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Convertino G, Talbot J, Mazzoni G. Psychophysiological indexes in the detection of deception: A systematic review. Acta Psychol (Amst) 2024; 251:104618. [PMID: 39642425 DOI: 10.1016/j.actpsy.2024.104618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 11/08/2024] [Accepted: 11/25/2024] [Indexed: 12/08/2024] Open
Abstract
Robust evidence on deception detection highlights that humans perform at chance level, especially when a truth-default cognitive threshold is crossed by the deceiver. This systematic review examined whether identification of deceptive stimuli elicits specific physiological responses in the detectors of deception. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, five databases were searched for human studies that evaluate physiological reactivity to deceptive stimuli, along with behavioural responses. Eleven studies (thirteen experiments) were included in a qualitative synthesis. Results show that deception detection is associated with higher activity in the prefrontal cortex and temporal lobe, with a specific involvement of the temporoparietal junction, alongside the cerebellum and cingulate cortex. Specific changes in other physiological activities (i.e., heart rate, skin temperature, motor excitability) also seem to be differently associated with the detection of deception. This review suggests that detecting deception should be considered a complex decision-making process and indicates that specific physiological activity is present across different types of deceptive stimuli. Implications are promising for further developments in security and forensic sciences.
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Affiliation(s)
- Gianmarco Convertino
- Faculty of Medicine and Psychology, "Sapienza", University of Rome, Rome, Italy.
| | - Jessica Talbot
- Faculty of Medicine and Psychology, "Sapienza", University of Rome, Rome, Italy
| | - Giuliana Mazzoni
- Faculty of Medicine and Psychology, "Sapienza", University of Rome, Rome, Italy; Department of Psychology, University of Hull, Hull, United Kingdom
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Scarpazza C, Gramegna C, Costa C, Pezzetta R, Saetti MC, Preti AN, Difonzo T, Zago S, Bolognini N. The Emotion Authenticity Recognition (EAR) test: normative data of an innovative test using dynamic emotional stimuli to evaluate the ability to recognize the authenticity of emotions expressed by faces. Neurol Sci 2024:10.1007/s10072-024-07689-0. [PMID: 39023709 DOI: 10.1007/s10072-024-07689-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 07/08/2024] [Indexed: 07/20/2024]
Abstract
Despite research has massively focused on how emotions conveyed by faces are perceived, the perception of emotions' authenticity is a topic that has been surprisingly overlooked. Here, we present the Emotion Authenticity Recognition (EAR) test, a test specifically developed using dynamic stimuli depicting authentic and posed emotions to evaluate the ability of individuals to correctly identify an emotion (emotion recognition index, ER Index) and classify its authenticity (authenticity recognition index (EA Index). The EAR test has been validated on 522 healthy participants and normative values are provided. Correlations with demographic characteristics, empathy and general cognitive status have been obtained revealing that both indices are negatively correlated with age, and positively with education, cognitive status and different facets of empathy. The EAR test offers a new ecological test to assess the ability to detect emotion authenticity that allow to explore the eventual social cognitive deficit even in patients otherwise cognitively intact.
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Affiliation(s)
- Cristina Scarpazza
- Department of General Psychology, University of Padova, Via Venezia 8, Padova, PD, Italy.
- IRCCS S Camillo Hospital, Venezia, Italy.
