Social signals
Evolutionary origins of language
Evolution and information
Simplicity Theory
Cognitive modelling of interest
Cognitive modelling of relevance
Cognitive modelling of meaning
Cognitive modelling of emotional intensity
Cognitive modelling of concept learning
Emergence as complexity drop
Qualia cannot be epiphenomenal
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Texts available on the Web have been generated by human minds. We observe that simple patterns are over-represented: abcdef is more frequent than arfbxg and 1000 appears more often than 1282. We suggest that word frequency patterns can be predicted by cognitive models based on complexity minimization. Conversely, the observation of word frequencies offers an opportunity to infer particular cognitive mechanisms involved in their generation.
We design a pattern mining algorithm to provide a summary of graphs in the form of a set of unexpected patterns, that is, patterns for which there is a drop between their expected complexity and the observed complexity.
In previous work, we proposed to generate Causal Bayesian Networks (CBN) as follows. Starting with considering all possible relations, we progressively discarded non-correlated variables. Next, we identified causal relations from the remaining correlations by employing “do-operations”. The obtained CBN could then be employed for causal inference. The main challenges of this approach included: “non-doable variables” and limited scalability. To address these issues, we propose three extensions: i) early pruning weakly correlated relations to reduce the number of required do-operations; ii) introducing aggregate variables that summarize relations between weakly-coupled sub-systems; iii) applying the method a second time to perform indirect do interventions and handle non-doable relations. Our proposal leads to a decrease in the number of operations required to learn the CBN and in an increased accuracy of the learned CBN, paving the way towards applications in large CPS.
To demonstrate their commitment, for instance during wartime, members of a group will sometimes all engage in the same ruinous display. Such uniform, high-cost signals are hard to reconcile with standard models of signaling. For signals to be stable, they should honestly inform their audience; yet, uniform signals are trivially uninformative. To explain this phenomenon, we design a simple model, which we call the signal runaway game. In this game, senders can express outrage at non-senders. Outrage functions as a second-order signal. By expressing outrage at non-senders, senders draw attention to their own signal, and benefit from its increased visibility. Using our model and a simulation, we show that outrage can stabilize uniform signals, and can lead signal costs to run away. Second-order signaling may explain why groups sometimes demand displays of commitment from all their members, and why these displays can entail extreme costs.
The paper presents a novel computational framework named CompLog. Inspired by probabilistic programming systems like ProbLog, CompLog builds upon the inferential mechanisms proposed by Simplicity Theory, relying on the computation of two Kolmogorov complexities (here implemented as min-path searches via ASP programs) rather than probabilistic inference.
With the increasing number of connected devices, complex systems such as smart homes record a multitude of events of various types, magnitude and characteristics. Current systems struggle to identify which events can be considered more memorable than others. In contrast, humans are able to quickly categorize some events as being more “memorable” than others. They do so without relying on knowledge of the system’s inner working or large previous datasets. Having this ability would allow the system to: (i) identify and summarize a situation to the user by presenting only memorable events; (ii) suggest the most memorable events as possible hypotheses in an abductive inference process. Our proposal is to use Algorithmic Information Theory to define a “memorability” score by retrieving events using predicative filters. We use smart-home examples to illustrate how our theoretical approach can be implemented in practice.
The Internet of Things (IoT) has been a prominent application for Intelligent Systems in recent years. While the increasing demand for explanations led to many advances in Explainable Artificial Intelligence (XAI), most solutions focus on systems where a single agent takes all decisions to be explained. However within the IoT context, Cyber-Physical Systems (CPS) are decentralized, with multiple agents coordinating their decisions to control the overall CPS. By contrast users expect coherent system-wide explanations, as if they were generated by a single agent. We propose a decentralized Explanation System generating such explanations while preserving the advantages of decentralized control: separation of concerns, heterogeneity, flexibility, ...Our architecture relies on: i) decentralized XAI Component specialists for providing partial explanations based on local knowledge; ii) a central generic Spotlight composing local explanations into a global explanation. We illustrate and qualitatively evaluate our approach via a proof-of-concept implementation for a smart home system.
We suggest that Bayes’ rule can be seen as a specific instance of a more general inferential template that can be expressed in terms of algorithmic complexities, namely through the measure of unexpectedness proposed by Simplicity Theory.
Comment décider si un événement est le fruit du hasard ou, au contraire, découle d’une causalité ciblée ? La question est fondamentale. Il en va des décisions que nous allons prendre et, parfois, de notre sécurité. Sur quels critères décidons- nous qu’un événement est ou n’est pas fortuit ? Si notre jugement en la matière est valide, comment expliquer qu’il puisse conduire des individus rationnels à rejeter systématiquement l’existence du hasard, pour lui préférer l’hypothèse de complots, d’infl uences magiques ou de la main du destin ? Et si cette capacité de jugement concernant le hasard n’est pas valide, comment expliquer que nous en soyons dotés ?
Le 13 septembre 1916, une éléphante de cinq tonnes prénommée Mary fut pendue à Erwin dans le Tennessee, devant un public de 2500 personnes, à l’aide d’une grue...
Human beings understand causal relationships through observations, actions and counterfactual reasoning. While data-driven methods achieve high levels of correlation detection, they mainly fall short of finding causal relations, notably being limited to observations only. In this paper, we propose an approach to learn causal models, combining observed data and selected interventions on the environment. We use this approach to generate Causal Bayesian Networks, which can be later used to perform diagnostic and predictive inference. We use our method on a smart home simulation, a use case where having knowledge of causal relations pave the way towards explainable systems. Our algorithm succeeds in generating a Causal Bayesian Network close to the simulation’s ground truth causal interactions, showing encouraging future prospects for application in real-life systems.
This MOOC is about applying Algorithmic Information Theory to Artificial Intelligence. Algorithmic information was discovered half a century ago. It is a great conceptual tool to describe what artificial intelligence actually does, and what it should do to make optimal choices.
Devrons-nous bientôt nous soumettre avec résignation à l’inévitable suprématie de l’intelligence artificielle ? Avant d’en appeler à la révolte, essayons de regarder à quoi nous avons affaire.
Human beings are talkative. What advantage did their ancestors find in communicating so much? Numerous authors consider this advantage to be “obvious” and “enormous”. If so, the problem of the evolutionary emergence of language amounts to explaining why none of the other primate species evolved anything even remotely similar to language. I propose to reverse the picture. On closer examination, language resembles a losing strategy. Competing for providing other individuals with information, sometimes striving to be heard, makes apparently no sense within a Darwinian framework. At face value, language as we can observe it should never have existed or should have been counter-selected. In other words, the selection pressure that led to language is still missing. The solution I propose consists in regarding language as a social signaling device that developed in the social context that is unique to our species.
A referring expression (RE) is a description that identifies a set of instances unambiguously. Mining REs from data finds applications in natural language generation, algorithmic journalism, and data maintenance. Since there may exist multiple REs for a given set of entities, it is common to focus on the most intuitive ones, ie, the most concise and informative. In this paper we present REMI, a system that can mine intuitive REs on large RDF knowledge bases. Our experimental evaluation shows that REMI finds REs deemed intuitive by users. Moreover we show that REMI is several orders of magnitude faster than an approach based on inductive logic programming.
Analogies are 4-ary relations of the form “A is to B as C is to D”. When A, B and C are fixed, we call analogical equation the problem of finding the correct D. A direct applicative domain is Natural Language Processing, in which it has been shown successful on word inflections, such as conjugation or declension. If most approaches rely on the axioms of proportional analogy to solve these equations, these axioms are known to have limitations, in particular in the nature of the considered flections. In this paper, we propose an alternative approach, based on the assumption that optimal word inflections are transformations of minimal complexity. We propose a rough estimation of complexity for word analogies and an algorithm to find the optimal transformations. We illustrate our method on a large-scale benchmark dataset and compare with state-of-the-art approaches to demonstrate the interest of using complexity to solve analogies on words.
