scholarly journals Psychometric and electrodermal activity data from an experimental paradigm of memory encoding with some items periodically followed by painful electric shock

Data in Brief ◽  
2020 ◽  
Vol 31 ◽  
pp. 105669
Author(s):  
Ally T. Citro ◽  
Caroline M. Norton ◽  
Samantha J. Pcola ◽  
Keith M. Vogt
Keyword(s):  
Electric Shock ◽  
Electrodermal Activity ◽  
Memory Encoding ◽  
Experimental Paradigm ◽  
Activity Data ◽  
Painful Electric Shock

scholarly journals Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data (Preprint)

2019 ◽  
Author(s):  
Donna L Coffman ◽  
Xizhen Cai ◽  
Runze Li ◽  
Noelle R Leonard
Keyword(s):  
Life Events ◽  
Adolescent Mothers ◽  
Electrodermal Activity ◽  
Real Life ◽  
Motion Artifacts ◽  
Ambulatory Setting ◽  
Activity Data ◽  
Dry Electrode ◽  
Challenges And Opportunities ◽  
Initial Processing

BACKGROUND Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. OBJECTIVE This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. METHODS We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. RESULTS We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. CONCLUSIONS The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.

Youth engagement during making: using electrodermal activity data and first-person video to generate evidence-based conjectures

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Victor R. Lee
Keyword(s):  
Design Methodology ◽  
Electrodermal Activity ◽  
Learning Activities ◽  
Youth Engagement ◽  
Time Intervals ◽  
Activity Data ◽  
Content Type ◽  
Psychophysiological Measures ◽  
Museum Data ◽  
Children's Museum

Purpose This paper aims to introduce and explores the use of electrodermal activity (EDA) data as a tool for obtaining data about youth engagement during maker learning activities. Design/methodology/approach EDA and survey data were collected from a yearlong afterschool maker program for teens that met weekly and was hosted at a children’s museum. Data from four youth who were simultaneously present for eight weeks were examined to ascertain what experiences and activities were more or less engaging for them, based on psychophysiological measures. Findings Most of the focal youth appeared to show higher levels of engagement by survey measures throughout the program. However, when examined by smaller time intervals, certain activities appeared to be more engaging. Planning of maker activities was one space where engagement was higher. Completing sewing projects with minimal social interaction appeared to be less engaging. Specific activities involving common maker technologies yielded mixed levels of engagement. Originality/value Some research is emerging that uses EDA data as a basis for generating inferences about various states while participating in maker learning activities. This paper provides a novel analysis building on some techniques established in the still emergent body of prior research in this area.

Behavioral consequence of painful electric shock

1962 ◽  
Vol 17 (2) ◽  
pp. 333-337 ◽  
Author(s):  
A. J. Dinnerstein ◽  
M. Lowenthal
Keyword(s):  
Reaction Time ◽  
Task Performance ◽  
Choice Reaction Time ◽  
Shock Intensity ◽  
Electric Shock ◽  
September 11 ◽  
Laboratory Simulation ◽  
Painful Electric Shock ◽  
Pathological Pain ◽  
The Difference

Choice reaction time and hand steadiness were studied under conditions in which correct performance of a task produced painful electric shock. Task performance deteriorated in response to shock. Deterioration was greater when shock was applied to the active hand than when applied to the passive hand. The hand steadiness test also involved variation in shock intensity and administration of aspirin or placebo. Tremor increased with shock intensity, and aspirin decreased the difference in performance between shock and nonshock trials. The methods employed offer a means of laboratory simulation of disability produced by pathological pain and a possible means of evaluation of analgesic effectiveness. Submitted on September 11, 1961

scholarly journals Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data

10.2196/17106 ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. e17106 ◽  
Author(s):  
Donna L Coffman ◽  
Xizhen Cai ◽  
Runze Li ◽  
Noelle R Leonard
Keyword(s):  
Life Events ◽  
Adolescent Mothers ◽  
Electrodermal Activity ◽  
Real Life ◽  
Motion Artifacts ◽  
Ambulatory Setting ◽  
Activity Data ◽  
Dry Electrode ◽  
Challenges And Opportunities ◽  
Initial Processing

Background Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting. Objective This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress. Methods We used data from a study examining the effects of stressful tasks on EDA of adolescent mothers (AMs). A biosensor band recorded EDA 4 times per second and was worn during an approximately 2-hour assessment that included a 10-min mother-child videotaped interaction. The initial processing included filtering noise and motion artifacts. Results We constructed the features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which AMs returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between-individual analyses and comparisons. Conclusions The algorithms we developed can be used to construct features for dry-electrode ambulatory EDA, which can be used by other researchers to study stress and anxiety.

Would You Deliver an Electric Shock in 2015? Obedience in the Experimental Paradigm Developed by Stanley Milgram in the 50 Years Following the Original Studies

2017 ◽  
Vol 8 (8) ◽  
pp. 927-933 ◽  
Author(s):  
Dariusz Doliński ◽  
Tomasz Grzyb ◽  
Michał Folwarczny ◽  
Patrycja Grzybała ◽  
Karolina Krzyszycha ◽  
...  
Keyword(s):  
Human Behavior ◽  
Electric Shock ◽  
Turning Point ◽  
Personality Characteristics ◽  
Experimental Paradigm ◽  
Ethical Considerations ◽  
Stanley Milgram

In spite of the over 50 years which have passed since the original experiments conducted by Stanley Milgram on obedience, these experiments are still considered a turning point in our thinking about the role of the situation in human behavior. While ethical considerations prevent a full replication of the experiments from being prepared, a certain picture of the level of obedience of participants can be drawn using the procedure proposed by Burger. In our experiment, we have expanded it by controlling for the sex of participants and of the learner. The results achieved show a level of participants’ obedience toward instructions similarly high to that of the original Milgram studies. Results regarding the influence of the sex of participants and of the “learner,” as well as of personality characteristics, do not allow us to unequivocally accept or reject the hypotheses offered.

scholarly journals Cross-Domain Classification of Physical Activity Intensity: An EDA-Based Approach Validated by Wrist-Measured Acceleration and Physiological Data

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2159
Author(s):  
Angelica Poli ◽  
Veronica Gabrielli ◽  
Lucio Ciabattoni ◽  
Susanna Spinsante
Keyword(s):  
Physical Activity ◽  
Support Vector Machine ◽  
Electrodermal Activity ◽  
Machine Learning Algorithms ◽  
Physiological Data ◽  
Support Vector ◽  
Physical Activity Intensity ◽  
Activity Data ◽  
Perceived Effort ◽  
Activity Intensity

Performing regular physical activity positively affects individuals’ quality of life in both the short- and long-term and also contributes to the prevention of chronic diseases. However, exerted effort is subjectively perceived from different individuals. Therefore, this work explores an out-of-laboratory approach using a wrist-worn device to classify the perceived intensity of physical effort based on quantitative measured data. First, the exerted intensity is classified by two machine learning algorithms, namely the Support Vector Machine and the Bagged Tree, fed with features computed on heart-related parameters, skin temperature, and wrist acceleration. Then, the outcomes of the classification are exploited to validate the use of the Electrodermal Activity signal alone to rate the perceived effort. The results show that the Support Vector Machine algorithm applied on physiological and acceleration data effectively predicted the relative physical activity intensities, while the Bagged Tree performed best when the Electrodermal Activity data were the only data used.

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