AIMC Topic: Guilt

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Bridging Neuroscience and Machine Learning: A Gender-Based Electroencephalogram Framework for Guilt Emotion Identification.

Sensors (Basel, Switzerland) Feb 17, 2025
This study explores the link between the emotion "guilt" and human EEG data, and investigates the influence of gender differences on the expression of guilt and neutral emotions in response to visual stimuli. Additionally, the stimuli used in the stu...
Male Support Vector Machine Young Adult Adult Sex Factors Neurosciences Guilt Emotions Electroencephalography Brain Female Humans Machine Learning
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Unveiling the perceptions of Syrian and Turkish citizens afflicted by survivor guilt in the aftermath of the 2023 earthquake: A study based on deep learning.

Asian journal of psychiatry Jun 7, 2023
Humans Deep Learning Earthquakes Refugees Syria Guilt
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Using machine learning analyses to explore relations between eyewitness lineup looking behaviors and suspect guilt.

Law and human behavior Feb 27, 2020
OBJECTIVE: We conducted 2 experiments using machine learning to better understand which lineup looking behaviors postdict suspect guilt., Hypotheses: We hypothesized that (a) lineups with guilty suspects would be subject to shorter viewing duration o...
Decision Making Mental Recall Child Support Vector Machine Criminal Law Recognition, Psychology Guilt Female Young Adult Male Adolescent Humans Machine Learning
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Subject-based discriminative sparse representation model for detection of concealed information.

Computer methods and programs in biomedicine Feb 20, 2017
BACKGROUND AND OBJECTIVES: The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each s...
Truth Disclosure Guilt Algorithms Learning Reproducibility of Results Healthy Volunteers Electroencephalography Artificial Intelligence Linear Models Sensitivity and Specificity Signal-To-Noise Ratio Electrodes Support Vector Machine Machine Learning Crime Random Allocation Humans Brain Mapping
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Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Psychiatry research Jul 5, 2015
Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to ad...
Guilt Diagnosis, Computer-Assisted Female Magnetic Resonance Imaging Algorithms Nerve Net Humans Sensitivity and Specificity Adult Depressive Disorder, Major Biomarkers Temporal Lobe Machine Learning Male Reference Values Case-Control Studies Echo-Planar Imaging Image Interpretation, Computer-Assisted
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