The standard view of classical cognitive science stated that cognition consists in the manipulation of language-like structures according to formal rules. Since cognition is ‘linguistic’ in itself, according to this view language is just a complex communication system and does not influence cognitive processes in any substantial way. This view has been criticized from several perspectives and a new framework (Embodied Cognition) has emerged that considers cognitive processes as non-symbolic and heavily dependent on the dynamical interactions between the cognitive system (...) and its environment. But notwithstanding the successes of the embodied cognitive science in explaining low-level cognitive behaviors, it is still not clear whether and how it can scale up for explaining high-level cognition. In this paper we argue that this can be done by considering the role of language as a cognitive tool: i.e. how language transforms basic cognitive functions in the high-level functions that are characteristic of human cognition. In order to do that, we review some computational models that substantiate this view with respect to categorization and memory. Since these models are based on a very rudimentary form of non-syntactic ‘language’ we argue that the use of language as a cognitive tool might have been an early discovery in hominid evolution, and might have played a substantial role in the evolution of language itself. (shrink)

The nature–nurture controversy has been with us since it was first outlined by Plato and Aristotle. Nobody likes it anymore. All reasonable scholars today agree that genes and environment interact to determine complex cognitive outcomes. So why does the controversy persist? First, it persists because it has practical implications that cannot be postponed (i.e., what can we do to avoid bad outcomes and insure better ones?), a state of emergency that sometimes tempts scholars to stake out claims they cannot defend. (...) Second, the controversy persists because we lack a precise, testable theory of the process by which genes and the environment interact. In the absence of a better theory, innateness is often confused with (1) domain specificity (outcome X is so peculiar that it must be innate), (2) species specificity (we are the only species who do X, so X must lie in the human genome), (3) localization (outcome X is mediated by a particular part of the brain, so X must be innate), and (4) learnability (we cannot figure out how X could be learned, so X must be innate). We believe that an explicit, plausible theory of interaction is now around the corner, and that many of the classic maneuvers to defend or attack innateness will soon disappear. In the interim, some serious errors can be avoided if we keep these confounded issues apart. That is the major goal of this chapter: not to attack innateness but to clarify what claims about innateness are (and are not) about. (shrink)

Simulations with neural networks living in a virtual environment can be used to explore and test hypotheses concerning concepts and language. The advantages that result from this approach include (1) the notion that a concept can be precisely defined and examined, (2) that concepts can be studied in both nonverbal and verbal artificial organisms, and (3) concepts have properties that depend on the environment as well as on the organism's adaptive behavior in response to the environment.

Human beings possess external stores in which they put all sorts of goods to use them at some later time. In this paper we investigate this typically human adaptation using agent-based simulations. We show that the use of external stores explains many aspects of human life, allowing the agents to reduce their dependence on both the environment and the current state of their body and to be more efficient in extracting the energy contained in the environment. We analyse the spatial (...) behaviour of agents with external stores located in specific positions of the environment and we find that these agents tend to develop a sedentary life. We discuss how stores can be at the origin of many human mental and social phenomena such as the acquisition of a more extended temporal perspective, specialisation in producing different types of goods, and exchange of goods. (shrink)

Human beings possess external stores in which they put all sorts of goods to use them at some later time. In this paper we investigate this typically human adaptation using agent-based simulations. We show that the use of external stores explains many aspects of human life, allowing the agents to reduce their dependence on both the environment and the current state of their body and to be more efficient in extracting the energy contained in the environment. We analyse the spatial (...) behaviour of agents with external stores located in specific positions of the environment and we find that these agents tend to develop a sedentary life. We discuss how stores can be at the origin of many human mental and social phenomena such as the acquisition of a more extended temporal perspective, specialisation in producing different types of goods, and exchange of goods. (shrink)

Human beings possess external stores in which they put all sorts of goods to use them at some later time. In this paper we investigate this typically human adaptation using agent-based simulations. We show that the use of external stores explains many aspects of human life, allowing the agents to reduce their dependence on both the environment and the current state of their body and to be more efficient in extracting the energy contained in the environment. We analyse the spatial (...) behaviour of agents with external stores located in specific positions of the environment and we find that these agents tend to develop a sedentary life. We discuss how stores can be at the origin of many human mental and social phenomena such as the acquisition of a more extended temporal perspective, specialisation in producing different types of goods, and exchange of goods. (shrink)

The theme of Aesthetics in Present Future concerns the new chances the arts have and the deep changes they are undergoing, due to the new media, and the digital world in which we are growingly immersed. That this world is to be understood from an aesthetic point of view, become clear if we think of how much of what we produce, and observe and study is offered through images in particular and perceptual means in general.

This article aims at contributing to the discussion about the relationships between ICT, computer science and policy-making by focusing on agent-based social simulation. Enabled, from a technical point of view, by the developments of Distributed Artificial Intelligence in the 1990s and by the features of the object-oriented programming paradigm, agent-based social simulations are a tool for the analysis of social dynamics that can be used also to support the design and the evaluation of public policies. After a brief description of (...) social simulation paradigm and of its impact on social sciences, the paper presents a simple agent-based model devised to analyze, even if in a very abstract way, a phenomenon that can rouse the interest of legal scientists: the interplay between damaging behaviors, punishment and social mechanisms of learning and imitation. Our goal is to show how agent-based simulation can be used not only to illuminate basic mechanisms underlying social phenomena but also to reflect, in an innovative way, on how society can deal with them. (shrink)

Mirror neurons may play a role in representing not only signs but also their meaning. Because actions are the only aspect of behavior that are inter-individually accessible, interpreting meanings in terms of actions might explain how meanings can be shared. Behavioral evidence and artificial life simulations suggest that seeing objects or processing words referring to objects automatically activates motor actions.

The article examines the question of how learning multiple tasks interacts with neural architectures and the flow of information through those architectures. It approaches the question by using the idealization of an artificial neural network where it is possible to ask more precise questions about the effects of modular versus nonmodular architectures as well as the effects of sequential versus simultaneous learning of tasks. A prior work has demonstrated a clear advantage of modular architectures when the two tasks must be (...) learned at the same time from the start, but this advantage may disappear when one task is first learned to a criterion before the second task is undertaken. Indeed, in some cases of sequential learning, nonmodular networks achieve success levels comparable to those of modular networks. In particular, if a nonmodular network is to learn two tasks of different difficulty and the more difficult task is presented first and learned to a criterion, then the network will learn the second, easier one without permanent degradation of the first one. In contrast, if the easier task is learned first, a nonmodular task may perform significantly less well than a modular one. It seems that the reason for this difference has to do with the fact that the sequential presentation of the more difficult task first minimizes interference between the two tasks. More broadly, the studies summarized in this article seem to imply that no single learning architecture is optimal for all situations. (shrink)