Abstract:The endophenotype concept was introduced into the literature by Gottesman and Gould in 1973 to bridge the gap between complex multi-factorial disease processes and the underlying genetic basis for such diseases. The concept has been useful in developing a more comprehensive approach to understanding a number of disease processes through the application of extra-genetic analytical approaches. The approach focuses on aspects of disease processes that are more directly linked to measurable functionand hence will tend to be more informative with respect to disease process identifi cation than the complex genetics which ultimately may underlie the disease process. The concept has been applied most rigorously to identifying psychopathologies such as schizophrenia. Recent work has established its successful application to a wider range of cognitive disorders such as major depression and anti-social behaviour. In the process, a considerable body of cognitive processing features has been elucidated in the context of investigating the cognitive basis for these cognitive disorders. These cognitive processing features have been termed 'endophenocognitypes.' This concept retains the scientifi c framework proposed by Gottesman, but emphasizes the building blocks relevant to cognitive functioning directly. The focus in this paper is to discuss why the endophenocognitypes might should be deployed in the context of modelling and the subsequent development of UIs.
Keywords:affective computing; cognitive performance support; endophenocognitype; endophenotype; man-machine interaction; user interface design.Reference to this paper should be made as follows: Revett, K. (2013)
Biographical note:The author is an associate professor at the British University in Egypt, where he teaches a variety of subjects within the computational neuroscience and artifi cial intelligence domains. His research focuses on the deployment of cognitive signals for UI interactions and related topics such as game play, artifi cial intelligence based game engine design and EEG signal processing. He received his PhD in computational neuroscience from the University of Maryland, College Park, USA in 1999. Since that time, he has engaged in studies examining the role of the brain in person/machine interactions.