logo

SCIENTIA SINICA Informationis, Volume 49, Issue 4: 422-435(2019) https://doi.org/10.1360/N112018-00286

Objective monitoring of attentional states based on collaborative force-position control tasks

More info
  • ReceivedOct 23, 2018
  • AcceptedDec 27, 2018
  • PublishedApr 11, 2019

Abstract

Accurate haptic interaction tasks require intensive activation of attentional resources. Exploring the relation between haptic channel and attention is not only conducive to understanding the interaction mechanism between human perception and cognition but is also important for the design of brain-computer interaction system based on haptic modality. In this paper, we built a virtual reality environment incorporating the multiregion force feedback and immersive visual displays and developed a collaborative force-position control task to monitor the attentional states objectively based on this platform. The force and position control performances of users at four different difficulty levels were used to measure the attentional level and attentional spotlight. The user's subjective assessment showed that the task can effectively activate attentional resources. The experimental results showed that the task can be used as an attentional "oscilloscope" to monitor the changes in the attentional level and target of attentional spotlight with a high temporal resolution, providing a behavioral ground truth for exploring attentional biomarkers.


Funded by

国家自然科学基金(61572055)


References

[1] Tang Y Y, Posner M I. Attention training and attention state training.. Trends Cognitive Sci, 2009, 13: 222-227 CrossRef PubMed Google Scholar

[2] Cho B H, Ku J, Jang D P. The effect of virtual reality cognitive training for attention enhancement.. CyberPsychology Behav, 2002, 5: 129-137 CrossRef PubMed Google Scholar

[3] Wang D X, Zheng Y L, Li T. Multi-modal human-machine interaction for human intelligence augmentation. Sci Sin-Inf, 2018, 48: 449-465 CrossRef Google Scholar

[4] Green C S, Bavelier D. Learning, attentional control, and action video games.. Curr Biol, 2012, 22: R197-R206 CrossRef PubMed Google Scholar

[5] Anguera J A, Boccanfuso J, Rintoul J L. Video game training enhances cognitive control in older adults. Nature, 2013, 501: 97-101 CrossRef PubMed ADS Google Scholar

[6] Wang D X, Zhang Y R, Yang X X. Force control tasks with pure haptic feedback promote short-term focused attention.. IEEE Trans Haptics, 2014, 7: 467-476 CrossRef PubMed Google Scholar

[7] Yang X X, Wang D X, Zhang Y R. An adaptive strategy for an immersive visuo-haptic attention training game. In: Proceedings of International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, London, 2016. 441--451. Google Scholar

[8] Zhang X M. Experimental psychology. Beijing: Beijing Normal University Publishing Group, 2011. Google Scholar

[9] Chun M M, Potter M C. A two-stage model for multiple target detection in rapid serial visual presentation.. J Exp Psychology-Human Perception Performance, 1995, 21: 109-127 CrossRef Google Scholar

[10] Moray N. Attention in Dichotic Listening: Affective Cues and the Influence of Instructions. Q J Exp Psychology, 1959, 11: 56-60 CrossRef Google Scholar

[11] Westerhausen R, Hugdahl K. The corpus callosum in dichotic listening studies of hemispheric asymmetry: a review of clinical and experimental evidence.. NeuroSci BioBehaval Rev, 2008, 32: 1044-1054 CrossRef PubMed Google Scholar

[12] Fan J, McCandliss B D, Sommer T. Testing the efficiency and independence of attentional networks.. J Cognitive Neuroscience, 2002, 14: 340-347 CrossRef PubMed Google Scholar

[13] Robertson I H, Manly T, Andrade J. `Oops¡: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 1997, 35: 747-758 CrossRef Google Scholar

[14] Shapiro K L, Raymond J E, Arnell K M. The attentional blink. Trends Cognitive Sci, 1997, 1: 291-296 CrossRef Google Scholar

[15] Marois R, Ivanoff J. Capacity limits of information processing in the brain.. Trends Cognitive Sci, 2005, 9: 296-305 CrossRef PubMed Google Scholar

[16] McVay J C, Kane M J. Drifting from slow to "D'oh": working memory capacity and mind wandering predict extreme reaction times and executive control errors.. J Exp Psychology-Learning Memory Cognition, 2012, 38: 525-549 CrossRef PubMed Google Scholar

[17] Rosenberg M D, Finn E S, Constable R T. Predicting moment-to-moment attentional state.. NeuroImage, 2015, 114: 249-256 CrossRef PubMed Google Scholar

[18] Rosenberg M, Noonan S, DeGutis J. Sustaining visual attention in the face of distraction: a novel gradual-onset continuous performance task.. Atten Percept Psychophys, 2013, 75: 426-439 CrossRef PubMed Google Scholar

[19] Esterman M, Noonan S K, Rosenberg M. In the zone or zoning out? Tracking behavioral and neural fluctuations during sustained attention.. Cerebral Cortex, 2013, 23: 2712-2723 CrossRef PubMed Google Scholar

[20] Li Y J, Qiu Y H, Zhu Y S. EEG signal analysis method and its application. Beijing: Science Press, 2009. Google Scholar

[21] Braboszcz C, Delorme A. Lost in thoughts: neural markers of low alertness during mind wandering.. NeuroImage, 2011, 54: 3040-3047 CrossRef PubMed Google Scholar

[22] Kam J W Y, Dao E, Farley J. Slow fluctuations in attentional control of sensory cortex.. J Cognitive Neuroscience, 2011, 23: 460-470 CrossRef PubMed Google Scholar

[23] Wang D X, Jiao J, Yang G. Force Maintenance Accuracy Using a Tool: Effects of Magnitude and Feedback.. IEEE Trans Haptics, 2016, 9: 432-436 CrossRef PubMed Google Scholar

[24] Sheng Z. Probability theory and mathematical statistics: third Edition. Beijing: Higher Education Press, 2001. Google Scholar

[25] Wang D X, Zhang X, Zhang Y R. Configuration-based optimization for six degree-of-freedom haptic rendering for fine manipulation.. IEEE Trans Haptics, 2013, 6: 167-180 CrossRef PubMed Google Scholar

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1