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Journal of Vision (2022) 22(1):12, 1–16 1
Diminishing sensitivity and absolute difference in value-driven
attention
Sunghyun Kim
Department of Psychology, Louisiana State University,
Baton Rouge, LA, USA
School of Psychology, Korea University, Seoul,
South Korea
Jason L. Harman Department of Psychology, Louisiana State University,
Baton Rouge, LA, USA
Melissa R. Beck Department of Psychology, Louisiana State University,
Baton Rouge, LA, USA
Kim and Beck (2020b) demonstrated that value-driven
attention is based on relative value rather than absolute
value, suggesting that prospect theory is relevant to our
understanding of value-driven attention. To further this
understanding, the present study investigated the
impacts of diminishing sensitivity on value-driven
attention. According to diminishing sensitivity, changes
in outcomes have greater impacts nearer the reference
point of 0 than farther from the point. Thus, the
difference between $1 and $100 looms larger than that
between $901 and $1000, due to their different ratios
(100/1 >1000/901). However, according to the absolute
difference hypothesis, the differences should have
similar impacts due to the absolute differences being the
same (100 – 1 =1000 – 901). Experiment 1 investigated
whether diminishing sensitivity operates in the modified
value-driven attention paradigm while controlling the
impact of absolute differences. In the training phase,
100-point and 1000-point color targets had references of
1-point and 901-point color targets, respectively. In the
test phase, 100-point color distractors attracted
attention more than 1000-point color distractors,
supporting the diminishing sensitivity hypothesis.
Experiment 2 examined the absolute difference
hypothesis while controlling the impact of diminishing
sensitivity. Contrary to the absolute difference
hypothesis, the test phase showed that 1000-point color
distractors (compared with 10-point colors for a 990
absolute difference in the training phase) failed to
attract attention more than 100-point color distractors
(compared with 1-point colors, for a 99 absolute
difference). These results suggest that diminishing
sensitivity rather than absolute difference influences
value-driven attention, further supporting the relevance
of prospect theory to value-driven attention.
Introduction
A selection process exists in perception via selective
attention, the process of focusing on a particular
stimulus out of many alternatives (Broadbent,
1958;Kahneman, 1973;Treisman & Geen, 1967).
This selection process is similar to the process of
making a choice among available alternatives in
decision-making (Edwards, 1954). Therefore, although
decision-making and selective attention occur in
dierent cognitive stages, they share a core concept of
selection. Furthermore, the selection processes interact
functionally. Attending to an item leads to an increase
in the likelihood of choosing the item (Krajbich, Armel,
& Rangel, 2010;Stewart, Hermens, & Matthews,
2016), and the decision to search for an item facilitates
attention toward the item (Desimone & Duncan, 1995;
Kim & Beck, 2020a;Kim & Cho, 2016;Wolfe, 1994).
In addition to the conceptual similarity and functional
link, decision-making and selective attention share a
critical factor aecting selection, value. More valuable
items are more likely to be chosen in decision-making
(Von Neumann & Morgenstern, 1947) and are more
likely to be attended to in perception (Anderson,
Laurent, & Yantis, 2011). However, decision-makers do
not objectively evaluate the value of items, but distort it
(Kahneman & Tversky, 1979;Thaler, 1980). The value
distortion in decision-making occurs in a predictable
way according to the psychological principles in
the value function of prospect theory (Tversky &
Kahneman, 1981). Then, the critical question is if the
value distortion found in decision-making will occur
in selective attention. In line with this, previous work
Citation: Kim, S., Harman, J. L., & Beck, M. R. (2022). Diminishing sensitivity and absolute difference in value-driven attention.
