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Mitsuhiko Hanada

Publications and source records attributed to Mitsuhiko Hanada.

4 recordsLinked to original sources

Computational analyses for illusory transformations in the optic flow field and heading perception in the presence of moving objects.

When we see a stimulus of a radial flow field (the target flow) overlapped with a lateral flow field or another radial flow field, the focus of expansion (FOE) of the target radial flow appears to be shifted in a direction. Royden and Conti [(2003). A model using MT-like motion-opponent operators explains an illusory transformation in the optic flow field. Vision Research, 43, 2811-2826] argued that local motion subtraction is crucial for explanation of this phenomenon. The flow field which causes the illusory displacement of FOE was computationally analyzed. It was shown that the flow field is approximately a rigid-motion flow; the flow can be generated by simulating a situation where an observer moves toward a stationary scene. The heading direction for the observer corresponds to the perceived position of the FOE of the radial flow pattern. It implies that any algorithms which assume rigidity of the scene and recover veridical heading explain the bias in perceived FOE. There is no need for local motion subtraction in order to explain the phenomena. Furthermore, the flow for an observer's translation in the presence of objects moving laterally or in depth was computationally analyzed. It was found that algorithms which minimizes standard error functions with less weights to the independently moving objects show similar biases in recovered heading to the bias of human observers. It implies that local motion subtraction is not necessary for explanation of the bias in perceived heading due to an object moving laterally or in depth, contrary to the argument of Royden [(2002). Computing heading in the presence of moving objects: a model that uses motion-opponent operators. Vision Research, 42, 3043-3058].

Algorithms↗

Phenomenal regression to the frontal and natural picture.

The retinal image of a figure on a slanted picture is narrower than that of a figure on a frontal picture. In this study, the perceived width of various figures (horizontal line segments, ellipses, faces, symbolic faces, and artistic pictures) on a slanted picture plane was measured. The width of the figures was magnified or reduced in order to vary the naturalness of the original figures. The perceived width was found to be much closer to the width of the original figures than to the retinal images of the slanted figures. The width of the original figures was also found to affect the perceived width of the slanted figures; the perceived width was observed to be more biased toward a more natural width. On the other hand, the naturalness of the figures did not affect the perceived slant. These results suggest that the visual system corrected the width of the figures on a slanted plane, taking into the account naturalness or prägnanz as well as the slant.

Discrimination Learning↗

An algorithmic model of heading perception.

On the basis of Hanada and Ejima's (2000) model, an algorithmic model was presented to explain psychophysical data of van den Berg and Beintema (2000) that are inconsistent with vector-subtractive compensation for the rotational flow. The earlier model was modified in order not to use vector-subtractive compensation for the rotational flow. The proposed model computes the center of flow first and then estimates self-rotation; finally, heading is recovered from the center of flow and the estimate of self-rotation. The model explains the data of van de Berg and Beintema (2000). A fusion model of rotation estimates from different sources (efferent signals, proprioceptive feedback, vestibular signals about eye and head rotation, and visual motion) was also presented.

Algorithms↗

Effects of the noise level on induced motion.

Motion in a part of the field induces motion in an adjoining region. In this study, it was investigated how the noise level affects induced motion of a counterphase flickering (target) grating due to adjacent drifting (inducer) gratings. It was shown that at low noise levels, motion contrast occurred, and at high noise levels, motion assimilation occurred. When the noise level was randomly set for each trial, the adaptive change with the noise level was also observed. The result suggests that the adaptive change occurs for a short period. It was also found that noise for the target as well as noise for the inducers contributes to the effect of noise on motion induction. It suggests that the overall noise level is crucial for the effect. The study provided evidence that motion integration changes from a spatially band-pass operation to a low-pass operation as the signal-to-noise ratio (SNR) decreases.

Contrast Sensitivity↗