Document Type



Master of Science


Computer Science

First Adviser

Muñoz-Avila, Héctor


This thesis discusses extensions and modifications to a model of semantic interference originally introduced by Oppenheim et al. The first of the two networks presented extends the original toy model to be able to operate over realistic feature-norm datasets. The second of the two networks presented modifies the operation of this extended network in order to artificially activate non-shared features of competitor words during the selection process. Both networks were extensively tested over a wide range of possible simulation configurations. Metrics were developed to aid in predicting the behavior of these networks given the structure of the data used in the simulations. The networks were also tested for noise tolerance and duration of interference retention over time. The results of these experiments show resultant semantic interference behavior consistent with predictions over the parameter space tested, as well as high noise tolerance and the expected reductions in semantic interference effects as the networks were artificially aged. The new network models could be used as simulation platforms for experiments that wish to examine the emergence of semantic interference over complex or large datasets.