Journal of Vision,22(1):12, 1–16, https://doi.org/10.1167/jov.22.1.12.
https://doi.org/10.1167/jov.22.1.12 Received April 22, 2021; published January 20, 2022 ISSN 1534-7362 Copyright 2022 The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 2
Figure 1. A typical value function of prospect theory
demonstrating diminishing sensitivity. The value function
describes how objective value (absolute value, outcomes) is
psychologically (subjectively) distorted. Specifically, the
difference between 10 and 5 and the difference between 100
and 105 are equal on the objective-value axis but differ on the
subjective-value axis.
shows that attention is attracted based on relative
value rather than absolute value (Kim & Beck 2020b).
The present study expands on this previous work by
investigating whether when a valuable item attracts
attention, the value of the item is distorted on the basis
of the diminishing sensitivity principle of prospect
theory (Kahneman & Tversky, 1979).
Prospect theory in selective attention
Traditional economic theory suggests how decision-
makers should behave to maximize benets, postulating
that decision-makers are rational. As a result, economic
theory does not t with how people actually make a
choice. To explain how decision-makers actually behave,
prospect theory (Kahneman & Tversky, 1979;Tversky
& Kahneman, 1992) posits that decision-makers
are irrational and explains how objective (absolute)
value (e.g., money, time, health) is distorted due
to the following psychological principles: reference
dependence, diminishing sensitivity, and loss aversion.
The principles are described in the value function
(see Figure 1). According to reference dependence,
value is determined from a reference point so that
relative value (e.g., higher, lower) but not absolute value
(e.g., $5, 100 points, 10 minutes) is critical. A reference
point can move, and multiple reference points may
exist depending on a given situation. Diminishing
sensitivity further species the impact of relative value
by suggesting that the proportion of a dierence
is critical so that earning an additional $5 leads to
more pleasure when $10 was expected than when
$100 was expected (15:10 >105:100). The dierence
between $10 and $15 looms bigger psychologically
than the dierence between $100 and $105, although
the objective (absolute) dierence is the same. Loss
aversion suggests that people are more sensitive to
potential losses than gains. Prospect theory has been
extensively validated and used in economics, marketing,
and politics because it predicts how people actually
make a decision (Barberis, 2013). Kahneman was
awarded the 2002 Nobel Memorial Prize in Economics
for developing prospect theory, and Tversky would
surely have shared the prize if he had not passed away
in 1996 (Barberis, 2013). The 2017 Nobel Memorial
Prize winner in Economics, Richard Thaler, played a
pivotal role in applying prospect theory to economics
and establishing the eld of behavioral economics
(Kahneman, 2011).
Interestingly, Kahneman and Tversky took
advantage of basic principles of perception (e.g.,
Weber–Fechner law) to develop prospect theory. The
two psychologists drew from the understanding that
basic cognitive principles operate across the early
(perception) and later (decision-making) cognitive
stages. For example, Kahneman and Tversky (1979,
p. 277) stated:
An essential feature of the present theory is that the carriers
of value are changes in wealth or welfare, rather than nal
states. This assumption is compatible with basic principles
of perception and judgment. Our perceptual apparatus is
attuned to the evaluation of changes or dierences rather
than to the evaluation of absolute magnitudes. When we
respond to attributes such as brightness, loudness, or tem-
perature, the past and present context of experience denes
an adaptation level, or reference point, and stimuli are per-
ceived in relation to this reference point. Thus, an object
at a given temperature may be experienced as hot or cold
to the touch depending on the temperature to which one
has adapted. The same principle applies to non-sensory at-
tributes such as health, prestige, and wealth.
This statement, in line with Thaler (1980,1999),
indicates that prospect theory may extend to selective
attention. In addition, the probability weighting
function (another aspect of prospect theory) was
demonstrated to operate in selective attention (Vincent,
2011). Also, decision-making and selective attention
share a core concept of selection (Edwards, 1954;
Kahneman, 1973) and interact functionally (Desimone
& Duncan, 1995;Kim & Beck, 2020a;Krajbich et
al., 2010;Stewart et al., 2016;Wolfe, 1994). These
allude to the extendibility of reference dependence
and diminishing sensitivity to selective attention. In
line with this, Kim and Beck (2020b) demonstrated
that the reference dependence principle of prospect
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 3
theory is present in selective attention. In the current
study, we expand on this previous research to show
that diminishing sensitivity is also present in selective
attention.
