In this paper we present a new model for the mechanism underlying what is traditionally known in immunology as the “selfnonself” distinction. It turns out that in operational terms, the distinction effected by this model of the immune system is between a sufficiently numerous set of antigens present from the start of the ontogeny of the system on the one hand, and isolated antigens first introduced after the system has reached maturity on the other. The coincidence between this “founder versus late” distinction and the traditional “somatic self-foreign pathogen” one is essentially contingent, an example of the purely opportunistic tinkering characteristic of biological organization in general. We conclude that the so-called “self-nonself” distinction in immunology is a misleading misnomer. This raises the question as to what would genuinely count as a “self-nonself” distinction, a fundamental question for biology in general and Artificial Life in particular.

Excerpt: I claim that a mechanism that reconstructs and re-coordinates processes, rather than stores and retrieves labeled descriptions or procedures, is more consistent with what we know about human memory and perception (Clancey 1991, 1994). Such a process memory possibly cannot be built today, because we don’t know how to build the kind of self-organizing mechanism that is required (cf. Freeman 1991). But articulating how human cognition is different from a classical architecture helps delineate what aspects of situated robotic designs are still cast in the classical mold and remain to be freed of prevailing assumptions about the nature of memory and representations.

Demonstrates that Vygotskian theory can support two opposing interpretations: supporting reform and undermining reform. Discussion is organized by: sociocultural perspectives, Marxist influences on historical analysis and the role of labor, semiotics and psychological tools, dialectic of thought and language, conceptual development, and learning and development.

Presents a theory of intellectual development in which human development depends on environment, self is autonomous and communal, diversity and dissent are anticipated, emotional intelligence is acknowledged, abstraction is reconceptualized and placed in a dialectic, learning is a reciprocal activity, and classrooms are interactions among interactions.

Excerpt: I draw from recent developments in philosophy, biology, ecological thought, phenomenology, and curriculum theory in an effort to re-formulate a response to the question, Why teach mathematics?

This article focuses on an artificial life approach to some important problems in machine learning such as statistical discrimination, curve approximation, and pattern recognition. We describe a family of models, collectively referred to as semi-algebraic networks (SAN). These models are strongly inspired by two complementary lines of thought: the biological concept of autopoiesis and morphodynamical notions in mathematics. Mathematically defined as semi-algebraic sets, SANs involve geometric components that are submitted to two coupled processes: (a) the adjustment of the components (under the action of the learning examples), and (b) the regeneration of new components. Several examples of SANs are described, using different types of components. The geometric nature of SANs gives new possibilities for solving the bias/variance dilemma in discrimination or curve approximation problems. The question of building multilevel semi-algebraic networks is also addressed, as they are related to cognitive problems such as memory and morphological categorization. We describe an example of such multilevel models.