• Journal of Computing Science and Engineering
• /
• v.7 no.2
• /
• pp.122-131
• /
• 2013

The MPI CyberMotion Simulator provides a unique motion platform, as it features an anthropomorphic robot with a large workspace, combined with an actuated cabin and a linear track for lateral movement. This paper introduces the simulator as a tool for studying human perception, and compares its characteristics to conventional Stewart platforms. Furthermore, an experimental evaluation is presented in which multimodal human control behavior is studied by identifying the visual and vestibular responses of participants in a roll-lateral helicopter hover task. The results show that the simulator motion allows participants to increase tracking performance by changing their control strategy, shifting from reliance on visual error perception to reliance on simulator motion cues. The MPI CyberMotion Simulator has proven to be a state-of-the-art motion simulator for psychophysical research to study humans with various experimental paradigms, ranging from passive perception experiments to active control tasks, such as driving a car or flying a helicopter.

• Journal of Digital Convergence
• /
• v.13 no.4
• /
• pp.357-368
• /
• 2015

People observe lots of events around the environment and we can easily recognize the nature of an event from the resulting optic flow. The questions are how do people recognize events and what is the information in the optic flow that enables observers to recognize events. Motor theorists claim that human observers exhibit special sensitivity when perceiving events like speech or biological motion, because we both produce and perceive those events. However, direct perception theorists suggested that speech or biological motion is not special from the perception of all other kinds of event. The purpose of this review article is to address this controversy to critique the motor theory and to describe a direct realist approach to event perception. It is important to understand the fundamental information of how human perceive event perception for the convergence on robotics.

• Journal of Internet Computing and Services
• /
• v.15 no.4
• /
• pp.9-20
• /
• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.

• Science of Emotion and Sensibility
• /
• v.21 no.3
• /
• pp.151-164
• /
• 2018

The appropriate interpretation of social cues is a crucial ability for everyday life. While processing socially relevant information, beyond the low-level physical features of the stimuli to emotional information is known to influence human cognition in various stages, from early perception to later high-level cognition, such as working memory (WM). However, it remains unclear how the influence of each type of emotional information on cognitive processes changes in response to what has occurred in the processing stage. Past studies have largely adopted face stimuli to address this type of research question, but we used a unique class of socially relevant motion stimuli, called biological motion (BM), which depicts various human actions and emotions with moving dots to exhibit the effects of anger, happiness, and neutral emotion on task performance in perceptual and working memory. In this study, participants determined whether two BM stimuli, sequentially presented with a delay between them (WM task) or one immediately after the other (perceptual task), were identical. The perceptual task showed that discrimination accuracies for emotional stimuli (i.e., angry and happy) were lower than those for neutral stimuli, implying that emotional information has a negative impact on early perceptual processes. Alternatively, the results of the WM task showed that the accuracy drop as the interstimulus interval increased was actually lower in emotional BM conditions than in the neutral condition, which suggests that emotional information benefited maintenance. Moreover, anger and happiness had distinct impacts on the performance of perception and WM. Our findings have significance as we provide evidence for the interaction of type of emotion and information-processing stage.

• Journal of Digital Convergence
• /
• v.15 no.12
• /
• pp.509-516
• /
• 2017

People observe a lot of events in our environment and we do not have any difficulty to perceive events including speech perception. Like perception of biological motion, two main theorists have debated on speech perception. The purpose of this review article is to briefly describe speech perception and compare these two theories of speech perception. Motor theorists claim that speech perception is special to human because we both produce and perceive articulatory events that are processed by innate neuromotor commands. However, direct perception theorists claim that speech perception is not different from nonspeech perception because we only need to detect information directly like all other kinds of event. It is important to grasp the fundamental idea of how human perceive articulatory events for the convergence on speech engineering. Thus, this basic review of speech perception is expected to be able to used for AI, voice recognition technology, speech recognition system, etc.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.8
• /
• pp.299-308
• /
• 2014

Various literatures related computing of information processing have been recently shown the researches inspired from the remarkably excellent human capabilities which recognize and categorize very complex visual patterns such as body motions and facial expressions. Applied from human's outstanding ability of perception, the classification function of visual sequences without context information is specially crucial task for computer vision to understand both the coding and the retrieval of spatio-temporal patterns. This paper presents a biological process based action recognition model of computer vision, which is inspired from visual information processing of human brain for action recognition of visual sequences. Proposed model employs the structure of neural fields of bio-inspired visual perception on detecting motion sequences and discriminating visual patterns in human brain. Experimental results show that proposed recognition model takes not only into account several biological properties of visual information processing, but also is tolerant of time-warping. Furthermore, the model allows robust temporal evolution of classification compared to researches of action recognition. Presented model contributes to implement bio-inspired visual processing system such as intelligent robot agent, etc.