Friday 24 March 2023

Locus of Control and Vulnerability to Peer Pressure: a Study of Adolescent Behavior in Urban Ghanaian Context

 Abstract

Peer pressure is one thing that every individual is vulnerable to and has faced before at some point in their lives. It is becoming a serious health problem, especially for adolescents as well as concerned parents because, though not all peer pressure leads to health-related concerns or is negative, most are that need curbing, or treatment. Thus, NGOs, youth organizations, social welfare as well as parents, are looking for different ways to tackle this health-related issue in society. This research investigated locus of control on vulnerability to peer pressure in the African context. A survey was conducted using 144 adolescents from 2 Senior High Schools in Ghana with ages ranging from 15 to 19 years old. The following data collection instruments were used: Informed consent, Demographic data which used to group the students into categories based on age, class form and gender; the Nowicki-Strickland test (1973) and the Steinberg and Monahan resistance to peer influence scale (2007) measuring Locus of Control and Resistance to Peer Influence respectively. Statistical methods such as the independent t-test and Pearson Product Moment Correlation Coefficient (Pearson r) were used to study the relationships between age, locus of control, and resistance to pressure in the urban context. The findings revealed that there was no significant effect of locus of control on vulnerability to peer pressure, which still indicates that our study may be more bent towards the general idea that individuals with an internal locus of control are more likely to resist the pressure to conform by their peers.


Keywords: Adolescents; Locus of control; Peer Pressure; Health problems; Internal and External locus of control; Resistance

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Thursday 24 November 2022

Happy Thanksgiving 2022!!!

 


This year yields it's harvest sharing aboundant blessings may your thanksgiving be blessed with fruitfullness and overflowing love.

May your Thanksgiving be full of peace, love and joy.

Tuesday 15 November 2022

Children’s Fear From Dentists, Based on Literature Data

 Abstract

Introduction: Providing quality dental services, priority is given to patients’ approach to painless treatment at the dentist. The pain experienced by previous dental treatments, triggers the appearance of the feeling of fear that precedes the next dental intervention.

This picture becomes even more difficult when talking about pediatric age and the feeling of fear or anxiety experienced by this age before dental treatment. Fear of dentists has its origins in childhood, so logically, if fear and anxiety about dental interventions are to be analyzed, the age of study on this topic should be specifically pediatric age.

Materials and methods: The study is of review type based on the collected articles on the assessment of fear and anxiety in pediatric ages. A total of 14 articles and literature sources, which were collected based on the study selection criteria. The study was designed in the context of coping with data figures published by various articles on dental fear and anxiety in pediatric age specifically.

Results: Based on the data collected, 23% of children reported emotional distress in dealing with the dentist where only 12% of them expressed feelings of fear.64% of patients appreciate the way the doctor dresses, with white aprons giving him more confidence and not fear.70% of pediatric patients did not have the feeling of fear of wearing the mask during dental treatments. Feelings of fear, according to children, leave for a clinic decorated according to age.

Conclusions: Maintaining oral hygiene leads to a healthy dental status that does not face pain originating from the tooth, and consequently, not recognizing the pain reduces sensitivity and expectation to dental fear or anxiety. The visual appearance of the clinic and the doctor speaking with body language affects the expectation at the level of fear perception before dental treatment.

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Friday 29 July 2022

Employees’ Sense of Entitlement Toward Their Supervisors and its Association with Burnout and Job Satisfaction: Assessing A Multidimensional Construct

 There has been increased interest on the part of both organizations and the academy in the entitlement attitudes of employees. The vast majority of studies on employee entitlement have construed it as a unidimensional dispositional trait and have generally revealed strong correlations between sense of entitlement and negative workplace behaviors, suggesting significant implications for organizational outcomes. The goal of the current study was to develop and validate a self-report measure that views employees’ sense of relational entitlement toward their supervisors (SRE-es) as multifactorial. Findings indicated initial evidence of the validity of the SRE-es three-factor structure, reflecting employees’ adaptive (assertive) as well as pathological (restricted or exaggerated) attitudes regarding the assertion of their needs and rights toward their supervisors. Findings also indicated that an assertive sense of entitlement was linked with high job satisfaction and low burnout. Conversely, an exaggerated sense of entitlement was associated with high burnout and low job satisfaction. Restricted sense of entitlement revealed a mixed trend, being linked with both burnout and job satisfaction. The potential uses of the SRE-es scale are discussed.

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Monday 18 July 2022

Children’s Fear From Dentists, Based on Literature Data

 

Abstract

Introduction: Providing quality dental services, priority is given to patients’ approach to painless treatment at the dentist. The pain experienced by previous dental treatments, triggers the appearance of the feeling of fear that precedes the next dental intervention.

This picture becomes even more difficult when talking about pediatric age and the feeling of fear or anxiety experienced by this age before dental treatment. Fear of dentists has its origins in childhood, so logically, if fear and anxiety about dental interventions are to be analyzed, the age of study on this topic should be specifically pediatric age.

