Modern techniques have been applied to brain modeling, based on recent approaches in the artificial intelligence field that use brain-like “connectionistic” computational architectures. The model proposed by Cohen and Servan-Schreiber uses a gain parameter which they identify with dopamine function. They apply their model to neuroleptically treated schizophrenia patients who show improved task performance which they link to increased dopamine function and increased gain in the prefrontal cortex. However, evidence indicates that antipsychotic medications block dopamine (especially D2) receptors, decreasing mesolimbic and mesocortical dopamine function. If therapeutic dosages of neuroleptics diminish dopamine function, this would decrease gain in context modules needed for adequate task performance. Schizophrenia patients would perform more poorly by further reducing gain in their already compromised context modules. The current investigators suggest three possible ways to resolve this difficulty, to explain why normals perform more poorly when taking neuroleptics, although acute schizophrenia patients' performance may be enhanced in several areas. Evidence would suggest that multiple processes occur simultaneously in neuroleptically treated patients with some processes counterbalancing others.