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Model-Free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data

Fig 7

Effects of attention on tuning curves.

Scatter plots of various features in the afix versus the ain condition. Orange error ellipses are centered on the mean feature values with half-axes corresponding to eigenvalues and -vectors of the feature-pair’s covariance matrix. Some outliers were omitted for better visualization. The indicated p-value in each panel corresponds to a Kruskal-Wallis test. A) Attention decreased (increased) the left peak—as measured by the feature normalizedPeakToPeakleft—in the spatially separated (transparent) paradigm. B) Attention increased the right peak in both paradigms according to the feature normalizedPeakToPeakright. C) Attention significantly increased the difference between left and right peak’s inner width—ΔInnerWidth—only for the spatially separated paradigm. Size of circles in panel C illustrates density of points at each particular coordinate (note that values of ΔInnerWidth from the direct method are quantized in steps of 30°due to the design of experimentally used stimuli). Altogether panels indicate that attention asymetrically expanded the right at the expense of the left peak for the spatially separated paradigm, but increased both peaks similarly for the transparent paradigm.

Fig 7

doi: https://doi.org/10.1371/journal.pone.0146500.g007