There is a need within human movement sciences for a markerless motion capture system, which is easy to use and sufficiently accurate to evaluate motor performance. This study aims to develop a 3D markerless motion capture technique, using OpenPose with multiple synchronized video cameras, and examine its accuracy in comparison with optical marker-based motion capture. Participants performed three motor tasks (walking, countermovement jumping, and ball throwing), and these movements measured using both marker-based optical motion capture and OpenPose-based markerless motion capture. The differences in corresponding joint positions, estimated from the two different methods throughout the analysis, were presented as a mean absolute error (MAE). The results demonstrated that, qualitatively, 3D pose estimation using markerless motion capture could correctly reproduce the movements of participants. Quantitatively, of all the mean absolute errors calculated, approximately 47% were <20 mm, and 80% were <30 mm. However, 10% were >40 mm. The primary reason for mean absolute errors exceeding 40 mm was that OpenPose failed to track the participant's pose in 2D images owing to failures, such as recognition of an object as a human body segment or replacing one segment with another depending on the image of each frame. In conclusion, this study demonstrates that, if an algorithm that corrects all apparently wrong tracking can be incorporated into the system, OpenPose-based markerless motion capture can be used for human movement science with an accuracy of 30 mm or less.

Gaze direction can be represented in terms not only of local-feature information (ie the location of the pupil in the eye socket), but also of an emergent property---whether the perceived gaze direction is straight or averted. To examine whether this emergent property is preferentially accessed when searching for an oddly directed gaze, we experimentally manipulated the local-feature information and the emergent property independently, in order to investigate the influences of both types of information on visual searches for an oddly directed gaze. We found that the primary determinant of search efficiency was not the local-feature information of eye region, but the emergent property--the perceived direction of the gaze. This finding is consistent with the idea that important social signals are recognised primarily by their emergent properties.

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge-based inference depending on the subjective difficulty of the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge-based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge-based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed.

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Previous studies showed that regular users have become an important source of innovation (called user innovation). Previous studies also suggested that two factors have significant impacts on user innovation: the social network structure of the users and neighbours' innovation performance (neighbours mean users having interactions with the focal user). However, in these studies, the influence of the two factors were only discussed separately, and it remained unclear whether these two factors interdependently affected a focal user's innovation. To examine the interplay between the network structure and the neighbours' innovation performance, we harnessed data sets from "Idea Storm", which collects data on user network and idea submission. Through panel regression analyses, we found that-within an open-network structure-the higher innovation performance of neighbours has a larger positive impact on the focal user's innovation ability. Conversely, in an enclosed network structure, neighbours' higher performance has a larger negative impact on the focal user's innovation ability. Our findings filled an important gap in understanding the interplay between the network structure and the neighbours' performance in user innovation. More broadly, these results suggested that the interplay between the neighbours and the network structures merits attention that even goes beyond user innovation.

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Sometimes we regard just an artifact as a lifelike one and other times not; it is considered to depend on how we deal and interact with the artifact. We experimentally examined whether differences in the manner of interacting with a moving robot (operating it or only observing its movements) influenced one's perception of the robot's animacy and, if so, whether the strength of this influence depended on the apparent goal-directedness of the robot's movements. We found that people only observing the robot perceived it most animated when its movements seemed most goal-directed but that people controlling the robot perceived it more animated when 1/f noise made its movements seem less goal-directed. Our perception of a moving object's animacy thus depends on whether we interact with the object or just observe it while someone else interacts with it. This result suggests that robotics researchers should design how a robot interacts with its users, in order to elicit higher degree of animacy perception for the robot.