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Journal Article

Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network

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Ficco, L., Mancuso, L., Manuello, J., Teneggi, A., Liloia, D., Duca, S., et al. (2021). Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network. Scientific Reports, 11: 16258. doi:10.1038/s41598-021-95603-5.

Cite as: https://hdl.handle.net/21.11116/0000-0008-FD51-0
According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its
upcoming states and refning these predictions through error signals. Despite extensive research
investigating the neural bases of this theory, to date no previous study has systematically attempted
to defne the neural mechanisms of predictive coding across studies and sensory channels, focussing
on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach
to address this issue. We frst use the Activation Likelihood Estimation (ALE) algorithm to detect
spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results
suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover,
we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique
reveals a large, bilateral predictive network, which resembles large-scale networks involved in taskdriven attention and execution. In sum, we fnd that: (i) predictive processing seems to occur more in
certain brain regions than others, when considering diferent sensory modalities at a time; (ii) there is
no evidence, at the network level, for a distinction between error and prediction processing.