Tag: motivational state

Effort Rewarded: Tools for Measuring Motivation

A job well done may be its own reward, but the mechanisms that drive humans to produce better results are far more complex. Much research has been devoted to the study of motivation and the psychophysiological processes that drive our behavior in pursuit of a goal, whether in relationships, on the job, or in education.

The nature of motivation is controversial, and a variety of competing theories have been put forward regarding what a motivational state comprises. Some researchers have argued that motivation is driven by specific human needs, like food, safety, or respect from one’s peers. Motivation may be categorized as intrinsic, meaning an individual pursues an activity because the activity itself is enjoyable or interesting, or extrinsic, which is when the pursuit offers some external reward.

Research into motivation has drawn interest across a wide range of fields, particularly in the areas of business and education. In the realm of business, corporations are especially focused on the motivation to work, which impacts productivity. In an era where buzzwords such as “quiet quitting” and “bare minimum Mondays” have entered the lexicon, companies are keen to know what motivates workers. Similarly, educators are looking to what types of intrinsic and extrinsic motivational factors are most effective in helping students achieve their learning goals. A key metric for measuring motivation is effort, which can be measured with a range of tools readily available to researchers.

work harderA 2020 Harvard University study published by the American Psychological Association investigated the differences in how adolescents and adults translated motivation into action based on the physical effort exerted for financial reward. Participants age 12 to 23 exerted force by squeezing a BIOPAC hand dynamometer in response to visual cues indicating monetary rewards from $.05 to $.75. The dynamometer, connected to a DA100C amplifier, measured grip force, which the participant saw displayed on a screen as a vertical bar. These tools provided the research team with a means of accurately demonstrating quantitative variations in motivational behavior across various age groups in pursuit of effort-based rewards.

A similar approach was used in a 2019 study from the Netherlands that examined eating behavior by “quantifying the dynamic aspects of appetitive motivation.” As with the above example, researchers relied on a BIOPAC device to measure grip effort, in this case, a bulb transducer connected to an MP36 data acquisition system running Biopac Student Lab software. By squeezing the bulb, participants were able to self-administer sips of chocolate milk via a tube in response to stimuli while experiencing various states of hunger and satiation. The process developed for this study, termed the “Grab-to-Eat Task”, or GET, successfully showed that handgrip effort provided an accurate metric for “evaluating motivational aspects of food consumption” with implications for further research into eating behavior and weight control.

Techniques for measuring motivation are not limited to grip effort. A 2019 study published by the National Institute of Health relied on cardiovascular signals to measure the willingness of older adults to engage in cognitively demanding tasks. Blood pressure and heart rate data were collected using a CNAP monitor connected to a BIOPAC MP150 system, which provided a detailed measure of effort exerted through the participants’ physiological response to each task.

These examples just begin to touch on the ways various psychophysiological measures are being applied to the study of motivation and effort. For additional insight, view the BIOPAC webinar on tools for measuring effort and motivation in the lab.

For more information on how to gather and integrate effort-related data into your motivation study, contact your regional BIOPAC sales representative.

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.

 

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