E-CAP 08 C. Menant 1 From meaningful information to representations, enaction and cognition E-CAP 08 Christophe Menant Bordeaux, France – June 2008 Extended Abstract The notions of information, representation and enaction entertain historical and complex relations with cognition. Historical relations because representational structures belong to the central hypothesis of cognitive sciences. Complex relations because cognitive sciences apply the notion of representation to animals, humans and robots, and also because the enactive approach tends to disregard the GOFAI type of representations. In this wide horizon of relations, we propose to look at a systemic approach that could bring up a common denominator for information and representations in the build up of cognition, and also keep a link with the enactive approach. Our purpose is to show that systems submitted to constraints can generate meaningful information to maintain their natures, and consequently build up meaningful representations that have some compatibility with the enactive approach. Such a systemic approach to the notion of meaningful information could then make available a link between enaction and meaningful representations. The first part of the presentation is about reminding that cognition does not exist per se, but is related to the system that builds it. We look at cognition as constituted by dynamic meaningful representations built up by systems that have constraints to satisfy in their environments. Cognition is considered here at the level of the system that builds it and uses it in order to maintain its nature in its environment. Such a systemic approach fits with evolution. Organisms build representations to cope with survival constraints (frogs build representations of moving black dots in order to satisfy food constraints). Humans build representations and cognition to satisfy constraints that are conscious and unconscious. Artificial systems can use representations and cognition to run activities related to constraints implemented by the designers or coming from the environment (a goal to reach being considered as a constraint to satisfy). In the second part of the presentation we define what are a meaningful information and a representation for a system submitted to a constraint in its environment, and we link these to the enactive approach. We define a meaningful information (a meaning) as an information generated by a system submitted to a constraint when it receives an external information that has a connection with the constraint. The meaning is precisely that connection. The meaning belongs to the interactions that link the system to its environment. The function of the meaning is to participate to the determination of an action that E-CAP 08 C. Menant 2 will be implemented in order to satisfy the constraint. (Menant, 2003). The satisfaction of the constraint goes with maintaining the nature of the system in its environment. A Meaning Generator System (MGS) is defined correspondingly. It is a building block for higher level systems. We present some characteristics of the MGS (groundings of a meaning, domain of efficiency and transfer of meanings, networking of meanings, evolutionary usage). The MGS approach is close to a simplified version of the Peircean triadic theory of signs (Menant, 2003, 2005 ). We define the representation of an item for a system as being the dynamic set of meaningful information corresponding to the item for the system in its environments (an elementary representation being made of a single meaningful information). These representations link the system to its environment by their meaningful components related to the nature of the system. These representations are different from the GOFAI ones. The possibilities for linking these notions of meaning and representation with the enactive approach come from the structure of the MGS: the need for an action is the cause of the meaning generation by and for the system. The action on the environment is for the system to maintain its nature (its identity). The MGS links together the generation of meaningful representations, the nature of the system, and the interactions with the environment. This can be considered as close to enacting a world by meaning generation (Di Paolo and all 2007), and to the enactive concept of sense making (De Jaegher, Di Paolo 2007). We propose that basing the definition of a representation on the notion of meaningful information generated by a system submitted to a constraint can open a way for making the notion of representation compatible with the enactive approach. In the third part of the presentation, we consider some cases of meaningful information and representations for organisms and for robots. Regarding organisms, the MGS can