Preference bias of head orientation in choosing between two non-durables.

The goal of this study is to investigate how customers' gaze, head and body orientations reflect their choices. Although the relationship between human choice and gaze behavior has been well-studied, other behaviors such as head and body are unknown. We conducted a two-alternatives-forced-choice task to examine (1) whether preference bias, i.e., a positional bias in gaze, head and body toward the item that was later chosen, exists in choice, (2) when preference bias is observed and when prediction of the resulting choice becomes possible (3) whether human choice is affected when the body orientations are manipulated. We used real non-durable products (cheap snacks and clothing) on a shopping shelf. The results showed that there was a significant preference bias in head orientation at the beginning 1 s when the subjects stood straight toward the shelf, and that the head orientation was more biased toward the selected item than the gaze and the center of pressure at the ending 1 s. Manipulating body orientation did not affect the result of choice. The preference bias detected by observing the head orientation would be useful in marketing science for predicting customers' choice.

Low-cost three-dimensional gait analysis system for mice with an infrared depth sensor.

Three-dimensional (3D) open-field gait analysis of mice is an essential procedure in genetic and nerve regeneration research. Existing gait analysis systems are generally expensive and may interfere with the natural behaviors of mice because of optical markers and transparent floors. In contrast, the proposed system captures the subjects shape from beneath using a low-cost infrared depth sensor (Microsoft Kinect) and an opaque infrared pass filter. This means that we can track footprints and 3D paw-tip positions without optical markers or a transparent floor, thereby preventing any behavioral changes. Our experimental results suggest with healthy mice that they are more active on opaque floors and spend more time in the center of the open-field, when compared with transparent floors. The proposed system detected footprints with a comparable performance to existing systems, and precisely tracked the 3D paw-tip positions in the depth image coordinates.

Statistical properties of support vector machines with forgetting factor.

Introducing a forgetting factor allows a support vector machine to solve time-varying problems adaptively. However, the exponential forgetting factor proposed in an earlier work does not ensure convergence of average generalization error even for a simple linearly separable problem. To guarantee convergence, we propose a factorial forgetting factor which decays factorially over time. We approximately derive the average generalization error of the factorial forgetting factor as well as that of the exponential forgetting factor using a simple one-dimensional problem, and confirm our theory by computer simulations. Finally, we show that our theory can be extended to arbitrary types of forgetting factors for simple linearly separable cases.