Attentional modulation of speed-change perception in the perifoveal and near-peripheral visual field

The same phase creates a unique visual rhythm unifying moving elements in time

Attention can be selectively tuned to particular features at different spatial locations or objects. The deployment of attention can be guided by properties, such as color, orientation, and so forth, as guiding features. What might be such guiding features for visual stimuli under dynamic rhythmic conditions? We asked specifically what might be the parameters that attract attention when perceiving a visual rhythm. We used a visual search paradigm, in which a dynamic search display consisted of vertically "bouncing balls" with regular rhythms. The search target was defined by a unique visual rhythm (i.e., with either a shorter or longer period) among rhythmic distractors sharing an identical period. We modulated amplitudes and phases of the distractor balls systematically. The results showed a crucial factor of the phase, not the amplitude. If the phase is violated, the target suddenly "pops out" as an "oddball," showing an efficient parallel search. The findings indicate in general the essential role of the phase in conjunction with amplitude and period for visual rhythm perception. Furthermore, a higher saliency of moving objects with a higher frequency component has also been disclosed.

Analysis based on neural representation of natural object surfaces to elucidate the mechanisms of a trained AlexNet model

Analysis and understanding of trained deep neural networks (DNNs) can deepen our understanding of the visual mechanisms involved in primate visual perception. However, due to the limited availability of neural activity data recorded from various cortical areas, the correspondence between the characteristics of artificial and biological neural responses for visually recognizing objects remains unclear at the layer level of DNNs. In the current study, we investigated the relationships between the artificial representations in each layer of a trained AlexNet model (based on a DNN) for object classification and the neural representations in various levels of visual cortices such as the primary visual (V1), intermediate visual (V4), and inferior temporal cortices. Furthermore, we analyzed the profiles of the artificial representations at a single channel level for each layer of the AlexNet model. We found that the artificial representations in the lower-level layers of the trained AlexNet model were strongly correlated with the neural representation in V1, whereas the responses of model neurons in layers at the intermediate and higher-intermediate levels of the trained object classification model exhibited characteristics similar to those of neural activity in V4 neurons. These results suggest that the trained AlexNet model may gradually establish artificial representations for object classification through the hierarchy of its network, in a similar manner to the neural mechanisms by which afferent transmission beginning in the low-level features gradually establishes object recognition as signals progress through the hierarchy of the ventral visual pathway.

Correspondence between Monkey Visual Cortices and Layers of a Saliency Map Model Based on a Deep Convolutional Neural Network for Representations of Natural Images

Attentional selection is a function that allocates the brain’s computational resources to the most important part of a visual scene at a specific moment. Saliency map models have been proposed as computational models to predict attentional selection within a spatial location. Recent saliency map models based on deep convolutional neural networks (DCNNs) exhibit the highest performance for predicting the location of attentional selection and human gaze, which reflect overt attention. Trained DCNNs potentially provide insight into the perceptual mechanisms of biological visual systems. However, the relationship between artificial and neural representations used for determining attentional selection and gaze location remains unknown. To understand the mechanism underlying saliency map models based on DCNNs and the neural system of attentional selection, we investigated the correspondence between layers of a DCNN saliency map model and monkey visual areas for natural image representations. We compared the characteristics of the responses in each layer of the model with those of the neural representation in the primary visual (V1), intermediate visual (V4), and inferior temporal (IT) cortices. Regardless of the DCNN layer level, the characteristics of the responses were consistent with that of the neural representation in V1. We found marked peaks of correspondence between V1 and the early level and higher-intermediate-level layers of the model. These results provide insight into the mechanism of the trained DCNN saliency map model and suggest that the neural representations in V1 play an important role in computing the saliency that mediates attentional selection, which supports the V1 saliency hypothesis.

Advantage of detecting visual events in the right hemifield is affected by reading skill

Visual perception is often not homogenous across the visual field and can vary depending on situational demands. The reasons behind this inhomogeneity are not clear. Here we show that directing attention that is consistent with a western reading habit from left to right, results in a ~32% higher sensitivity to detect transient visual events in the right hemifield. This right visual field advantage was largely reduced in individuals with reading difficulties from developmental dyslexia. Similarly, visual detection became more symmetric in skilled readers, when attention was guided opposite to the reading pattern. Taken together, these findings highlight a higher sensitivity in the right visual field for detecting the onset of sudden visual events that is well accounted for by left hemisphere dominated reading habit.