Does the brain activity underlying the production of deception differ depending on whether or not one believes their deception can be detected? To address this question, we had participants commit a mock theft in a laboratory setting, and then interrogated them while they underwent functional MRI (fMRI) scanning. Crucially, during some parts of the interrogation participants believed a lie-detector was activated, whereas in other parts they were told it was switched-off. We were thus able to examine the neural activity associated with the contrast between producing true vs. false claims, as well as the independent contrast between believing that deception could and could not be detected. We found increased activation in the right amygdala and inferior frontal gyrus (IFG), as well as the left posterior cingulate cortex (PCC), during the production of false (compared to true) claims. Importantly, there was a significant interaction between the effects of deception and belief in the left temporal pole and right hippocampus/parahippocampal gyrus, where activity increased during the production of deception when participants believed their false claims could be detected, but not when they believed the lie-detector was switched-off. As these regions are associated with binding socially complex perceptual input and memory retrieval, we conclude that producing deceptive behavior in a context in which one believes this deception can be detected is associated with a cognitively taxing effort to reconcile contradictions between one's actions and recollections.
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http://dx.doi.org/10.3389/fnhum.2013.00016 | DOI Listing |
J Coll Physicians Surg Pak
January 2025
Anti-Quackery Committee, Sindh Healthcare Commission, Karachi, Pakistan.
Fraudulent medical practices remain prevalent globally, fueled by digital platforms spreading false claims. This study analysed 3,327 published studies and 400 grey literature sources, selecting 38 studies and 19 excerpts for detailed examination. It identified 126 distinct quackery techniques and products used throughout history.
View Article and Find Full Text PDFJ Appl Psychol
January 2025
Advanced Research Institute of Business, School of Economics and Management, Tongji University.
Leaders, often perceived as possessing exceptional confidence and competence, are not immune to feelings of self-doubt. Leader impostorism describes the experience that one's attributes, experiences, skills, and abilities fall short of the standards expected in the leadership role, resulting in a sense of deception in fulfilling leadership responsibilities. While existing research has examined the antecedents and individual outcomes of leader impostorism, its implications for leaders' treatment of subordinates remain largely unexplored.
View Article and Find Full Text PDFInfluencers are content creators who post online about their lives and can amass a significant following. Influencers can be dangerous by negatively affecting their followers' body image and marketing products in a deceptive way. The limited academic writings which consider influencer regulation note an incongruency between influencer conduct and the corresponding regulatory system.
View Article and Find Full Text PDFJ Allergy Clin Immunol Glob
February 2025
Big Data Department, Faculdade Israelita de Ciências da Saúde Albert Einstein, São Paulo, Brazil.
Background: The use of artificial intelligence (AI) in scientific writing is rapidly increasing, raising concerns about authorship identification, content quality, and writing efficiency.
Objectives: This study investigates the real-world impact of ChatGPT, a large language model, on those aspects in a simulated publication scenario.
Methods: Forty-eight individuals representing 3 medical expertise levels (medical students, residents, and experts in allergy or dermatology) evaluated 3 blinded versions of an atopic dermatitis case report: one each human written (HUM), AI generated (AI), and combined written (COM).
PLoS One
December 2024
Faculty of Economics and Management, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
In this paper, we investigate how technology has contributed to experimental economics in the past and illustrate how experimental economics can contribute to technological progress in the future. We argue that with machine learning (ML), a new technology is at hand, where for the first time experimental economics can contribute to enabling substantial improvement of technology. At the same time, ML opens up new questions for experimental research because it can generate previously impossible observations.
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