Les sociétés de chasseurs-cueilleurs n’ont pas d’écoles. Elles accumulent pourtant des savoirs, elles possèdent des langues et des cultures sophistiquées. Si l’on compare notre espèce aux autres primates, tout est différent. Les cultures animales existent, mais elles sont si restreintes qu’elles sont longtemps passées inaperçues aux yeux des éthologues. Pourquoi existe-t-il tant de « savoirs » dans notre espèce ? Et pourquoi les transmettons-nous ? Si la question semble saugrenue, c’est parce que nous avons perdu de vue le caractère apparemment contre-nature de ce comportement.
This book explores the unity of life. It proposes that the concept of information is the inner essence of what we today call life.
The importance of information for our species is obvious. Human beings are highly dependent on information, constantly exchanging with conspecifics. In a less apparent way, we are the product of genetic and epigenetic information which determines our development in a given environment from a fertilized egg to the adult stage. Even less apparent is that information plays a determining role in ecosystems. This observation may include the prebiotic systems in which life emerged.
Our claim is that Nature processes information continuously. This means that even beyond living entities, we can see messages and decoding procedures. Nature can be said to send messages to its own future and then to decode them. Nature "talks" to itself! The systematic organization of messages suggests that, in some respects, we should even speak of the "languages" of Nature.
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.
Si vous marchez à reculons, les traces de pas que vous voyez devant vous sont les vôtres. Aucun robot, aucune intelligence artificielle (IA) ne sait ce genre de choses, sauf si l’on a pensé à les lui dire. Les IA sont-elles si intelligentes que cela ? À bien y regarder, elles apparaissent très intelligentes et très stupides à la fois. Pour quelle raison ? En sera-t-il toujours ainsi ? Dans ce livre, Jean-Louis Dessalles aborde ces questions d’une manière précise et accessible à tous. Chaque lecteur trouvera dans ce livre de quoi le surprendre. Il nous parle du passé, du présent et du futur des IA. Il évoque même ce qui, selon lui, leur manque pour devenir... intelligentes.
En 2013, une étude alarmiste1 annonce un risque de disparition imminente, en quatre ans seulement, de la moitié des emplois aux États-Unis par l’introduction massive de l’intelligence artificielle dans le monde du travail. Or, rien de tel ne s’est produit2. Se pourrait-il que ces discours qui présentent l’IA comme un bouleversement absolu soient juste un moyen d’attirer l’attention, extrapolant abusivement une réalité bien modeste faite de techniques balbutiantes ?
We remember a small proportion of our experiences as events. Are these events selected because they are useful and can be proven true, or rather because they are unexpected?
Several computational methods have been proposed to evaluate the relevance of an instantiated cause to an observed consequence. The paper reports on an experiment to investigate the adequacy of some of these methods as descriptors of human judgments about causal relevance.
This paper provides an updated formalization of the operation of contrast, and shows that, by applying it on conceptual spaces, membership functions to categories as e.g. those captured by adjectives or directional relationships emerge as a natural by-product. Because the outcome of contrast depends not only on the objects contrasted (a target and a reference, as for instance a prototype), but also on the frame in which those are contained, it is argued that contrast enables a continuous contextualization, offering a basis for "on the fly" predication. This investigation is used for inferring general requirements for the application of contrast and its generalization, and for comparison with current practices in the conceptual space literature.
Self-sacrifice can be modeled as costly social signal carried to the ultimate extreme. Such signaling may be evolutionarily stable if social status is in part inherited.
Analogical reasoning is a cognitively fundamental way of reasoning by comparing two pairs of elements. Several computational approaches are proposed to efficiently solve analogies: among them, a large number of practical methods rely on either a parallelogram representation of the analogy or, equivalently, a model of proportional analogy. In this paper, we propose to broaden this view by extending the parallelogram representation to differential manifolds, hence spaces where the notion of vectors does not exist. We show that, in this context, some classical properties of analogies do not hold any longer. We illustrate our considerations with two examples: analogies on a sphere and analogies on probability distribution manifold.
Responsibility, as referred to in everyday life, as explored in moral philosophy and debated in jurisprudence, is a multiform, ill-defined but inescapable notion for reasoning about actions. Its presence in all social constructs suggests the existence of an underlying cognitive base. Following this hypothesis, and building upon simplicity theory, the paper proposes a novel computational approach.
Within the general objective of conceiving a cognitive architecture for image interpretation able to generate outputs relevant to several target user profiles, the paper elaborates on a set of operations that should be provided by a cognitive space to guarantee the generation of relevant descriptions. First, it attempts to define a working definition of contrast operation. Then, revisiting well-known results in cognitive studies, it sketches a definition of similarity based on contrast, distinguished from the metric defined on the conceptual space.
Analogical reasoning is still a difficult task for machines. In this paper, we consider the problem of analogical reasoning and assume that the relevance of a solution can be measured by the complexity of the analogy. This hypothesis is tested in a basic alphanumeric micro-world.
Incremental learning designates online learning of a model from streaming data. In non-stationary environments, the process generating these data may change over time, hence the learned concept becomes invalid. Adaptation to this non-stationary nature, called concept drift, is an intensively studied topic and can be reached algorithmically by two opposite approaches: active or passive approaches. We propose a formal framework to deal with concept drift, both in active and passive ways. Our framework is derived from the Minimum Description Length principle and exploits the algorithmic theory of information to quantify the model adaptation. We show that this approach is consistent with state of the art techniques and has a valid probabilistic counterpart.
People avoid changing subject abruptly during conversation. There are reasons to think that this constraint is more than a social convention and is deeply rooted in our cognition. We show here that the phenomenon of topic connectedness is an expected consequence of the maximization of unexpectedness and that it is predicted by Simplicity Theory.
L’un des grands mérites que l’on doit reconnaître à Condillac est d’avoir posé la question de l’origine du langage, à une époque où la réponse évidente de l’origine divine obscurcissait le champ de l’investigation scientifique. D’une évidence, il a fait une question, et cette question continue de nous hanter. L’enjeu, derrière la question de l’origine du langage, est de comprendre l’origine d’un ensemble facultés mentales par laquelle nous aimons à nous distinguer des autres animaux. De tous nos prédécesseurs, à travers les quelque trois cent mille générations qui nous séparent de nos ancêtres simiesques, lequel a émis la première phrase, le premier mot, le premier geste référentiel ? Pourquoi est-ce arrivé dans notre lignée, et seulement dans notre lignée ? Comment et pourquoi cette innovation s’est-elle amplifiée jusqu’à produire le langage tel qu’il est pratiqué actuellement par tous les êtres humains ?
Pour Darwin, les facultés mentales de l’être humain diffèrent de celles des autres animaux par leur degré et non par leur nature. Pourtant, l’analyse des compétences cognitives humaines révèle certaines opérations qui ne prennent leur sens que par rapport au langage. Par exemple, les mécanismes qui nous permettent de combiner les significations ou de former des prédicats sont essentiels pour raconter ou pour argumenter. En revanche, ils n’ont pas de rôle comportemental évident. Il est alors tentant de penser que ces mécanismes cognitifs sont propres à notre espèce et qu’ils sont apparus dans un ordre défini au cours de la phylogenèse, pour remplir des fonctions langagières particulières.
Human conversation has a particular structure that bears no resemblance with any other known communication system. People’s spontaneous talking comes in two forms: narratives and collective argumentative reasoning. This characteristic conversational structure cannot be fortuitous. Conversation is a costly behaviour, if only by the time and energy it demands. Surprisingly, there have been few attempts to relate conversational structure to any biological function it may have. This chapter illustrates conversational structure with examples and explores the issue of its biological purpose.
We propose to apply Simplicity Theory (ST) to model interest in creative situations. ST has been designed to describe and predict interest in communication. Here we use ST to derive a decision rule that we apply to a simplified version of a creative game, the Poietic Generator. The decision rule produces what can be regarded as an elementary form of creativity. This study is meant as a proof of principle. It suggests that some creative actions may be motivated by the search for unexpected simplicity.
