Associative memory in chronic schizophrenia: a computational model.

We developed a computer model to simulate associative memory recall of patients with chronic schizophrenia. Model inputs consisted of words derived from normative data that differed in terms of connectivity and network size, with the former quantitatively represented by parametric weights and the latter by the specific number of word associates that formed a particular network. Previous behavioral studies of normal subjects indicated better recall for words of high connectivity-small network (HCSN), followed by low connectivity-small network (LCSN), high connectivity-large network (HCLN), and low connectivity-large network (LCLN). This pattern of recall differed from that observed in behavioral studies of schizophrenic patients, which showed better recall for high connectivity words, regardless of network size. Holding constant network size while manipulating connection weights effectively simulated this schizophrenic pattern of recall. That is, manipulation of parametric weights coupled with a slight increase in noise significantly and reliably elicited the response pattern of abnormal connectivity demonstrated in the prior behavioral study of patients with chronic schizophrenia. An increase in noise was a necessary, but insufficient step in modeling the response pattern of abnormal connectivity. These findings provide support for the use of computational models to investigate dynamics of associative word recall in patients with chronic schizophrenia.