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Sara Aghajari, Sam Ling; Attentional load parametrically modulates responses within human FEF and early visual cortex. Journal of Vision 2017;17(10):527. doi: https://doi.org/10.1167/17.10.527.
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Although attention is known to modulate visual responses, the computations by which this modulatory influence emerges remain unclear. Here, we used fMRI to parametrically assess the nature of modulatory signals in regions of the putative dorsal attentional network and their effect on the responses within visual cortex. To do so, we developed a paradigm that allowed for fine-grained manipulation of attentional load, a feature lacking in common attentional tasks. In this task, which we coin the Numerosity Judgement Paradigm (NJP), participants viewed a dense array of small, colored (red or blue) rectangles near fixation. They were asked to judge, from trial to trial, which of the two colors was dominant in this array. Changing the ratio of the rectangles with the dominant color enabled us to modulate the amount of attentional load directed toward this array; when the ratio was close to 1, accomplishing the task did not require much attention, but when the ratio was closer to 0.5, more attentional effort should have been directed toward the array. Around the NJP stimulus, an annulus comprised of pink noise was shown to participants; this allowed us to test the degree of attentional withdrawal from fixation. As expected, we observed a drop in performance for the NJP as a function of task difficulty, which coincided with an enhancement in activity within the dorsal attentional network, particularly in the frontal eye fields (FEF). This parametric increase in the activity of the attentional areas was accompanied by a graded decrease in responses within early visual areas corresponding to the unattended noise stimulus. These results give a precise profile of the relationship between the rise and fall of activity within attentional networks and visual areas, demonstrating how attention operates as a modulatory signal.
Meeting abstract presented at VSS 2017
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