Deficits in learning and memory are among the most robust correlates of schizophrenia. It has been hypothesized that these deficits are in part due to reduced conscious recollection and increased reliance on familiarity assessment as a basis for retrieval. The Remember-Know (R-K) paradigm was administered to 35 patients with chronic schizophrenia and 35 healthy controls. In addition to making "remember" and "know" judgments, the participants were asked to make forced choice recognition judgments with regard to details about the learning episode. Analyses comparing response types showed a significant reduction in "remember" responses and a significant increase in "know" responses in schizophrenia patients relative to controls. Both patients and controls recalled more details of the learning episode for "remember" compared to "know" responses, although, in particular for "remember" responses, patients recalled fewer details compared with controls. Notably, patients recognized fewer inter-item but not intra-item stimulus features compared with controls. These findings suggest deficits in organizing and integrating relational information during the learning episode and/or using relational information for retrieval. A Dual-Process Signal Detection interpretation of these findings suggests that recollection in chronic schizophrenia is significantly reduced, while familiarity is not. Additionally, a unidimensional Signal Detection Theory interpretation suggests that chronic schizophrenia patients show a reduction in memory strength, and an altered criterion on the memory strength distribution for detecting new compared with old stimuli but not for detecting stimuli that are remembered versus familiar. Taken together, these findings are consistent with a deficit in recollection and increased reliance on familiarity in making recognition memory judgments in chronic schizophrenia.

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<strong>Hallway testing, Remote Usability testing, Expert review, Automated expert review and A/B testing are the methods commonly used for Usability testing. However, there is no reliable system that integrates Brain Computer Interface (BCI) into the testing process with focus given towards user emotion analysis using electroencephalography (EEG) signals. This paper proposes a system that would be able to identify user emotions while they are conducting usability tests and the results would be able to increase the accuracy of the usability test. In the proposed system the results of the usability test would be displayed in real time on a dashboard and a summary report can be generated for distribution. </strong>

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