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The human body and mind share an intrinsic link, both influencing and reflecting the health and general well-being of the other. Within the field of psychophysiology, researchers gain a wealth of insight into mental processes and disorders by studying physiological signals recorded from both the brain and body. Signals such as electrodermal activity (EDA), skin temperature (SKT), electroencephalogram (EEG), heart rate variability (HRV), and functional near-infrared spectroscopy (fNIRS) provide just a few of the tools helping us to better understand these mind-body interactions.

A wide range of research technologies has made these signals increasingly accessible in the lab as well as in everyday environments, allowing studies to place participants in increasingly naturalistic settings while gathering critical physiological data. In this way, researchers can provide a more accurate picture of how study participants react to stress and arousal, experience emotion, or interact within group environments.

One recent study used EDA and heart rate (HR) to examine the role of emotion in student success within computer-based learning environments (CBLE). As discussed in previous posts, a range of signals can be used to measure the body’s physiological response to emotion, otherwise known as “affect.” By gaining a better understanding of learners’ emotional responses to CBLE stimuli, researchers in this study hope to apply their data to improvements in CBLE design. EDA data was gathered from participants via SS57LA EDA leads while HR signals were collected with SS2LB shielded lead sets, both of which were connected to a BSL MP36 data acquisition and analysis system. Participants were shown imagery designed to evoke a negative emotional reaction before being asked to read a complex academic paper. The study was able to establish a link between the participants’ emotions and their comprehension of the reading material.

head with brain exposedIn another study, researchers used EDA and skin conductance response (SCR) to measure participants’ reactions to conditioned stimuli (CS) and unconditioned stimuli (US). The goal was to determine if fear-inducing memories could be modified either pharmacologically or through behavior modification to overcome fear. An MP36R System with AcqKnowledge software allowed analysis of SCR signals gathered via SS3LA finger transducers across a range of experiments designed to trigger fear-inducing memories. While the overall results were inconclusive, the SCR signals provided valuable feedback regarding the participants’ response to stimuli.

While EDA is a common thread through many of these psychophysiology studies, other signals provide researchers with a more complete and nuanced data set from which to draw their conclusions. Researchers exploring the factors influencing musical preferences combined EDA data with ECG and facial electromyography (fEMG) signals to assess how listeners reacted to familiar and unfamiliar musical stimuli. Data from all three signals were fed to an MP36 data acquisition and analysis system. The study found a strong correlation between musical preferences and familiarity with material as reflected by the subjects’ emotional responses.

These examples only scratch the surface of how psychophysiological research is being aided by an array of signals, all made accessible with available technology. To learn more about how to implement these signals in your next study, see BIOPAC’s Psychophysiology Applications webpage.

Contact your BIOPAC sales representative to learn more about the wide range of data-gathering tools available to help you construct your next psychophysiology study.

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