A quantitative framework of attentional selection

Attentional selection is an essential prerequisite for adaptive behaviour. The brain receives a vast amount of information from our sensory organs. Fully processing all of this information would exceed the capacity of the human brain, therefore attention selects only some of this information for in-depth processing at the expense of other, less relevant information. Attention is a key concept in the understanding of mental abilities: it critically influences perception, memory, cognition, emotion and consciousness, so that what we see, remember, think, feel and experience is, to a large extent, a function of selective attention.

Selectively attending to a stimulus enhances its processing in visual cortex and increases accuracy and speed of motor responses. While the connection between these findings seems obvious, our understanding of this connection is surprisingly vague: we know that, in the average of many trials, measures of cortical processing and behavioural performance are enhanced when the eliciting stimulus is attended, but we have very limited understanding of how these attentional effects are quantitatively related.

The proposed project aims to develop a quantitative model of visual attention that directly links electrophysiological measures of stimulus processing in visual cortex with behavioural outcomes. This will be achieved in a sequence of three connected objectives:

First, we will examine how concurrent attentional selection of different dimensions (e.g. location, colour, orientation or motion) affects stimulus processing. This will allow us to better understand attentional selection in complex realistic situations and facilitates quantitative modelling in the subsequent experiments by restricting model parameters.

Second, we will conduct a series of three EEG experiments in order to uncover the quantitative connections between attentional modulation of stimulus processing in visual cortex and behavioural measures. This will significantly enhance our understanding of how attention supports adaptive behaviour.

Third, we will investigate how monitoring of behavioural performance is used to readjust attentional model parameters over time in order to optimise subsequent performance. This will help us understand why performance on the same task fluctuates over time. It also provides important information on why attentional model parameters take on specific values.

The answers to these questions fundamentally contribute to our understanding of visual attention and perception. This research may have implications for research in special populations (e.g. children and older participants, patients with attentional disorders) as the experimental protocols and modelling approaches of the present project could be applied to gain a better understanding of group differences. Additionally, this work may facilitate the further development of Brain Computer Interfaces (BCIs), which allow subjects to convey their intentions by shifting attention and which commonly employ similar technical approaches as the present project.