LONGTEMPS considérée comme une question taboue par les scientifiques, l’origine du langage est devenue un thème à la mode. On peut s’en féliciter, tant il est au centre de ce qui nous distingue des autres animaux. Mais sachant que le langage ne laisse pas de fossiles, peut-on faire autre chose qu’émettre des hypothèses invérifiables à propos de son origine ?
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.
Considering concepts as mental representations, should we regard them as perceptual prototypes or rather as symbolic entities?
Acceptable arguments must be logically relevant. This paper describes an attempt to retro-engineer the human argumentative competence. The aim is to produce a minimal cognitive procedure that generates logically relevant arguments at the right time. Such a procedure is proposed as a proof of principle. It relies on a very small number of operations that are systematically performed: logical conflict detection, abduction and negation. Its eventual vali-dation however depends on the quality of the available domain knowledge.
Quelles sont les propriétés dont doit jouir une histoire pour être une histoire ? Il est possible de répondre à cette question en se plaçant dans le cadre de la modélisation cognitive. La notion centrale développée ici est celle de simplicité cachée. Pour être intéressante, une situation imaginaire doit comporter une « révélation » qui simplifie une situation perçue comme complexe. Ce cadre conceptuel peut avoir des retombées qui débordent le domaine de la production narrative. Il concerne potentiellement toute création de l’esprit censée intéresser autrui. Ceci inclut les récits, mais aussi les objets ou les projets, dès lors que ces objets ou ces projets se voient dotés d’une valeur narrative.
Human beings do assess probabilities. Their judgments are however sometimes at odds with probability theory. One possibility is that human cognition is imperfect or flawed in the probability domain, showing biases and errors. Another possibility, that we explore here, is that human probability judgments do not rely on a weak version of probability calculus, but rather on complexity computations. This hypothesis is worth exploring, not only because it predicts some of the probability ‘biases’, but also because it explains human judgments of uncertainty in cases where probability calculus cannot be applied. We designed such a case in which the use of complexity when judging uncertainty is almost transparent.
Why is a red face not really red? How do we decide that this book is a textbook or not? Conceptual spaces provide the medium on which these computations are performed, but an additional operation is needed: Contrast.
Unexpectedness is a major factor controlling interest in narratives. Emotions, for instance, are felt intensely if they are associated with unexpected events. The problem with generating unexpected situations is that either characters, or the whole story, are at risk of being no longer believable. This issue is one of the main problems that make story design a hard task. Writers face it on a case by case basis. The automatic generation of interesting stories requires formal criteria to decide to what extent a given situation is unexpected and to what extent actions are kept believable. This paper proposes such formal criteria and makes suggestions concerning their use in story generation systems.
De nombreux comportements humains n’ont pas pour effet de procurer un bénéfice immédiat. Alors quel est leur rôle ? Attirer les amitiés, à l’image de ce que l’on peut observer dans les réseaux sociaux numériques.
What is language good for? For a long time, the question has remained not only unanswered, but not even asked. The classic ‘reason’ invoked to avoid the issue was that language benefited the species as a whole. This way of reasoning is simply wrong (Williams 1966). If information has any value, it is in the interest of no one to give it for free. And if information has no value, why are there ears ready to listen to it? The reason why we talk, and so much, still requires a biological and social explanation.
This study is an attempt to determine how much individuals should invest in social communication, depending on the type of relationships they may form. Two simple models of social relationships are considered. In both models, individuals emit costly signals to advertise their "quality" as potential friends. Relationships are asymmetrical or symmetrical. In the asymmetrical condition (first model), we observe that low-quality individuals are discouraged from signaling. In the symmetrical condition (second model), all individuals invest in communication. In both models, high-quality individuals ("elite") do not compete and signal uniformly. The level of this uniform signal and the size of the elite turn out to be controlled by the accuracy of signals. The two models may be relevant to several aspects of animal and human social communication.
Though human beings are experts in the determination of aspectual relations, current models of Aspect lack principled parsimony. We show that even on a limited segment of language, determining aspectual interpretations seems to require much ad hoc information. Our suggestion is to give parsimony first priority. The model we present in this paper is limited in scope, but its complexity is bounded in principle.
Human beings devote a considerable share of their time, maybe one third of the day (Mehl & Pennebaker 2003:866), to sharing information with conspecifics about often futile but sometimes consequential topics. This behavior is unique in nature. How can we account for the existence of honest communication in a Darwinian world where individuals are inevitably in competition with each other? The task proves much harder than what was thought in the past decades. The problem should bother all scientists, and more broadly any person wondering about human nature.
The challenge of narrative automatic generation is to produce not only coherent, but interesting stories. This study considers the problem within the Simplicity Theory framework. According to this theory, interesting situations must be unexpectedly simple, either because they should have required complex circumstances to be produced, or because they are abnormally simple, as in coincidences. Here we consider the special case of narratives in which characters perform actions with emotional consequences. We show, using the simplicity framework, how notions such as intentions, believability, responsibility and moral judgments are linked to narrative interest.
The human mind is known to be sensitive to complexity. For instance, the visual system reconstructs hidden parts of objects following a principle of maximum simplicity. We suggest here that higher cognitive processes, such as the selection of relevant situations, are sensitive to variations of complexity. Situations are relevant to human beings when they appear simpler to describe than to generate. This definition offers a predictive (i.e. refutable) model for the selection of situations worth reporting (interestingness) and for what individuals consider an appropriate move in conversation.
L’existence des capacités syntaxiques qui permettent aux êtres humains de manier des langues complexes reste mystérieuse. Pour certains auteurs, ces capacités seraient apparues totalement par hasard au cours de l’évolution et leur application à la communication serait fortuite. Nous essayons ici de montrer comment la modélisation de l’interface syntaxe-sémantique permet d’envisager un tout autre scénario. L’aptitude à manier des structures syntaxiques serait apparue en deux temps et serait liée à une nouvelle capacité sémantique, la formation des prédicats. La récursivité serait apparue lors de la deuxième étape, comme un moyen de lier les prédicats entre eux pour permettre la détermination de leurs arguments.
Current models of temporality in language are either inaccurate or too complex to be cognitively plausible. We present a cognitive model of the computation of aspect in French. Our approach emphasizes the importance of minimalism for cognitive plausibility: structures and computation are kept simple and combinatorial explosion is avoided. Though the model and its current implementation remain partial for now, our approach opens the way to a generic and cognitively plausible method for the determination of aspect.
Several studies have highlighted the combined role of emotions and reasoning in the determination of judgments about morality. Here we explore the influence of Kolmogorov complexity in the determination, not only of moral judgment, but also of the associated narrative interest. We designed an experiment to test the predictions of our complexity-based model when applied to moral dilemmas. It confirms that judgments about interest and morality may be explained in part by discrepancies in complexity. This preliminary study suggests that cognitive computations are involved in decision-making about emotional outcomes.
Near-miss experiences are one of the main sources of intense emotions. Despite people’s consistency when judging near-miss situations and when communicating about them, there is no integrated theoretical account of the phenomenon. In particular, individuals’ reaction to near-miss situations is not correctly predicted by rationality-based or probability-based optimization. The present study suggests that emotional intensity in the case of near-miss is in part predicted by Simplicity Theory.
Commonsense wisdom dictates that mutual understanding grows with cognitive harmony. Communication seems impossible between people who do not share values, beliefs and concerns. If brought to the extreme, this statement however neglects the fact that the formation of social bonds crucially depends on the expression of cognitive dissonance.
Le langage ne vise pas seulement à transmettre des informations utiles. Il sert aussi à se mettre en valeur en racontant de bonnes histoires qui doivent répondre à des caractéristiques très précises.
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 iscloser to what we should expect from a naturally selected competence.
