What are reaction time indices of automatic imitation measuring?

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Humans unintentionally copy other people’s gestures, postures and speech patterns. This behaviour has been termed ‘automatic imitation’, and is thought to play a crucial role in the development of social relationships by building affiliation and rapport between interaction partners. The current paper critically evaluates reaction time measures of automatic imitation and demonstrates that it is currently unclear what reaction time indices measure, due to a lack of research assessing dimensions of validity and domain-specificity. Based on this assessment of the literature, three suggestions are made for future research. First, studies that assess the validity of reaction time paradigms as an index of automatic imitation are required. That is, the extent that processes involved in reaction time measures of automatic imitation also operate in real-world copying behaviours needs firmly establishing. Second, the extent that component processes of automatic imitation rely on domain-specific, domain-general or a combination of both types of cognitive system needs establishing. Moreover, theories of imitation that move beyond neat divisions between domain-specific and domain-general systems should be prioritised. Third, a case is made for why methodological rigour requires far more emphasis in order to support the development of a cumulative science of automatic imitation. In sum, by providing a critical review of the current literature, as well as suggesting new directions for research questions, theories and methods, this paper aims to stimulate discussion regarding optimal approaches to studying automatic imitation. Moreover, the analysis and recommendations presented here generalise to other domains of social cognition, such as gaze perception and perspective taking, which also rely on similar reaction time paradigms.
Original languageEnglish
Pages (from-to)240-254
JournalConsciousness and Cognition
Early online date13 Sep 2018
Publication statusPublished - Oct 2018

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