The role of brain oscillations in feature integration

Our sensory system is able to build a unified perception of the world, which although rich, is limited and inaccurate. Sometimes, features from different objects are erroneously combined. At the neural level, the role of the parietal cortex in feature integration is well-known. However, the brain dynamics underlying correct and incorrect feature integration are less clear. To explore the temporal dynamics of feature integration, we studied the modulation of different frequency bands in trials in which feature integration was correct or incorrect. Participants responded to the color of a shape target, surrounded by distractors. A calibration procedure ensured that accuracy was around 70% in each participant. To explore the role of expectancy in feature integration, we introduced an unexpected feature to the target in the last blocks of trials. Results demonstrated the contribution of several frequency bands to feature integration. Alpha and beta power was reduced for hits compared to illusions. Moreover, gamma power was overall larger during the experiment for participants who were aware of the unexpected target presented during the last blocks of trials (as compared to unaware participants). These results demonstrate that feature integration is a complex process that can go wrong at different stages of information processing and is influenced by top-down expectancies. When exploring the brain dynamics of feature integration, previous studies have only explored one frequency band. We explore if and how different cognitive failures can lead to incorrect feature integration by exploring the modulation of different frequency bands. Our results support that feature integration can go wrong at different stages including pre-stimulus attentional processes (alpha), perceptual representations (post-stimulus, alpha and beta), and working memory (gamma).

Automated summary

Our sensory system is capable of creating a unified perception of the world, but sometimes it combines features from different objects incorrectly. This study examines the temporal dynamics of feature integration and identifies the contribution of different frequency bands. Additionally, the role of expectancy in feature integration is explored, revealing the influence of top-down factors.