For at least 100 000 years, human beings have been talking the way we do. Language is universally used by most individuals in every culture several hours each day, primarily during conversational chatter (Dunbar 1998). How did our species come to adopt such a strange behavior in the course of its evolution? The question has been considered in turn as obvious and baffling. A proper approach to the reasons why we talk requires that the biological function of language be understood, and pragmatics is the right place to seek out that function.
Algorithmic probability is traditionally defined by considering the output of a universal machine fed with random programs. This definition proves inappropriate for many practical applications where probabilistic assessments are spontaneously and instantaneously performed. In particular, it does not tell what aspects of a situation are relevant when considering its probability ex-post (after its occurrence). As it stands, the standard definition also fails to capture the fact that simple, rather than complex outcomes are often considered improbable, as when a supposedly random device produces a repeated pattern. More generally, the standard algorithmic definition of probability conflicts with the idea that entropy maximum corresponds to states that are both complex (unordered) and probable. We suggest here that algorithmic probability should rather be defined as a difference in complexity. We distinguish description complexity from generation complexity. Improbable situations are situations that are more complex to generate than to describe. We show that this definition is more congruent with the intuitive notion of probability.
Where does human language come from? The ‘greatest problem in science’, according to Bickerton (2009), remains a mystery. This new volume offers a partial but important map of current ideas on the problem. The book is stimulating because of the issues it raises and surprising in the issues it ignores.
Human communication involves a huge cost. Conversation takes up about one third of our awake time; children must learn ten new words a day during ten years; getting first-hand information is time-consuming and may involve risks; and we need to support disproportionate brains to store episodes worth telling during verbal interactions. Why are we (apparently) the only species that shows this behavior, in apparent contradiction with Darwinian principles? This is the real mystery about language.
Nous proposons l’esquisse d’un modèle de calcul des relations temporelles dans le langage. Ce modèle cherche à satisfaire une contrainte de minimalisme cognitif, de manière à assurer une économie dans le calcul, dans la mémoire et dans les structures lors de l’analyse des informations temporelles contenues dans les énoncés. L’objectif est, à terme, de parvenir à une méthode générique et cognitivement plausible pour la détermination du temps, de l’aspect et de leurs implications logiques. L’originalité de ce modèle est d’isoler les opérations topologiques des autres traitements sémantiques.
In the human species, individuals establish social bonds mainly based on communication. Among the qualities that are used by individuals to include other individuals in their social network, the ability to demonstrate one’s relevance in the eye of others proves crucial. In this respect, relevance can be more important than sharing a common culture or a common language. Fortunately, the principles that govern relevance in communication seem to be universal and deeply rooted in our biology, enabling any two individuals in our species to become friends, regardless of their differences.
Language, from its early hominin origin to now, was not primarily being used for practical purposes. We suggest that an essential function of protolanguage was to signal ‘noteworthy’ events, as humans still systematically do. 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.
Dans son dernier livre, le linguiste Derek Bickerton s’attaque à ce qu’il qualifie de "plus grande énigme de la science": l’origine du langage. Une faculté humaine radicalement différente de la communication animale, comme l’explique l’extrait présenté ici en avant-première.
Nous défendons ici l’idée que le langage humain est né d’une compétition inédite dans le monde animal, la compétition informationnelle. De nombreux aspects de notre mode de communication, notamment sa modalité essentiellement orale, ses lexiques pléthoriques, son caractère déplacé (hors du ‘ici et maintenant'), son mode dialogique, toutes choses parfaitement mystérieuses autrement, trouvent une explication dans le fait que les locuteurs sont engagés dans une compétition communicationnelle de laquelle les gagnants retirent un bénéfice social. Cette explication de l’émergence du langage ne se limite pas à imaginer un intérêt pour l’auditeur, mais également pour le locuteur. Elle est donc recevable dans un cadre darwinien.
This symposium discusses J.-L. Dessalles’s account of the evolution of language, which was presented in Why we Talk (Oxford Univ. Press 2007).
The feeling of good or bad luck occurs whenever there is an emotion contrast between an event and an easily accessible counterfactual alternative. This study suggests that cognitive simplicity plays a key role in the human ability to experience good and bad luck after the occurrence of an event.
Individuals devote one third of their language time to mentioning unexpected events. We try to make sense of this universal behaviour within the Costly Signalling framework. By systematically using language to point to the unexpected, individuals send a signal that advertises their ability to anticipate danger. This shift in display behaviour, as compared with typical displays in primate species, may result from the use by hominins of artefacts to kill.
Human language is still an embarrassment for evolutionary theory, as the speaker’s benefit remains unclear. The willingness to communicate information is shown here to be an evolutionary stable strategy (ESS), even if acquiring original information from the environment involves significant cost and communicating it provides no material benefit to addressees. In this study, communication is used to advertise the emitter’s ability to obtain novel information. We found that communication strategies can take two forms, competitive and uniform, that these two strategies are stable and that they necessarily coexist.
La coopération est l’un des piliers, voire un axiome, des sciences sociales. Elle seule permet à des individus non apparentés de vivre ensemble. Même la guerre, sorte d’autodestruction des sociétés, repose sur une coopération efficace. Et, pour prendre un exemple que je connais bien, le langage est présenté aux étudiants comme un cas emblématique de coopération, consistant en un échange d’informations. C’est pourtant à propos du langage que j’ai eu mes premiers doutes. J’ai alors tenté de m’attaquer au pilier, armé du canif des modélisateurs, la simulation.
Certains chercheurs, probablement la vaste majorité, restent d’une grande prudence lorsqu’ils expriment leurs idées. Derek Bickerton n’est certainement pas de ceux-là. La prudence scientifique est un moyen de ne pas trop heurter la pensée, souvent contradictoire, des collègues. C’est aussi un moyen de ne pas prendre de risques, de ne pas se voir reprocher plus tard que l’on s’est trompé. Les demi-teintes, les nuances et les compromis conceptuels ne font pas partie du monde de Derek Bickerton. Dans ses écrits comme lors de ses interventions publiques, il montre une fougue et une prise de risque que nombre de jeunes chercheurs pourraient lui envier. Bickerton nous dit : « Voilà comment le langage humain a émergé ! ». Il nous dit même : « Pour la première fois, quelqu’un va vous dire comment le langage a émergé ». Il ne s’agit pas d’immodestie. Il s’agit de passion. Cette passion, il nous la communique, pour notre plus grand plaisir.
Deux présidents emblématiques des États-Unis ont été assassinés à 1OO ans d’intervalle et leur histoire présente plusieurs points communs. Pourquoi notre cerveau est-il irrésistiblement attiré par de telles coïncidences, y cherchant des marques du destin ?
This study is an attempt to measure the variations of interest aroused by conversational narratives when definite dimensions of the reported events are manipulated. The results are compared with the predictions of the Complexity Drop Theory, which states that events are more interesting when they appear simpler, in the Kolmogorov sense, than anticipated.
Why do human beings tirelessly strive to provide information to conspecifics? Human language seems to benefit listeners more than speakers. It seems to be an exception in a Darwinian world in which organisms are primarily concerned with their own survival.
Cette étude vise à mesurer les variations de l’intérêt suscitées par un événement lorsque certaines dimensions définies sont manipulées. Les résultats sont comparés aux prédictions de la théorie du décalage de complexité, selon laquelle les événements sont d’autant plus intéressants qu’ils sont plus simples qu’attendu.
Le prix de certaines informations est devenu négatif : nous sommes prêts à payer pour ne pas recevoir la plupart des messages qui assaillent nos boîtes électroniques. À l’inverse, le coût de l’information pertinente, ou le temps nécessaire pour la trouver, risque d’augmenter indéfiniment. Pourrons-nous échapper à cette malédiction ?
