2020
DOI: 10.1101/2020.12.15.422942
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Pitfalls in Post Hoc Analyses of Population Receptive Field Data

Abstract: Data binning can cope with overplotting and noise, making it a versatile tool for comparing many observations. However, it goes awry if the same observations are used for binning and contrasting. This creates an inherent circularity, leaving noise and regression to the mean insufficiently controlled. Here, we use population receptive field analyses – where data binning is commonplace – as an example to expose this flaw through simulations and empirical repeat data.

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“…Additional analyses, e.g., the dependency of reproducibility in different eccentricity bins of the visual field of view were not included in this work, since recent findings ( Stoll et al, 2022 ) suggest systematic influences of the binning method when multiple runs are included. In this case, the voxels for the computation of correlations within an eccentricity band would be chosen based on one run, with the other run not considered, biasing the results.…”
Section: Discussionmentioning
confidence: 99%
“…Additional analyses, e.g., the dependency of reproducibility in different eccentricity bins of the visual field of view were not included in this work, since recent findings ( Stoll et al, 2022 ) suggest systematic influences of the binning method when multiple runs are included. In this case, the voxels for the computation of correlations within an eccentricity band would be chosen based on one run, with the other run not considered, biasing the results.…”
Section: Discussionmentioning
confidence: 99%