Bruckner, R. | bioRxiv Science

bioRxiv2019

Power, positive predictive value, and sample size calculations for random field theory-based fMRI inference

Recent discussions on the reproducibility of task-related functional magnetic resonance imaging (fMRI) studies have emphasized the importance of power and sample size calculations in fMRI study planning. In general, statistical power and sample size calculations are dependent on the statistical inference framework that is used to test hypotheses. Bibliometric analyses suggest that random field theory (RFT)-based voxel- and cluster-level fMRI inference are the most commonly used approaches for the statistical evaluation of task-related fMRI data. However, general power and sample size calculations for these inference approaches remain elusive. Based on the mathematical theory of RFT-based inference, we here develop power and positive predictive value (PPV) functions for voxel- and cluster-level inference in both uncorrected single test and corrected multiple testing scenarios. Moreover, we apply the theoretical results to evaluate the sample size necessary to achieve desired power and PPV levels based on an fMRI pilot study.

neuroscience↗

bioRxiv2019

Statistical context dictates the relationship between feedback-related EEG signals and learning

Successful decision-making requires learning expectations based on experienced outcomes. This learning should be calibrated according to the surprise associated with an outcome, but also to the statistical context dictating the most likely source of surprise. For example, when occasional changepoints are expected, surprising outcomes should be weighted heavily, demanding increased learning. In contrast, when signal corruption is expected to occur occasionally, surprising outcomes can suggest a corrupt signal that should be ignored by learning systems. Here we dissociate surprising outcomes from the degree to which they demand learning using a predictive inference task and computational modeling. We show that the updating P300, a stimulus-locked electrophysiological response previously associated with adjustments in learning behavior, does so conditionally on the source of surprise. Larger P300 signals predicted greater learning in a changing context, but predicted less learning in a context where surprise was indicative of a one-off outlier (oddball). The conditional predictive relationship between the P300 and learning behavior was persistent even after adjusting for known sources of learning rate variability. Our results suggest that the P300 provides a surprise signal that is interpreted by downstream learning processes differentially according to statistical context in order to appropriately calibrate learning across complex environments.

neuroscience↗