Hole superiority effect with 3D figures formed by binocular disparity

Where there is no object formation, there is no perceptual organization: Evidence from the configural superiority effect

Object formation is considered the aim of perceptual organization, but such a proposition has been neglected in empirical studies. In the current study, we investigated the role of object formation in configural superiority. Essentially, discrimination on bar orientations was enhanced by adding a right angle to each of the bars. Such facilitation is due to the emergent feature (EF) of closure formed by combining the bars with right angles. To study object formation, visual stimuli were generated by random dot stereograms to form objects or holes in 3D. Behaviorally, we found that the EF of closure facilitated oddball discrimination on objects, as demonstrated by previous studies, but did not facilitate oddball discrimination on holes with the same shape as objects. Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data showed that the EF of closure increased the object classification accuracy compared to the holes in the lateral occipital cortex (LOC), where object information is encoded, but not in the early visual cortex (EVC). The neural representations of objects and holes with and without EFs were further investigated using representational similarity analysis. The results demonstrate that in the LOC, the neural representations of objects with EFs showed a greater difference than those of the other three, that is, objects without EFs and holes with or without EFs. However, the uniqueness of objects with EFs was not observed in the EVC. Thus, our results suggest that the EF of closure, which leads to the configural superiority effect, only emerges for objects but not for holes, and only in the LOC but not the EVC. Our study provides the first empirical evidence suggesting that object formation plays an indispensable role in perceptual organization.

A Neural Network Model With Gap Junction for Topological Detection

Visual information processing in the brain goes from global to local. A large volume of experimental studies has suggested that among global features, the brain perceives the topological information of an image first. Here, we propose a neural network model to elucidate the underlying computational mechanism. The model consists of two parts. The first part is a neural network in which neurons are coupled through gap junctions, mimicking the neural circuit formed by alpha ganglion cells in the retina. Gap junction plays a key role in the model, which, on one hand, facilitates the synchronized firing of a neuron group covering a connected region of an image, and on the other hand, staggers the firing moments of different neuron groups covering disconnected regions of the image. These two properties endow the network with the capacity of detecting the connectivity and closure of images. The second part of the model is a read-out neuron, which reads out the topological information that has been converted into the number of synchronized firings in the retina network. Our model provides a simple yet effective mechanism for the neural system to detect the topological information of images in ultra-speed.