“So full of artless jealousy is guilt, It spills itself in fearing to be spilt”
—Gertrude, William Shakespeare’s “Hamlet”
In Act 4 of Shakespeare’s famous tragedy, Hamlet’s mother Gertrude points out that the harder one tries to conceal guilt, the easier it is to expose. Little does she realize that she may have hit upon how future researchers approach the study of guilt in the lab. For practical purposes, it is a primary motivator in moral action and impacts how an individual responds to certain stimuli.
Psychologists have developed a myriad of tests to gauge guilt in a clinical setting such as the Test of Self-Conscious Affect (TOSCA), a multiple-choice test that tracks responses to various moral scenarios. While such techniques have been useful in linking guilt with conditions such as depression and anxiety, they have also shown their limitations in providing a deeper understanding of guilt’s influence on behavior, development, and psychological health.
In a recent BIOPAC webinar, UCSB professor Dr. Hongbo Yu addressed these limitations. Scenario-based tasks, he points out, are non-committal in examining the causes of guilt and do a poor job of measuring behavioral tendencies linked to guilt. Interaction-based research methods, however, allow participants to experience emotions in real-time, allow direct control of the causes of guilt in the lab, and allow for direct measurement of responses to stimuli.
As discussed in prior posts on affect, the body’s physiological manifestation of emotion, there is a range of methods and physiological signals available to researchers for measuring affect and gaining a better understanding of emotional states like guilt. Dr. Yu utilized eye-tracking, electrodermal activity (EDA), and functional magnetic resonance imaging (fMRI) in his examples. These are just a few of the signals currently being used in the lab to measure guilt.
One of the practical applications of guilt research is detecting when a subject is concealing the truth, as one would with a polygraph test. A recent study conducted by researchers at the University of Wisconsin-Madison’s Department of Psychology sought to gauge deception among research participants taking the Concealed Information Test (CIT). The goal was to improve upon traditional polygraph test signals such as heart rate and respiratory rate. Respiration data was gathered using a respiration transducer belt while ECG signals were collected with BIOPAC electrodes. In addition, researchers gathered EDA data using a transducer and finger electrodes. The study found that the EDA signal was significantly more effective in accurately measuring physiological responses during the test procedure.
A 2018 study by researchers at Liverpool Hope University looked at the link between guilt and aggression in children by examining activity in the autonomic nervous system during so-called “bad behavior.” The study measured respiratory sinus arrhythmia (RSA) and skin conductance in children 4–8 years old while they imagined engaging in antisocial activities. EDA and ECG signals were gathered via wireless amplifiers and fed to an MP150 data acquisition unit (superseded by the MP160) running AcqKnowledge software. The study found a linkage between physiological responses to aggression and guilt that may provide a better understanding of the mechanisms behind guilt and related behavior in early and middle childhood.
These are just a few examples of how physiological signals can be applied to measuring guilt in research. BIOPAC offers a wide range of data acquisition, stimulus presentation, and VR options suitable for creating custom guilt research environments in the lab.
For additional information, contact your BIOPAC representative to learn more about the range of signals that can be utilized to gather data on guilt and other emotional states in the lab.