pcbi.1010557.g005.tif (1.07 MB)

Influence of sample size on model recovery.

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posted on 2023-12-13, 18:37 authored by Arnaud Poublan-Couzardot, Françoise Lecaignard, Enrico Fucci, Richard J. Davidson, Jérémie Mattout, Antoine Lutz, Oussama Abdoun

(A) Decreasing sample size has the effect of degrading the confusion matrix towards more “blurred” (i.e. uniform) matrices. (B) Representing confusion matrices by their distance from the identity and the uniform matrices (I and U, respectively), emphasizes the specific influences of sample size and SNR on model discrimination. While low SNR leads to highly precise—i.e. deterministic—but biased confusion matrices (marked as B on the graph), low sample size results in random model selection.

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using electroencephalographic data two model families two main hypotheses two independent studies reports indicating spatial first ran simulations different theoretical accounts controlled experimental parameters complex brain response bayesian model averaging bayesian learning later auditory deviance detection physiological mechanisms within parameter models within neurophysiological mechanisms underlying bayesian learning models resolved approach revealed 190 ms post early mmn window adaptation family revealed tested models generative mechanisms resolved fashion 180 ms temporal dissociation sufficiently distinguishable sufficient snr stimulus depending snr ).sequential dynamics recurrent stimuli ongoing debate observed properties noise ratio neuronal adaptation mmn ),mismatch responses mismatch negativity methodological limitations human mmn high evidence direct comparisons compared adaptation certain level adaptation rates 82 ),220ms ).

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