Philosophical Psychology 13 (1):5 – 45 (2000)
|Abstract||This paper outlines an original interactivist-constructivist (I-C) approach to modelling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realized in the brain. Two key ideas for conceptualizing intelligence within this framework are developed. These are: (1) intelligence is centrally concerned with the capacity for coherent, context-sensitive, self-directed management of interaction; and (2) 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 modelling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski's recent neural constructivism 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 fourfold reflection on the divergence in cognitive modelling philosophy between the I-C and the traditional computational information processing approaches.|
|Keywords||No keywords specified (fix it)|
|Through your library||Configure|
Similar books and articles
Pavel Prudkov (2010). A View on Human Goal-Directed Activity and the Construction of Artificial Intelligence. Minds and Machines 20 (3):363-383.
Johan J. Bolhuis (1997). Learning, Development, and Synaptic Plasticity: The Avian Connection. Behavioral and Brain Sciences 20 (4):559-560.
Ron Sun, The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach.
Chris Thornton (1997). Brave Mobots Use Representation: Emergence of Representation in Fight-or-Flight Learning. Minds and Machines 7 (4):475-494.
Enrico Blanzieri (1997). Dynamical Learning Algorithms for Neural Networks and Neural Constructivism. Behavioral and Brain Sciences 20 (4):559-559.
Robert P. Farrell & C. A. Hooker (2007). Applying Self-Directed Anticipative Learning to Science II: Learning How to Learn Across a Revolution in Early Ape Language Research. Perspectives on Science 15 (2):222-255.
R. P. Farrell & C. A. Hooker (2009). Error, Error-Statistics and Self-Directed Anticipative Learning. Foundations of Science 14 (4).
Robert P. Farrell & C. A. Hooker (2007). Applying Self-Directed Anticipative Learning to Science I: Agency, Error, and the Interactive Exploration of Possibility Space in Early Ape-Langugae Research. Perspectives on Science 15 (1):87-124.
C. A. Hooker (2009). Interaction and Bio-Cognitive Order. Synthese 166 (3):513 - 546.
Added to index2009-01-28
Total downloads13 ( #87,931 of 549,087 )
Recent downloads (6 months)2 ( #37,333 of 549,087 )
How can I increase my downloads?