Cognitive ethologists are in need of a good theoretical framework for attributing intentional states. Heyes and Dickinson (1990) present criteria that they claim are necessary for an intentional explanation of behavior to be justified. They suggest that questions of intentionality can only be investigated under controlled laboratory conditions and they apply their criteria to laboratory experiments to argue that the common behavior of approaching food is not intentional in most animals. We dispute the details of their argument and interpretation of (...) the laboratory experiments. While criteria such as those suggested have a role to play in comparative studies of cognition, both laboratory and field studies are important for assessing the applicability of intentional explanations across different taxa. (shrink)

We propose here to clarify some of the relations existing between information and meaning by showing how meaningful information can be generated by a system submitted to a constraint. We build up definitions and properties for meaningful information, a meaning generator system and the domain of efficiency of a meaning (to cover cases of meaningful information transmission). Basic notions of information processing are used.

In this paper, we use the Meaning Generator System (MGS) presented at Gathering in Biosemiotics 2 [1] to analyse the evolution of meaningful information generation through different steps of the evolution of life. Taking as a starting point the usage of MGS for vital constraint satisfaction in basic life (paramecium), we develop its application for more complex living elements up to the case of non-human primate. The thread we follow is relative to the identification of new constraints that can appear (...) through evolution of life, and correspondingly participate to generation of new types of meaningful information. We show that beside the complexification of vital constraints to be satisfied, and in addition to the corresponding enrichment of their satisfaction processes, there is a step in evolution that naturally introduces some specific new constraint in living elements. This step is the one corresponding to the performance of self-representation. Self-representation appeared in evolution at the level of non-human primates. We present the content of self-representation and show that it has a direct consequence on the living element in terms of a new type of constraint to be satisfied. We show that this new constraint participates to the generation of a new set of meaningful information via the MGS, and that the satisfaction of the constraint introduces some natural formulation of emotion generation during the evolution of life. [1]. (shrink)

Understanding computation as “a process of the dynamic change of information” brings to look at the different types of computation and information. Computation of information does not exist alone by itself but is to be considered as part of a system that uses it for some given purpose. Information can be meaningless like a thunderstorm noise, it can be meaningful like an alert signal, or like the representation of a desired food. A thunderstorm noise participates to the generation of meaningful (...) information about coming rain. An alert signal has a meaning as allowing a safety constraint to be satisfied. The representation of a desired food participates to the satisfaction of some metabolic constraints for the organism. Computations on information and representations will be different in nature and in complexity as the systems that link them have different constraints to satisfy. Animals have survival constraints to satisfy. Humans have many specific constraints coming in addition. And computers will compute what the designer and programmer ask for. We propose to analyze the different relations between information, meaning and representation by taking an evolutionary approach on the systems that link them. Such a bottom-up approach allows starting with simple organisms and avoids an implicit focus on humans, which is the most complex and difficult case. To make available a common background usable for the many different cases, we use a systemic tool that defines the generation of meaningful information by and for a system submitted to a constraint [Menant, 2003]. This systemic tool allows to position information, meaning and representations for systems relatively to environmental entities in an evolutionary perspective. We begin by positioning the notions of information, meaning and representation and recall the characteristics of the Meaning Generator System (MGS) that link a system submitted to a constraint to its environment. We then use the MGS for animals and highlight the network nature of the interrelated meanings about an entity of the environment. This brings us to define the representation of an item for an agent as being the network of meanings relative to the item for the agent. Such meaningful representations embed the agents in their environments and are far from the Good Old Fashion Artificial Intelligence type ones. The MGS approach is then used for humans with a limitation resulting of the unknown nature of human consciousness. Application of the MGS to artificial systems brings to look for compatibilities with different levels of Artificial Intelligence (AI) like embodied-situated AI, the Guidance Theory of Representations, and enactive AI. Concerns relative to different types of autonomy and organic or artificial constraints are highlighted. We finish by summarizing the points addressed and by proposing some continuations. (shrink)