emotional state Archives

Game On: Applying Physiological Data in Video Game Design and Research

Ever since players first started feeding quarters into arcade games and began plugging in home systems in the late twentieth century, researchers have looked for practical tools for both shaping video game design and measuring their effects on players. Physiological signals filled this niche area of research. Researchers began investigating the connection between physiological science and gaming in the 1980s and 1990s, when early studies explored the effects of video games on heart rate and arousal. These first studies focused on issues such as aggression and overstimulation, but research has since expanded to encompass the cognitive and emotional aspects of gameplay. An early research project by NASA, for example, examined how biofeedback could be applied to game design to enhance traditional game control methods while also improving players’ cognitive abilities.

Today, physiological monitoring is not only a method for psychological inquiry but also a vital asset in improving user experience (UX) research, game accessibility, and even driving therapeutic game development. As wearable sensors and game engines grow more sophisticated, the integration of physiological signals into game design is opening new frontiers for immersive, responsive, and health-conscious gaming. By analyzing real-time bodily signals, such as heart rate (HR), electroencephalography (EEG), electromyography (EMG), electrodermal activity (EDA), respiration, photoplethysmography (PPG), and electrooculography (EOG), researchers and developers can gain a deeper understanding of how players emotionally and physically respond to game elements.

A study published in the International Journal of Human-Computer Studies investigated whether players’ self-reported preferences for first-person shooter (FPS) game dynamics match their emotional reactions during both gameplay and viewing. Researchers recruited 24 active gamers and divided them into two groups: those who enjoyed the fast-paced action of FPS games and those who didn’t. While participants played an FPS game and later watched a recording of it, the team measured electrodermal activity (EDA), heart rate, and facial muscle responses, specifically expressions associated with frowning (corrugator supercilii) and smiling (zygomaticus major), to capture emotional arousal and valence. Physiological signal data were recorded using BIOPAC EDA, PPG, and EMG amplifiers, which were connected to a data acquisition and analysis system running AcqKnowledge software. Although both groups reported more positive feelings and higher excitement after actively playing rather than watching, the data revealed a deeper story: players who disliked the game showed more substantial rises in arousal during play. Those who favored the dynamics, however, showed steadier emotional responses across both conditions. Muscle activity patterns also differed. Those who enjoyed FPS dynamics exhibited sharper increases in frowning-related muscle activity during play than watchers, indicating nuanced emotional engagement. Overall, the study found that what people say they prefer does reflect in their bodies’ emotional reactions. Still, those preferences also shape whether playing or spectating feels more intense, highlighting how combining self-reports with physiological signals can deepen an understanding of game design and player experience.

A team of researchers from Bournemouth University’s Centre for Digital Entertainment investigated how players’ bodies react during intense engagement, or flow, in virtual reality (VR) games. The goal was to capture objective physiological signals, such as heart rate and eye-blink patterns, alongside subjective self-reports to see how well a game’s design induced flow versus boredom or anxiety. The researchers had participants play a custom VR “tower defense” game while monitored with BIOPAC equipment, including a two-channel Biopac Student Lab (BSL) data acquisition system and physiological modules to record HRV via ECG, as well as EOG and EEG signals. The team then utilized clustering and machine-learning classifiers to decode flow states with high accuracy. The research team also successfully identified that high flow correlated with fewer blinks and longer blink intervals, indicating a state of deep concentration. The study not only demonstrates the value of combining physiological and self-report data but also shows how wearable sensors can inform design decisions in VR games by providing real-time feedback on player experience.

Beyond game design, a primary area of research focus has been on the long-term psychological, neurological, and physiological impact that games have on players. A study published in the Journal of Media Psychology explored how experiences with violent video games might reduce prosocial behavior, such as the motivation to help others, through physiological changes. Researchers recruited 155 Chinese college students who reported their exposure to violent games (in terms of violence, bloodiness, and duration) and completed simple self-reports measuring their willingness to help and the time they took to help others in controlled scenarios. Researchers used a BIOPAC data acquisition unit running AcqKnowledge software to record relevant physiological data. A three-lead ECG amplifier, respiration module, and respiration belt were used to record ECG and breathing signals across baseline, violent-film task, and during recovery periods. Researchers calculated the reduction in respiratory sinus arrhythmia (RSA), a marker of parasympathetic nervous system engagement. They found that higher levels of violent-game experience predicted less RSA reduction during stress, which in turn was linked to reduced prosocial behavior. The study’s findings suggest that playing violent video games may impair physiological readiness to respond in an empathetic manner to others, showing RSA reduction to be a key influence. The study’s findings demonstrate how physiological and psychophysiological signals provide a deeper understanding of the impacts of video games, while also guiding design decisions.

