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How to build a machine with emergent representational content

Profile image of Mark BickhardMark Bickhard

1991

Abstract

Abstract Encodingism as a fundamental model of the nature of representation rests on a logical incoherence. This incoherence is manifested, among many other ways, in the empty symbol problem—the inability to provide any representational content to the symbols upon which cognitive science depends. Interactivism is model of representation that avoids the incoherences and aporias of encodingism—in fact, it explains them.

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Mark Bickhard

Journal of Experimental & Theoretical Artificial …, 1993

Thanks and acknowledgements are due to many people for comments, suggestions, questions, objections, and discussions that have contributed to this paper. These include: Abstract This article focuses on the problem of representational content. Accounting for representational content is the central issue in contemporary naturalism: it is the major remaining task facing a naturalistic conception of the world.

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Interactivism mechanized: bridging the gap between cognition, correspondence, and computation
Marcin Miłkowski

Phenomenology and Cognitive Sciences, 2025

This paper augments Mark Bickhard's interactivist model by incorporating mechanistic explanation, computation, and a non-encodingist correspondence. It argues for a mechanistic framework (drawing on the new mechanism's focus on organized entities and activities) that preserves core interactivist principles: process ontology, representation as emergent from interaction, rejection of encodingism, and the centrality of system-detectable error. However, grounding representational normativity solely in action outcomes or self-maintenance faces limitations. This paper proposes instead that normativity arises from epistemic norms within cognitive problem-solving, encompassing alethic, pragmatic, and economic dimensions addressed through bounded rationality under functional constraints. A novel framework integrating interactivism with this mechanistic view, computation, and infocorrespondence (a specific, non-encodingist correspondence based on information channels) is developed. This framework emphasizes semantic information processed by computational mechanisms, with inconsistency detection between information vehicles (leveraging informational redundancy) serving as a key system-accessible proxy for representational error (falsity). This synthesis offers a more robust account of error detection, the truth-success relationship, and the grounding of epistemic normativity, thereby enriching interactivist theory.

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From meaningful information to representations, enaction and cognition (2008 E-CAP08)
Christophe Menant

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 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 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. http://www.mdpi.org/entropy/papers/e5020193.pdf * Menant, C. 2005. "Information and Meaning in Life, Humans and Robots" FIS 2005 Paris. http://www.mdpi.org/fis2005/F.45.paper.pdf * 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.Proust.pdf

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