be used in an evolutionary context by looking at the evolution of the systems and of the constraints. Purpose is to modelize the generation of meanings and of representations in order to make available a tool usable for different levels of evolution, as evolution has a place in cognitive sciences (Proust, 2007). Constraints for basic life are survival constraints (individual and species). Group life constraints are also to be considered. Reaching the level of humans in evolution brings in new constraints that cannot be clearly identified as they have to take into account human consciousness which is today a mystery (the "hard problem"). On an evolutionary standpoint, human constraints come in addition to the ones existing for non human organisms. We can make some hypothesis on the nature of human constraints (Maslow pyramid based constraints, anxiety limitation...). For robots, the MGS is initially based on the design of the robot. The meaning generated within a robot is initially derived from the constraints implemented by the designer and from the environment. But some non calculable or non predictable evolutions of the E-CAP 08 C. Menant 3 robot can introduce meanings that look proper to the robot. This last point can be linked to the notion of autonomy in robots. In such examples, the dynamic management of meanings thru the MGSs in their environments keeps the link with the enactive approach. We finish the presentation by summarising the points addressed and by proposing several continuations. * De Jaegher, H. and Di Paolo E. 2007. "Participatory Sense-Making An Enactive Approach to Social Cognition" To appear in Phenomenology and the Cognitive Sciences, 2007 http://www.informatics.sussex.ac.uk/users/ezequiel/DeJaegher&DiPaolo2007 .pdf * Di Paolo, E., Rohde, M., De Jaegher, H. 2007 "Horizons for the Enactive Mind: Values, Social Interaction, and Play". To appear in Enaction: Towards a New Paradigm for Cognitive Science, J. Stewart, O. Gapenne, and E. A. Di Paolo (Eds), Cambridge, MA: MIT Press, forthcoming http://www.informatics.sussex.ac.uk/users/ezequiel/DiPaoloetal_csrp587.pdf * Menant, C. 2003. "Information and Meaning" Entropy 2003, 5, 193-204: https://philpapers.org/rec/MENIAM-2, * Menant, C. 2005. "Information and Meaning in Life, Humans and Robots" FIS 2005 Paris: https://philpapers.org/rec/MENIAM * Proust, J. 2007. "Why evolution has to matter to cognitive psychology and to philosophy of mind", Biological Theory, 2007, 2. http://jeannicod.ccsd.cnrs.fr/docs/00/13/93/30/PDF/Biological_Theory.Prous t.pdf E-CAP 08 C. Menant 4 Presentation 1) Cognition process. System submitted to constraints a) Cognition, Cognitive Systems. Definitions: * Cognition as a cycle of anticipation, assimilation, and adaptation embedded in, contributing to, and benefiting from a continuous process of action and perception. (D. Vernon 2006) * Cognition for animals, humans and robots. * Cognitive process uses transfer of information b) Cognition and systems submitted to constraints. * No cognition per se. *"earliest living organisms ... subject to a constraint of viability" (Stewart, J. 1996) * Cognition: system/agent that has constraints to satisfy in its environment. * system – constraint – perception/action Mouse – Survival Seen cat as danger, actions Bicycle rider – Equilibrium Pressure on palm of hand on handle bar, actions Researcher Need to understand/discover Conference as new data, actions Car driver In time arrival Seen traffic jam as delay, actions Robot Goal to reach Identified light as direction, actions E-CAP 08 C. Menant 5 2) Constraints. Meaning generation. Meaningful representation. Enaction a) Constraints. Meaning generation (Menant 2003, 2005) Identification of the connection between received information and constraint (Acid in water) (Paramecium) Constraint of the system (To stay alive) (To move away from acid area) Meaningful information (Acid non compatible with staying alive) Incident information (Connection between acid and staying alive) Determination of action satisfying the constraint of the system SYSTEM SUBMITTED TO A CONSTRAINTReception * Paramecium submitted to a "stay alive" constraint (Constraint of the system). * Acid non compatible with staying alive (Meaningful information). * Paramecium moves away from acid area (Action to satisfy the constraint). * Meaning by the system and for the system. * A meaning is a meaningful information that is created by a system submitted to a constraint when it receives an external information that has a connection with the constraint. The meaning is formed of the connection existing between the received information and the constraint of the system. The function of the meaning is to participate to the determination of an action that will be implemented in order to