Physiological signals help researchers identify moments of excitement, stress, immersion, or frustration. Such insights are increasingly being used to refine gameplay mechanics, balance difficulty, and enhance player engagement through adaptive or personalized game experiences. They also reveal the short- and long-term effects gameplay has on both individuals and society. For more ideas on how to apply these signals in your research, visit our Webinars page for information on wearable devices, mobile data acquisition, and other helpful topics.

Are you planning a research project related to video games? Reach out to our customer service specialists to get all the help you need in mapping out and equipping your study team.

Facing Fear: Measuring What Scares Us

In many countries, October ushers in the season of ghosts, goblins, and all manner of frightening things that inhabit our deepest fears. While the popularity of Halloween reflects the thrills many derive from a good scare, facing one’s fears is not always fun and games. Our fears can protect us from danger, but they can also hamper individuals from performing the simplest tasks and negatively impact one’s quality of life and mental health.

The study of fear conditioning and fear learning sheds light on how humans and animals develop responses to threatening stimuli. Research often relies on psychophysiological measurements—techniques that capture the body’s physical responses to fear. Key among these measurements are skin conductance response (SCR), electrodermal activity (EDA), heart rate variability (HRV), respiration, and electromyography (EMG).

SCR and EDA track changes in sweat gland activity, providing a direct measure of emotional arousal when a person experiences fear. HRV and respiration rate reveal the autonomic nervous system’s reaction to stress, indicating shifts between sympathetic (fight-or-flight) and parasympathetic (rest-and-digest) states. EMG measures muscle activity, especially facial muscles, to detect subtle expressions or physical tension associated with fear. Understanding these mechanisms has practical applications in understanding and treating anxiety disorders, phobias, and post-traumatic stress disorder (PTSD). By analyzing the body’s automatic fear responses, scientists can develop targeted therapies to help individuals recondition or dampen these reactions.

A study published in the Journal of Behavior Therapy and Experimental Psychiatry used a range of indices to assess learned fear (unconditioned stimulus expectancy or US-expectancy) in individuals. These included both self-reporting and measurements of autonomic response such as skin conductance. Researchers using E-Prime Stimulus Presentation Systems, paired various neutral visual stimuli with unconditioned stimuli in the form of electrical shocks delivered via a constant voltage stimulator coupled with a BIOPAC STM100C amplifier. After a brief break, both neutral stimuli were administered without shock. Skin conductance response data was gathered and analyzed during each phase with a BIOPAC data acquisition and analysis system and software. Results showed a positive correlation between US-expectancy ratings and skin conductance response.

Researchers at the University of Bologna in Italy took a different approach to measuring the psychophysiological mechanisms of fear learning by focusing on HRV. In their study, published in the journal Psychophysiology, they explained that “understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders.” Researchers gathered SCR and electrocardiogram (ECG) data. As with the previous study, mild electrical shocks were applied as unconditioned stimuli and paired with neutral or conditioned stimuli (CS). SCR signals were acquired using a skin conductance transducer attached to the participants’ fingertips connected to a BIOPAC EDA amplifier. ECG data was collected using disposable surface electrodes connected to an ECG100C amplifier. EDA and ECG signals were processed with a BIOPAC data acquisition and analysis system running AcqKnowledge software, which calculated HRV from the ECG data. Researchers concluded that “learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli.” They added that the study’s findings “provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness.”

Another study published in the journal Psychophysiology examines fear learning by applying a broad range of physiological signals and responses to determine which provides the “optimal measurement of human trace fear conditioning.” For this study, researchers compared SCR, pupil size response, heart period response (via ECG), respiration amplitude response, and electromyography (EMG) to measure participants’ responses to electrical current, audio, and visual stimulation. EDA data was gathered via a BIOPAC EDA amplifier, while ECG, EMG, and respiration data were fed to BIOPAC ECG100C, EMG100C, and RSP100C amplifiers, respectively. All data was processed using BIOPAC data acquisition and analysis systems and software. Results of the experiment suggested that startle eye-blink response through EMG provided the most sensitive metric for measuring fear conditioning.

Fear is just one of the emotions that can be studied through the body’s affective response to stimuli. Read more about the relationship between affect and emotion or watch our webinar on Emotion and Objective Physiological Measures.

Are you facing the reality of putting together your own fear-based study? Don’t be afraid to contact your local BIOPAC customer service representative for help finding the right tools for the job.

Guilt Trips: Taking Measure of Our Moral Compass

“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.

Measuring Moods: Signals for Insight into Emotion

“Do you ever look at someone and wonder what is going on inside their head?”

Joy, an anthropomorphized manifestation of happiness, delivers this line at the opening of Pixar’s 2015 animated hit “Inside Out,” a film that explores the emotional struggles of its young protagonist. She could have been speaking for generations of researchers who have plumbed the depths of the human psyche for a better understanding of the mechanisms behind our feelings, moods, and emotions.

