shared:research:attention

Attentional enhancement via selection and pooling of early sensory responses in human visual cortex

Our world is filled with multiple distractions - flashing images on a television screen, blinking lights, blaring horns. How is our brain able to focus attention only on relevant stimuli? The brain might turn up the sensory gain of responses (B above) or turn down noise in sensory cortical circuits responding to the relevant stimulus (C above) - thus enhancing our sensitivity. Alternatively (or in addition to), the brain might efficiently select just the most relevant sensory responses for routing to higher perceptual and action related areas (D above) - thus improving behavioral sensitivity by blocking out irrelevant signals. We studied contrast discrimination performance when subjects were cued to a single (focal attention) or multiple locations (distributed attention), while concurrently measuring cortical responses using fMRI. Using computational models we found that improved behavioral performance could be quantitatively accounted for by a model which included efficient selection of sensory signals using a max-pooling selection rule, but not by models that only allowed behavior to be improved by sensitivity enhancement. The max-pooling rule simply selected responses based on the magnitude of response. We conclude that attention enhanced behavioral performance predominantly by enabling efficient selection of the behaviorally relevant sensory signals.
Pestilli, F., Carrasco, M., Heeger, D. J. and Gardner, J. L. (2011) Attentional enhancement via selection and pooling of early sensory responses in human visual cortex. Neuron 72:832-46 DOI <Preview by John T. Serences>	pdf	SI
Abstract To characterize the computational processes by which attention improves behavioral performance, we measured activity in visual cortex with functional magnetic resonance imaging as humans performed a contrast-discrimination task with focal and distributed attention. Focal attention yielded robust improvements in behavioral performance that were accompanied by increases in cortical responses. Using a quantitative analysis, we determined that if performance were limited only by the sensitivity of the measured sensory signals, the improvements in behavioral performance would have corresponded to an unrealistically large (approximately 400%) reduction in response variability. Instead, behavioral performance was well characterized by a pooling and selection process for which the largest sensory responses, those most strongly modulated by attention, dominated the perceptual decision. This characterization predicts that high contrast distracters that evoke large sensory responses should have a negative impact on behavioral performance. We tested and confirmed this prediction. We conclude that attention enhanced behavioral performance predominantly by enabling efficient selection of the behaviorally relevant sensory signals.
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Hara, Y. and Gardner, J. L. (2014) Encoding of graded changes in spatial specificity of prior cues in human visual cortex. Journal of Neurophysiology 112:2834-49. DOI		pdf
Abstract Prior information about the relevance of spatial locations can vary in specificity; a single location, a subset of locations or all locations may be of potential importance. Using a contrast-discrimination task with 4 possible targets, we asked whether performance benefits are graded with the spatial specificity of a prior cue and whether we could quantitatively account for behavioral performance with cortical activity changes measured by blood oxygenation level dependent (BOLD) imaging. Thus we changed the prior probability that each location contained the target from 100 to 50 to 25% by cueing in advance 1, 2 or 4 of the possible locations. We found that behavioral performance (discrimination thresholds) improved in a graded fashion with spatial specificity. However, concurrently measured cortical responses from retinotopically-defined visual areas were not strictly graded; response magnitude decreased when all four locations were cued (25% prior probability) relative to the 100 and 50% prior probability conditions, but no significant difference in response magnitude was found between the 100 and 50% prior probability conditions for either cued or uncued locations. Also, while cueing locations increased responses relative to non-cueing, this cue-sensitivity was not graded with prior probability. Further, contrast-sensitivity of cortical responses, which could improve contrast discrimination performance, was not graded. Instead, an efficient-selection model showed that even if sensory responses do not strictly scale with prior probability, selection of sensory responses by weighting larger responses more can result in graded behavioral performance benefits with increasing spatial specificity of prior information.
Hara Y., Pestilli F. and Gardner J. L. (2014). Differing effects of attention in single-units and populations are well predicted by heterogeneous tuning and the normalization model of attention. Frontiers in Computational Neuroscience 8:12. DOI		pdf
Abstract Single-unit measurements have reported many different effects of attention on contrast-response (e.g. contrast-gain, response-gain, additive-offset dependent on visibility), while functional imaging measurements have more uniformly reported increases in response across all contrasts (additive-offset). The normalization model of attention elegantly predicts the diversity of effects of attention reported in single-units well-tuned to the stimulus, but what predictions does it make for more realistic populations of neurons with heterogeneous tuning? Are predictions in accordance with population-scale measurements? We used functional imaging data from humans to determine a realistic ratio of attention-field to stimulus-drive size (a key parameter for the model) and predicted effects of attention in a population of model neurons with heterogeneous tuning. We found that within the population, neurons well-tuned to the stimulus showed a response-gain effect, while less-well-tuned neurons showed a contrast-gain effect. Averaged across the population, these disparate effects of attention gave rise to additive-offsets in contrast-response, similar to reports in human functional imaging as well as population averages of single-units. Differences in predictions for single-units and populations were observed across a wide range of model parameters (ratios of attention-field to stimulus-drive size and the amount of baseline response modifiable by attention), offering an explanation for disparity in physiological reports. Thus, by accounting for heterogeneity in tuning of realistic neuronal populations, the normalization model of attention can not only predict responses of well-tuned neurons, but also the activity of large populations of neurons. More generally, computational models can unify physiological findings across different scales of measurement, and make links to behavior, but only if factors such as heterogeneous tuning within a population are properly accounted for.