Les être humains, dans leur milieu naturel, utilisent le langage pour bavarder. C’est lors de ce comportement étrange et faussement futile qu’ils constituent leur réseau social. Je montre comment cette fonction permet d’expliquer l’existence du langage dans un cadre darwinien. Je montre également pourquoi d’autres modèles, proposés dans le passé, échouent face aux contraintes darwiniennes
Homo homini lupus, l’être humain est un loup pour ses semblables. En même temps, homo sapiens est la seule espèce dont les membres rendent systématiquement des services à leurs congénères non apparentés. Les populations humaines sont-elles un mélange nécessaire où cohabitent les individus agressifs et les altruistes ? Si l’on en croit une étude récente (Choi & Bowles 2007), les agressifs et les altruistes pourraient être les mêmes personnes !
Selection through iterated learning explains no more than other non-functional accounts, such as universal grammar, why language is so well-designed for communicative efficiency. It does not predict several distinctive features of language like central embedding, large lexicons or the lack of iconicity, that seem to serve communication purposes at the expense of learnability.
Human beings share a common competence for generating relevant arguments. We hypothesize the existence of a cognitive procedure that enables them to determine the content of their arguments. We consider that this procedure must be simple to have cognitive plausibility. This paper is an attempt to determine central aspects of this cognitive procedure. The originality of the present approach is to analyse spontaneous argument generation as a process in which arguments either signal problems or aim at solving previously acknowledged problems.
Language, from its early hominin origin to now, was not primarily being used for practical purposes. We suggest that an essential function of protolanguage was to signal ‘noteworthy’ events, as humans still systematically do. 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.
This chapter provides a critical survey of emergence definitions both from a conceptual and formal standpoint. The notions of downward / backward causation and weak / strong emergence are specially discussed, for application to complex social system with cognitive agents. Particular attention is devoted to the formal definitions introduced by (Müller 2004) and (Bonabeau & Dessalles, 1997), which are operative in multi-agent frameworks and make sense from both cognitive and social point of view. A diagrammatic 4-Quadrant approach, allow us to understanding of complex phenomena along both interior/exterior and individual/collective dimension.
Les conversations quotidiennes constituent une arène permanente où se joue l’essentiel de notre existence sociale. Dans ce jeu proprement humain, la pertinence est le principal critère. Nous possédons tous une intuition précise de ce qui rend une histoire ou un argument pertinent et nous sommes hypersensibles aux défauts de pertinence.
The fact that human beings universally put much energy and conviction in reporting events in daily conversations demands an explanation. After having observed that the selection of reportable events is based on unexpectedness and emotion, we make a few suggestions to show how the existence of narrative behaviour can be consistent with the socio-political theory of the origin of language.
Individuals have an intuitive perception of what makes a good coincidence. Though the sensitivity to coincidences has often been presented as resulting from an erroneous assessment of probability, it appears to be a genuine competence, based on non-trivial computations. The model presented here suggests that coincidences occur when subjects perceive complexity drops. Co-occurring events are, together, simpler than if considered separately. This model leads to a possible redefinition of subjective probability.
Nous définissons la complexité cognitive comme une notion dérivée de la complexité de Kolmogorov. Nous montrons qu’une partie importante de ce qui retient l’intérêt des êtres humains, notamment lors de la sélection des événements spontanément signalés ou rapportés, peut être prédite par un saut de complexité cognitive. Nous évaluons les conséquences de ce modèle pour l’étude de la pertinence conversationnelle.
Most of the situations of daily life that arouse human interest are experienced as unexpected. Highly unexpected events are preferentially memorised and are systematically signalled or reported in conversation. Probability theory is shown to be inadequate to predict which situations will be perceived as unexpected. We found that unexpectedness is best explained using Kolmogorov complexity, which is a strong indication that human individuals have an intuitive access to what was thought to be only an abstract mathematical notion. Many important and previously disparate facts about human communicative behaviour are shown to result from the cognitive ability to detect complexity shifts.
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 which are worth telling. The function of episodic memory, in our view, is to accumulate stories that are relevant to recount in conversation.
Why do human beings tirelessly strive to provide information to conspecifics? Human language seems to benefit listeners more than speakers. It seems to be an exception in a Darwinian world in which organisms are primarily concerned with their own survival.
L’existence des capacités syntaxiques qui permettent aux êtres humains de manier des langues complexes reste mystérieuse. Pour certains auteurs, ces capacités seraient apparues totalement par hasard au cours de l’évolution et leur application à la communication serait fortuite. Nous essayons ici de montrer comment la modélisation de l’interface syntaxe-sémantique permet d’envisager un tout autre scénario. L’aptitude à manier des structures syntaxiques serait apparue en deux temps et serait liée à une nouvelle capacité sémantique, la formation des prédicats. La récursivité serait apparue lors de la deuxième étape, comme un moyen de lier les prédicats entre eux pour permettre la détermination de leurs arguments.
This paper explores a few consequences of the hypothesis that language evolved for the benefit of speakers. The hypothesis, supported by recent Darwinian scenarios of language emergence, explains why speech production organs were dramatically transformed through evolution, while auditory systems remained practically unchanged. It also explains the need for huge vocabularies and for large episodic memory, and it dismisses the possibility of gesture-first scenarios of language origins.
Les scientifiques de ce début de siècle sont maintenant convaincus, non seulement que la question de l’origine du langage mérite d’être posée, mais aussi que la réponse est loin d’être évidente. L’enjeu est d’importance, car comprendre la raison de l’existence du langage pourrait nous conduire à porter un regard entièrement nouveau sur notre propre espèce.
Comment le langage est-il apparu ? Certes pas parce qu’il fallait que l’on parle... L’éthologie, la paléoanthropologie, la linguistique, servent ici de guides précieux dans une véritable enquête qui nous mène sur les traces des premiers humains. Existe-t-il des méthodes qui nous permettraient de reconstituer une éventuelle ‘langue mère’ ? Comment un ‘protolangage’ se serait-il complexifié pour d’atteindre l’immense sophistication de nos langues actuelles ? Le langage, universel dans notre espèce et exception dans le règne animal, constituerait-il une anomalie de l’évolution ? Les rôles joués par le langage et l’avantage évolutif qu’ils induisent seraient une des clés permettant de répondre à ces questions.
The existence of syntactic abilities allowing human beings to process complex languages remains mysterious. According to some authors, these abilities appeared by mere chance at some point in evolution, and their use in communication is, in some way, fortuitous. We try here to show how a simple model of the syntax-semantic interface allows us to consider a quite different scenario. The ability to process syntactic structures would have appeared in a two-step evolutionary process and would be the consequence of a new semantic ability, the ability to form predicates. Recursion is claimed to have appeared in the second step, as a way to link predicates for their arguments to be determined.
The systematic and universal communicative behaviour that drives human beings to give honest information to conspecifics during long-lasting conversational episodes still represents a Darwinian paradox. Attempts to solve it by comparing conversation with a mere reciprocal cooperative information exchange is at odds with the reality of spontaneous language use. The Costly Signalling Theory has recently attracted attention as a tentative explanation of the evolutionary stability of language. Unfortunately, it makes the wrong prediction that only elite individuals would talk. I show that as far as social bonding is assortative in our species, generalised signalling through language becomes a viable strategy to attract allies.
This paper explores a few consequences of the hypothesis that language evolved for the benefit of speakers. The hypothesis, supported by recent Darwinian scenarios of language emergence, explains why speech production organs were dramatically transformed through evolution, while auditory systems remained practically unchanged. It also explains the need for huge vocabularies and for large episodic memory, and it dismisses the possibility of gesture-first scenarios of language origins.
Though the ability of human beings to deal with probabilities has been put into question, the assessment of rarity is a crucial competence underlying much of human decision-making and is pervasive in spontaneous narrative behaviour. This paper proposes a new model of rarity and randomness assessment, designed to be cognitively plausible. Intuitive randomness is defined as a function of structural complexity. It is thus possible to assign probability to events without being obliged to consider the set of alternatives. The model is tested on Lottery sequences and compared with subjects’ preferences.