| | - Chiara Gramegna
- Ph.D. Program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Cristiano Costa
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | | | - Maria Cristina Saetti
- Neurology Unit, IRCCS Fondazione Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alice Naomi Preti
- Ph.D. Program in Neuroscience, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Teresa Difonzo
- Neurology Unit, Foundation IRCCS Ca' Granda Hospital Maggiore Policlinico, Milano, Italy
| | - Stefano Zago
- Neurology Unit, Foundation IRCCS Ca' Granda Hospital Maggiore Policlinico, Milano, Italy
| | - Nadia Bolognini
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuropsychology, Department of Neurorehabilitation Sciences, IRCCS Istituto Auxologico Italiano, Milano, Italy
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Zhou X, Jenkins R, Zhu L. An Honest Joker reveals stereotypical beliefs about the face of deception. Sci Rep 2023; 13:16649. [PMID: 37789048 PMCID: PMC10547800 DOI: 10.1038/s41598-023-43716-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 09/26/2023] [Indexed: 10/05/2023] Open
Abstract
Research on deception detection has mainly focused on Simple Deception, in which false information is presented as true. Relatively few studies have examined Sophisticated Deception, in which true information is presented as false. Because Sophisticated Deception incentivizes the appearance of dishonesty, it provides a window onto stereotypical beliefs about cues to deception. Here, we adapted the popular Joker Game to elicit spontaneous facial expressions under Simple Deception, Sophisticated Deception, and Plain Truth conditions, comparing facial behaviors in static, dynamic nonspeaking, and dynamic speaking presentations. Facial behaviors were analysed via machine learning using the Facial Action Coding System. Facial activations were more intense and longer lasting in the Sophisticated Deception condition than in the Simple Deception and Plain Truth conditions. More facial action units intensified in the static condition than in the dynamic speaking condition. Simple Deception involved leaked facial behaviors of which deceivers were unaware. In contrast, Sophisticated Deception involved deliberately leaked facial cues, including stereotypical cues to lying (e.g., gaze aversion). These stereotypes were inaccurate in the sense that they diverged from cues in the Simple Deception condition-the actual appearance of deception in this task. Our findings show that different modes of deception can be distinguished via facial action analysis. They also show that stereotypical beliefs concerning cues to deception can inform behavior. To facilitate future research on these topics, the multimodal stimuli developed in this study are available free for scientific use.
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Affiliation(s)
- Xingchen Zhou
- Department of Psychology, Fudan University, Handan Road 220, Shanghai, 200433, SH, People's Republic of China
| | - Rob Jenkins
- Department of Psychology, University of York, York, UK
| | - Lei Zhu
- Department of Psychology, Fudan University, Handan Road 220, Shanghai, 200433, SH, People's Republic of China.
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Straulino E, Scarpazza C, Sartori L. What is missing in the study of emotion expression? Front Psychol 2023; 14:1158136. [PMID: 37179857 PMCID: PMC10173880 DOI: 10.3389/fpsyg.2023.1158136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/06/2023] [Indexed: 05/15/2023] Open
Abstract
While approaching celebrations for the 150 years of "The Expression of the Emotions in Man and Animals", scientists' conclusions on emotion expression are still debated. Emotion expression has been traditionally anchored to prototypical and mutually exclusive facial expressions (e.g., anger, disgust, fear, happiness, sadness, and surprise). However, people express emotions in nuanced patterns and - crucially - not everything is in the face. In recent decades considerable work has critiqued this classical view, calling for a more fluid and flexible approach that considers how humans dynamically perform genuine expressions with their bodies in context. A growing body of evidence suggests that each emotional display is a complex, multi-component, motoric event. The human face is never static, but continuously acts and reacts to internal and environmental stimuli, with the coordinated action of muscles throughout the body. Moreover, two anatomically and functionally different neural pathways sub-serve voluntary and involuntary expressions. An interesting implication is that we have distinct and independent pathways for genuine and posed facial expressions, and different combinations may occur across the vertical facial axis. Investigating the time course of these facial blends, which can be controlled consciously only in part, is recently providing a useful operational test for comparing the different predictions of various models on the lateralization of emotions. This concise review will identify shortcomings and new challenges regarding the study of emotion expressions at face, body, and contextual levels, eventually resulting in a theoretical and methodological shift in the study of emotions. We contend that the most feasible solution to address the complex world of emotion expression is defining a completely new and more complete approach to emotional investigation. This approach can potentially lead us to the roots of emotional display, and to the individual mechanisms underlying their expression (i.e., individual emotional signatures).
