Serial Order in Reading Aloud: Connectionist Models and Neighborhood Structure

Besides averaging over the 30 trials per condition, each mean of these charts also averages over the two input distributionconditions and the linear and quadratic function condition, as these four cases are frequently observed violations of the statistical assumptions in nonlinear function approximationwith locally linear models. In Figure Ib the number of factors equals the underlying dimensionality of the problem, and all algorithms are essentially performing equallywell. For perfectly Gaussian distributions in all random variables (not shown separately), LWFA's assumptions are perfectly fulfilled and it achieves the best results, however, almost indistinguishable closely followed by LWPLS. For the ''unequal noise condition", the two PCA based techniques, LWPCA and LWPCR, perform the worst since--as expected-they choose suboptimal projections. However, when violating thestatistical assumptions, LWFA loses parts of its advantages, such that the summary resultsbecome fairly balanced in Figure lb. The quality of function fitting changes significantly when violating the correct number of factors, as illustrated in Figure I a,c.