satisfy the constraint of the system. E-CAP 08 C. Menant 6 b) Meaning Generator System (Menant 2003, 2005) * MGS Properties Constructivist approach. Embedded in perception/action. Dynamically links a system to its environment for constraint satisfaction. Building block. Links with memory, simulation, scenarios, other MGSs, .... Can modify functions/identity/constraints of higher level systems. Gives meaning for the system to its environment. Groundings in/out. Constraint linked to the nature of system. Transmission and networkings of meanings. Evolutionary approach. Close to a simplified version of the Peircean triadic approach on sign. * Meaning generation is part of cognition. E-CAP 08 C. Menant 7 c) Representation as set of meaningful information * "Representations do not exist independent of individuals for whom they are "meaningful"." (Scheutz, M. 1999) * Representation as a set of meanings relative to the represented item: (ex: representation of a cat for a mouse) Real time sensory meaning of item. (seen cat is a danger) Available action scenarios relative to item, with real time update. (stand still, run away. Options updatable by cat movements) Memorized past experiences of item. (new cat or known fast/dumb cat. Cat's habits, past experiences) Anticipated evolutions of item (simulation of direction options) Covers what is represented and the content of the representation. d) Meaning Generation, Enactive approach and Representations * Meaning generation is part of enaction * "five key "pillars" to the enactive approach: A dynamical systems perspective and emergence, embodiment, biological autonomy, "sense-making" (the creation of meaning), and experience" (McGann, M. 2006) * "sense-making, which we identified as one of the central concepts of the enactive approach" (Di Paolo and all, 2007) * " Weber & Varela (2002) mention valence to refer to initial forms of meaninggeneration in the autopoietic system: "Stimuli from outside enter the sphere or relevance of such a unit only by their existential meaning for the keeping of the process of self-establishment." (Colombetti, G. 2008). * "in an enactive perspective, meaning is inseparable from the whole of contextdependent, life motivated, embodied activity, without being at all a hazy concept beyond the reach of scientific understanding." (Di Paolo and all, 2007) * Representations, as made of meaningful information, are to be part of enaction E-CAP 08 C. Menant 8 3) Examples of meaningful information/representations 4) Conclusion and continuations a) Summary of points addressed: Cognition is related to systems having constraints to satisfy in an environment. Meaning as generated by a system for constraint satisfaction. Part of cognition. Meaning Generator System: building block linking system to its environment. Representation: integrated set of meanings relative to the represented item. (Senses, memory, scenarios, anticipation, ...). Meaningful information/representations to be part of Enaction. b) Continuation: Enaction and first person experience. Enaction and agency, self, identity, autonomy. Position of phenomenology in cognitive sciences. Evolution from matter to unicellular life (constraints). Nature and content of human specific constraints. Meaning generation at embodiment/enactive levels. E-CAP 08 C. Menant 9 References * Colombetti, G. 2008 « Enaction, sense-making and emotion » (To appear in Stewart, J., Gapenne, O. & Di Paolo, E. (eds). Enaction: Towards a New Paradigm for Cognitive Science. Cambridge MA: MIT Press. 2008. Forthcoming.) . Enaction, Sense-Making and Emotion * Di Paolo, E., Rohde, M., and De Jaegher, H. 2007. "Horizons for the Enactive Mind: Values, Social Interaction, and Play" To appear in Enaction: Towards a New Paradigm for Cognitive Science, J. Stewart, O. Gapenne, and E. A. Di Paolo (Eds), Cambridge, MA: MIT Press, forthcoming. * McGann, M. "What is "Enactive" Cognition?" euCognition http://www.eucognition.org/wiki/index.php?title=Cognition_Briefings * Menant, C. 2003 " Information and Meaning". Entropy 2003, 5, 193-204. https://philpapers.org/rec/MENIAM-2 * Menant, C. 2005 " Information and Meaning in Life, Humans and Robots": * Foundations of Information Sciences (Paris, July 2005). https://philpapers.org/rec/MENIAM * Scheutz, M. (1999). "The Ontological Status of Representations". In A. Riegler, M. Peschl \& A. von Stein (eds.) Understanding Representation in the Cognitive Sciences. Plenum Academic / Kluwer Publishers: Holland, 33--38. * Stewart, J. (1996). "Cognition = Life: Implications for higher-level cognition". Behavioural Processes 35: 311-326. * Vernon, D. "What is cognition ? One view of cognitive systems" euCognition Wiki (http://www.eucognition.org/wiki/index.php?title=What_is_Cognition%3F_One_View_of_C ognitive_Systems)