Visuomotor information may be better conveyed through a first-person perspective than through a third-person perspective. However, few reports have shown a clear behavioral advantage of the first-person perspective because of the confounding factor of spatial stimulus-response compatibility. Most imitation studies have utilized visuospatial imitation tasks in which participants use the same bod...

Yu et al (Reports, 10 March 2017, p. 1072) state that contagious itch occurs in mice based on imitative scratching in normal mice observing excessive scratching in genetically modified demonstrator mice. However, despite employing multiple behavioral analysis approaches, we were unable to extend these findings to normal mice observing the well-established histamine model of acute itch in demons...

The 'mirror neuron system' (MNS), located within inferior parietal lobe (IPL) and inferior frontal gyrus (IFG), creates an internal motor representation of the actions we see and has been implicated in imitation. Recently, the MNS has been implicated in non-identical responses: when the actions we must execute do not match those that we observe. However, in such conflicting situations non actio...

Humans automatically imitate other people's actions during social interactions, building rapport and social closeness in the process. Although the behavioral consequences and neural correlates of imitation have been studied extensively, little is known about the neural mechanisms that control imitative tendencies. For example, the degree to which an agent is perceived as human-like influences a...

Recent research has shown that infants and young children up to the age of 5 years are more likely to imitate in-group members than out-group members. Cues like gender, race, age, and language are robust indicators for social categories and, thus, for group membership. Concerning imitation, research mainly focuses on language and accent, whereas race indicated by physical appearance is rarely i...

Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised ...