It is well known that the brain (especially the cortex) is structurally separable into two hemispheres. Many neuropsychological studies show that the process of ambiguity resolution requires the intact functioning of both cerebral hemispheres. Moreover, these studies suggest that while the Left Hemisphere (LH) quickly selects one alternative, the Right Hemisphere (RH) maintains alternate meanings. However, these hemispheres are connected through the corpus callosum and presumably the exchange of information is useful. In addition, many works show that the Left Hemisphere (LH) is more influenced by the phonological aspect of written words whereas lexical processing in the Right Hemisphere (RH) is more sensitive to visual form. This distinction suggests that the interconnections between the hemispheres may be used to strengthen or correct incorrect interpretations by one hemisphere. We test this hypothesis by (Ⅰ) postulating that in the Left Hemisphere (LH) orthography, phonology and semantics are interconnected while (Ⅱ) the Right Hemisphere (RH), phonology is not connected directly to orthography and hence its influence must be mitigated by semantical processing (Ⅲ) seeing if corrections in ambiguous word processing can be aided by information in the other hemisphere. We investigate this by complementary human psychophysical experiments and by dual (one RH and one LH) computational neural network model architecturally modified from Kawamoto's (1993) model to follow our hypothesis. Since the different models have different rates of convergence, we test (Ⅲ) by halting processing, and using an analogue to priming to compare the rate of convergence to a corrected semantics in the LH working alone and working with information obtained from the RH at the same point in processing. In this paper we present results of the computational model and show that (Ⅰ) the results obtained from the two hemispheres separately are analogous to the human experiments and (Ⅱ) the use of the RH information does indeed help such corrections.
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