we will put intelligence and free will to the test and debate John Gray's thesis that, outside of science, the idea of human progress is a myth and that " knowledge does not make us free. " In addition to the critical evaluation of research methodology, issues of medical, ethical, social, political significance will be discussed. (3 Lecture hours; 0.5 credit) We will address these questions using both Socratic and Scientific methods with the following objectives: 1. To develop a multidisciplinary perspective of human cognition and behaviour, from basic molecular processes within neurons to the neural pathways that regulate the body and which determine individual action and social interaction. 2. To develop an appreciation of how the neurophysiology of the brain encodes and integrates information about the physical, mental, and social domains of the human condition, thus creating neural 'maps of meaning', and the implications for individuals and society. 3. To develop and apply the principles of critical thinking to scientific and other claims, including the ability to make objective, evidence-based arguments and identify and refute illogical arguments by challenging the assumptions underlying various belief systems. 4. To develop transferrable skills essential for career success, including critical analysis, knowledge synthesis and application, collaboration, and oral and written communication skills.
Animal Minds and Animal Emotions1
American Zoologist, 2000
The possibility of conscious experiences of emotions in non-human animals has been much less explored than that of conscious experiences associated with carrying out complex cognitive tasks. However, no great cognitive powers are needed to feel hunger or pain and it may be that the capacity to feel emotions is widespread in the animal kingdom. Since plants can show surprisingly sophisticated ''choice'' and ''decision-making'' mechanisms and yet we would not wish to imply that they are conscious, attribution of emotions to animals has to be done with care. Whether or not an animal possesses anticipatory mechanisms associated with positive and negative reinforcement learning may be a guide as to whether it has evolved emotions.