Neuroimaging studies employ different classes of optical illusions to identify which areas of the visual pathway are involved in visual perception and how these interact in feedback and feedforward loops to integrate specific visual features in a univocal percept. Here, we conducted a systematic review and an ALE meta-analysis of fMRI studies that contrasted activations in response to illusory contours, geometrical and motion illusions, to reveal the cortical regions involved in the processing of illusory percepts, and to highlight the networks contributing to visual contextual integration. We pooled the studies into two subgroups to reveal the areas of overlapping activation related to static visual illusions and motion illusions. The general ALE revealed a bilateral network of visual areas encompassing both ventral and dorsal visual regions (bilateral inferior and middle temporal gyri; right superior parietal gyrus). The two individual ALE analyses both revealed bilateral activations in the occipital cortex. However, the static illusions network included a cluster in the right superior parietal cortex, not present in the motion illusion network, which instead comprised specific activations in the right precentral gyrus. Overall, the present study describes the networks of areas involved in the perception of contextual visual illusions and, specifically, in response to static and motion-related ones, laying the foundation for a neural based classification system of illusions. These results are coherent with accounts supporting that visual perception relies on interactions between areas of the ventral and dorsal visual pathways, thus arguing against a strict division of labor between the two streams.

Neural Networks Underlying Static and Motion Visual Illusions: An ALE Meta-analysis / VON GAL, Alessandro; Boccia, Maddalena; Nori, Raffaella; Verde, Paola; Giannini, Anna Maria; Piccardi, Laura. - (2023). (Intervento presentato al convegno American Psychological Association 2023 Convention tenutosi a Washington D.C., USA).

Neural Networks Underlying Static and Motion Visual Illusions: An ALE Meta-analysis

Alessandro von Gal
Primo
;
Maddalena Boccia;Paola Verde;Anna Maria Giannini;Laura Piccardi
2023

Abstract

Neuroimaging studies employ different classes of optical illusions to identify which areas of the visual pathway are involved in visual perception and how these interact in feedback and feedforward loops to integrate specific visual features in a univocal percept. Here, we conducted a systematic review and an ALE meta-analysis of fMRI studies that contrasted activations in response to illusory contours, geometrical and motion illusions, to reveal the cortical regions involved in the processing of illusory percepts, and to highlight the networks contributing to visual contextual integration. We pooled the studies into two subgroups to reveal the areas of overlapping activation related to static visual illusions and motion illusions. The general ALE revealed a bilateral network of visual areas encompassing both ventral and dorsal visual regions (bilateral inferior and middle temporal gyri; right superior parietal gyrus). The two individual ALE analyses both revealed bilateral activations in the occipital cortex. However, the static illusions network included a cluster in the right superior parietal cortex, not present in the motion illusion network, which instead comprised specific activations in the right precentral gyrus. Overall, the present study describes the networks of areas involved in the perception of contextual visual illusions and, specifically, in response to static and motion-related ones, laying the foundation for a neural based classification system of illusions. These results are coherent with accounts supporting that visual perception relies on interactions between areas of the ventral and dorsal visual pathways, thus arguing against a strict division of labor between the two streams.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1686693
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