Fig. 1: Model mimicry: many models produce the same pattern of results.
From: Model mimicry limits conclusions about neural tuning and can mistakenly imply unlikely priors
In generative forward modeling, EEG data are simulated from models that use different sets of orientation tuning functions (top row). Decoding results (mean-centered decoding accuracy, mean-centered precision, and bias) as a function of orientation are shown (3 bottom rows) for simulations using different underlying example models. Blue error areas are 95% confidence intervals of the mean of the simulated instances (n = 36) of each model for each decoding metric. A Preferred tuning model: Tuning functions are unevenly spaced along the orientation space, with more clustering at vertical, and even more at horizontal orientations. This is the best fitting model from Harrison and colleagues1. B Width model: Tuning curve widths are uneven, with narrowest tuning for obliques, wider tuning for vertical and widest tuning for horizontal. C Gain model: Uneven tuning curve gain across orientations space, with more gain at cardinals that is highest for horizontal orientations. D Signal-to-noise (SNR) model: Tuning curves are uniform, but signal strength is orientation specific. Source data are provided as a Source Data file.