Read more about this article:

https://lupinepublishers.com/psychology-behavioral-science-journal/fulltext/childrens-fear-from-dentists-based-on-literature-data.ID.000219.php

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Friday 7 January 2022

Lupine Publishers | Effect of Transcranial Magnetic Stimulation and Cranial Electrotherapy Stimulation on Heroin Craving

 Lupine Publishers | Scholarly Journal Of Psychology And Behavioral Sciences


Introduction

Addiction is defined as a chronic relapsing neurobiological brain disease. The rate of drug addiction in Egypt is twice the global rate so working on the development of new preventive and treatment modalities is crucial. One of the most prevalent features of opioid addiction is the tendency to relapse on the drug even weeks, months or years after stoppage of opioid use. Exposures to stress or conditioned cues related to heroin abuse are distinct conditions that induce relapse. Craving (the intense desire to take a drug) is a central aspect of drug addiction and a contributing factor in relapse after period of abstinence. The National Institute on Drug Abuse reports that 40 to 60 % of people treated for substance use disorders relapse. For heroin, the numbers tend to be approximately twice that rate, 59% of which occurred within 1 week of discharge [1].

Aim of the Work

The aim of this work was to assess the efficacy of transcranial magnetic stimulation (TMS) and cranial electrotherapy stimulation (CES) on craving of heroin use disorder.

Subjects and Methods

The design of the current research was randomized controlled clinical trial to assess the effectiveness and outcome of Transcranial Magnetic Stimulation (TMS) and Cranial Electrotherapy Stimulation (CES) on heroin abuse craving. This study was done in Neuropsychiatry Department and Psychiatry and Neurology Center, Tanta University over a period of 27 months started from June 2018 through September 2020 on eighty patients who fulfilled the diagnostic criteria of heroin use disorder according to DSM-5.

Inclusion criteria consisted of heroin abusers aged 18-30 years. Exclusion criteria included patients with current medications which may alter EEG activity as anticonvulsants or patients with history of any neurological disorder that would result in abnormal EEG activity or current substance abuse other than heroin and the presence of implanted devices as cardiac pacemaker. Patients were randomized either to receive active TMS (20 patients) versus sham TMS (20 patients) or to receive active CES (20 patients) versus sham CES (20 patients). All patients were subjected to history taking included personal or drug history, systemic or mental state examination and urine drug screen. Craving was induced by cues of heroin blocks or pictures before applying the questionnaire or performing the EEG. Craving was assessed objectively by EEG or subjectively by BSCS at base line before starting TMS or CES, then at the next day after completion of 10 session of TMS or CES and after one or three months follow up.

For EEG recordings, participants were asked to close their eyes and to avoid mental activities as well as movements or muscular contractions during the recordings. EEG was recorded with Neurofax nihon Kohden QP-110 AK from scalp locations placed according to 10-20 international system. Resting EEG was recorded for 5 min to identify the baseline background activity of each patient. The EEG was digitized, and fast Fourier transformation (FFT) was performed. Fourier analysis converts a signal from its original domain (often space or time) to a representation in the frequency domain and vice versa. To calculate EEG power, the frequency spectrum was divided into 0.2 Hz bands and collapsed into EEG frequency bands of delta (1- 3.9 Hz), theta (4.0-7.9 Hz), alpha (8.0-12.9 Hz) and beta (13.0- 30 Hz). Each power value represented 5 seconds, and we analyzed 30 seconds of recording per case. Then we designated these power values as average percentages of total power. These were usually called delta, theta, alpha, and beta power ratios. Then, we exposed each patient to cue-induced craving pictures or previous heroin use situations according to each patient`s history of intake for recording a baseline EEG. Instructions were to sit relaxed, still and to carefully attend to the cues without employing distracting thoughts. The frequency domain analysis was performed using the Fast Fourier Transform (FFT) algorithm to calculate absolute (μV2/Hz) power density, relative (%) power density and mean frequency (Hz) within each of the sub-bands. The absolute power of a band is the integral of all of the power values within its frequency range. Relative power (RP) indices for each band were derived by expressing absolute power in each frequency band as a percent of the absolute power (AP) summed over the four frequency bands. Absolute power was log transformed (log x) and, relative power variables were transformed by log {x/1-x} in order to normalize the distribution of the data. EEG frequency (Hz) indices were found to be normally distributed and thus did not require transformation [2]. The BSCS is a self-report instrument to assess craving for heroin abuse over a 24-hour period. Patients were asked to complete a scale, rating the intensity, frequency and length of their cravings. Ratings were then scored on a scale of 0 to 12 (0 meaning no cravings; 12 meaning the patient was experiencing severe cravings) [3].

TMS Protocol

Group I patients (40 patients) received TMS sessions after one week of heroin abstinence. Before starting the study, individual TMS motor thresholds were determined for each participant. Individual motor threshold (MT) was determined similar to the method described [4]. TMS intensity was varied using an ascending staircase procedure and the motor evoked potential (MEP) of the abductor pollicis brevis muscle was assessed. High Frequency rTMS (HF rTMS) at 10 Hz (24 trains, 5 s per train, 25 s intertraininterval, i.e. 1200 pulses, 90% MT) was applied via a figure-eight coil with an outer winding of 70 mm connected to a Magstim Rapid-2 stimulator [5] targeting the left DLPFC. The duration of each session (real or sham TMS) will be 20 minutes for 5 days per week for 2 weeks, so each patient received 10 sessions. Sham stimulation was administrated at the same location, strength and frequency with the coil angled 45o away from the skull. This method reproduced sound and some somatic sensation (vibration and contraction of scalp muscles) that resemble active stimulation while generating intracerebral voltage approximately 1/3 that of active TMS stimulation [6].

