2019 VOL.2 Jun No.3

• Title: Research on the neuropsychological mechanism of fraudulent lie detection for criminal responsibility evaluation
• Name: Ke Li
• Company: School of Politics and Law,Tibet University
• Abstract:

  With the continuous progress of cognitive neuroscience technology, we will also have a more accurate and comprehensive understanding of human behavior, especially the study of fraud, violence and mental disorders, which will inevitably play a more important role in legal affairs. On the macroscopic aspect, through its important role in the judicial practice, cognitive neuroscience will bring an impact on the current judicial system and social civilization, which is the real significance of the realization of the neural law. In the micro aspect, the study of cognitive neuroscience is not only conducive to judicial practice, but also more reasonable assessment of individual criminal responsibility, so that justice can be extended. It will also have a far-reaching impact on the correction of criminal evidence and the treatment of the mentally ill, even to the prevention of antisocial behavior and the accuracy of the risk assessment of criminals. Based on this, time sequence model of fraudulent lie detection neuropsychological mechanism based on time scale mapping mechanism for criminal responsibility assessment is proposed in this paper. The influence of cognitive neuroscience on the assessment of criminal responsibility in judicial practice is discussed. A time scale mapping mechanism between different speech features is constructed to effectively observe the temporal change characteristics of the speech characteristic signals of the tester. The three dimensions of arousal, valence and nervousness are used to show the state of lying and non-lying, and to study the differences of lying states in different dimensions. The mapping relationship between basic voice parameters and wake-up dimensions, the mapping relationship between psycho acoustic parameters and titer dimensions, and the mapping relationship between the depth feature parameters and the tension dimension are built. The judgment of fraudulent lie detection psychological mechanism for criminal responsibility evaluation is realized. The results show that the WPBCC-2 parameter is more discriminative than the MFCC parameter, which makes the built model have the best identification ability. It can realize accurate judgments for whether the tester is lying, improve the accuracy of lie detection, and provide effective guidance for assessing the criminal responsibility of mental illness.

• Keyword: Criminal responsibility; assessment; fraudulent lie detection; neuropsychological mechanism
• DOI: 10.12250/jpciams2019030117
• Citation form: Ke Li.Research on the neuropsychological mechanism of fraudulent lie detection for criminal responsibility evaluation[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 74-83.

[1]Sun D C, Law S O. Comprehensive Construction for the Distinguished Criminal Imputation Theory by Replacing of the Ground of Criminal Responsibility Theory. Journal of Physical Chemistry B 2016;116(9):2677-2686.

[2]Chen Z. Construction of double value of criminal responsibility based on value structure analysis. Boletin Tecnico/technical Bulletin, 2017;55(11):133-139.

[3]Wang X, Li H C, Zhang L. Correlation between theory of mind and empathy among alexithymia college students. Chinese Journal of School Health 2017;38(5):693-695.

[4]Xiao L, Li Y, Han G, et al. PHY-layer Spoofing Detection with Reinforcement Learning in Wireless Networks. IEEE Transactions on Vehicular Technology 2016;PP(99):1-1.

[5]Liu Z H. Research on the Principle of Social Responsibility of Economic Law. Marketing Management Review 2017;(1):133-133.

[6]Chang H K, Sun Y K, Chan G P. Adaptive complex-EKF-based DOA estimation for GPS spoofing detection. Iet Signal Processing 2018;12(2):174-181.

[7]Wang H, Yu G L. Research on the Current Situation and Countermeasures of Mental Health Education in Higher Vocational Colleges. Chinese Vocational and Technical Education 2016;(2):10-14.

[8]Shi C H. Simulation of Speech Signal Optimization in Reverberation Environment. Computer Simulation 2017;34(2):217-220.

[9]Wang N, Li W, Jiang T, et al. Physical layer spoofing detection based on sparse signal processing and fuzzy recognition. Iet Signal Processing 2017;11(5):640-646.

[10]He S T, Jiang J C, Ge L J. Evolution of Cultural Psychology and Invisible Connection of Psychological Health Education in Colleges and Universities. Heilongjiang Researches on Higher Education 2017;(11):127-131.