Reference dependence in selective attention
Reference dependence suggests that the value of
an object is determined by a reference point of the
object. For example, when you have expected to gain
$1, receiving $10 will give rise to pleasure. When
you have expected to gain $20, receiving $10 will
lead to disappointment. The reference points of 1
and 20 determine the subjective value of 10. That is,
prospect theory suggests that relative value (high or low
compared with a reference point), not absolute value
($10), is critical to perceived value.
Kim and Beck (2020b) demonstrated that reference
dependence operates in selective attention by applying
the reference dependence principle to value-driven
attention. Value-driven attention (Anderson et al.,
2011;Bucker & Theeuwes, 2017;Chelazzi, Perlato,
Santandrea, & Della Libera, 2013;Della Libera &
Chelazzi, 2009;Hickey, Chelazzi, & Theeuwes, 2010;Le
Pelley, Pearson, Porter, Yee, & Luque, 2019;Mine &
Saiki, 2015;Roper, Vecera, & Vaidya, 2014) suggests
that more valuable stimuli are attended more. Therefore,
value-driven attention is useful for exploring whether
more valuable stimuli are attended more based on the
value function (the psychological principles) of prospect
theory. However, the classic paradigm of value-driven
attention (Anderson, 2016) is not sucient to test if the
value function of prospect theory applies to selective
attention.
The classic paradigm of value-driven attention
consists of training and test phases (e.g., Anderson et
al., 2011). In a typical training phase where associative
learning between color and reward occurs, search
targets are red and green. One of the two target colors
is randomly selected and presented among dierent
color stimuli on each trial. Therefore, both target
colors are potential targets on each trial during the
training phase. After locating the target (either red or
green), participants are asked to report whether the
orientation of a line inside the target is horizontal or
vertical by pressing a corresponding key. Response
time for pressing the key is measured. Reward is given
for a correct response. Critically, red is associated with
high reward and green with low reward (color–reward
associations counterbalanced across participants). In a
typical test phase, it is examined if the more valuable
color (red), previously associated with higher reward in
training, attracts attention more than the less valuable
color (green), previously associated with lower reward
in training. In the test phase, color is task-irrelevant
because search targets are unique shapes (e.g., a white
diamond among white circles, a white circle among
white diamonds). Critically, on some trials, one of
distractors is equiprobably either red or green. Despite
color being task-irrelevant and no reward being given in
the test phase, search is slower when red distractors, the
previously high-valued color in training, are presented
than when green distractors, the previously low-valued
color in training, are presented. The delay with the
high-valued compared with the low-valued color
distractors suggests that more valuable stimuli attract
attention more.
The results of the classic paradigm, however, cannot
demonstrate if the value-driven attention eect was due
to relative or absolute value (Anderson, 2016). The test
target colors (red and green) are reference points for
one another during the training phase in the classic
paradigm. Red is both absolutely and relatively high
compared with green, making the classic paradigm
unable to answer whether high-valued color distractors
capture attention more because they are associated with
a higher relative or absolute value.
Kim and Beck (2020b) demonstrated that reference
dependence operates in selective attention by modifying
the classic paradigm to allow for reference dependence
to be tested. Unlike in the typical value-driven attention
paradigm, in Kim and Beck (2020b) the test target
colors (red and green) had dierent reference points
in the training phase. For example, while target colors
were red and yellow in blocks 1, 3, and 5, they were
green and blue in blocks 2, 4, and 6. Then, in blocks 1,
3, and 5, rewards were given for only red and yellow
(never for green and blue), allowing red and yellow
to be one context and compared with each other. In
blocks 2, 4, and 6, rewards were given for only blue
and green (never for red and yellow), allowing green
and blue to be the other context and compared with
each other. Accordingly, red and green had yellow
and blue reference points, respectively, in the training.