CES Protocol

Group II patients (40 patients) received CES sessions after one week of heroin abstinence. Twenty-minute (5 days per week for 2 weeks) application of 10 sessions of CES using alphastim technology (Electromedical products international Inc., Mineral wells, Texas; www.alpha-stim.com ). Earclips electrodes were moistened with a conducting solution and attached to the earlobes. Current levels of the CES device (which range from 100 to 500 microamperes) were adjusted following manufacture recommendations to a comfortable level just below where vertigo is experienced. Sham CES was administrated at same location, strength and frequency but with CES device turned on and off. The collected data were organized, tabulated and statistically analyzed using SPSS version 19 (Statistical Package for Social Studies) created by IBM. There were descriptive and comparative types, where quantitative data were summarized as mean and standard deviations while qualitative data were summarized as numbers and percentages. A comparison was made using Paired student test (t - test) in case of two groups quantitative data. The chi-square test(X2) and Fisher exact test (FET) were used for qualitative data. Differences were considered significant if the P value was 0.05 or less. The study’s protocol was approved by The Research Ethics Committee and Quality Assurance Unit, Faculty of Medicine, Tanta University. Participations were voluntary, informed consents were obtained from all included patients and the possible risks were clarified.

Results

The current study showed that the mean age of patients was (24.8 ± 3.8) without significant difference among different study groups (Table 1). The participants in this study were all male (100%). Five heroin abuser females refused to participate in our study secondary to fear and stigma despite maintained privacy and confidentiality of the data. According to the marital status of the participants, half of them (50%) were single. Technical educational level was the commonest among participants. Most of participants were in the middle and low social class levels. According to the family characters, most families were of extended type. Tobacco abuse was the commonest substance of abuse by families followed by cannabis and heroin. Antisocial personality disorder represented the main personality disorder among participants. Peer influence (62.5%) and curiosity represented the main causes of heroin abuse between study group’s patients. According to addiction severity index, drug abuse was considered the main problem among this study participants followed by employment problems. Intravenous injection (63.7%) of heroin was the commonest form of heroin intake among all participants. There was no significance regarding the daily dose of heroin or the duration of abuse among the study groups. The current study showed no significant difference regarding the BSCS baseline scores between all study groups before performing any TMS or CES sessions. The current study showed significant decrease in BSCS values after (10 sessions of active TMS or active CES) and after one month and three months follow up with least values in active TMS group which indicate the least craving in active TMS group (Tables 2 & 3). After three months follow up there was slight increase in BSCS values which indicate more heroin craving. Despite these observed increase in BSCS values with follow up, the active TMS group subjects showed the least increase among all participants and still had significant difference with baseline craving (Figure 1). Power ratios are an index of EEG power reflecting changes in the balance of EEG power by frequency band. The current study showed no significant difference regarding mean log transformed beta band power at baseline before performing any TMS or CES sessions (Table 3). The current study showed significant decrease in mean log transformed beta band power values (after 10 sessions TMS or CES) and after one month follow up with least values in active TMS group which indicate less craving in this group (Figure 2). This study showed no significant difference between mean baseline beta band frequencies at F3 and F4 among study groups with higher values at F3 than F4 which indicated relative greater activation of left frontal hemisphere than the right one. The mean beta band frequency in active TMS group after performing 10 sessions TMS or after one month follow up at F3 was lower than F4 which correlated with the effectiveness of active TMS in modulation of brain activity in left prefrontal region (Table 4).

 

Discussion

Craving for heroin can be described as a powerful urge to use heroin again. Patients with craving have an intense desire to use heroin accompanied by vivid day dreaming about using heroin or difficulty in focusing on anything other than getting it. The current study showed that the mean age of patients was (24.8 ± 3.8) without significance among different study groups. The participants in this study were all male (100%). The higher ratio in heroin abuse in males may be attributable to the more freedom that males can obtain in our society and the easier ways to obtain the drugs than females. In Egypt, females still have lower prevalence rates than men due to culture effect. The involvement of 100% male gender in this research may provide an advantage of avoidance the genderassociated differences in craving as a result of hormonal changes throughout the menstrual cycle but it also lacking the advantage of gender difference studying [7]. The current study showed no significance regarding the BSCS baseline measures between all study groups but after 10 sessions TMS or CES these values were decreased with significant difference in active TMS and CES groups which indicate significant reduction of heroin craving but the maximum decrease was noticed in subjects who received active TMS sessions than active CES. So, both active TMS and active CES were significantly effective in reducing heroin craving but TMS was significantly more effective than CES. After three months follow up there was slight increase in BSCS values which indicate more heroin craving. Despite these observed increase in BSCS values with follow up, the active TMS group subjects showed the least increase among all participants and still had significant difference with baseline craving.

