Using a cognitive architecture to examine what develops

Different theories of development propose alternative mechanisms by which development occurs. Cognitive architectures can be used to examine the influence of each proposed mechanism of development while keeping all other mechanisms constant. An ACT-R computational model that matched adult behavior in solving a 21-block pyramid puzzle was created. The model was modified in three ways that corresponded to mechanisms of development proposed by developmental theories. The results showed that all the modifications (two of capacity and one of strategy choice) could approximate the behavior of 7-year-old children on the task. The strategy-choice modification provided the closest match on the two central measures of task behavior (time taken per layer, r = .99, and construction attempts per layer, r = .73). Modifying cognitive architectures is a fruitful way to compare and test potential developmental mechanisms, and can therefore help in specifying “what develops.

Knowledge-based cascade-correlation

Neural network modeling typically ignores the role of knowledge in learning by starting from random weights. A new algorithm extends cascade-correlation by recruiting previously learned networks as well as single hidden units. Knowledge-based cascade-correlation (KBCC) finds, adapts, and uses its relevant knowledge to speed learning. In this paper, we describe KBCC and illustrate its performance on a small, but clear problem. 1 Existing knowledge and new learning Most research on learning in neural networks has assumed that learning is done "from scratch", without the influence of previous knowledge. However, it is clear that when people learn, they make extensive use of their existing knowledge [1-3]. Use of prior knowledge in learning is responsible for the ease and speed with which people are able to learn new material, and for interference effects. A major limitation of neural network models of human cognition and learning is that these networks begin learning from only a random set of connection weights. This implements a tabula rasa view of each learning task that few contemporary researchers would accept. In this paper, we propose a fundamental extension of cascade-correlation (CC), a generative learning algorithm that has been useful in the simulation of cognitive development [4-9]. CC builds its own network topology by recruiting new hidden units into a feed-forward network as needed in order to reduce network error [10]. Our extension, called knowledge-based cascade-correlation (KBCC) recruits previously learned networks in addition to the untrained hidden units recruited by CC. We refer to existing networks as potential source knowledge and to a current learning task as a target. Previously learned source networks compete with each other and with single hidden units to be recruited into the target network. KBCC is similar to recent neural network research on transfer [11], sequentia

A Biased Review of Sociophysics

Various aspects of recent sociophysics research are shortly reviewed: Schelling model as an example for lack of interdisciplinary cooperation, opinion dynamics, combat, and citation statistics as an example for strong interdisciplinarity.Comment: 16 pages for J. Stat. Phys. including 2 figures and numerous reference