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How are second-order (texture-defined) and third-order (pattern-tracking) motions processed in our brains? As shown here in the context of an ambiguous motion task involving a nominal second-order stimuli first devised by Werkhoven et al., [Werkhoven, P., Sperling, G. & Chubb, C. (1993) Vision Res. 33, 463-485.], the observers fell into two distinct groups based on the direction of perceived motion. The differences were interpreted in terms of the algorithms used to extract motion: one group by using a second-order motion process and the other by using a third-order motion process. This was investigated further using a dual-task paradigm in which the interference between two tasks indicated the nature of processing involved. Observers who used third-order motion processing experienced interference with letter recognition and a more severe interference in dual third-order motion tasks. Observers who used second-order motion processing experienced interference with another second-order motion detection but not with letter recognition. Insofar as task interference implies the need for attention, the complex interference effects and the apparently paradoxical interference effects of second-order motion perception imply that there are multiple forms of attention. Whether two tasks interfere depends on whether they require the same form of attention. Insofar as spatio-temporal processing is assumed to be carried out in the dorsal stream and pattern recognition in the ventral stream, the interference patterns suggest that second-order motion may be computed entirely in the dorsal stream, and third-order motion may involve two computational processes, one of which shares computational resources with the letter recognition task in the ventral stream.

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Internet and cloud-based robot services have been attractive and developed. Nowadays, how to enhance the controllability of intelligent robots is an important issue for researchers. This study designs a Kinect Motion Oriented Intelligent-Robot Environment, which is called K-MORE. K-MORE is designed for easily controlling intelligent robots, which is composed of Sensing-based Interactive Robot (SIR) service platform and Cloud Environment. K-MORE is based on open service platforms, including Arduinos, Google Android, and Google App Engine (GAE). Robots not only can be controlled by a smartphone but also can be triggered by events via GAE or Ontology Case-based Reasoning (OCBR). Furthermore, users can use a smartphone to control robots. K-MORE is well-integrated with users, intelligent robots, sensors and the cloud environment. Finally, K-MORE can be an inference model for researchers when developing a robotics-based open service platform in the future.