Interaction of surface pattern and contour shape in the tilt after effects evoked by symmetry

Sakai, Ko; Sakata, Yui; Kurematsu, Ken

doi:10.1038/s41598-021-87429-y

Cited by 5 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although a substantial number of natural objects are symmetric and the solid perceptual representation of symmetry has been reported ( Sharman and Gheorghiu, 2018 ; Sakai et al, 2021 ), only a fraction of V4 neurons showed significant tuning to symmetry in the present experiment. The limited spatial extent of the receptive filed might account for this discrepancy.…”

Section: Discussioncontrasting

confidence: 57%

Representation of Natural Contours by a Neural Population in Monkey V4

Machida,

Shishikura,

Yamane

et al. 2024

eNeuro

Self Cite

View full text Add to dashboard Cite

The cortical visual area, V4, has been considered to code contours that contribute to the intermediate-level representation of objects. The neural responses to the complex contour-features intrinsic to natural contours are expected to clarify the essence of the representation. To approach the cortical coding of natural contours, we investigated the simultaneous coding of multiple contour-features in monkey (Macaca fuscata) V4 neurons and their population-level representation. A substantial number of neurons showed significant tuning for two or more features such as curvature and closure, indicating that a substantial number of V4 neurons simultaneously code multiple contour-features. A large portion of the neurons responded vigorously to acutely curved contours that surrounded the center of classical receptive field, suggesting that V4 neurons tend to code prominent features of object contours. The analysis of mutual information (MI) between the neural responses and each contour feature showed that the most neurons exhibited similar magnitudes for each type of MI, indicating that many neurons showed the responses depended on multiple contour-features. We next examined the population-level representation by using multi-dimensional scaling analysis. The neural preferences to the multiple contour-features and that to natural stimuli compared to silhouette stimuli increased along with the primary and secondary axes, respectively, indicating the contribution of the multiple contour-features and surface textures in the population responses. Our analyses suggested that V4 neurons simultaneously code multiple contour-features in natural images and represent contour and surface properties in population.Significance StatementContours of natural objects are often complex but the visual system extracts their features and efficiently represent their shape. Neurons in the intermediate-level visual cortex, V4, play crucial roles for the representation of natural contours. Analyzing the electrophysiological data, we found that V4 neurons simultaneously code multiple contour-features such as curvature, closure, and orientation, and represent the prominent contours such as corners and bumps. For instance, a number of neurons responded to acute curvatures that enclosed object extent. Mutual information and population analyses showed that the responses of many neurons depend on multiple contour-features and represent the contours and surfaces in population. A population of V4 neurons seems to encode complex but prominent contours for the representation of natural objects.

show abstract

Section: Discussioncontrasting

confidence: 57%

Representation of Natural Contours by a Neural Population in Monkey V4

Machida,

Shishikura,

Yamane

et al. 2024

eNeuro

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, contour shapes and high-contrast textures are good candidates to examine the relevance to neural responses. The integration and interaction of contour shapes and surface textures are also crucial in the examination [ 50 ]. It is also important to investigate the interactions with depth cues.…”

Section: Discussionmentioning

confidence: 99%

Figure-ground responsive fields of monkey V4 neurons estimated from natural image patches

et al. 2022

Self Cite

View full text Add to dashboard Cite

Neurons in visual area V4 modulate their responses depending on the figure-ground (FG) organization in natural images containing a variety of shapes and textures. To clarify whether the responses depend on the extents of the figure and ground regions in and around the classical receptive fields (CRFs) of the neurons, we estimated the spatial extent of local figure and ground regions that evoked FG-dependent responses (RF-FGs) in natural images and their variants. Specifically, we applied the framework of spike triggered averaging (STA) to the combinations of neural responses and human-marked segmentation images (FG labels) that represent the extents of the figure and ground regions in the corresponding natural image stimuli. FG labels were weighted by the spike counts in response to the corresponding stimuli and averaged over. The bias due to the nonuniformity of FG labels was compensated by subtracting the ensemble average of FG labels from the weighted average. Approximately 50% of the neurons showed effective RF-FGs, and a large number exhibited structures that were similar to those observed in virtual neurons with ideal FG-dependent responses. The structures of the RF-FGs exhibited a subregion responsive to a preferred side (figure or ground) around the CRF center and a subregion responsive to a non-preferred side in the surroundings. The extents of the subregions responsive to figure were smaller than those responsive to ground in agreement with the Gestalt rule. We also estimated RF-FG by an adaptive filtering (AF) method, which does not require spherical symmetry (whiteness) in stimuli. RF-FGs estimated by AF and STA exhibited similar structures, supporting the veridicality of the proposed STA. To estimate the contribution of nonlinear processing in addition to linear processing, we estimated nonlinear RF-FGs based on the framework of spike triggered covariance (STC). The analyses of the models based on STA and STC did not show inconsiderable contribution of nonlinearity, suggesting spatial variance of FG regions. The results lead to an understanding of the neural responses that underlie the segregation of figures and the construction of surfaces in intermediate-level visual areas.

show abstract