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4. Personality Sensing for Theory Development and Assessment in the Digital Age

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   Harari, Gabriella M.; Vaid, Sumer S.; Müller, Sandrine R.; Stachl, Clemens; Marrero, Zachariah; Schoedel, Ramona; Bühner, Markus; Gosling, Samuel D. (September 2020, European Journal of Personality)

   null (Ed.)

   People around the world own digital media devices that mediate and are in close proximity to their daily behaviours and situational contexts. These devices can be harnessed as sensing technologies to collect information from sensor and metadata logs that provide fine–grained records of everyday personality expression. In this paper, we present a conceptual framework and empirical illustration for personality sensing research, which leverages sensing technologies for personality theory development and assessment. To further empirical knowledge about the degree to which personality–relevant information is revealed via such data, we outline an agenda for three research domains that focus on the description, explanation, and prediction of personality. To illustrate the value of the personality sensing research agenda, we present findings from a large smartphone–based sensing study ( N = 633) characterizing individual differences in sensed behavioural patterns (physical activity, social behaviour, and smartphone use) and mapping sensed behaviours to the Big Five dimensions. For example, the findings show associations between behavioural tendencies and personality traits and daily behaviours and personality states. We conclude with a discussion of best practices and provide our outlook on how personality sensing will transform our understanding of personality and the way we conduct assessment in the years to come. © 2020 European Association of Personality Psychology 

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   Park, Hyoshin; Ono, Masahiro; Posselt, Derek (August 2023, 2023 IEEE Space Computing Conference (SCC))

   Recent advances in computer vision for space exploration have handled prediction uncertainties well by approximating multimodal output distribution rather than averaging the distribution. While those advanced multimodal deep learning models could enhance the scientific and engineering value of autonomous systems by making the optimal decisions in uncertain environments, sequential learning of those approximated information has depended on unimodal or bimodal probability distribution. In a sequence of information learning and transfer decisions, the traditional reinforcement learning cannot accommodate the noise in the data that could be useful for gaining information from other locations, thus cannot handle multimodal and multivariate gains in their transition function. Still, there is a lack of interest in learning and transferring multimodal space information effectively to maximally remove the uncertainty. In this study, a new information theory overcomes the traditional entropy approach by actively sensing and learning information in a sequence. Particularly, the autonomous navigation of a team of heterogeneous unmanned ground and aerial vehicle systems in Mars outperforms benchmarks through indirect learning. 

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