When today’s researchers talk about emotion, they typically do so in terms of affect, the underlying physiological and psychological experience of feelings and emotions as a reaction to stimuli. Affect and emotion are not the same thing, per se, but are fundamentally connected. While the feelings we describe as fear, joy, love, hate, etc. remain abstract, affect is more concrete. BIOPAC co-founder and head of R&D describes it as “the measurable quality or manifestation of emotion in the human body.”

This manifestation can be measured in the human nervous system’s response to stimuli. Depending on the nature of the stimuli and how this sensory data is interpreted by the central nervous system, an array of physiological responses can take place: heart rate changes, the stomach and muscle groups tense, skin temperature responds, triggering sweat glands, among others.

The signals corresponding to these reactions provide researchers with valuable data to help them better understand affect and emotion. These signals—electroencephalogram (EEG), electrodermal activity (EDA), electromyogram (EMG), heart rate, and respiratory rate, to name a few—can be recorded and measured using a range of currently available sensing technologies.

As an example, a 2019 study published through the Queensland University of Technology used respiration signals as a means of classification for human emotions such as happiness, sadness, surprise, disgust, fear, and anger. In the study, breathing signals, including airflow rate and volume, were acquired and recorded using a BIOPAC MP36 data acquisition unit while participants were shown seven film clips, each associated with a different emotion. The study concluded that the respiratory data was 80% effective in accurately classifying affective responses to the film stimuli.

A team of researchers at the Khwaja Fareed University of Engineering and Information Technology in Pakistan took their investigation into the connection between physiological signals and emotion a step further. They used a similar approach by measuring participant response to movie clips, however this time, four types of physiological signals were acquired, including ECG, EMG, respiration, and skin conductivity (SC). The data was acquired with a wide range of signal sensors and analyzed via a BIOPAC MP160 DAQ System.

Signal sensing technology also allows researchers to study emotion in multiple participants simultaneously, as was the case in a 2020 dyadic study of how a parent’s stress and emotional suppression affects their children. The study measured physiological responses of both parents and children using ECG and impedance cardiography (ICG) via a BIOPAC MP150 data acquisition unit.

The mysteries of human emotion are being unlocked through a growing understanding of the affective response to stimuli. For more information on how physiological signals can be gathered and analyzed for this purpose, see our app note on emotional state physiology as well as our webinar Emotion and Objective Physiological Measures.

Contact BIOPAC for the latest information on how we can help you measure affect in the lab.

Circumplex Model of Affect and Motivational State

By Alan Macy, BIOPAC Systems, Inc.

The Circumplex Model of Affect was first described in 1980 by James Russell. Affective states arise from the behavior of two independent neurophysiological systems, the arousal and valence systems. Affective states are a function of these two systems. The circumplex model is two-dimensional, with arousal and valence defined as orthogonal (perpendicular) axes. The arousal axis, plotted vertically, ranges from zero arousal to high arousal.The valence axis, plotted horizontally, ranges from negative to positive. Objective physiological indexes of affect or emotion are available. As valence examples, positive affect is indicated by increasing zygomaticus activity and negative affect is indicated by increasing corrugator activity. Physiological example indices for arousal include heart rate and electrodermal activity.

Perhaps even more fundamental to emotional state is the concept of motivational state. Motivational state is indexed by specific, bodily expressed, physiological states that can easily be measured. Motivational state is based on the concept of core relational themes, called “challenge” and “threat”. During the course of living, humans relate to difficult environmental circumstances as a combination of challenge and threat. A challenge response is similar to the aerobic physiological response and involves increased stroke volume and cardiac output, unchanged or increased heart rate, decreased vascular resistance and relatively unchanged blood pressure. This response is indicative of efficient mobilization of available energy for coping with circumstances. A threat response is characterized by increased heart rate and blood pressure, increased or little changed vascular resistance, decreased or unchanged stroke volume and relatively unchanged cardiac output. A challenge response occurs when the subject experiences sufficient resources to meet circumstantial demands in a performance situation where goals are important. A threat response occurs when the subject experiences insufficient resources to meet those same circumstantial demands. If a circumstance is determined to be challenging, then subject performance is usually adequate. If a circumstance is determined to be threatening, then subject performance tends to worsen. Motivational state information can be used to better reflect objective differences between similar circumplex model defined emotions, such as anger and fear.

Electrodermal activity (EDA) is a physiological signal that indicates increased SNS activity. EDA is representative of changes in the electrical conductance of the skin due to eccrine (sweat) gland activity. SNS activity increases sweat gland secretions. Eccrine glands only receive activation signals from the SNS, so increased EDA is an indicator of increased arousal.

Skin temperature (SKT) changes are primarily driven by variations in blood flow. These local variations are mainly caused by changes in vascular resistance or arterial blood pressure. Local vascular resistance is modulated by smooth muscle activity, which is mediated by the SNS. SKT variation reflects SNS activity, and is an indicator of emotional state. In particular, fingertip temperature is a marker for sympathetic induced changes in microcirculation.