This allowed for the independent manipulation of
the relative and absolute value of the test target color
(red and green). In the test phase, red and green
were presented as distractor colors like the classic
paradigm. Kim and Beck (2020b) found evidence that
the relatively high-valued color distractors delayed
search compared with the relatively low-valued color
distractors when the absolute value of the colors
was the same. However, the absolutely high-valued
color distractors did not delay the search compared
with the absolutely low-valued color distractors when
the relative value of the colors was the same. The
ndings suggest that more valuable stimuli receive
higher attentional priority due to relative but not
absolute value, and reference points play a critical role
in determining subjective value (reference dependence).
Reference dependence in selective attention (Kim &
Beck, 2020b) is consistent with Anderson’s assumption
that, “although never directly manipulated in a
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 4
single experiment, it has become clear that relative
or normalized value, rather than associated value in
an absolute sense, biases attention” (Anderson, 2016,
p. 28).
Diminishing sensitivity in selective attention
The present study extends on Kim and Beck
(2020b) by investigating what type of relative value is
critical in attentional priority: diminishing sensitivity
(proportional dierence) versus absolute dierence.
The diminishing sensitivity principle reects the basic
psychological principle of the Weber–Fechner law,
which states that people respond to changes in physical
stimuli by comparing the changed value to the original
value (Kahneman & Tversky, 1979); therefore, not only
is the relative value important, but the proportion of the
dierence between the value and the reference is also
critical (Thaler, 1980,1999). For example, it is easier
to notice the dierence of 1 kg between 1 kg and 2
kg than between 10 kg and 11 kg (Stevens, 1957). In
line with this, the value function of prospect theory
shows that the marginal impact of a change diminishes
with the distance from a regular reference point of 0
(Kahneman & Tversky, 1979). That is, diminishing
sensitivity explains that relative values (e.g., $2 is more
than $1; $11 is more than $10) are based on a relative
dierence ($2/$1 =2; $11/$10 =1.1) rather than an
absolute dierence ($2 – $1 =$1; $11 – $10 =$1).
The diminishing sensitivity principle has been
demonstrated empirically in judgment and decision-
making tasks. Thaler (1980) showed that $5 seems like
a lot to save on a $25 radio but not much on a $500
television. This is because the dierence between 20
and 25 looms larger than the dierence between 495
and 500, although the actual dierence is the same, $5.
This foundational nding was replicated in Tversky
and Kahneman (1981), where people were more
sensitive to the dierence between $10 and $15 than
between $120 and $125, indicating that the dierence
between 10 and 15 was psychologically larger than the
dierence between 120 and 125. However, according
to the economic theory, only absolute dierences
should matter (Tirole, 1988). This idea follows from
rational utility maximization and is an unchallenged
assumption in economic theory. Thus, in the example
of Tversky and Kahneman (1981), the economic theory
suggests that the impact of the dierences should be
psychologically similar because the absolute dierences
are the same (15 – 10 =125 – 120). In Kim and
Beck (2020b), absolute dierences and diminishing
sensitivity (proportional dierences) of rewards were
not controlled. Thus, the present study expands on
this previous research by examining if diminishing
sensitivity operates in selective attention by using
the empirical nding from decision making (Thaler,
1980;Tversky & Kahneman, 1981) and modifying the
value-driven attention paradigm (Kim & Beck, 2020b).
Experiment 1
In the training phase (see Figure 2), associative
learning occurs between color and reward. Test target
colors were paired with reference target colors within
particular context blocks to control reference points of
the test target colors. Across participants, the test target
colors (red or green), the reference target colors (yellow
or blue), and the block order (ABABAB or BABABA)
were fully counterbalanced. For ease of explanation,
we will provide an example of what a given participant
could have received. In this example, in blocks 1, 3,
and 5, one of two target colors (red and yellow) was
randomly chosen and presented on each trial. Red gave
100 points and yellow gave 1 point on each trial when
a correct response was made. Therefore, the 100-point
Figure 2. Examples of the training phase in Experiment 1.In
blocks 1, 3, and 5, search targets are yellow and red, giving 1
point and 100 points, respectively. In blocks 2, 4, and 6, search
targets are blue and green, giving 901 points and 1000 points,
respectively.
