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. 2020 Dec 2;14:541920. doi: 10.3389/fnbeh.2020.541920

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Copyright © 2020 Moore, Khang and Francis.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Signal- vs. noise-correlation. Plotted in red are the R trial datapoints from all datasets for each NHP and task, while blue datapoints are from the NR trials. Cyan line is a linear fit to the NR trials and green are linear fits to the R data. The x-axis is signal-correlation while the y-axis is the noise-correlation. All datasets had highly significant linear fits between most of the variables involved, including trial-type, y-intercept and slope of the linear fits. All datasets for NHP-Zi, and the manual datasets for NHP-Ac also showed highly significant fits for signal-correlation *trial-type vs. noise-correlation. Statistics included below from the ANOVA output for the relationships between signal-correlation, noise-correlation and trial-type. MATLAB aoctool was utilized for this analysis of covariance (ANCOVA). Number of data sets was (N = 3 for all but NHP-Ac Manual N = 4, see “Materials and Methods” section for trial and unit #s). NHP-Ac manual task, group (R vs. NR), p = 0, F_(1,61,743) = 63, group * X (signal correlation) p = 0, F_(1,61,743) = 100. NHP-Ac OT, group, p = 0, F_(1,49,682) = 35. NHP-Zi manual task group p = 0.0004, F_(17,524) = 13, group * X p = 0.001, F_(17,524) = 11. NHP-Zi OT group p = 0, F_(19,016) = 17, group * X F_(19,016) = 23, p = 0.