Spontaneous conversations involve a considerable amount of event reporting. Eggins and Slade (1997:265) observed that storytelling alone filled up to 43% of the three hour corpus of casual conversation they collected. In our corpus of family conversations, they may constitute from one third to two thirds of spoken time. One remarkable phenomenon about conversational stories is that they most often concern unexpected states of affairs, or states of affairs that are presented as such. Another, no less remarkable, phenomenon is that individuals quite often tend to diminish the originality of others’ stories. We wall such reactions trivialization.
La conversation humaine agit comme un filtre extraordinairement sélectif : seule une infime partie des situations que les locuteurs ont vécues ou ont pu connaître sera jugée digne d’être rapportée aux interlocuteurs. L’un des objectifs de la recherche sur le langage consiste à rechercher des critères permettant de prévoir si une situation sera perçue comme suffisamment « intéressante » si elle est mentionnée en conversation. Nous montrons ici que le caractère inattendu de certaines situations, qui conduit souvent à ce qu’elles soient rapportées en conversation, est lié à des écarts de complexité, et que ce phénomène peut s’expliquer dans le cadre plus général de la théorie « shannonienne » de la communication événementielle.
Communication of honest information is known to be fundamentally unstable in populations of selfish agents. As agents have more interest in benefiting from others’ information than in giving away their own knowledge, general muteness is the only attractor. We develop an alternative model along the lines of the general Theory of Honest Signaling. In our model, agents communicate to display their ability to get original information.
Un aspect important des interactions humaines est lié au fait que les individus exigent les uns des autres que leurs messages apparaissent comme intéressants, les autres messages étant perçus comme inutiles, gênants, voire ineptes. Nous proposons ici un modèle de l’intérêt, for-mé à partir de l’observation des conversations spontanées. Nous vérifions que de fortes contraintes portent sur le contenu des messages admissibles. Nous identifions en particulier une classe de messages "inté-ressants" ignorée des modèles habituels : les messages portant sur un état de fait improbable, que nous analy-sons comme associés à une valeur informationnelle élevée. Les applications potentielles de ce modèle vont de la sélection automatique des informations à l’interaction humain-machine
The evolutionary story proposed in the target paper makes no difference between semantic representations underlying language and more general cognitive representations, at work in perception and action, which humans share with apes and probably other mammals. Though semantic representations supporting language are grounded in perception, some of them, specifically predicative structures, should rather be considered a distinctive feature of human communication system. Any evolutionary scenario about language should explain how human minds evolved to form the kind of thoughts that are communicated through language.
Pourquoi le langage est-il apparu? La réponse courante - pour se transmettre des informations au sein d’un groupe - n’est pas évidente au regard des contraintes de l’évolution. Les origines du langage seraient plutôt à chercher dans d’autres raisons: maintenir des liens d’amitié par exemple.
The present paper provides a formal definition of emergence, operative in multi-agent framework designed by Agent Oriented Programming, and which makes sense from both a cognitive and an economics point of view.
Recursion has a function: it gives a new role to predicates. The main predicate in a sentence expresses a thought for argumentative purposes. The main predicate is what is really meant, what is offered to the addressees’ critique (in the case of argumentation) or to their appraisal (in the case of event report). Thanks to recursion, other predicates can be introduced to determine arguments. They help addressees determine what x refers to in the scene.
Les êtres humains parviennent à communiquer et à argumenter en tenant compte, avec une aisance spectaculaire, des relations temporelles entre les situations. Pourtant, la plupart des modèles du temps échouent à donner une explication cognitivement plausible de cette performance. L’une des difficultés principales des modèles existants est qu’ils utilisent des ensembles infinis d’instants ou d’intervalles, ce qui est irréaliste du point de vue de la modélisation cognitive. Le modèle que nous esquissons ici propose une approche non-réaliste de la construction cognitive du temps. Il parvient à éviter l’écueil des ontologies temporelles infinies, au prix d’un changement radical dans la manière de considérer la conceptualisation du temps.
People put words together, often in an innovative way, to create genuine new meanings. According to the compositionality principle, the meaning of an expression is entirely deducible from the meanings of its components. The principle alone does not, however, tell how composed meanings are formed. Most attempts to solve the compositionality problem led authors to assign fixed conceptual structures to words.,We suggest another solution. In our model, conceptual representations are ephemeral. They are constructed “on the fly” and they do not survive the context that gave birth to them.
Language has long been thought to be the inevitable outcome of some general evolutionary trend towards more complexity and more intelligence. On such a pathway towards progress, our species would just happen to be more advanced than others. In the light of modern evolution theory, this picture turns out to be fundamentally wrong.
Les modèles de l’interaction rencontrent un certain nombre de contraintes, comme éviter de postuler des structures ou des procédures qui ne pourraient pas être hébergées par un cerveau humain ou un dispositif matériel. En particulier, un modèle de l’interaction doit renoncer à toute structure ou procédure infinie. Dans ce papier, nous montrons comment la prise en compte de cette contrainte nous amène à proposer, dans le cas particulier du temps, un modèle qui renonce à certains présupposés classiques, notamment l’existence d’une structure temporelle globale. L’abandon de cette hypothèse nous conduit à adopter une approche procédurale de la construction des relations temporelles. Le cas du temps est proposé comme un exemple montrant qu’il est possible de rapprocher les modèles formels de la performance humaine qu’ils sont censés expliquer.
Quoi de plus naturel que d’écouter ceux qui ont des choses intéressantes à dire ? Et quoi de plus naturel, pour chacun d’entre nous, que de chercher à satisfaire l’auditoire potentiel qui se trouve en chaque être humain, à commencer par nos proches ? Si nos congénères voient un intérêt dans nos informations, pourquoi les donnons-nous ainsi gratuitement ? À première vue, un tel comportement est inexplicable au regard de la théorie de la sélection naturelle.
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.
Human beings of both sexes take any opportunity to show their informational abilities through language.
La raison première pour laquelle le comportement de langage existe dans notre espèce est à rechercher dans l’utilisation que nous en faisons et dans l’impact biologique que cette utilisation peut avoir sur la survie et la reproduction des individus. Nous analysons l’une de ces utilisations, que nous qualifions de shannonienne et qui consiste à attirer systématiquement l’attention sur les nouveautés. Nous suggérons que l’emploi shannonien du langage est révélateur de son utilisation première et constituait même la raison d’être de ce qu’il est convenu d’appeler le protolangage.
Animal behavior is often altruistic. In the frame of the theory of natural selection, altruism can only exist under specific conditions like kin selection or reciprocal cooperation. We show that reciprocal cooperation, which is generally invoked to explain non-kin altruism, requires very restrictive conditions to be stable. Some of these conditions are not met in many cases of altruism observed in nature. In search of another explanation of non-kin altruism, we consider Zahavis’s theory of prestige. We extend it to propose a ‘political’ model of altruism. We give evidence showing that non-kin altruism can evolve in the context of inter-subgroup competition. Under such circumstances, altruistic behavior can be used by individuals to advertise their quality as efficient coalition members. In this model, only abilities which positively correlate with the subgroup success can evolve into altruistic behaviors.
Postulating a variety of mutually isolated thought domains for pre-linguistic 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.
A number of concepts are included in the term ‘consciousness’. We choose to concentrate here on phenomenal consciousness, the process through which we are able to experience aspects of our environment or of our physical state. We probably share this aspect of consciousness with many animals which, like us, feel pain or pleasure and experience colours, sounds, flavours, etc. Since phenomenal consciousness is a feature of some living species, we should be able to account for it in terms of natural selection. Does it have an adaptive function, or is it an epiphenomenon ? We shall give arguments to reject the second alternative. We propose that phenomenal properties of consciousness are involved in a labelling process that allows us to discriminate and to evaluate mental representations. We also discuss to what extent consciousness as such has been selected for this labelling function.
