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    Negotiation Skills Training Intervention Based on Automated Recognition of Human Emotion and Non-Verbal Behaviour

    HCI 2018

    Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI 2018)

    Belfast, UK, 4 - 6 July 2018


    Nicole Shumskaya



    This research explores the effect of ‘social signals’ feedback intervention based on automated recognition of affect and non-verbal behaviours within the context of negotiation skills training. The work uses several off-the-shelf technologies; Sociometric badges, iMotions Biometric Research Platform and Nemesysco Layered Voice Analysis, to recognise and analyse emotional expressions, vocal emotions and body movement. A controlled experiment compared standard negotiation skills feedback to feedback augmented with emotion and sensor-based social skills evaluation to explore whether negotiation performance and use of social signals vary depending on feedback condition.

    The study focusses on paired-negotiation tasks with three conditions: control (standard feedback) vs. two experimental conditions; one where both negotiators in the pair received the augmented feedback; one where only one of the pair received the augmented feedback. We collect objective and subjective measures of negotiation performance, and emotion and social signals data in order to test the following hypotheses: H1: measurable changes in social signals will be evident following training in negotiation skills; changes will be greater in those who receive social signals feedback & H2: training using social signals feedback will result in differences in negotiation outcomes (measured objectively and subjectively).


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