首页|New Findings from Nanyang Technological University Describe Advances in Robotics (A stimulus exposure of 50 ms elicits the uncanny valley effect)
New Findings from Nanyang Technological University Describe Advances in Robotics (A stimulus exposure of 50 ms elicits the uncanny valley effect)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in robotic s. According to news reporting from Nanyang Technological University by NewsRx j ournalists, research stated, "The uncanny valley (UV) effect captures the observ ation that artificial entities with near-human appearances tend to create feelin gs of eeriness. Researchers have proposed many hypotheses to explain the UV effe ct, but the visual processing mechanisms of the UV have yet to be fully understo od." Funders for this research include Nanyang Technological University. The news correspondents obtained a quote from the research from Nanyang Technolo gical University: "In the present study, we examined if the UV effect is as acce ssible in brief stimulus exposures compared to long stimulus exposures (Experime nt 1). Forty-one participants, aged 21-31, rated each human-robot face presented for either a brief (50 ms) or long duration (3 s) in terms of attractiveness, e eriness, and humanness (UV indices) in a 7-point Likert scale. We found that bri ef and long exposures to stimuli generated a similar UV effect. This suggests th at the UV effect is accessible at early visual processing. We then examined the effect of exposure duration on the categorisation of visual stimuli in Experimen t 2. Thirty-three participants, aged 21-31, categorised faces as either human or robot in a two-alternative forced choice task. Their response accuracy and vari ance were recorded. We found that brief stimulus exposures generated significant ly higher response variation and errors than the long exposure condition. This i ndicated that participants were more uncertain in categorising faces in the brie f exposure condition due to insufficient time. Further comparisons between Exper iment 1 and 2 revealed that the eeriest faces were not the hardest to categorise ."
Nanyang Technological UniversityEmergi ng TechnologiesMachine LearningRobotRobotics
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