SENSORY MAPPING ADAPTATION UNDER MULTIPLE TASK SCENARIOS

Demands on auditory perception change constantly with natural changes in everyday acoustic environments. Mechanisms, such as attentional feedback, direct the brain to adapt processing of the incoming signal to maximize its ability to detect the presence of a sound of interest or enhance its representation. These top-down feedback processes induce adaptation of the spectrotemporal representation of incoming sounds in a manner that enhances our ability to perform the desired task. In this work, we propose a computational model to implement and study sensory mapping adaptation under different task-demands. We propose a common processing framework to examine how sensory mapping adaptation manifests under different task-driven conditions like speech enhancement and robust speech activity detection. Objective measures of speech enhancement and discrimination are used to quantify the impact of the adaptation under different contexts and its impact on performance outcomes.