This downward shift of BSCS values after 10 sessions TMS or CES confirm the acute efficacy of these maneuvers to decrease craving in heroin use disorder patients especially in active TMS group but the observed slight increase of BSCS scores (which indicate more heroin craving) especially after three months follow up may confirm the need for further booster TMS or CES sessions throughout a longitudinal six month protocol. A prominent factor for the increase of craving within three months follow up is the presence of environmental drug cues. Continuous changes in reward and memory brain circuitry which are related to drug dependence result in high sensitivity to drug-linked cues during abstinence [8]. Furthermore, impairment in the regulation of the hypothalamic-pituitary-adrenal axis caused by opioid dependence is correlated to enhanced sensitivity to stressors during abstinence. Such abstinence related impairment of reward and stress response systems may explain the subjective experiences reported by study participants during abstinence and the constant desire for drugs even in case patients are in residence and living far from social, drug use-related, environmental stimuli of craving. Shi et al study corroborated our findings regarding a significant reduction of heroin craving among participants after one month follow up after abstinence [9]. On the contrary, Fathi et al declared a reduction of heroin craving for one month follow up but without statistical significance. Such a difference may arise from the different psychometric test used, small sample size and absence of drug cues [10].

According to Jack et al in a systematic review and meta-analysis regarding effect of rTMS on craving in substance dependence patients, rTMS revealed a significant anti-craving effect of the left DLPFC in patients with substance dependence. Excitatory rTMS targeting left DLPFC shows promise in reducing both craving and substance consumption, which may be a result of dopamine release and/or activation of the dorsal PFC executive functioning system. However, this effect was limited in duration, as indicated by a nonsignificant treatment effect at follow-up [11]. CES may produce its effect on decreasing heroin craving with ear-clip electrodes through achieving parasympathetic nervous system dominance by stimulating the auricular branch of the vagus nerve. Withdrawal symptoms including craving are basically manifestations of sympathetic nervous system over activity. CES also improves anxiety and frequent insomnia which are common symptoms in the early stage of recovery from heroin and these symptoms are the main precursor for relapse. In our current study, these effects appear to be session linked with increased craving or desire to take a heroin again after stoppage of CES sessions especially with continued follow up for three months. The current study showed that the mean log transformed beta band power values at base line had no significant difference between the study groups but after 10 sessions TMS or CES these values were decreased with significant difference in both active TMS or active CES study groups which indicate less craving but the maximum decrease was noticed in subjects who received active TMS sessions. So, both active TMS and active CES were significantly effective in reducing heroin craving but TMS was significantly more effective than CES. Active TMS group showed significant decrease of absolute beta band power values from baseline to 10 sessions application or after one or three months follow up. These results were in agreement with Jurgen et al who showed that absolute beta power significantly decreased with rTMS sessions but this study results were related only to the acute effects of TMS without further follow up. Jurgen reported that absolute beta band power was significantly decreased more with active TMS versus sham TMS. He postulated that active TMS session might mimic craving action on brain reward functions producing an increase of dopamine release with further cognitive inhibitory control mechanism [12].

Our study showed no significant difference between mean baseline beta band frequencies at F3 and F4 among study groups with higher values at F3 than F4 which indicated relative greater activation of left frontal hemisphere than the right one. The mean beta band frequency in active TMS group after performing 10 sessions TMS and after one and three months follow up at F3 was lower than F4 which correlated with the effectiveness of active TMS in modulation of brain activity in left prefrontal region. We reported that the left frontal hemisphere had greater activation than the right hemisphere after cue induction and therefor the choice of left DLPFC for neuromodulation of substance induced craving is an optimum location to manipulate. Our findings were in agreement [3,5].

Conclusion

Transcranial magnetic stimulation (TMS) and cranial electrotherapy stimulation (CES) are effective non-invasive treatment modalities for the acute reduction of heroin craving with significant reduction of heroin craving through three months after heroin abstinence. So, TMS or CES are considered valuable noninvasive devices to overcome the risky period of heroin craving especially after one month of heroin abstinence. So, TMS or CES represented new effective method for relapse prevention in heroin use disorder. Active TMS shows significant difference for reduction of heroin craving than sham TMS. Active TMS is more effective treatment modality for reduction of heroin craving than active CES. The observed slight increase of BSCS scores or the beta band power readings (which indicate more heroin craving) especially after three months follow up may confirm the need for further booster TMS or CES sessions throughout a longitudinal six-month protocol. Despite this observed slight increase of BSCS scores or the beta band power within three months after abstinence, the BSCS scores or the beta band power still have significant difference than the baseline readings. The left frontal hemisphere has greater activation than the right hemisphere after cue induction. Therefore, the choice of left DLPFC for neuromodulation of substance induced craving is an optimum location to manipulate

Recommendations

We recommend the use of rTMS or CES as non-invasive treatment options for acute reduction of heroin craving which provide improvement in the adherence to the recovery programs and prevention of relapse. We recommend the use of TMS for acute reduction of heroin craving as a superior efficient tool than CES. However, CES provides an easily accessible home treatment option for reduction of heroin craving especially if TMS apparatus is not available or if there are any contraindications of TMS use. We recommend the need to adopt a uniform prolonged treatment protocol and optimize the number of rTMS sessions in relation to treatment of patients with heroin use disorder during the whole abstinence months. We recommend the restrict prevention of heroin abuse patients during the abstinence period from exposure to heroin cues which induce craving that represents an important cause for heroin relapse. Violent craving can erupt suddenly into consciousness after exposure to drug cues, acting as a persistent cause for relapse. We recommend the presence of comprehensive effective rehabilitation program for six months for heroin abusers till complete normalization of brain neurobiological changes associated with heroin abuse.

