Humans are very good at prioritising competing processing demands. In particular, perception of a salient environmental event can interrupt ongoing processing, causing attention, and accompanying processing resources, to be redirected to the new event. A classic example of this is the well-known Cocktail Party Effect. Not only are we easily able to follow just one conversation when several people are speaking, but the occurrence of a salient phrase in a peripheral conversation stream, such as somebody mentioning our name, causes auditory attention to be redirected. It is also clear that emotions, motivation and physiological state in general, play a key role in such prioritisation In contrast, artificial systems do less well. For example, they are often bad at adjusting their processing to salient events, especially when assessing salience is context dependent. Thus, they may fail to respond appropriately to a salient event or at the other extreme, they may interrupt processing unnecessarily frequently in response to contextually low salience events.
A big hindrance to constructing systems that are sensitive in this respect was that it was not fully understood how humans adapted their behaviour according to salience. However, through the combination of behavioural experimentation and the recent application of brain imaging, modern cognitive and neural sciences are starting to clarify the underlying mechanism. In particular, a number of experimental paradigms, which fall broadly within the study of human attention, have started to reveal how real-time constraints and sensitivity to salient events are reconciled in humans. Two such experimental paradigms are the attentional blink and psychological refactory period.
The proposed PhD will investigate the construction of computational models of these cognitive phenomena, with particular emphasis on neural level modeling. The modeling work will be guided by (and will also guide) the converging evidence now being made available by behavioural studies and brain mapping (both fMRI and EEG). The models that we will construct will both elucidate the human system and inform the construction of artificial systems by using the suite of computational models as general-purpose specifications of such systems.