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【论文】唐红红,X Lu, Z Cui, C Feng, Q Lin,崔学刚,苏凇(通讯作者),刘超:“Resting-state functional connectivity and deception: exploring individualized deceptive propensity by machine learning”
发布日期: 2018-12-21  浏览次数:

唐红红,X Lu, Z Cui, C Feng, Q Lin,崔学刚,苏凇(通讯作者),刘超(2018). Resting-state functional connectivity and deception: exploring individualized deceptive propensity by machine learning. Neuroscience. Vol 395, 101-112. (SCI期刊, SSCI索引, JCR Q2分区, Impact Factor=3.382, 论文被评选为Neuroscience杂志当期封面文章)文章链接:https://www.sciencedirect.com/science/article/pii/S0306452218307061

ABSTRACT:

Individuals show marked variability in determining to be honest or deceptive in daily life. A large number of studies have investigated the neural substrates of deception; however, the brain networks contributing to the individual differences in deception remain unclear. In this study, we sought to address this issue by employing a machine-learning approach to predict individuals’ deceptive propensity based on the topological properties of whole-brain resting-state functional connectivity (RSFC). Participants finished the resting-state functional MRI (fMRI) data acquisition, and then, one week later, participated as proposers in a modified ultimatum game in which they spontaneously chose to be honest or deceptive. A linear relevance vector regression (RVR) model was trained and validated to examine the relationship between topological properties of networks of RSFC and actual deceptive behaviors. The machine-learning model sufficiently decoded individual differences in deception using three brain networks based on RSFC, including the executive controlling network (dorsolateral prefrontal cortex, middle frontal cortex, and orbitofrontal cortex), the social and mentalizing network (the temporal lobe, temporo-parietal junction, and inferior parietal lobule), and the reward network (putamen and thalamus). These networks have been found to form a signaling cognitive framework of deception by coding the mental states of others and the reward or values of deception or honesty, and integrating this information to make a final decision about being deceptive or honest. These findings suggest the potential of using RSFC as a task-independent neural trait for predicting deceptive propensity, and shed light on using machine-learning approaches in deception detection.

该论文与以下论文为系列论文:

唐红红,Wang S., Liang Z., W Sinnott-Armstrong, 苏凇(通讯作者), 刘超 (2018).Are proselfs more deceptive and hypocritical? Social image concerns in appearing fair. Frontiers in Psychology. 9: 2286. (SSCI, JCR Q2分区, Impact Factor=2.089); Published online 2018 Nov 21.  https://www.frontiersin.org/articles/10.3389/fpsyg.2018.02268/full

唐红红. Ye P, Wang S, Zhu R, 苏凇(通讯作者),童璐琼, 刘超 (2017) Stimulating the Right Temporoparietal Junction with tDCS Decreases Deception in Moral Hypocrisy and Unfairness. Frontiers in Psychology. 8:2033.  https://www.frontiersin.org/articles/10.3389/fpsyg.2017.02033/full


 

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