Limitations

The need for further studies with longer follows up duration (six months) which may offer a more informative data about neurobiological changes for the whole abstinence period in heroin use disorder. The need for further studies to identify the neurobiological changes for heroin craving in females for proper assessment of the gender difference.

Declaration

Ethics approval and consent to participate section

- The manuscript was approved from The Research Ethics Committee and Quality Assurance Unit, Faculty of Medicine, Tanta University.

- The study’s protocol had permitted by The Research Ethics Committee and Quality Assurance Unit, Faculty of Medicine, Tanta University. Participations were voluntary, informed consents were approved by all participants’’ guardian and any possible risks were clarified.

Consent of publication: Not applicable.

Competing interests: All authors disclose that they have no competing interests related to the study.

Availability of data and materials: The datasets used and/ or analyzed during the current study are available from the corresponding author on reasonable request.

Authors Contributions:

MSK: participated in the study’s design, patients’ selection, statistical analysis, data analysis, references collection, manuscript writing and revision and final approval, WSB: participated in the study’s design, patients’ selection, EEG interpretation, statistical analysis, data analysis, manuscript writing, revision and final approval, AAH: participated in patients’ assessment and inclusion, data analysis, psychometric scale analysis, statistical analysis, references collection, manuscript writing, revision and final approval, GTS: participated in the study’s design, patients’ selection, and evaluation, data analysis, references collection. EAG: participated in study’s idea and design, patients’ assessment and inclusion, data analysis, references collection, manuscript writing, revision and final approval.

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Friday 10 December 2021

Lupine Publishers | Losses Loom Larger Than Gains: Neural Correlates Of Behavioral Gain-Loss Asymmetry

 Lupine Publishers | Scholarly Journal of Psychology and Behavioral Sciences

Introduction

Thomas Hobbes (1588-1679) proposed that voluntary behavior is governed by the principle of hedonism, that is, an individual’s sole intrinsic good is the overall pursuit of pleasure. A hedonist strives to maximize net pleasure by minimizing pain. Utility theory, which is a cornerstone of the rational perspective of economics, is rooted in the hedonist principle. However, the psychology of Homo economicus-a rational and self-interested individual with relatively stable preferences-has been challenged by numerous psychologists and behavioral economists. The purpose of our research was to explore the effects of small monetary gains and losses on choice behavior using a computerized game and to determine gain/loss ratio differences using both behavioral and electroencephalographic (EEG) measures.

A prominent example of gain-loss asymmetry is that losses loom larger than gains [1-4], meaning that the aversion to a loss of a certain magnitude is greater than the attraction to a gain of the same absolute magnitude. Such asymmetry is an indication that humans are sometimes biased in their decision making. Accordinng to Kahneman and Tversky [9], “The asymmetry of pain and pleasure is the ultimate justification of loss aversion in choice” [p. 157]. Kahneman, Knetsch, and Thaler [8] reported that “The existing evidence suggests that the ratio of the slopes of the value function in two domains, for small or moderate gains or losses of money, is about 2:1” [p. 199].

Rasmussen and Newland [14] reported a behavior-analytic experiment in which participants played a customized computer game that sometimes-produced reinforcers (gains) and sometimes punishers (losses) in the form of points exchangeable for money or, conversely, the loss of points and money. Their design included two alternating conditions. In one condition, a pair of reinforcement schedules were concurrently available. The ratio of reinforcer frequencies was adjusted systematically. The other condition consisted of the same pairs of reinforcement schedules, but a schedule of punishment was overlaid onto one of the reinforcement schedules. The authors reasoned that, in this way, they could measure the effect of punishers as the difference in the response ratios when punishers were delivered on one of the alternatives versus when punishers were absent from both alternatives. They concluded that the mean asymmetry ratio was approximately 3:1 (loss:gain) on average.

Kahneman [7] asserted that “the brain’s response to variations of probabilities is strikingly similar to the decision weights estimated from choices” [p. 315]. The electroencephalograph (EEG) may be used to record scalp visual-evoked potentials (VEPs), including event-related potentials (ERPs) [3,11]. Sokol-Hessner and Rutledge [15] reported that “Research on the neuroscientific basis of loss aversion has identified several critical neural components, suggesting a model of loss aversion in the human brain and providing links to the neuroscience of affect” [p. 315]. They reviewed several loss-aversion studies of young adults and adults and found that the means of loss aversion (computationally formalized as a multiplicative weight on losses relative to gains) in their model were between 1.3 and 2.5.

Kahneman and Tversky’s research method was cognitive. Rasmussen and Newland’s method was behavioral as was ours. However, we added an electrophysiological measure of the asymmetry of gains and losses. In the electrophysiological component of our study, the amplitudes of P300 waves elicited by gains and losses while playing the video game were measured in real time and converted to a gain/loss ratio. We hypothesized that the amplitudes of P300 waves recorded during a gain/loss behavioral procedure, when expressed as gain:loss ratios, would be directly related to their behavioral counterparts.

