Humans are adept at distinguishing between stimuli that are very similar, an ability that is particularly crucial when the outcome is of serious consequence (e.g. for a surgeon or air traffic controller). Traditionally, selective attention was thought to facilitate perception by increasing the gain of sensory neurons tuned to the defining features of a behaviorally relevant object (e.g. color, orientation, etc.). In contrast, recent mathematical models counter-intuitively suggest that in many cases attentional gain should be applied to neurons that are tuned away from relevant features, especially when discriminating highly similar stimuli. Here we used psychophysical methods to critically evaluate these ‘ideal observer’ models. The data demonstrate that attention enhances the gain of the most informative sensory neurons, even when these neurons are tuned away from the behaviorally relevant target feature. Moreover, the degree to which an individual adopted optimal attentional gain settings by the end of testing predicted success rates on a difficult visual discrimination task, as well as the amount of task improvement that occurred across repeated testing sessions (learning). Contrary to most traditional accounts, these observations suggest that the primary function of attentional gain is not simply to enhance the representation of target features, but to optimize performance on the current perceptual task. Additionally, individual differences in gain suggest that the operating characteristics of low-level attentional phenomena are not stable trait-like attributes and that variability in how attention is deployed may play an important role in determining perceptual abilities.

Sameer presented a poster at SfN 2008 in Washington DC titled "Estimating the influence of spatial attention in human visual cortex using voxel-based tuning functions". Poster reprint can be found here.

In order to form stable perceptual representations, populations of sensory neurons must pool their output to
overcome physiological noise; selective attention is then required to ensure that behaviorally relevant stimuli
dominate these ‘population codes’ to gain access to awareness. However, the role that attention plays in shaping
population response profiles has received little direct investigation, in part because most traditional
neurophysiological methods cannot simultaneously assess changes in activity across large populations of sensory
neurons. Based on single-unit recording studies, current theories hold that attending to a relevant feature sharpens
the population response profile and improves the signal-to-noise ratio of the resulting perceptual representation.
Here, we test this hypothesis using fMRI and an analysis approach that estimates the influence of feature-based
attentional modulations on population response profiles. We first derive orientation tuning functions for single
voxels in human primary visual cortex, and then use these tuning functions to sort voxels according to their
orientation preference. We then show that selective attention systematically biases population response profiles so
that behaviorally relevant stimuli are represented in the visual system at the expense of behaviorally irrelevant
stimuli. Collectively, the present results (1) provide a new approach for precisely characterizing feature-selective
responses in human sensory cortices and (2) reveal how behavioral goals can shape population response profiles
to support the formation of coherent perceptual representations.

link to paper here

link to paper here