In the present study we propose an evaluation method for the eye attraction (the probability of gaze following the first saccade) to object in visual search. We measured eye movements during visual search to clear eye attraction phenomena, and particularly investigated the effect of peripheral visual field on eye attraction according to visual feature dimensions. Eye movement data indicated that the probability of gaze following the first saccade gradually weakens in peripheral areas of visual field regardless of any visual feature dimension, and also depends on the number of object of the same visual feature or the same combination as visual feature. Therefore, the ease of gaze following the first saccade was formulated by taking consideration of such functions of eye attraction to object. As a result, our proposed model using the value can estimate the actual probability of gaze following the first saccade with high accuracy.

Using the computer graphics images of fabric that were produced with the Oren-Nayar-Blinn model and the Oren-Nayar model in computer graphics software, we examined the rendering parameters (“Specular”, “Diffuse”, and “Roughness”) of images which give similar subjective impression to actual fabric. We also considered the influence of the parameter values on subjective impression of the images. The result showed that when the parameter “Diffuse” was equal to 90 or 100, and the parameter “Roughness” was equal to 30 in both models, the subjective evaluations were the most similar to those of real fabric. It was also shown that the value of the parameter “Diffuse” greatly contributed to the appearance of fabric: as the value increased, the appearance of fabric images became brighter, softer, and smoother.

A method is described that reconstructs population receptive field (pRF) properties in human visual cortex using functional MRI. This data-analysis technique fits a model of the pRF to the fMRI timeseries. It is able to reconstruct visual field maps and additional properties of the underlying neural population, such as quantitative estimates of the pRF size and surround. The pRF sizes vary systematically between visual field maps and as a function of eccentricity. As we change the stimulus, we expect different contributions from the underlying neural population and different pRF sizes. Different neural contributions to the pRF can be teased apart by comparing pRF size estimates from different stimuli within the same cortical location. In this fashion, the pRF method provides an additional technique to elucidate the neural computations of the human visual system.