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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