This is the first systematic investigation of the very long-term effects of severe closed head injury (CHI) on objective measures of memory, and the first to employ both a normal control group and an 'other injury' control group consisting of spinal cord injury (SCI) patients. The CHI group displayed significantly poorer performance on every memory measure, and the effect sizes were large. This impairment in episodic memory is neither due to pre-injury nor post-injury differences between CHI and normal control subjects because the same differences were found when the CHI group was compared to a group of SCI patients. The findings demonstrate severe impairment in learning and retention many years after sustaining a severe CHI, which is likely in part due to the bilateral hippocampal damage shown in neuropathological studies. This life-long memory impairment needs to be addressed by community service programs.

Neural networks can be used as a tool in the explanation of neuropsychological data. Using the Hebbian Learning Rule and other such principles as competition and modifiable interlevel feedback, researchers have successfully modeled a widely used neuropsychological test, the Wisconsin Card Sorting Test. One of these models is reviewed here and extended to a qualitative analysis of how verbal fluency might be modeled, which demonstrates the importance of accounting for the attentional components of both tests. Difficulties remain in programming sequential cognitive processes within a parallel distributed processing (PDP) framework and integrating exceedingly complex neuropsychological tests such as Proverbs. PDP neural network methodology offers neuropsychologists co-validation procedures within narrowly defined areas of reliability and validity.

No abstract

No abstract

No abstract