ABAIS: Affect and Belief Adaptive Interface System

Currently, the majority of decision-support systems assume normative performance and fail to adapt to individual differences. This is particularly true with respect to affective states and individual beliefs, which can have profound impact on performance, particularly in complex, crisis situations. We propose to develop an Affect and Belief Adaptive Interface System (ABAIS) capable of compensating for performance biases caused by users' affective states and active beliefs. The ABAIS architecture implements an adaptive methodology consisting of four steps: sensing/inferring user affective state and performance-relevant beliefs; identifying their potential impact on performance; selecting a compensatory strategy; and implementing this strategy in terms of specific GUI adaptations. ABAIS provides a generic adaptive framework for exploring a variety of user state assessment methods (e.g., knowledge-based, self-reports, diagnositc tasks, physiological sensing), and GUI adaptation strategies (e.g., content- and format-based). The ABAIS performance bias prediction is based on existing empirical findings from emotion research and knowledge of specific task requirements. We propose to develop ABAIS using COTS software and an existing decision-support pilot-aiding system GUI, and demonstrate its functionality in the context of an Air Force combat ask, which is particularly susceptible to anxiety-induced biases.