Method

Participants

The participants were 16 male undergraduate students enrolled at Brigham Young University (BYU), Provo, UT, USA 84602. They were recruited through an online recruitment platform following approval of our research protocol by the BYU Institutional Review Board.

Materials and procedures

We developed a computer game to produce behavioral data. Participants played the game in an experimental room, 9 ft by 9 ft, containing a table and chair. The table held a Dell® desktop computer (the game computer) equipped with a 17-in monitor and a mouse. The room was windowless and artificially illuminated. The computer had an Ethernet connection to a separate, identical computer that was in an adjacent room and that hosted the Emotive EPOC® Brainwear® software for recording the EEG and to monitor its functioning. The Emotive EPOC® device was placed on the participant’s head and contained 14 scalp electrodes. Written informed consent was obtained from all participants prior to the first experimental session. The participant was seated in front of the computer monitor and asked to read the instructions (written in English) for the game that appeared there. He was invited to play the video game in a series of 36-min sessions in which he could earn points on the screen. The net earnings were paid to the participant at the end of each session. In addition, the participants received a $50 bonus at the completion of the study. There were seven sessions. EEG recording was continuous during each session.

The SubSearch Game

Participants played SubSearch using the computer mouse to guide an underwater submarine and to retrieve as many yellow objects as possible before reaching the sea floor. When the cursor rested on the submarine, moving it moved the submarine. If the submarine was placed over a yellow object, clicking the mouse retrieved the object. Underwater barriers complicated the submarine’s movement between objects. Once the submarine descended to the sea floor, it was returned to the surface for a new descent, this time with more frequent barriers. Thus, the game became progressively more difficult as it continued. Only one panel was operative at a time. The other panel was darkened, and motion was paused. The game was played in two different vertical panels separated by a vertical line. Each panel was associated with its own interdependent concurrent variable-interval (inter conc VI VI) schedules of reinforcement. There was also a conjoint VI schedule of punishment during certain conditions on the left side of the screen. Unlike the traditional conc VI VI schedule in which the two schedules are independent of each other, the interdependent version assigned a reinforcer according to a preset probability generator. If, for example, the generator was set to assign twice as many reinforcers to the left panel than to the right panel (pL = 0.67), and the next reinforcer was assigned to the right panel, then it would be necessary for the participant to produce that reinforcer before the next one would be assigned. Thus, the interdependent schedule reduced the likelihood of extreme position (left or right) biases and assured that the scheduled proportion of reinforcers) between the two panels remained close to the proportion of those that were delivered.

After the participant clicked the “Start-OK” message on the screen, a 36-min session commenced. The game allowed the participant to move the cursor from one panel to the other. However, each switch produced a changeover delay of 2 s. During this interval, no reinforcers or punishers were delivered. Gains and losses were signaled by separate on-screen messages, each accompanied by a distinctive sound. For 0.5 s prior to the on-screen signal of a gain or of a loss, a fixation signal (a white + sign) was presented on the screen and followed in the same location by a message indicating either “Collect a coin to continue” for gains or “Insert a coin to continue” for losses. The gain-message appeared for 1 sec. Then the tab located at the bottom of the screen between the two counters began to blink. The game resumed after the participant clicked on the tab. Counters on each side of the tab displayed the net points for the respective side of the screen.

Each click during a session was coded, time stamped, and saved to an external MySQL database. The summary statistics included the total time spent responding in each panel, the total number of clicks that occurred in each panel, the total numbers of reinforcers and punishers that occurred in each panel, and the total number of changeovers. Each session consisted of a fixed sequence of six 6-min conditions (conditions 1-6). Three of them (1, 3, and 5) contained conc VI VI schedules of reinforcement only and three (2, 4, and 6) contained conc VI VI schedules of reinforcement and a conjoint VI schedule of punishment on the left side of the screen. Table 1 summarizes the scheduled frequencies of reinforcers and punishers in each condition. Condition 1 featured a conc VI60-s VI20-s schedule, meaning that 25% of the total reinforcers were allocated to the left panel and 75% to the right panel. There was no schedule of punishment. Condition 2 featured the same conc VI60-s VI-20 schedule of reinforcement plus a VI60-s schedule of punishments. In other words, 100% of the punishers were allocated to the left panel and no punishers to the right panel. The other four conditions featured different reinforcer ratios. Each unpunished condition was followed by a similar condition that included punishers only in the left panel under the same schedule as the reinforcers that were delivered in that panel. Each condition was accompanied by a different background color in each panels, for a total of six different colors. It should be noted that the values of the VI schedules in each concurrent pair of reinforcement schedules were selected to produce the same overall rate of reinforcement despite the difference in their ratios (1:3 in conditions 1 and 2, 1:1 in conditions 3 and 4, and 3:1 in conditions 5 and 6). The ratio of reinforcers to punishers was always 1:1.

