“I Spy with my Little Eye, Something that is a Face…”: A Brain Network for Illusory Face Detection

Thome, Ina; Hohmann, Daniela Michelle; Zimmermann, Kristin; Smith, Marie L.; Kessler, Roman; Jansen, Andreas

doi:10.1093/cercor/bhab199

Cited by 8 publications

(5 citation statements)

References 77 publications

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“…Similar to the core brain network underlying face processing ( 44 , 45 ) most of the previous work (not only with Face-n-Thing images used here but also with Arcimboldo-like images and prototype faces containing just a few blobs located in accordance with a coarse face scheme) speaks rather in favor of a predominantly right-hemispheric brain processing [( 16 , 25 – 27 , 43 ) but cf. ( 46 )]. For instance, over the right hemisphere, the N170 is larger in response to faces and Face-n-Thing images than to objects ( 5 ).…”

Section: Discussionmentioning

confidence: 99%

Dynamic brain communication underwriting face pareidolia

Romagnano,

Kubon,

Sokolov

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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Face pareidolia is a tendency to seeing faces in nonface images that reflects high tuning to a face scheme. Yet, studies of the brain networks underwriting face pareidolia are scarce. Here, we examined the time course and dynamic topography of gamma oscillatory neuromagnetic activity while administering a task with nonface images resembling a face. Images were presented either with canonical orientation or with display inversion that heavily impedes face pareidolia. At early processing stages, the peaks in gamma activity (40 to 45 Hz) to images either triggering or not face pareidolia originate mainly from the right medioventral and lateral occipital cortices, rostral and caudal cuneus gyri, and medial superior occipital gyrus. Yet, the difference occurred at later processing stages in the high-frequency range of 80 to 85 Hz over a set of the areas constituting the social brain. The findings speak rather for a relatively late neural network playing a key role in face pareidolia. Strikingly, a cutting-edge analysis of brain connectivity unfolding over time reveals mutual feedforward and feedback intra- and interhemispheric communication not only within the social brain but also within the extended large-scale network of down- and upstream regions. In particular, the superior temporal sulcus and insula strongly engage in communication with other brain regions either as signal transmitters or recipients throughout the whole processing of face-pareidolia images.

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Section: Discussionmentioning

confidence: 99%

Dynamic brain communication underwriting face pareidolia

Romagnano,

Kubon,

Sokolov

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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“…Kanwisher et al, 1997; Rossion et al, 2000), there were more face-selective voxels in the right FFA compared to the left (see Table 1). The voxel coordinates were in correspondence with previour research (see Table 1; Fahrenfort et al, 2012; Goffaux et al, 2011; Kanwisher et al, 1997; Rossion et al, 2003a; Thome et al, 2021). Additionally, we used the data obtained from the functional localiser to confirm that voxels in V1 showed greater responses to scrambled compared to intact faces (e.g.…”

Section: Methodsmentioning

confidence: 99%

Backward masking reveals coarse-to-fine dynamics in human V1

Schuurmans¹,

Bennett²,

Petras³

et al. 2023

Preprint

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Natural images exhibit luminance variations aligned across a broad spectrum of spatial frequencies (SFs). It has been proposed that, at early stages of processing, the coarse signals carried by the low SF (LSF) of the visual input are sent rapidly from primary visual cortex (V1) to ventral, dorsal and frontal regions to form a coarse representation of the input, which is later sent back to V1 to guide the processing of fine-grained high SFs (i.e., HSF). We used functional resonance imaging (fMRI) to investigate the role of human V1 in the coarse-to-fine integration of visual input. We disrupted the processing of the coarse and fine content of full-spectrum human face stimuli via backward masking of selective SF ranges (LSFs: <1.75cpd and HSFs: >1.75cpd) at specific times (50, 83, 100 or 150ms). In line with coarse-to-fine proposals, we found that (1) the selective masking of stimulus LSF disrupted V1 activity in the earliest time window, and progressively decreased in influence, while (2) an opposite trend was observed for the masking of a stimulus' HSF. This pattern of activity was found in V1, as well as in ventral (i.e. the Fusiform Face area, FFA), dorsal and orbitofrontal regions. We additionally presented participants with contrast negated stimuli. While contrast negation significantly reduced response amplitudes in the FFA, as well as coupling between FFA and V1, coarse-to-fine dynamics were not affected by this manipulation. The fact that V1 response dynamics to strictly identical stimulus sets differed depending on the masked scale adds to growing evidence that V1 role goes beyond the early and quasi-passive transmission of visual information to the rest of the brain. It instead indicates that V1 may yield a 'spatially registered common forum' or 'blackboard' that integrates top-down inferences with incoming visual signals through its recurrent interaction with high-level regions located in the inferotemporal, dorsal and frontal regions.

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“…Currently, the mainstream object detection algorithms are divided into single-stage and two-stage detection algorithms. The YOLO series belongs to the single-stage detection category, which has the characteristics of high computational efficiency, making it suitable for detecting faces occluded by masks in practical scenarios (Thome et al, 2021). Therefore, the proposed algorithm selects the YOLOv5s network as the benchmark model, which is a lightweight network with a high recognition rate.…”

Section: Improved Yolov5 Algorithmmentioning

confidence: 99%

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

Qi,

Wang,

Dong

2024

International Journal of Gaming and Computer-Mediated Simulations

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This paper proposes an advanced approach for detecting faces with mask occlusion based on YOLOv5 to address various challenges encountered in face detection, including illumination blur and occlusion. The proposed methodology involves the integration of a convolutional block attention module into the backbone network and different network levels in the neck of the YOLOv5s. This approach enables the suppression of irrelevant features and emphasizes the identification of masked facial features. Replacing the conventional loss function with the Focal Loss function addresses the problem of sample imbalance. The enhanced YOLOv5s network was applied to detect mask-occluded faces. Empirical evaluations were conducted on the WIDER Face and AIZOO datasets. The simulation comparison results demonstrate that the proposed method achieves superior real-time detection performance, fulfilling the objective of developing a lightweight detection model.

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“I Spy with my Little Eye, Something that is a Face…”: A Brain Network for Illusory Face Detection

Cited by 8 publications

References 77 publications

Dynamic brain communication underwriting face pareidolia

Dynamic brain communication underwriting face pareidolia

Backward masking reveals coarse-to-fine dynamics in human V1

An Improved Face Mask Detection Simulation Algorithm Based on YOLOv5 Model

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