White Paper Series2021-08-16T11:53:15+00:00

Science has brought us far enough to predict soft skills. Human behaviour is a window into internal psychological functions. A fair understanding of the connections between behaviours and what drives them brings value wherever humans are at the centre of processes such as managing a company’s workforce. Human factors such as personality, soft skills, and emotions are the main drivers of successful matches between people and their environment: their job, team members, personalized consumables and services.

Until recent years, firms relied on small windows of behavioural data to gain insight into human factors affecting their business. However, thanks to massive technological innovations in the past decade, we have entered an age of ubiquitous computing, multiplying and magnifying the windows by which we can observe and interpret human behaviour. In particular, high-quality video has become widely available thanks to cameras in smartphones and user-friendly sharing platforms.

These innovations unlock new opportunities in the field of human-machine interactions. Coupled to recent developments in behavioural psychology on the one side and AI/machine learning on the other side, automatic detection, analysis and understanding of human behaviour have become a reality.

As a leader in that field, Vima is ideally positioned to help global companies develop new capabilities thanks to its scientifically validated and proprietary solutions in Behavioural Intelligence. Applications are multiple: human resources (recruitment and organizational development), eSport, market research (consumer behaviour), health and safety).

These scientifically oriented series outline the field of Behavioural Intelligence and explain the core psychological phenomena and how to measure them. New computational technologies are introduced and explained how they help to understand human behaviour. Vima is situated at this intersection, offering the best understanding of human behaviour using behavioural AI solutions.

Ames, D. R., & Kammrath, L. K. (2004). Mind-reading and metacognition: Narcissism, not actual competence, predicts self-estimated ability.

Journal of Nonverbal Behavior, 28(3), 187–209. https://doi.org/10.1023/B:JONB.0000039649.20015.0e

Breil, S., Osterholz, S., Nestler, S., & Back, M. D. (in press). Contributions of nonverbal cues to the accurate judgment of personality traits. http://doi.org/https://doi.org/10.31234/osf.io/mn2je

Dael, N., Bianchi-Berthouze, N., Kleinsmith, A., & Mohr, C. (2016).

Measuring body movement: Current and future directions in proxemics and kinesics. In D. Matsumoto, H. C. Hwang, & M. G. Frank (Eds.), APA handbook of nonverbal communication. (pp. 551–587).

Washington: American Psychological Association. http://doi.org/10.1037/14669-022

Eyben, F., Wöllmer, M., Schuller, B. (2010). Opensmile: the munich versatile and fast open-source audio feature extractor. In Proceedings of the 18th ACM international conference on Multimedia (MM ’10).

Association for Computing Machinery, New York, NY, USA, 1459– 1462. https://doi.org/10.1145/1873951.1874246

Frauendorfer, D., Schmid Mast, M., Nguyen, L., & Gatica-Perez, D. (2014). Nonverbal social sensing in action: Unobtrusive recording and extracting of nonverbal behaviour in social interactions illustrated with a research example. Journal of Nonverbal Behaviour, 38(2), 231–245. http://doi.org/10.1007/s10919-014-0173-5

Frauendorfer, D., & Mast, M. S. (2015). The Impact of Nonverbal Behavior in the Job Interview. In The Social Psychology of Nonverbal Communication (pp. 220–247). London: Palgrave Macmillan UK. http://doi.org/10.1057/9781137345868_11

Gill, A. J., & Oberlander, J. (2002). Taking care of the linguistic features of extraversion. Proceedings of the Annual Meeting of the Cognitive Science Society, 24(24), 363–368.

Hall, J. A., Schmid Mast, M., & West, T. V. (Eds.). (2016). The Social Psychology of Perceiving Others Accurately. Cambridge: Cambridge University Press. http://doi.org/10.1017/CBO9781316181959

John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (pp. 102– 138). New York, NY, USA: Guilford Press

Kleinberg, J., Ludwig, J., Mullainathan, S., & Sunstein C.R. (2019).

Discrimination in the Age of Algorithms. http://dx.doi.org/10.2139/ssrn.3329669

Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007). Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of Artificial Intelligence Research, 30, 457–500. https://doi.org/10.1613/jair.2349

Nguyen, L., Frauendorfer, D., Schmid Mast, M., & Gatica-Perez, D. (2014). Hire me: Computational Inference of Hirability in Employment Interviews Based on Nonverbal Behaviour. IEEE Transactions on Multimedia, 16(4), 1018–1031. http://doi.org/10.1109/TMM.2014.2307169

Nguyen, L., & Gatica-Perez, D. (2016). Hirability in the Wild: Analysis of Online Conversational Video Resumes. IEEE Transactions on Multimedia, 18(7), 1422–1437. http://doi.org/10.1109/TMM.2016.2557058

Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77(6), 1296–1312. https://doi.org/10.1037/0022-3514.77.6.1296

Tausczik, Y. R., & Pennebaker, J. W. (2010). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676

Scherer, K. R., Bänziger, T., & Roesch, E. B. (Eds.). (2010). A blueprint for affective computing: A sourcebook and manual. New York: Oxford University Press.

Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695–729. https://doi.org/10.1177%2F0539018405058216

Scherer, K. R., Bänziger, T., & Roesch, E. B. (Eds.). (2010). A blueprint for affective computing: A sourcebook and manual. New York: Oxford University Press.

Schmid Mast, M., Gatica-Perez, D., Frauendorfer, D., Nguyen, L., & Choudhury, T. (2015). Social Sensing for Psychology. Current Directions in Psychological Science, 24(2), 154–160. http://doi.org/10.1177/0963721414560811

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need.

Advances in Neural Information Processing Systems, 2017December, 5999–6009.

Download all White papers


View our recent LinkedIn activity. Follow Vima on LinkedIn in order to get more updates.

Vima’s LinkedIn Page


Social Wall


  • Rue Marconi 19, 1920 Martigny, Switzerland
  • + 41 79 708 59 49
  • sales@vima.swiss
Go to Top