Neural networks underlying visual illusions: An activation likelihood estimation meta-analysis

It is now online the final published version of the Article Neural networks underlying visual illusions: An activation likelihood estimation meta-analysis by Alessandro von Gal, Maddalena Boccia, Raffaella Nori, Paola Verde, Anna Maria Giannini, and Laura Piccardi published in NeuroImage. In the present study, we conducted an Activation Likelihood Estimation (ALE) meta-analysis and meta-analytic connectivity modeling on fMRI studies using static and motion illusions to reveal the neural signatures of illusory processing and to investigate the degree to which different areas are commonly recruited in perceptual inference. The resulting networks encompass ventral and dorsal regions, including the inferior and middle occipital cortices bilaterally in both illusions. The static and motion illusion networks selectively included the right posterior parietal and ventral premotor cortex, respectively. Overall, these results describe a network of areas crucially involved in perceptual inference relying on feedback and feed-forward interactions between areas of the ventral and dorsal visual pathways. The same network is proposed to be involved in hallucinogenic symptoms characteristic of schizophrenia and other disorders, with crucial implications in using illusions as biomarkers.

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