Cognition as management of meaningful information. Proposal for an evolutionary approach.

摘要

Humans are cognitive entities. Our behaviors and ongoing interactions with the environment are\udthreaded with creations and usages of meaningful information, be they conscious or unconscious.\udAnimal life is also populated with meaningful information related to the survival of the individual\udand of the species. The meaningfulness of information managed by artificial agents can also be\udconsidered as a reality once we accept that the meanings managed by an artificial agent are\udderived from what we, the cognitive designers, have built the agent for.\udThis rapid overview brings to consider that cognition, in terms of management of meaningful\udinformation, can be looked at as a reality for animal, humans and robots. But it is pretty clear\udthat the corresponding meanings will be very different in nature and content. Free will and selfconsciousness\udare key drivers in the management of human meanings, but they do not exist for\udanimals or robots. Also, staying alive is a constraint that we share with animals. Robots do not\udcarry that constraint.\udSuch differences in meaningful information and cognition for animal, humans and robots could\udbring us to believe that the analysis of cognitions for these three types of agents has to be done\udseparately. But if we agree that humans are the result of the evolution of life and that robots are a\udproduct of human activities, we can then look at addressing the possibility for an evolutionary\udapproach at cognition based on meaningful information management. A bottom-up path would\udbegin by meaning management within basic living entities, then climb up the ladder of evolution\udup to us humans, and continue with artificial agents.\udThis is what we propose to present here: address an evolutionary approach for cognition, based\udon meaning management using a simple systemic tool.\udWe use for that an existing systemic approach on meaning generation where a system submitted\udto a constraint generates a meaningful information (a meaning) that will initiate an action in order\udto satisfy the constraint [1,2]. The action can be physical, mental or other.\udThis systemic approach defines a Meaning Generator System (MGS). The simplicity of the MGS\udmakes it available as a building block for meaning management in animals, humans and robots.\udContrary to approaches on meaning generation in psychology or linguistics, the MGS approach is\udnot based on human mind. To avoid circularity, an evolutionary approach has to be careful not to\udinclude components of human mind in the starting point.\udThe MGS receives information from its environment and compares it with its constraint. The\udgenerated meaning is the connection existing between the received information and the\udconstraint. The generated meaning is to trigger an action aimed at satisfying the constraint. The\udaction will modify the environment, and so the generated meaning. Meaning generation links\udagents to their environments in a dynamic mode. The MGS approach is triadic, Peircean type.\udThe systemic approach allows wide usage of the MGS: a system is a set of elements linked by a\udset of relations. Any system submitted to a constraint and capable of receiving information from\udits environment can lead to a MGS. Meaning generation can be applied to many cases, assuming\udwe identify clearly enough the systems and the constraints. Animals, humans and robots are then\udagents containing MGSs. Similar MGSs carrying different constraints will generate different\udmeanings. Cognition is system dependent.\udWe first apply the MGS approach to animals with “stay alive” and “group life” constraints. Such\udconstraints can bring to model many cases of meaning generation and actions in the organic\udworld. However, it is to be highlighted that even if the functions and characteristics of life are well\udknown, the nature of life is not really understood. Final causes are difficult to integrate in our\udtoday science. So analyzing meaning and cognition in living entities will have to take into account\udour limited understanding about the nature of life. Ongoing research on concepts like autopoiesis\udcould bring a better understanding about the nature of life [3].\udWe next address meaning generation for humans. The case is the most difficult as the nature of\udhuman mind is a mystery for today science and philosophy. The natures of our feelings, free will\udor self-consciousness are unknown. Human constraints, meanings and cognition are difficult to\uddefine. Any usage of the MGS approach for humans will have to take into account the limitations\udthat result from the unknown nature of human mind.\udWe will however present some possible approaches to identify human constraints where the MGS\udbrings some openings in an evolutionary approach [4, 5]. But it is clear that the better human\udmind will be understood, the more we will be in a position to address meaning management and\udcognition for humans. Ongoing research activities relative to the nature of human mind cover\udmany scientific and philosophical domains [6].\udThe case of meaning management and cognition in artificial agents is rather straightforward with\udthe MGS approach as we, the designers, know the agents and the constraints. In addition, our\udevolutionary approach brings to position notions like artificial constraints, meaning and autonomy\udas derived from their animal or human source.\udWe next highlight that cognition as management of meaningful information by agents goes\udbeyond information and needs to address representations which belong to the central hypothesis\udof cognitive sciences.\udWe define the meaningful representation of an item for an agent as being the networks of\udmeanings relative to the item for the agent, with the action scenarios involving the item.\udSuch meaningful representations embed the agents in their environments and are far from the\udGOFAI type ones [4]. Meanings, representations and cognition exist by and for the agents.\udWe finish by summarizing the points presented and highlight some possible continuations.\ud[1] C. Menant "Information and Meaning" http://cogprints.org/3694/\ud[2] C. Menant “Introduction to a Systemic Theory of Meaning” (short paper)\udhttp://crmenant.free.fr/ResUK/MGS.pdf\ud[3] A. Weber and F. Varela “Life after Kant: Natural purposes and the autopoietic foundations of\udbiological individuality”. Phenomenology and the Cognitive Sciences 1: 97–125, 2002.\ud[4] C. Menant "Computation on Information, Meaning and Representations. An Evolutionary\udApproach" http://www.idt.mdh.se/ECAP-2005/INFOCOMPBOOK/CHAPTERS/10-Menant.pdf\udhttp://crmenant.free.fr/2009BookChapter/C.Menant.211009\ud[5] C. Menant "Proposal for a shared evolutionary nature of language and consciousness"\udhttp://cogprints.org/7067/\ud[6] Philpapers “philosophy of mind” http://philpapers.org/browse/philosophy-of-mind