Les interventions, au cours d’un dialogue argumentatif, sont logiquement reliées les unes aux autres. De ce fait, l’agencement des répliques prend une forme arborescente, qui apparaît comme une structure fractale : la structure locale d’une partie du dialogue ressemble à la structure qui l’englobe. Nous montrons ensuite comment une telle structure peut être vue comme le résultat de l’application récursive d’une procédure de génération d’arguments. Nous envisageons enfin la faisabilité d’une capacité artificielle de dialogue construite autour d’une telle procédure récursive.
Comme toutes les caractéristiques physiques et toutes les dispositions comportementales universelles de notre espèce, la capacité de langage est un produit de la sélection naturelle. Quel avantage particulier a-t-elle procuré à nos ancêtres pour qu’ils se mettent à parler ?
La capacité de langage est souvent présentée comme l’aboutissement inévitable d’une évolution qui va de l’amibe à l’homme. En acquerrant les prédispositions nécessaires à l’usage de la parole, notre espèce aurait simplement franchi une étape supplémentaire. Pourtant, ce comportement auquel nous consacrons une part significative de notre temps éveillé est, par bien des aspects, différent de la communication animale. En reliant la structure de chaque composante du langage (phonologie, syntaxe, sémantique, pragmatique) à ses possibles fonctions, l’auteur révèle un paradoxe : pourquoi les être humains cherchent-ils inlassablement à fournir des informations à leurs congénères ? Le comportement langagier semble faire exception à la théorie darwinienne, qui prévoit que les organismes se préoccupent avant tout de leur propre survie. Pour résoudre ce paradoxe, l’auteur nous demande de remonter aux origines du langage. Il en vient à l’idée que l’émergence de notre manière de communiquer est liée au mode d’organisation particulier des groupes humains. Les premières formes de langage seraient apparues, chez les hominidés, comme un moyen pour les individus de se choisir afin de former des coalitions. Ainsi, loin de résulter d’une tendance évolutive générale, l’apparition du langage serait une conséquence de l’organisation sociale singulière de notre espèce.
Language is the main distinctive feature of our species. Why do we feel the urge to communicate with our fellows, and why is this form of communication, characterised by relevance, unique in animal kingdom ? In this chapter, we will first stress this specificity of human communication. In a second part, using computer evolutionary simulations, we will dismiss the usual claim that human communication is a specific form of reciprocal cooperation. A Darwinian account of language requires that we find a selective advantage in the communication act. We will propose, in the third part of this chapter, that such an advantage can be found if we consider language activity in the broader frame of human social organisation. In the continuation of the ‘chimpanzee politics’ studied by de Waal (1982), the ability to form large coalitions must have been an essential feature of hominid societies (Dunbar 1996). We will suggest that relevant speech originated in this context, as a way for individuals to select each other to form alliances.
The study of language use, usually called pragmatics, reveals that the competence of speakers is not monolithic. It can be split into two quite distinct behaviors. The first one deals with salient events; the second one deals with problematic situations. We claim that the second ability emerged long after the first one in hominid evolutionary history. A consistent scenario is that communication about salient events is what the protolanguage hypothesized by Bickerton (1990) was used for. The detection and collective processing of problematic situations can be understood as an additional ability which gave rise to modern language.
Animal behavior is often altruistic. In the frame of the theory of natural selection, altruism can only exist under specific conditions like kin selection or reciprocal cooperation. We show that reciprocal cooperation, which is generally invoked to explain non-kin altruism, requires very restrictive conditions to be stable. Some of these conditions are not met in many cases of altruism observed in nature. In search of another explanation of non-kin altruism, we consider Zahavis’s theory of prestige. We extend it to propose a ‘political’ model of altruism. We give evidence showing that non-kin altruism can evolve in the context of inter-subgroup competition. Under such circumstances, altruistic behavior can be used by individuals to advertise their quality as efficient coalition members. In this model, only abilities which positively correlate with the subgroup success can evolve into altruistic behaviors.
Bias is always present in learning systems. There is no perfect, universal, way of learning that would avoid any ‘innate’ predetermination. However, all biases should not be considered equivalent. Usually, it is implicitly regarded as desirable to avoid anisotropic biases when designing a learning mechanism, especially when it is intended as a cognitive model of some human or animal learning ability. Anisotropic bias necessarily involves some ad hoc a priori knowledge that severely limits the generality of the learning device.
Students’ errors become manifest through erroneous behaviours noticed by the teacher. However, addressing behavioural deviation alone is not sufficient to design appropriate feedback. We propose here a model of student error, based on a separation between procedural and logical knowledge. This model is tested through its ability to predict the observed behaviour of subjects solving the Tower of Hanoi problem. Using this model, we are able to propose a ‘deep’ error classification, based on the observation of the internal representations of the system when it generates deviant behaviours. From this characterisation of errors, we aim at designing a critiquing system. Such a system will deliver more elaborate feedback to the learner, from which we hope better pedagogical efficiency and better acceptability.
The question of the epiphenomenality of consciousness can be addressed from an evolutionary perspective. If phenomenal consciousness is not an evolutionary epiphenomenon, but is it part of our phenotype, we should conclude that consciousness is not a functional or neuronal epiphenomenon.
A number of concepts are included in the term ‘consciousness’. We choose to concentrate here on phenomenal consciousness, the process through which we are able to experience aspects of our environment or of our physical state. We probably share this aspect of consciousness with many animals which, like us, feel pain or pleasure and experience colours, sounds, flavours, etc. Since phenomenal consciousness is a feature of some living species, we should be able to account for it in terms of natural selection. Does it have an adaptive function, or is it an epiphenomenon ? We shall give arguments to reject the second alternative. We propose that phenomenal properties of consciousness are involved in a labelling process that allows us to discriminate and to evaluate mental representations. We also discuss to what extent consciousness as such has been selected for this labelling function.
We deal here with the problem of the origin of language from the point of view of pragmatics. Our aim is to show that any scenario of language origin should explain the relevance phenomenon. Why do people feel obliged to be relevant in casual conversation ? Analysing the structure of relevance leads to unexpected conclusions : relevant information is valuable, therefore language seems to be altruistic. As a consequence, from a Darwinian perspective, speakers should be rare and continually prompted for their knowledge. What we observe, however, is the exact opposite : in many situations, speakers repeatedly strive to make their point, while listeners systematically evaluate what they hear. A possible solution to this paradox is that language is not altruistic and that relevant information is traded for status. The observation of spontaneous conversation provides some evidence that supports such a hypothesis.
The distinction between declarative and procedural knowledge is a well-accepted one. However, few models offer a consistent implementation of this distinction. We present such a system, based on a strict separation of logical and calculation capabilities, designed to model aspects of human problem solving behaviour. We have tested our approach on the Tower of Hanoi task by comparing the results provided by our model with the performance of novice subjects. We also compared these results with the performance of a few other computational models. These comparisons are quite promising. Our model has been designed to be simple and psychologically plausible. Its current implementation is still basic. We expect further improvement from the joint introduction of two separate learning abilities, a logical one and a procedural one.
The purpose of this paper is to suggest that many argumentative moves in casual dialogues can be explained in terms of conflicting desires and conflicting beliefs, in such a way that some of these moves may be predicted. Participants appraise the different outcomes of the conflicting situation and try to find, together, through dialogue, a solution that they consider as acceptable. We show how realistic dialogues can emerge through a simple recursive process from an initial cognitive conflict. This model is implemented in our program PARADISE which can reconstruct the argumentative moves of some real conversations.
The purpose of this paper is to propose a refinement of the notion of innateness. If we merely identify innateness with bias, then we obtain a poor characterisation of this notion, since any learning device relies on a bias that makes it choose a given hypothesis instead of another. We show that our intuition of innateness is better captured by a characteristic of bias, related to isotropy. Generalist models of learning are shown to rely on an ‘isotropic’ bias, whereas the bias of specialised models, which include some specific a priori knowledge about what is to be learned, is necessarily ‘anisotropic’. The so-called generalist models, however, turn out to be specialised in some way: they learn ‘symmetrical’ forms preferentially, and have strictly no deficiencies in their learning ability. Because some learning beings do not always show these two properties, such generalist models may be sometimes ruled out as bad candidates for cognitive modelling.
