Modeling Behavior Dynamics Using Computational Psychometrics Within Virtual Worlds

Overview

Journal Front Psychol

Date 2015 Nov 24

PMID 26594193

Citations 15

Authors

Pietro Cipresso

Affiliations

Soon will be listed here.

Abstract

In case of fire in a building, how will people behave in the crowd? The behavior of each individual affects the behavior of others and, conversely, each one behaves considering the crowd as a whole and the individual others. In this article, I propose a three-step method to explore a brand new way to study behavior dynamics. The first step relies on the creation of specific situations with standard techniques (such as mental imagery, text, video, and audio) and an advanced technique [Virtual Reality (VR)] to manipulate experimental settings. The second step concerns the measurement of behavior in one, two, or many individuals focusing on parameters extractions to provide information about the behavior dynamics. Finally, the third step, which uses the parameters collected and measured in the previous two steps in order to simulate possible scenarios to forecast through computational models, understand, and explain behavior dynamics at the social level. An experimental study was also included to demonstrate the three-step method and a possible scenario.

Citing Articles

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Extended Reality for the Clinical, Affective, and Social Neurosciences.

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References

Mauri M, Cipresso P, Balgera A, Villamira M, Riva G . Why is Facebook so successful? Psychophysiological measures describe a core flow state while using Facebook. Cyberpsychol Behav Soc Netw. 2011; 14(12):723-31. DOI: 10.1089/cyber.2010.0377. View

Goodmurphy C, Ovalle W . Morphological study of two human facial muscles: orbicularis oculi and corrugator supercilii. Clin Anat. 1999; 12(1):1-11. DOI: 10.1002/(SICI)1098-2353(1999)12:1<1::AID-CA1>3.0.CO;2-J. View

Stanney K, Salvendy G, Deisinger J, DiZio P, Ellis S, Ellison J . Aftereffects and sense of presence in virtual environments: formulation of a research and development agenda. Int J Hum Comput Interact. 2001; 10(2):135-87. DOI: 10.1207/s15327590ijhc1002_3. View

Mauri M, Magagnin V, Cipresso P, Mainardi L, Brown E, Cerutti S . Psychophysiological signals associated with affective states. Annu Int Conf IEEE Eng Med Biol Soc. 2010; 2010:3563-6. PMC: 3059749. DOI: 10.1109/IEMBS.2010.5627465. View

Baumgartner T, Valko L, Esslen M, Jancke L . Neural correlate of spatial presence in an arousing and noninteractive virtual reality: an EEG and psychophysiology study. Cyberpsychol Behav. 2006; 9(1):30-45. DOI: 10.1089/cpb.2006.9.30. View

Batut B, Parsons D, Fischer S, Beslon G, Knibbe C . In silico experimental evolution: a tool to test evolutionary scenarios. BMC Bioinformatics. 2014; 14 Suppl 15:S11. PMC: 3851946. DOI: 10.1186/1471-2105-14-S15-S11. View

Newman M . The structure of scientific collaboration networks. Proc Natl Acad Sci U S A. 2001; 98(2):404-9. PMC: 14598. DOI: 10.1073/pnas.98.2.404. View

Gomez-Marin A, Paton J, Kampff A, Costa R, Mainen Z . Big behavioral data: psychology, ethology and the foundations of neuroscience. Nat Neurosci. 2014; 17(11):1455-62. DOI: 10.1038/nn.3812. View

Mathersul D, Williams L, Hopkinson P, Kemp A . Investigating models of affect: relationships among EEG alpha asymmetry, depression, and anxiety. Emotion. 2008; 8(4):560-72. DOI: 10.1037/a0012811. View

10.

Xu Y, Liu P, Li X, Ren W . Discovering the influences of complex network effects on recovering large scale multiagent systems. ScientificWorldJournal. 2014; 2014:407639. PMC: 3996872. DOI: 10.1155/2014/407639. View

11.

Newman M, Strogatz S, Watts D . Random graphs with arbitrary degree distributions and their applications. Phys Rev E Stat Nonlin Soft Matter Phys. 2001; 64(2 Pt 2):026118. DOI: 10.1103/PhysRevE.64.026118. View

12.

Diego M, Field T, Hernandez-Reif M . CES-D depression scores are correlated with frontal EEG alpha asymmetry. Depress Anxiety. 2001; 13(1):32-7. View

13.

Gordon E, Palmer D, Cooper N . EEG alpha asymmetry in schizophrenia, depression, PTSD, panic disorder, ADHD and conduct disorder. Clin EEG Neurosci. 2010; 41(4):178-83. DOI: 10.1177/155005941004100404. View

14.

Moulton S, Kosslyn S . Imagining predictions: mental imagery as mental emulation. Philos Trans R Soc Lond B Biol Sci. 2009; 364(1521):1273-80. PMC: 2666712. DOI: 10.1098/rstb.2008.0314. View

15.

Barthelemy M, Barrat A, Pastor-Satorras R, Vespignani A . Velocity and hierarchical spread of epidemic outbreaks in scale-free networks. Phys Rev Lett. 2004; 92(17):178701. DOI: 10.1103/PhysRevLett.92.178701. View

16.

Kunzmann U, Kupperbusch C, Levenson R . Behavioral inhibition and amplification during emotional arousal: a comparison of two age groups. Psychol Aging. 2005; 20(1):144-58. DOI: 10.1037/0882-7974.20.1.144. View

17.

Giakoumis D, Drosou A, Cipresso P, Tzovaras D, Hassapis G, Gaggioli A . Using activity-related behavioural features towards more effective automatic stress detection. PLoS One. 2012; 7(9):e43571. PMC: 3446965. DOI: 10.1371/journal.pone.0043571. View

18.

Nikulin V, Brismar T . Long-range temporal correlations in alpha and beta oscillations: effect of arousal level and test-retest reliability. Clin Neurophysiol. 2004; 115(8):1896-908. DOI: 10.1016/j.clinph.2004.03.019. View

19.

Niemiec A, Lithgow B . Alpha-band characteristics in EEG spectrum indicate reliability of frontal brain asymmetry measures in diagnosis of depression. Conf Proc IEEE Eng Med Biol Soc. 2007; 2005:7517-20. DOI: 10.1109/IEMBS.2005.1616251. View

20.

. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996; 93(5):1043-65. View