Viviani, G. | Search bioRxiv

bioRxiv2026

Cracking the predictive stability code: dual trial-by-trial spatio-temporo-spectral dynamics of task representation directional tuning by Bayesian conflict expectations

Cognitive stability, or task focus, enables goal-directed behavior through representational dynamics: by prioritizing task-relevant representations over competing task-irrelevant ones. Beyond corrective reactive mechanisms operating after conflict arises, stability can be deployed predictively through the statistical learning of conflict regularities, either proactively, guided by global conflict expectations, or through a predictive reactive mode guided by item-specific ones. Yet, the representational mechanisms enabling such predictive adjustments remain underspecified. We addressed this by re-analyzing EEG data from a spatial Stroop task concurrently manipulating list-wide (LWPC) and item-specific (ISPC) proportion congruency. Moving beyond existing analytical approaches, we developed a framework combining trial-by-trial Bayesian conflict-expectancy modeling with a novel spatio-temporo-spectral searchlight Representational Similarity Analysis, and leveraged directional representational modeling complemented by ridge-regression decoding. We first identified the necessary computational substrate for stability predictive reconfigurations: the spatio-temporo-spectral patterns encoding task (Target, Distractor) and conflict-expectancy (LWPC, ISPC) representations, the latter representations encoded already in the pre-stimulus windows. Crucially, conflict expectations modulated Target and Distractor encoding strength with distinct spatio-temporal and spectral dynamics, revealing a functional dissociation. LWPC-induced proactive stability follows a "Target-first" policy, primarily strengthening the Target early post-stimulus via Alpha and lower-Beta patterns, weakening Distractor encoding only later on; conversely, ISPC-induced predictive reactive stability follows a "Distractor-first" policy, rapidly weakening Distractor encoding via response-locked posterior Beta patterns, strengthening the Target only downstream. Our study refines theoretical cognitive control models, incorporating a mechanistic explanation for predictive stability, while also establishing a multidimensional methodological template for interrogating the underlying substrates of adaptive behavior and beyond.

neuroscience↗

bioRxiv2023

lmeEEG: Mass linear mixed-effects modeling of EEG data with crossed random effects

BackgroundMixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models. New methodHere, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG. ResultslmeEEG showed excellent performance properties in terms of power and false positive rate. Comparison with existing methodslmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster). ConclusionslmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.

neuroscience↗