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Affiliation(s)
- Elisa Straulino
- Department of General Psychology, University of Padova, Padova, Italy
- *Correspondence: Elisa Straulino,
| | - Cristina Scarpazza
- Department of General Psychology, University of Padova, Padova, Italy
- IRCCS San Camillo Hospital, Venice, Italy
| | - Luisa Sartori
- Department of General Psychology, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
- Luisa Sartori,
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Zhao X, Liu Y, Chen T, Wang S, Chen J, Wang L, Liu G. Differences in brain activations between micro- and macro-expressions based on electroencephalography. Front Neurosci 2022; 16:903448. [PMID: 36172039 PMCID: PMC9511965 DOI: 10.3389/fnins.2022.903448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/23/2022] [Indexed: 12/04/2022] Open
Abstract
Micro-expressions can reflect an individual's subjective emotions and true mental state and are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, the current approach based on image and expert assessment-based micro-expression recognition technology has limitations such as limited application scenarios and time consumption. Therefore, to overcome these limitations, this study is the first to explore the brain mechanisms of micro-expressions and their differences from macro-expressions from a neuroscientific perspective. This can be a foundation for micro-expression recognition based on EEG signals. We designed a real-time supervision and emotional expression suppression (SEES) experimental paradigm to synchronously collect facial expressions and electroencephalograms. Electroencephalogram signals were analyzed at the scalp and source levels to determine the temporal and spatial neural patterns of micro- and macro-expressions. We found that micro-expressions were more strongly activated in the premotor cortex, supplementary motor cortex, and middle frontal gyrus in frontal regions under positive emotions than macro-expressions. Under negative emotions, micro-expressions were more weakly activated in the somatosensory cortex and corneal gyrus regions than macro-expressions. The activation of the right temporoparietal junction (rTPJ) was stronger in micro-expressions under positive than negative emotions. The reason for this difference is that the pathways of facial control are different; the production of micro-expressions under positive emotion is dependent on the control of the face, while micro-expressions under negative emotions are more dependent on the intensity of the emotion.
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Affiliation(s)
- Xingcong Zhao
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Ying Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- School of Music, Southwest University, Chongqing, China
| | - Tong Chen
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Shiyuan Wang
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Jiejia Chen
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Linwei Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Guangyuan Liu
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
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Park J, Gu J, Kim HY. “Do not deceive me anymore!” interpretation through model design and visualization for instagram counterfeit seller account detection. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2022.107418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Research on the Training and Management of Industrializing Workers in Prefabricated Building with Machine Vision and Human Behaviour Modelling Based on Industry 4.0 Era. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9230412. [PMID: 35720888 PMCID: PMC9200531 DOI: 10.1155/2022/9230412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/05/2022] [Accepted: 05/11/2022] [Indexed: 12/03/2022]
Abstract
As countries around the world pay more and more attention to the sustainable development of the construction industry, the prefabricated building model has become the best construction type to achieve energy conservation and emission reduction. However, the prefabricated building entails higher technical requirements, and the workers involved in the construction must be trained to reduce the risks. For China, where the demographic dividend is gradually disappearing, how to quickly promote the industrializing workers process has become an urgent issue. This research focuses on the training and management of industrializing workers in prefabricated building. First, the facial images of the participants were collected from the actual test data, and the changes of participants' facial expressions were analyzed through multitask convolutional neural network-Lighten Facial Expression Recognition (MTCNN-LFER). The results of the analysis were plugged into the facial expression recognition and evaluation model for industrializing workers training in this research to calculate the weights, and then all the weights were clustered through the improved SWEM-SAM method. The results show the following: (1) the values of objective data were used to judge the participating workers' mastery of each knowledge and to evaluate whether they are qualified. (2) The evaluation results were used to analyze the risk events that may be caused by participating workers.
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Identifying Faked Responses in Questionnaires with Self-Attention-Based Autoencoders. INFORMATICS 2022. [DOI: 10.3390/informatics9010023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Deception, also known as faking, is a critical issue when collecting data using questionnaires. As shown by previous studies, people have the tendency to fake their answers whenever they gain an advantage from doing so, e.g., when taking a test for a job application. Current methods identify the general attitude of faking but fail to identify faking patterns and the exact responses affected. Moreover, these strategies often require extensive data collection of honest responses and faking patterns related to the specific questionnaire use case, e.g., the position that people are applying to. In this work, we propose a self-attention-based autoencoder (SABA) model that can spot faked responses in a questionnaire solely relying on a set of honest answers that are not necessarily related to its final use case. We collect data relative to a popular personality test (the 10-item Big Five test) in three different use cases, i.e., to obtain: (i) child custody in court, (ii) a position as a salesperson, and (iii) a role in a humanitarian organization. The proposed model outperforms by a sizeable margin in terms of F1 score three competitive baselines, i.e., an autoencoder based only on feedforward layers, a distribution model, and a k-nearest-neighbor-based model.
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