Affect Prediction for Emotional Design: A Comparison Study of Physiological and Subjective Self-Report Data

2011 ◽  
Author(s):  
Feng Zhou ◽  
Xingda Qu ◽  
Jianxin Roger Jiao ◽  
Martin G. Helander
Keyword(s):  
User Satisfaction ◽  
Value Added ◽  
Self Report ◽  
Physiological Data ◽  
Physiological Measures ◽  
Emotional Design ◽  
Affective States ◽  
Subjective Measures ◽  
Report Data ◽  
Self Report Data

Emotional design has attracted much attention due to its important role in the development of products and services towards high value-added user satisfaction and performance enhancement. However, how to predict users’ affective states in real time and without having to interrupt the user is critical to emotional design. This study compared affect prediction between using physiological measures and using self-report subjective measures. Specifically, an experiment was designed to elicit seven different affective states using standardized affective pictures as visual stimuli. Each stimulus was presented for 6 seconds and multiple physiological signals were measured, including facial electromyography, respiration rate, electroencephalography, and skin conductance response. Subjective ratings were also recorded immediately after stimulus presentation. Three data mining methods (i.e., decision rules, k-NN, and decomposition tree) based on the rough set theory were applied to construct prediction models from physiological measures and subjective measures, respectively. We obtained the highest mean prediction rate at 73.69% for physiological models and 52.43% for subjective models, respectively, across the 7 affective states. It demonstrates that physiological data are able to predict better result than subjective self-report data did and that physiological computing offers great potential for the development of emotional design.

scholarly journals Can passive measurement of physiological distress help better predict suicidal thinking?

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Evan M. Kleiman ◽  
Kate H. Bentley ◽  
Joseph S. Maimone ◽  
Hye-In Sarah Lee ◽  
Erin N. Kilbury ◽  
...  
Keyword(s):  
Negative Affect ◽  
Suicide Risk ◽  
Electrodermal Activity ◽  
Model Fit ◽  
Self Report ◽  
Outcome Variable ◽  
Physiological Data ◽  
Suicidal Thinking ◽  
Report Data ◽  
Self Report Data

AbstractThere has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants were suicidal psychiatric inpatients (n = 25, 56% female, M age = 33.48 years) who completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect). These findings suggest that physiological data, under certain contexts (e.g., when combined with self-report data), may be useful in better predicting—and ultimately, preventing—acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking.

scholarly journals Can Passive Measurement of Physiological Distress Help Better Predict Suicidal Thinking?

2021 ◽  
Author(s):  
Evan Kleiman ◽  
Kate Bentley ◽  
Joseph Maimone ◽  
Sarah Lee ◽  
Erin Kilbury ◽  
...  
Keyword(s):  
Negative Affect ◽  
Suicide Risk ◽  
Electrodermal Activity ◽  
Model Fit ◽  
Self Report ◽  
Outcome Variable ◽  
Physiological Data ◽  
Suicidal Thinking ◽  
Report Data ◽  
Self Report Data

Abstract There has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants (n=25, 56% female, M age= 33.48 years) completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect).These findings suggest that physiological data, under certain contexts, may be useful in better predicting -- and ultimately, preventing -- acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking

scholarly journals Influence of environmental variables on fear of crime: Comparing self-report data with physiological measures in an experimental design

2017 ◽  
Vol 13 (4) ◽  
pp. 537-545 ◽  
Author(s):  
Francisco J. Castro-Toledo ◽  
Juan O. Perea-García ◽  
Rebeca Bautista-Ortuño ◽  
Panagiotis Mitkidis
Keyword(s):  
Experimental Design ◽  
Environmental Variables ◽  
Fear Of Crime ◽  
Self Report ◽  
Physiological Measures ◽  
Report Data ◽  
Self Report Data

A Study of Biofeedback in a Gaming Environment

2010 ◽  
pp. 326-345 ◽  
Author(s):  
Xin Du ◽  
Stephen R. Campbell ◽  
David Kaufman
Keyword(s):  
Self Report ◽  
Physiological Data ◽  
Data Sets ◽  
Preliminary Results ◽  
Learning Anxiety ◽  
Report Data ◽  
Gaming Environment ◽  
Self Report Data ◽  
Behavior And Cognition

This chapter reports on a study of biofeedback in a gaming environment incorporating the acquisition and analysis of physiological data sets in tandem with other behavioral and self-report data sets. Preliminary results presented here provide some groundwork toward subsequent study in this area, as more comprehensive and detailed treatments will require further research. The main contribution and focus of this chapter concerns our experiences in applying methods not typically available to educational researchers. Our results are promising, though they cannot be taken to be definitive. Further developments and applications of these methods will lead to more detailed investigations as to what people may learn or gain from biofeedback in gaming environments, along with interdependencies of biofeedback and gaming pertaining to affect, motivation, behavior and cognition, and perhaps especially, to learning anxiety.