Electrocortical activity

The head-mounted instrument was a wireless Bluetooth® Smart device (2.4GHz band) with 14 electrodes that transmitted at a sample rate of 128 Hz. It provided access to raw, densearray, high-quality EEG data with software subscription (EMOTIV Brainware®. The resolution was 14 bits with 1 LSB = 0.51 μV). The bandwidth was 0.2 -43Hz with digital notch filters at 50 Hz and 60 Hz. It included a digital 5th-order Sinc filter and a dynamic range (input referred) of 8400μV. It was AC coupled and powered by a lithium polymer battery (480 mV). The device sent the EEG data via a Bluetooth® connection to the computer to be recorded. In the game computer, certain in-game events, such as displaying a gain or a loss message on the monitor, triggered a signal to the second computer, it also compiled the data, temporally aligning the EEG data with the 8-bit codes received from the game and saved them to the hard disk. Because of the limitations of Bluetooth® range, both computers were in the same room, but the interface and the monitor for the second computer was in an adjacent room. The final output was a large csv file that contained a time-step column, the 14 electrode channels, and markers for each SubSearch on-screen message.

Event related potential analysis

VEPs are electrical potentials initiated by brief visual stimuli and are recorded from the scalp overlying the visual cortex. Amplitude (measured in μV) is defined as the difference between the mean pre-stimulus baseline voltage and specific voltages (positive and negative) measured within a time window. Latency (measured in ms) was defined as the time from stimulus onset to the point at which amplitude was measured within the window [11]. The ERP contains distinct waveforms that may be correlated with specific cognitive activities [2]. The labels N50, P100, N100, P200, N200, and P300 are commonly used, where P and N indicate positive or negative deflections, respectively, and the number indicates an ordinal position in the waveform. It should be noted that the P200, N200, and P300 are specifically ERPs; however, we used the term ERP to refer to all of the VEP components. Gehring and Willoughby [5] recorded brain activity coincident with monetary gains and losses and concluded that the amplitude was greater for losses than for gains. The ERP data were imported using EEGLab® with the ERPLab® add-on. EEGLab® is an interactive Matlab® toolbox for processing continuous and event-related EEG, magnetoencephalographic, and other electrophysiological data to produce independent component analysis, time/frequency analysis, artifact rejection, event-related statistics, and several modes of visualization of the averaged and single-trial data. A 1Hz high-pass filter, followed by a 50 Hz low-pass filter, was applied to the in-session recordings. Epochs were created for each gain or loss in the SubSearch game and ranged from 1,000 ms before the message appeared to 2,000 ms after it disappeared. Any epoch that contained an amplitude exceeding 150 mV was rejected. The epochs were averaged for gains and losses separately, resulting in a pair of summative waves (gain and loss) for each participant in each session. Then grand averages were created. The P300 component of the VEP was the focus of our analysis. The P300 wave was measured as the maximum positive deflection occurring between 250 msec and 500 msec following the presentation of brief visual stimulus. Yeung and Sanfey [17] found that, in studies of choice, the P300 can be influenced by several factors, including the magnitude of the chosen option, the valence and magnitude of the alternative option, and the relative value of the alternative outcome in comparison with the chosen outcome.

 

The data analysis consisted of signal filtering, amplifying, and averaging the EEG during the 1-s epoch immediately prior to the onset of a message on the monitor screen and during the 2-s epoch following the offset of the message. The analysis of the averaged ERPs focused on the previously indicated components of the average signal, with each component characterized by its amplitude, polarity (positive or negative), and latency. An ERP waveform consists of a series of peaks (here termed positive peaks) and troughs (negative peaks), but these voltage deflections reflect the sum of several relatively independent underlying, or latent, components. Isolating the latent components from the observable peaks and troughs of the waveform was challenging. The SubSearch game was designed to minimize latent components and to make sure that the evoked P300 was, as much as possible, a direct result of the experimental design. An important objective of the design was to separate the processes related to monetary gains and losses from possible confounding factors. The EEG does not only include ERPs but also other, “noisy” signals. The method we used to reduce the latter signals was signal averaging. All of the analyses featured epochs that were time-locked to the onset of the fixation signal that preceded the on-screen messages announcing reinforcers and punishers. Additionally, we examined the modulating effects of valence and magnitude on the ERP.

 

Figure 1 depicts the sequence of events in the computer game. Each ERP epoch began with a blank screen that appeared simultaneously with scheduled delivery of a reinforcer or punisher. Five- hundred ms later, a fixation mark appeared on the screen. After another 500 ms had passed, it disappeared, and the gain or loss message appeared on the screen. It marked the onset of the P300 waveform. Analysis of the epoch began 500 ms previous to the fixation signal. Immediately following the presentation of the reinforcer (or punisher) message, which remained on the screen until the participant resumed the game, there was a 1000-ms delay until the tab at the bottom of the screen between the two cumulative counters began to blink. During this interval, the game was inoperative and remained so until the participant clicked the tab.

Behavioral data analysis

Thorndike [16] formulated the basic principle of the law of effect, which stated that actions followed by feelings of satisfaction are more likely to be repeated, but actions followed by feelings of annoyance are less likely Herrnstein [6,13] produced systematic work on behavioral choice involving schedules of reinforcement. He found that, over time, the proportion of responses to an alternative matched the proportion of reinforcers received for responding to that alternative. If twice as many reinforcers were provided to one of two alternatives, then, on average, twice as many responses were directed to that alternative once response allocation was stable. Herrnstein summarized this regularity as follows and termed it the matching law of distributed (ongoing) choice between alternatives:

B refers to number of behavioral responses and R to the number of reinforcers that the responses produced. The two alternatives are designated by subscripts. This version of the matching law is actually a special case of the generalized matching law (GML; Baum, 1974):

that is, b = s= 1.0.

