Semantic Wavelet-Induced Frequency-Tagging (SWIFT) Periodically Activates Category Selective Areas While Steadily Activating Early Visual Areas

PLoS One

Rufin VanRullen¹  Roger Koenig-Robert²  Naotsugu Tsuchiya³ 

[1] CNRS, UMR5549, Centre de Recherche Cerveau et Cognition, Faculté de Médecine de Purpan, 31052 Toulouse, France;School of Psychological Sciences, Faculty of Biomedical and Psychological Sciences, Monash University, Melbourne, Australia;Université de Toulouse, Centre de Recherche Cerveau et Cognition, Université Paul Sabatier, 31052 Toulouse, France

关键词: Functional magnetic resonance imaging;    Vision;    Neuronal tuning;    Visual system;    Neurons;    Visual pathways;    Magnetic resonance imaging;    Psychophysics;   

DOI  :  10.1371/journal.pone.0144858

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

Primate visual systems process natural images in a hierarchical manner: at the early stage, neurons are tuned to local image features, while neurons in high-level areas are tuned to abstract object categories. Standard models of visual processing assume that the transition of tuning from image features to object categories emerges gradually along the visual hierarchy. Direct tests of such models remain difficult due to confounding alteration in low-level image properties when contrasting distinct object categories. When such contrast is performed in a classic functional localizer method, the desired activation in high-level visual areas is typically accompanied with activation in early visual areas. Here we used a novel image-modulation method called SWIFT (semantic wavelet-induced frequency-tagging), a variant of frequency-tagging techniques. Natural images modulated by SWIFT reveal object semantics periodically while keeping low-level properties constant. Using functional magnetic resonance imaging (fMRI), we indeed found that faces and scenes modulated with SWIFT periodically activated the prototypical category-selective areas while they elicited sustained and constant responses in early visual areas. SWIFT and the localizer were selective and specific to a similar extent in activating category-selective areas. Only SWIFT progressively activated the visual pathway from low- to high-level areas, consistent with predictions from standard hierarchical models. We confirmed these results with criterion-free methods, generalizing the validity of our approach and show that it is possible to dissociate neural activation in early and category-selective areas. Our results provide direct evidence for the hierarchical nature of the representation of visual objects along the visual stream and open up future applications of frequency-tagging methods in fMRI.

【 授权许可】

CC BY