Abstract This paper outlines an original interactivist-constructivist (I-C) approach to modeling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realised in the brain. Two key ideas for conceptualising intelligence within this framework are developed. These are: (i) intelligence is centrally concerned with the capacity for coherent, contextsensitive, self-directed management of interaction, (ii) the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process modulation which allows a system to ‘steer ’ itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified (e.g. genetically) learning is an important component of intelligence. A model of self-directed anticipative learning (SDAL) is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined, problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modeling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski’s recent neural constructivism (NC) paradigm, enriched by Montague and Sejnowski’s dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a four-fold reflection on the divergence in cognitive modeling philosophy between the I-C and the traditional computational information processing (CIP) approaches. 1.

Forty-two! yelled Loonquawl. Is that all you ve got to show for seven and a half million years w ork? I checked it very thoroughly, said the computer, and that quite definitely is the answer. I think the problem, to be quite honest with you, is that you ve never actually known what the question is. But it was the Great Question! The Ultimate Question of Life, the Universe and Everything, howled Loonquawl. Yes, sai d Deep Thou ght wit h th e air of on e wh o suffers fools gla dly, but what a ctually is it? Douglas Adams, The Hitchhikers Guide to the Galaxy 1

In this paper we outline a theory of the nature of self-directed agents. On our account what is distinctive about self-directed agents is that they are able to anticipate interaction processes and evaluate their performance. This allows self-directed agents to modify their behaviour context sensitively so as to improve the achievement of goals, and in certain instances construct new goals. We contrast self-directedness with reactive action processes that are not modifiable by the agent, though they may be modified by supra-agent processes such as populational adaptation or external design. Self-directedness lies at the nexus of a set of issues concerning the evolution and nature of intentionality, intelligence and agency. It provides the core of a biologically grounded account of intentional agency. 1