Cracking the predictive stability code: dual trial-by-trial spatio-temporo-spectral dynamics of task representation directional tuning by Bayesian conflict expectations
- 1Department of Neuroscience, University of Padova
Abstract
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.
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BibTeX
@misc{viviani2026cracking,
title = {{Cracking the predictive stability code: dual trial-by-trial spatio-temporo-spectral dynamics of task representation directional tuning by Bayesian conflict expectations}},
author = {G. Viviani and A. Visalli and M. Montefinese and I. Di Pietro and A. Vallesi and M. Ruz and E. Ambrosini},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.20.752953},
url = {https://www.biorxiv.org/content/10.64898/2026.09.20.752953v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Cracking the predictive stability code: dual trial-by-trial spatio-temporo-spectral dynamics of task representation directional tuning by Bayesian conflict expectations
AU - G. Viviani
AU - A. Visalli
AU - M. Montefinese
AU - I. Di Pietro
AU - A. Vallesi
AU - M. Ruz
AU - E. Ambrosini
PY - 2026
DA - 2026/09/20
PB - bioRxiv
DO - 10.64898/2026.09.20.752953
UR - https://www.biorxiv.org/content/10.64898/2026.09.20.752953v1
AB - 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.
ER -