Blood Volume Pulse (BVP), also referred to as the Pulse Plethysmogram (PPG), is reflective of variations in microcirculation. As vasculature resistance to flow increases, subject to increased SNS, microcirculation decreases. Accordingly a reduction in PPG (BVP) amplitude reflects increased SNS activity.

The heart and brain are connected bidirectionally via the vagus nerve. Vagal PsNS stimulation, from the brain, influences the heart via the sino-atrial (SA) node. Baroreceptor signals, from the heart, travel back along the vagus nerve to affect the brain. The SA node is the pacemaker of the heart. The SA node receives inputs from both the SNS and PsNS. The SA node can be considered to be a spike-train generator whose inter-spike (firing) interval is modulated by both SNS and PsNS activity levels. Because both SNS and PsNS activity influence SA node spike firing, heart rate behavior can be considered to be dependent on a range of emotional states. SNS activity increases heart rate and PsNS activity decreases heart rate.

Heart rate variability (HRV) is a measure of heart rate changes over time. HRV has a frequency range of 0.003 to 0.4 Hz and is considered to have four sub-bands: HF, LF, VLF and ULF. High frequency HRV (HF-HRV) band power is in the range of 0.18 to 0.4 Hz and reflects primarily PsNS influences. HF-HRV band power reflects modulation of vagus nerve activity, as driven by respiration. This modulation is called respiratory sinus arrhythmia (RSA). Low frequency HRV (LF-HRV) band power is in the range of 0.05-0.15 Hz and appears to reflect both SNS and PsNS influences. Very low frequency HRV (VLF-HRV) band power is in the range of 0.003-0.05 Hz and may reflect cardiovascular disease, thermo-regulatory cycles and blood plasma renin activity. Ultra low frequency HRV (ULF-HRV) band power is in the range of DC-0.003 Hz and may reflect circadian rhythm activity.

PsNS influences occur over the LF and HF frequency ranges of HRV. SNS influences drop off at about 0.15 Hz and higher. PsNS influences can affect heart rate in a fraction of a second, but SNS influences can only affect the heart rate after a few seconds. Accordingly, PsNS influences are uniquely capable of producing high speed changes in the heart rate. PsNS innervation of the heart is controlled by the right vagus nerve via the SA node. PsNS induced changes in heart rate are associated with vagal nerve activity that is modulated by respiration. Expiration causes an increase in vagal nerve activity, so heart rate decreases. Inspiration causes suppression of vagal nerve activity, so heart rate climbs back up. This combined action is known as respiratory sinus arrhythmia (RSA). RSA is considered, in part, to be a marker for vagal control of heart rate and also for emotional regulation. Total HRV appears to be positively correlated with valence.

When attention increases, there is a short term deceleration of heart rate. Arousal is also correlated to a long term acceleration in heart rate. Heart rate also provides an indication of valence. Compared to neutral stimuli, both positive and negative stimuli first exhibit a short term decrease in heart rate. Over the long term, positive stimuli are correlated with an increase in heart rate while negative stimuli usually are correlated to a decrease in heart rate.

Respiratory activity occurs via periodic contraction and relaxation of respiratory muscles, including the diaphragm, intercostal and abdominal muscles. The motor outputs for controlling respiration are generated by efferent neurons in the spinal cord. There are autonomic and voluntary breathing pathways to these respiratory-related efferent neurons. A variety of afferent (sensory) inputs influence respiratory rate and tidal volume in support of the body’s metabolic demands. SNS activity increases respiratory rate and PsNS activity reduces respiratory rate.

Several EEG studies suggest that emotional valence is linked to frontal lobe activation, typically indexed by alpha power. Positive valence is correlated to increased activation of the left frontal lobe and negative valence is correlated to increased activation of the right frontal lobe. Other studies have suggested that frontal lobe EEG activation asymmetry reflects the relative balance of motivational state, more so than emotional valence. In this work, enhanced left frontal lobe activation predicted dispositional states towards challenge and enhanced right frontal lobe activation predicted dispositional states towards threat.

Artwork adapted from from Russell, J. A. (1980). A circumplex model of affect.Journal of Personality and Social Psychology, 39, 1161–1178.

For more information on BIOPAC’s wide range of tools for recording, displaying, classifying, and analyzing psychophysiological measurements—including EEG, ECG, EDA, PPG, and emotional states—visit BIOPAC’s applications pages and view BIOPAC’s full line of physiology data acquisition and analysis systems, electrodes, amplifiers, and wearable, wireless transmitters and loggers.

BIOPAC Systems, Inc. provides life science researchers and educators with data acquisition and analysis systems that inspire people and enable greater discovery about life. Visit us at www.biopac.com.