A recent paper does a magnificent job of marshaling many sources of information on attention and developing a theory to fit those pieces of research. (Timothy J. Buschman, Sabine Kastner. From Behavior to Neural Dynamics: An Integrated Theory of Attention. Neuron, 2015; 88 (1): 127 DOI: 10.1016/j.neuron.2015.09.017). “The brain has a limited capacity and therefore needs mechanisms to selectively enhance the information most relevant to one’s current behavior. We refer to these mechanisms as ‘‘attention.’’ Attention acts by increasing the strength of selected neural representations and preferentially routing them through the brain’s large-scale network. This is a critical component of cognition and therefore has been a central topic in cognitive neuroscience. Here we review a diverse literature that has studied attention at the level of behavior, networks, circuits, and neurons. We then integrate these disparate results into a unified theory of attention.”
They concentrate on visual attention because there has been most research in that area. Recent work has pointed to the visual cortex creating a ‘dictionary’ of objects and object features through learning. The learning process captures the regularities of the world and visual representations are coded in this ‘dictionary’. “Importantly, embedding object-based representations will ensure that the system is tolerant to noise as any input will be transformed by the learned object dictionary: signals that match an expected pattern will be boosted, while signals that are orthogonal to representations in the dictionary will be ignored. As the dictionary has been trained to optimally represent the world, this means the system will, in effect, perform pattern completion, settling on nearby ‘‘known’’ representations, even when provided with a noisy input.” These representations are what top-down and bottom-up attention controls act on.
Their theory proposes a cascade and its regular reset.
(1) Attention can either be (a) automatically grabbed by salient stimuli or (b) guided by task representations in frontal and parietal regions to specific spatial locations or features.
(2) The pattern-completion nature of sensory cortex sharpens the broad top-down attentional bias, restricting it to perceptually relevant representations. Interactions with bottom-up sensory drive will emphasize specific objects.
(3) Interneuron-mediated lateral inhibition normalizes activity and, thus, suppresses competing stimuli. This results in increased sensitivity and decreased noise correlations.
(4) Lateral inhibition also leads to the generation of high-frequency synchronous oscillations within a cortical region. Inter-areal synchronization follows as these local oscillations synchronize along with the propagation of a bottom-up sensory drive. Both forms of synchrony act to further boost selected representations.
(5) Further buildup of inhibition acts to ‘‘reset’’ the network, thereby restarting the process. This reset allows the network to avoid being captured by a single stimulus and allows a positive-only selection mechanism to move over time.