A Theory and Computational Model of Auditory Selective Attention

The auditory system must separate an acoustic mixture in order to create a perceptual description of each sound source. It has been proposed that this is achieved by a process of auditory scene analysis (ASA) in which a number of streams are produced, each describing a single sound source. Few computer models of ASA attempt to incorporate attentional effects, since ASA is typically seen as a precursor to attentional mechanisms. This assumption may be flawed: recent work has suggested that attention plays a key role in the formation of streams, as opposed to the conventional view that attention merely selects a pre-constructed stream. This study presents a conceptual framework for auditory selective attention in which the formation of groups and streams is heavily influenced by conscious and subconscious attention. This framework is implemented as a computational model comprising a network of neural oscillators which perform stream segregation on the basis of oscillatory correlation. Within the network, attentional interest is modelled as a gaussian distribution in frequency. This determines the connection weights between oscillators and the attentional process the attentional leaky integrator (ALI). A segment or group of segments are said to be attended to if their oscillatory activity coincides temporally with a peak in the ALI activity. The output of the model is an ‘attentional stream’: a description of which frequencies are being attended at each epoch. The model successfully simulates a range of psychophysical phenomena. Furthermore, a number of predictions are made and a psychophysical experiment is conducted to investigate the time course of attentional allocation in a binaural streaming task. The results support the model prediction that attention is subject to a form of ‘reset’ when the attentional focus is moved in space.