Functional heterogeneity in the left lateral posterior parietal cortex during visual and haptic crossmodal dot‐surface matching

Yang, Jiajia; Yu, Yanan; Shigemasu, Hiroaki; Kadota, Hideki; Nakahara, Kiyoshi; Kochiyama, Takanori; Ejima, Yoshimichi; Wu, Jinglong

doi:10.1002/brb3.2033

Cited by 5 publications

(8 citation statements)

References 49 publications

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“…One possible explanation for this result is that the perception of global and local features did not occur through completely separate pathways (Stilla and Sathian, 2008 ). Information about different properties may be integrated into the brain (Kim et al 2015 ; Sathian 2016 ; Yang et al, 2021a , b ). Our recent study also provided evidence to support this idea by demonstrating that haptic curve processing and roughness processing share a large proportion of cortical networks (Yang et al 2021a , b ).…”

Section: Discussionmentioning

confidence: 99%

“…Information about different properties may be integrated into the brain (Kim et al 2015 ; Sathian 2016 ; Yang et al, 2021a , b ). Our recent study also provided evidence to support this idea by demonstrating that haptic curve processing and roughness processing share a large proportion of cortical networks (Yang et al 2021a , b ). Moreover, information processing of different properties may share the same system and, therefore, interact.…”

Section: Discussionmentioning

confidence: 99%

“…A series of coarse stimulus sets combining local features (i.e., raised-dot texture) and global features (i.e., curved surface), which were changed in a parametric manner, were used in Experiments 1–4 (Yang et al 2021a , b ). The texture of stimuli that changed on the order of millimeters was defined as a local feature, while the global feature of the surfaces changed on the order of centimeters.…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, we used the same series of haptic stimuli that combines local features (raised-dot spacing) and global features (cycle number of the curved surface) that was used in our previous study (Yang et al 2021a , b ) to investigate haptic roughness perception. A total of 25 three-dimensional (3D) printed stimuli (five kinds of curves × five varieties of dot spacing) were made by modulating these two features.…”

Section: Introductionmentioning

confidence: 99%

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Global surface features contribute to human haptic roughness estimations

Yang

et al. 2022

Exp Brain Res

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Previous studies have paid special attention to the relationship between local features (e.g., raised dots) and human roughness perception. However, the relationship between global features (e.g., curved surface) and haptic roughness perception is still unclear. In the present study, a series of roughness estimation experiments was performed to investigate how global features affect human roughness perception. In each experiment, participants were asked to estimate the roughness of a series of haptic stimuli that combined local features (raised dots) and global features (sinusoidal-like curves). Experiments were designed to reveal whether global features changed their haptic roughness estimation. Furthermore, the present study tested whether the exploration method (direct, indirect, and static) changed haptic roughness estimations and examined the contribution of global features to roughness estimations. The results showed that sinusoidal-like curved surfaces with small periods were perceived to be rougher than those with large periods, while the direction of finger movement and indirect exploration did not change this phenomenon. Furthermore, the influence of global features on roughness was modulated by local features, regardless of whether raised-dot surfaces or smooth surfaces were used. Taken together, these findings suggested that an object’s global features contribute to haptic roughness perceptions, while local features change the weight of the contribution that global features make to haptic roughness perceptions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global surface features contribute to human haptic roughness estimations

Yang

et al. 2022

Exp Brain Res

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“…This process involves the extraction of different stimulus features in the human skin. Then, primary brain areas and higher association areas combine these features and integrate them with prior knowledge [12][13][14][15][16][17][18][19]. On the descending motor pathway, the interaction of exteroceptive and proprioceptive is important.…”

Section: Introductionmentioning

confidence: 99%

Human Somatosensory Processing and Artificial Somatosensation

Wang

Yang

2021

Cyborg Bionic Syst

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In the past few years, we have gained a better understanding of the information processing mechanism in the human brain, which has led to advances in artificial intelligence and humanoid robots. However, among the various sensory systems, studying the somatosensory system presents the greatest challenge. Here, we provide a comprehensive review of the human somatosensory system and its corresponding applications in artificial systems. Due to the uniqueness of the human hand in integrating receptor and actuator functions, we focused on the role of the somatosensory system in object recognition and action guidance. First, the low-threshold mechanoreceptors in the human skin and somatotopic organization principles along the ascending pathway, which are fundamental to artificial skin, were summarized. Second, we discuss high-level brain areas, which interacted with each other in the haptic object recognition. Based on this close-loop route, we used prosthetic upper limbs as an example to highlight the importance of somatosensory information. Finally, we present prospective research directions for human haptic perception, which could guide the development of artificial somatosensory systems.

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