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Articles Published in EJPA 2009–2010

2011 ◽  
Vol 27 (4) ◽  
pp. 290-298 ◽  
Author(s):  
Tuulia M. Ortner ◽  
Isabella Vormittag
Keyword(s):  
Psychological Assessment ◽  
Rating Scales ◽  
Self Report ◽  
Related Field ◽  
Broad Scope ◽  
Future Challenges ◽  
Health Related ◽  
Report Data ◽  
Self Report Data

With reference to EJPA’s unique and broad scope, the current study analyzed the characteristics of the authors as well as the topics and research aims of the 69 empirical articles published in the years 2009–2010. Results revealed that more than one third of the articles were written by authors affiliated with more than one country. With reference to their research aims, an almost comparable number of articles (1) presented a new measure, (2) dealt with adaptations of measures, or (3) dealt with further research on existing measures. Analyses also revealed that most articles did not address any particular field of application. The second largest group was comprised of articles related to the clinical field, followed by the health-related field of application. The majority of all articles put their focus on investigating questionnaires or rating scales, and only a small number of articles investigated procedures classified as tests or properties of interviews. As to further characteristics of the method(s) used, a majority of EJPA contributions addressed self-report data. Results are discussed with reference to publication demands as well as the current and future challenges and demands of psychological assessment.

Does Dispositional Mindfulness Predict the Development of Grit?

2018 ◽  
Vol 39 (2) ◽  
pp. 76-87 ◽  
Author(s):  
Buaphrao Raphiphatthana ◽  
Paul Jose ◽  
Karen Salmon
Keyword(s):  
Undergraduate Students ◽  
Online Survey ◽  
Cross Sectional Study ◽  
Self Report ◽  
Dispositional Mindfulness ◽  
Cross Sectional ◽  
Report Data ◽  
Self Report Data ◽  
Acting With Awareness ◽  
Mindfulness Facets

Abstract. Grit, that is, perseverance and passion for long-term goals, is a novel construct that has gained attention in recent years ( Duckworth, Peterson, Matthews, & Kelly, 2007 ). To date, little research has been performed with the goal of identifying the antecedents of grit. Thus, in order to fill this gap in the literature, self-report data were collected to examine whether mindfulness, a mindset of being-in-the-present in a nonjudgmental way, plays a role in fostering grittiness. Three hundred and forty-three undergraduate students completed an online survey once in a cross-sectional study, and of these, 74 students completed the survey again 4.5 months later. Although the cross-sectional analyses identified a number of positive associations between mindfulness and grit, the longitudinal analysis revealed that the mindfulness facets of acting with awareness and non-judging were the most important positive predictors of grit 4.5 months later. This set of findings offers implications for future grit interventions.

The Effects of Questionnaire Length and Behavioral Consequences on Careless Responding

2020 ◽  
Vol 36 (2) ◽  
pp. 410-420 ◽  
Author(s):  
Anthony M. Gibson ◽  
Nathan A. Bowling
Keyword(s):  
Self Report ◽  
Randomized Experiments ◽  
High Quality ◽  
Buffering Effect ◽  
Careless Responding ◽  
Main Effect ◽  
Limited Support ◽  
Report Data ◽  
Self Report Data ◽  
Consistent Support

Abstract. The current paper reports the results of two randomized experiments designed to test the effects of questionnaire length on careless responding (CR). Both experiments also examined whether the presence of a behavioral consequence (i.e., a reward or a punishment) designed to encourage careful responding buffers the effects of questionnaire length on CR. Collectively, our two studies found (a) some support for the main effect of questionnaire length, (b) consistent support for the main effect of the consequence manipulations, and (c) very limited support for the buffering effect of the consequence manipulations. Because the advancement of many subfields of psychology rests on the availability of high-quality self-report data, further research should examine the causes and prevention of CR.

Sign in / Sign up

Export Citation Format

Share Document