The parameters in this power function reflect a bias (b) for one source of reinforcement over the other and sensitivity (s) to changes in the distribution of reinforcers between the two sources, respectively. Under logarithmic transformation, the GML becomes a linear equation:

We used a procedure like that of Rasmussen and Newland and applied both the subtractive model as well as an indirect model to our results. The latter does not directly include punishers but, instead, represents the effect of punishers by variations in the effects of reinforcers. In other words, using this model, it is not necessary to include both reinforcers and punishers in the equation to measure their asymmetrical effect. All analyses were conducted using IBM SPSS Statistics 23 [13] and Microsoft Excel®. Measures included in the analyses were the number of responses (clicks) to the left and right alternatives (BL and BR) and the number of reinforcers provided by each (RL an d RR). The results were analyzed using equation 3. Asymmetry ratios were computed using antilogarithms of the bias estimates from the linear regressions for the conditions in which only reinforcers were used and those in which reinforcers and punishers were both used.

Results

Behavioral gain: loss asymmetry

Loss amplitudes were higher than gain amplitudes with an asymmetry ratio of 3.40. Table 2 contains the overall mean values of b for the No-punishers and With-punishers conditions, the 95% confidence intervals, and the asymmetry ratios for each session. Tables 3 and 4 contain the overall mean responses, obtained reinforcers, obtained punishers, and changeovers for the Nopunishers and With-Punishers conditions for the unpunished alternatives, respectively, for each of the participants. Table 5 is a summary of the overall estimated values of s and b in the Nopunishers and With-punishers conditions, and the asymmetry ratio for each participant.

 

 

Figure 2 displays the overall mean P300 waveform and other waveforms at electrode sites F3 (Panel B) and O1 (Panel C) for all participants according to the timeline. The approximate locations of the electrodes also appear. The reaction time (RT) was measured as the interval that began with the onset of the on-screen message and continued until the blinking tab was clicked. The mean RT was slightly shorter for the gain message (1512 ms) than for the loss message (1665 ms). In Figure 3, the overall mean amplitudes and standard errors are shown for each session: green bars for gains and red bars for losses. The blue lines represent the overall mean asymmetry ratio in each session. Five component waveforms appear, including the P300. The mean overall symmetry ratio for the P300 waveform was 1.99.

Correlation of the behavioral results with the electrophysiological results

Figure 4 displays the means of the asymmetry ratios derived from the two categories of data (behavioral on the x-axis and EEG on the y-axis) for each participant, together with the regression equation and R^2 (%VAC). Overall, the correlation coefficient was 0.852 and R^2 = 0.738.

Figure 4: Linear Regression of the Asymmetry Ratios for Each Participant Derived From the Behavioral Data (X-Axis) and From the EEG data (Y-Axis).

 

Discussion

Our results demonstrated a direct, robust correlation between behavioral and EEG measures of gain-loss asymmetry. The overall mean asymmetry ratio from our behavioral results was higher (M = 3.40) than that from the cognitive results previously cited [5,17] and from Rasmussen and Newland’s [14] behavioral study (M ≈ 3.0). For our EEG results, M = 1.99. One possible reason for the larger ratio from our behavioral results may be the use of the EMOTIV EPOC+® and the incumbency on participants to wear it throughout each session and, at the same time, to avoid unnecessary movements (which could have interrupted Bluetooth® interconnectivity or added noise to the EEG record). Notable aspects of the research method and data analysis included the use of the interdependent schedules of reinforcement, the inclusion of six different schedules within the same session, and the achievement of relative stability of participants’ performance within seven sessions [12,16], and that our participants were men, but there were 16 of them in our study and five in theirs. We, too, utilized a computer game as the behavioral task, though the on-screen presentation in our game was more complex than in theirs, as was the concatenation of six conditions within a session as opposed to their presentation of a single condition over consecutive sessions. Our use of interdependent VI VI schedules of reinforcement produced ratios of reinforcement consistently closer to the programmed ratios than in their study, where the disparity between programmed and actual ratios produced by conc VI VI schedules of reinforcement was substantial. Also, when a VI schedule of punishment was conjoined with the reinforcement schedule in their procedure, the scheduled rate of punishment was half that of the scheduled rate of reinforcement as opposed to being the same rate, as in our experiment. As with reinforcers, their results displayed a considerable disparity between the scheduled and actual rates of punishers.

Finally, our study included the measurement of EEG activity concurrent with participants’ distributed choice behavior. The temporal alignment of the EEG record with the record of behavior during the same SubSearch session was critical to demonstrating the correlation between the two sets of data. This requirement was fraught with unanticipated complications and required extensive revision and testing of the communication protocol between both computers (see the earlier description of the procedure) before it was sufficiently reliable for use in the research we reported. Perhaps the most noteworthy outcome of our study was the strength of the positive correlation between the behaviorally derived and EEGderived asymmetry ratios. Though no extant models account for that relationship, its demonstration invites further investigation. For the present, we have concluded that losses affect ongoing behavior in a distributed-choice procedure more intensely than gains do and that this differential effect may be quantified using the record of brain activity that occurs simultaneously with distributed choice behavior. It is as if the brain holds a mirror to externally observable behavior, or vice versa.

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