Eye movement evidence for the V1 Saliency Hypothesis and the Central-peripheral Dichotomy theory in an anomalous visual search task.

Typically, searching for a target among uniformly tilted non-targets is easier when this target is perpendicular, rather than parallel, to the non-targets. The V1 Saliency Hypothesis (V1SH) - that V1 creates a saliency map to guide attention exogenously - predicts exactly the opposite in a special case: each target or non-target is a pair of equally-sized disks, a homo-pair of two disks of the same color, black or white, or a hetero-pair of two disks of the opposite color; the inter-disk displacement defines its orientation. This prediction - parallel advantage - was supported by the finding that parallel targets require shorter reaction times (RTs) to report targets' locations. Furthermore, it is stronger for targets further from the center of search images, as predicted by the Central-peripheral Dichotomy (CPD) theory entailing that saliency effects are stronger in peripheral than in central vision. However, the parallel advantage could arise from a shorter time required to recognize - rather than to shift attention to - the parallel target. By gaze tracking, the present study confirms that the parallel advantage is solely due to the RTs for the gaze to reach the target. Furthermore, when the gaze is sufficiently far from the target during search, saccade to a parallel, rather than perpendicular, target is more likely, demonstrating the Central-peripheral Dichotomy more directly. Parallel advantage is stronger among observers encouraged to let their search be guided by spontaneous gaze shifts, which are presumably guided by bottom-up saliency rather than top-down factors.

Parallel Advantage: Further Evidence for Bottom-up Saliency Computation by Human Primary Visual Cortex.

Finding a target among uniformly oriented non-targets is typically faster when this target is perpendicular, rather than parallel, to the non-targets. The V1 Saliency Hypothesis (V1SH), that neurons in the primary visual cortex (V1) signal saliency for exogenous attentional attraction, predicts exactly the opposite in a special case: each target or non-target comprises two equally sized disks displaced from each other by 1.2 disk diameters center-to-center along a line defining its orientation. A target has two white or two black disks. Each non-target has one white disk and one black disk, and thus, unlike the target, activates V1 neurons less when its orientation is parallel rather than perpendicular to the neurons' preferred orientations. When the target is parallel, rather than perpendicular, to the uniformly oriented non-targets, the target's evoked V1 response escapes V1's iso-orientation surround suppression, making the target more salient. I present behavioral observations confirming this prediction.

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.