Two different conceptions of emergence are reconciled as two instances of the phenomenon of detection. In the process of comparing these two conceptions, we find that the notions of complexity and detection allow us to form a unified definition of emergence that clearly delineates the role of the observer.
Disposons-nous d’une grande liberté lorsque nous choisissons de communiquer ? Non, bien sûr, pas toujours, mais dans les situations sociales décontractées comme la conversation entre amis, personne ne pourrait prétendre que notre comportement est fortement contraint. Quoique... Il semble que nous soyons soumis, sans en avoir conscience la plupart du temps, à une contrainte extrêmement sévère : la contrainte de pertinence. Lors d’une conversation spontanée, une réplique non pertinente provoque un rejet systématique (« Pourquoi dis-tu cela ? ») plus ou moins agressif. Plus généralement, tout acte de communication se doit d’être pertinent. Un être humain qui ne produit plus d’énoncés pertinents est vite considéré comme un malade mental. D’où vient cette contrainte, comment fonctionne-t-elle, quel est son rôle ?
Le fonctionnement cognitif d’un individu et le fonctionnement de la communication sociale sont deux phénomènes soumis à des contraintes assez différentes. Il n’est donc pas évident d’imaginer un parallélisme étroit entre ces deux processus. Pourtant la question de la parenté entre la pensée et le langage a été maintes fois abordée : pensons-nous avec des mots, la pensée est-elle un langage intériorisé, le langage précède-t-il la pensée chez l’enfant, etc. ? Je propose aussi d’aborder cette question, mais sous un angle original. La caractéristique du langage qui est retenue ici n’est pas la faculté d’agencer des mots pour produire des phrases qui évoquent une signification. Si l’on regarde le langage au niveau pragmatique, en retenant seulement de la faculté langagière le fait qu’elle permet d’enchaîner des arguments pertinents, alors force est de constater, dans le détail, une forte similitude entre le déroulement du flux de la pensée et celui de la communication sociale par excellence : la conversation. En d’autres termes, je suggère que l’enchaînement des pensées obéit aux contraintes de pertinence. Si l’on accepte de considérer une telle hypothèse, alors on doit envisager la possibilité que la structure de la pensée privée, constitutive de l’intelligence des individus humains, soit phylogénétiquement une conséquence des exigences de la communication sociale.
Améliorer l’aérodynamique d’une voiture, gérer un portefeuille boursier, aiguiller des messages dans un commutateur téléphonique, tous ces problèmes techniques peuvent être résolus d’une manière biologique ! Depuis des millions d’années, la nature résout des problèmes très variés (locomotion, perception, protection, camouflage, ...) en utilisant toujours la même « méthode » : les variations génétiques et l’évolution par sélection. Les algorithmes génétiques résultent de la transposition informatique de la génétique et de l’évolution naturelles.
Knowledge elicitation is a critical problem in computerized learning environments that make use of a knowledge base. Fortunately, contrary to usual expertise elicitation situations, didactic scientific knowledge is quite often well formalized, and authors are used to deal with the logical organization of the domain they teach. We want to propose here an original tool, a logical spreadsheet which, if included in an authoring package, will help authors organize concepts and at the same time make both conception and maintenance of didactic knowledge bases much easier.
The lack of a unifying conceptual framework for representing, characterizing and dealing with emergence and emergent phenomena,led us to study the building of such a framework, based on the notions of levels of organization and of levels of detection. Information theory and concepts related to theories of complexity will help us understand the nature of emergent phenomena.
Conceptual knowledge is a fundamental part of what is taught to engineering students. However most efforts in C.A.L. research are devoted to helping students acquire new skills, not concepts. We describe here a research project that aims at providing the student with relevant conceptual explanations whenever these are needed. We try first to describe what a relevant explanation should be and how it could be generated. Then we consider the possibility of coupling the explanation module with a simulation program so that part of the knowledge used in explanations is extracted from the simulation.
Emergence seems to be a central concept in Artificial Life, Cognitive Science, and many other related domains, but the meaning of which is not really agreed upon. In this paper, we critically review some major conceptions of emergence and give some examples of phenomena that are usually considered emergent.
L’étudiant qui cherche à acquérir un savoir-faire, ici la maîtrise de Prolog, a aussi besoin de connaissances conceptuelles. Pour répondre à ce type de besoin, nous avons développé un système qui permet à l’étudiant de simuler l’exécution de son programme Prolog, mais qui lui offre aussi la possibilité de soumettre ce programme au regard critique de SAVANT 3. Ce dernier système a été conçu pour soutenir une argumentation avec l’étudiant. Il est utilisé ici pour critiquer la justesse et l’efficacité du programme écrit par l’étudiant, ce qui permet à celui-ci de corriger d’éventuelles fautes conceptuelles. L’étudiant peut ainsi faire tourner son programme et observer son exécution, pour ensuite "discuter" de ce qu’il a écrit avec SAVANT 3. Nous abordons la question de savoir s’il est possible et souhaitable d’étendre ce qui n’est pour l’instant qu’une maquette à des situations réelles (par ex. programme Prolog complexe) et à des sujets quelconques (économie, architecture de réseau, etc.).
Genetic algorithms are considered as an original way to solve problems, probably because of their generality and of their "blind" nature. But GAs are also unusual since the features of many implementations (among all that could be thought of) are principally led by the biological metaphor, while efficiency measurements intervene only afterwards. We propose here to examine the relevance of these biomimetic aspects, by pointing out some fundamental similarities and divergences between GAs and the genome of living beings shaped by natural selection. One of the main differences comes from the fact that GAs rely principally on the so-called implicit parallelism, while giving to the mutation/selection mechanism the second role. Such differences could suggest new ways of employing GAs on complex problems, using complex codings and starting from nearly homogeneous populations.
We present here an analysis of a specific form of explanation that can be found in naturally occurring conversations, and that may be needed by users of KBS: explanations as answers to surprises that follow a discrepancy between expectations and reality. We describe a tutoring system based on this type of explanation: SAVANT3 systematically looks for reasons to be surprised, so that the student feels compelled to give explanations. We examine the requirements that a system has to meet to be able to produce this kind of explanation based on a preliminary surprise.
We try to show here how the structure of conversations can be explained by taking into account the logical knowledge that the speakers must possess to perform their replies. This study starts with the careful examination of observed excerpts taken from recorded spontaneous conversations. Next we express the minimal knowledge of each speaker by means of a special logical representation (modalities and paradoxical clauses). The PARADISE program is then able to reconstruct the dynamic chaining of replies from this static knowledge. The capabilities of PARADISE allow us to make three points. First they legitimize the use of logic and present it as an essential tool for spontaneous human speech analysis. Second, the strategies used by PARADISE give some indication of the unconscious strategies used by human speakers. And third, we mention how these results could lead to significant improvements of man-machine interface in knowledge-based systems.
On ne saurait imaginer l’enseignement du siècle prochain sans ordinateur. Certains affirment même que quelques séances où l’étudiant interagit avec la machine remplaceront bon nombre d’heures passées à écouter le monologue du professeur, à déchiffrer des livres ou à peiner sur des exercices. Pourtant, malgré la dimension de l’enjeu, personne n’est en mesure, à cette date, d’indiquer la manière de doter l’ordinateur d’une compétence suffisante pour qu’il tienne son rôle dans un tel scénario. Les principes qui sont à la base du système SAVANT3, développé à Télécom Paris, pourraient constituer un ingrédient de cet Enseignement Assisté par Ordinateur du futur.
The present study shows that there is a qualitative difference between concept and skill acquisition, and that it may have some consequences on the design of C.A.I. courseware. We show for instance that concept learning is essentially a logical process, based on rule acquisition or modification, and that conversation (free dialogue) is best suited for concept transmission. This paper describes a mixed-initiative dialogue module which is part of the ‘SAVANT 3’ CAI system.