The success of a connectionist model of cognitive development on the balance scale task is due to manipulations which impede convergence of the backpropagation learning algorithm. The model was trained at different levels of a biased training environment with exposure to a varied number of training instances. The effects of weight updating method and modifying the network topology were also examined. In all cases in which these manipulations caused a decrease in convergence rate, there was an increase in the proportion of psychologically realistic nms. W e conclude that incremental connectionist learning is not sufficient for producing psychologically successful connectionist balance scale models, but must be accompanied by a slowing of convergence.

The Cascade-Correlation connectionist architecture was used to model human cognitive development on balance scale problems. The simulations were characterized by gradual expansion of the training patterns, training bias in favor of equal distance problems, and test problems balanced for torque distance. Both orderly rule stages and torque difference effects were obtained. Analyses of the development of network structure revealed progressive sensitivity to distance information. It was noted that information salience effects, such as that for torque difference, are particularly difficult to capture in symbolic level models.

Study of cognitive development on the balance scale task has inspired a wide range of human and computational work. The task requires that children predict the outcome of placing a discrete number of weights at various distances on either side of a fulcrum. The current project examined the adequacy of the symbolic learning algorithm C4.5 as a model of cognitive transition on this task. Based on a set of novel assumptions, our C4.S simulations were able to exhibit regularities found in the human data including orderly stage progression, U-shaped development, and the torque difference effect. Unlike previous successful models of the task, the current model used a single free parameter, is not restricted in the size of the balance scale that it can accommodate, and does not require the assumption of a highly structured output representation or a training environment biased towards weight or distance information. The model makes a number of predictions differing from those of previous computational efforts.

Work on surface sensing in bacterial biofilms has focused on how cells transduce sensory input into cyclic diguanylate (c-di-GMP) signaling, low and high levels of which generally correlate with high-motility planktonic cells and low-motility biofilm cells, respectively. Using Granger causal inference methods, however, we find that single-cell c-di-GMP increases are not sufficient to imply surface commitment. Tracking entire lineages of cells from the progenitor cell onward reveals that c-di-GMP levels can exhibit increases but also undergo oscillations that can propagate across 10 to 20 generations, thereby encoding more complex instructions for community behavior. Principal component and factor analysis of lineage c-di-GMP data shows that surface commitment behavior correlates with three statistically independent composite features, which roughly correspond to mean c-di-GMP levels, c-di-GMP oscillation period, and surface motility. Surface commitment in young biofilms does not correlate to c-di-GMP increases alone but also to the emergence of high-frequency and small-amplitude modulation of elevated c-di-GMP signal along a lineage of cells. Using this framework, we dissect how increasing or decreasing signal transduction from wild-type levels, by varying the interaction strength between PilO, a component of a principal surface sensing appendage system, and SadC, a key hub diguanylate cyclase that synthesizes c-di-GMP, impacts frequency and amplitude modulation of c-di-GMP signals and cooperative surface commitment.