The biological function of human reasoning abilities cannot be to improve shared knowledge. This is at best a side effect. A more plausible function of argumentation, and thus of reasoning, is to advertise one's ability to detect lies and errors. Such selfish behavior is closer to what we should expect from a naturally selected competence.

Episodic memory is certainly a unique endowment, but its primary purpose is something other than to provide raw material for creative synthesis of future scenarios. Remembered episodes are exactly those that are worth telling. The function of episodic memory, in my view, is to accumulate stories that are relevant to recount in conversation.

A modular analysis of spontaneous language use provides support for the existence of an identifiable step in language evolution, protolanguage. Our suggestion is that a grammarless form of expression would have evolved to signal unexpected events, a behavior still prevalent in our species. Words could not be so specific as to refer to whole, non-recurring, situations. They referred to elements such as objects or locations, and the communicated event was inferred metonymically. Compositionality was achieved, without syntax, through multi-metonymy, as words (...) referring to elements of the same situation were concatenated into proto-utterances. (shrink)

A modular analysis of spontaneous language use provides support for the existence of an identifiable step in language evolution, protolanguage. Our suggestion is that a grammarless form of expression would have evolved to signal unexpected events, a behavior still prevalent in our species. Words could not be so specific as to refer to whole, non-recurring, situations. They referred to elements such as objects or locations, and the communicated event was inferred metonymically. Compositionality was achieved, without syntax, through multi-metonymy, as words (...) referring to elements of the same situation were concatenated into proto-utterances. (shrink)

Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the (...) form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection. (shrink)

Et si certaines entités vivantes n'étaient pas matérielles? Potentiellement éternelles, en lutte pour la survie, elles évoluent. Elles constituent ce qui unit les êtres à travers le temps. Elles sont le fil de la vie. Ces entités vivantes immatérielles sont des informations. Elles existent à travers nous, dans nos gènes, dans notre culture, dans nos écosystèmes. La vie produit l'information, lit l'information et se définit par l'information qu'elle porte. Ce livre nous aide à comprendre le monde vivant d'une manière toute (...) nouvelle! Il est le résultat de discussions passionnées entre trois chercheurs qui, chacun à sa manière, étaient parvenus au même questionnement à propos de la nature. Ils nous proposent une nouvelle description du vivant, où la lutte pour l'existence n'est pas celle des êtres, mais des messages qui passent à travers eux et dont ils sont les hôtes éphémères. (shrink)

Predicates involved in language and reasoning are claimed to radically differ from categories applied to objects. Human predicates are the cognitive result of a contrast between perceived objects. Object recognition alone cannot generate such operations as modification and explicit negation. The mechanism studied by Hurford constitutes at best an evolutionary prerequisite of human predication ability.

Selection through iterated learning explains no more than other non-functional accounts, such as Universal Grammar (UG), why language is so well designed for communicative efficiency. It does not predict several distinctive features of language, such as central embedding, large lexicons, or the lack of iconicity, which seem to serve communication purposes at the expense of learnability.

Postulating a variety of mutually isolated thought domains for prelinguistic creatures is both unparsimonious and implausible, requiring unexplained parallel evolution of each separate module. Furthermore, the proposal that domain-general concepts are not accessible without prior exposure to phonetically realized human language utterances cannot be implemented by any concept-acquisition mechanism.

This symposium discusses J.-L. Dessalles's account of the evolution of language, which was presented in Why we Talk (OUP 2007).

This symposium discusses J.-L. Dessalles's account of the evolution of language, which was presented in Why we Talk (OUP 2007).