Cognitive neuroscientists have been grappling with two related experimental design problems. First, the complexity of neuroimaging data (e.g. often hundreds of thousands of correlated measurements) and analysis pipelines demands bespoke, non-parametric statistical tests for valid inference, and these tests often lack an agreed-upon method for performing a priori power analyses. Thus, sample size determination for neuroimaging studies is often arbitrary or inferred from other putatively but questionably similar studies, which can result in underpowered designs -- undermining the efficacy of neuroimaging research. Second, when meta-analyses estimate the sample sizes required to obtain reasonable statistical power, estimated sample sizes can be prohibitively large given the resource constraints of many labs. We propose the use of sequential analyses to partially address both of these problems. Sequential study designs -- in which the data is analyzed at interim points during data collection and data collection can be stopped if the planned test statistic satisfies a stopping rule specified a priori -- are common in the clinical trial literature, due to the efficiency gains they afford over fixed-sample designs. However, the corrections used to control false positive rates in existing approaches to sequential testing rely on parametric assumptions that are often violated in neuroimaging settings. We introduce a general permutation scheme that allows sequential designs to be used with arbitrary test statistics. By simulation, we show that this scheme controls the false positive rate across multiple interim analyses. Then, performing power analyses for seven evoked response effects seen in the EEG literature, we show that this sequential analysis approach can substantially outperform fixed-sample approaches (i.e. require fewer subjects, on average, to detect a true effect) when study designs are sufficiently well-powered. To facilitate the adoption of this methodology, we provide a Python package "niseq" with sequential implementations of common tests used for neuroimaging: cluster-based permutation tests, threshold-free cluster enhancement, t-max, F-max, and the network-based statistic with tutorial examples using EEG and fMRI data.
Cognitive scientists differentiate the “minimal self” – subjective experiences of agency and ownership in our sensorimotor interactions with the world – and the “narrative self” that encompasses those beliefs about the self that are sustained over time. How exactly moment-to-moment experiences are integrated into narrative beliefs, however, remains an open question. We administered a battery of sensorimotor tasks and surveys to index subjects’ (n = 195) propensity to classify stimuli as self-caused and their metacognitive monitoring of such agency judgements, and we compared these behavioral metrics to trait-level beliefs about their own agency. Subjects who were less sensitive to sensory control cues in the sensorimotor tasks also reported lower trait-level agency beliefs. Importantly, however, this relationship all but disappears when controlling for metacognitive accuracy. These results suggest narrative beliefs about self-agency are not just the sum of individual experiences of self-causation but rather the product of a metacognitively-driven integration process.
The frequency-following response (FFR) is a phase-locked evoked response recorded at the scalp that directly mirrors the frequency content of acoustic stimuli. While once believed to be primarily of subcortical origin, MEG and EEG research has more recently shown that the FFR originates from multiple subcortical and cortical neural sources (Coffey et al., 2016; Hartmann and Weisz, 2019; Tichko and Skoe, 2017). The present study tests whether the frequency of the stimulus can be reliably decoded from frequency-specific power of single-trial high-density EEG, despite the extremely small amplitude of the FFR relative to the background EEG. Since the amplitude of the FFR contributions from the multiple constituent cortical and subcortical sources can be modulated independently, it is reasonable to posit that the scalp distribution of the FFR may be malleable to psychological processes such as selective attention. Past studies on the attentional modulation of the FFR have been inconclusive (e.g., Forte et al., 2017; Hoormann etal., 2004). We therefore also examine the accuracy of the single- trial FFR-based decoding as a function of different attention manipulations.
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