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 5
Figure 3. Examples of the test phase. Search targets are unique
shapes. On half of the trials, no color distractor was presented
(top). On the remaining half, one of distractors was
equiprobably either red (middle) or green (bottom). No reward
was given in the test phase.
color and the 1-point color were available targets in
the blocks. A reference point was set within each block
rather than between blocks because a reference point is
chosen among available alternatives (Elliott, Agnew,
& Deakin, 2008;Kahneman & Tversky, 1979;Thaler,
1980,1999;Tremblay & Schultz, 1999). In previous
experiments investigating relative value, availability was
a core factor in determining a reference point (Elliott
et al., 2008;Tremblay & Schultz, 1999). Therefore, the
1-point color became a reference point of the 100-point
color based on availability. In blocks 2, 4, and 6, one
of two target colors (green and blue) was randomly
presented on each trial. Green gave 1000 points, and
blue gave 901 points. Therefore, the 1000-point color
and the 901-point color were available targets in the
blocks, and the 901-point color became a reference
point of the 1000-point color. Across participants, test
target colors (red or green) were randomly assigned to
100 or 1000, reference target colors (yellow or blue)
were randomly assigned to 1 or 901, and blocks (1, 3,
and 5 or 2, 4, and 6) were randomly assigned between
high (1 point and 100 points) or low (901 and 1000
points) relative context for each participant.
In the test phase (see Figure 3), search targets
were dened by a unique shape; therefore, color was
task-irrelevant. Also, no reward was given in the test
phase. Critically, on some trials, one of distractors
was either red (which had been the 100-point color
with the reference point of the 1-point color in the
training phase) or green (which had been the 1000-point
color with the reference point of the 901-point color
in the training phase). According to the diminishing
sensitivity principle (Kahneman & Tversky, 1979;
Thaler, 1980), the dierence between 100 and 1 should
be psychologically bigger than the dierence between
1000 and 901, although the objective (absolute)
dierence is the same.
If the diminishing sensitivity principle operates
in selective attention, the 100-point color distractor
should attract attention more than the 1000-point color
distractor. Accordingly, search should be delayed more
when the 100-point color distractor is presented than
when the 1000-point color distractor is presented during
the test phase: the diminishing sensitivity hypothesis.In
contrast, if attentional selection operates according to
economic theory, the 1000-point color distractor and
the 100-point color distractor should attract attention
similarly because the absolute value dierences are the
same (1000 – 901 =100 – 1) in the training phase: the
absolute dierence hypothesis.
Method
Participants
Seventy-two undergraduate students with normal
or corrected-to-normal vision participated for course
credit (mean age =19.3 years; 51 female). G*Power
was used to calculate the needed sample size. We used
a power of 0.8 and an alpha of 0.05. To determine the
eect size, we looked to the Kim and Beck (2020b)
study, which had an eect size of 0.30 (Cohen’s d)
for the critical comparison between high-value and
low-value color distractors in the test phase. For the
current study, we predicted a small to medium eect
size because proportional dierences increased (from
18 times to 90 times) compared with Kim and Beck
(2020b). Therefore, we ran the G*Power test with an
eect size of 0.40 and found that a minimum sample
size was 52.
Apparatus and stimuli
Stimuli were presented on a 20-inch monitor. The
distance between the participants and the monitor
was approximately 60 cm but was not constrained.
Experiments were programmed and administered
using MATLAB (MathWorks, Natick, MA) and
Psychophysics Toolbox software.
In the training phase (see Figure 2), each trial
consisted of xation, search, blank, and feedback
displays. The background of the screen was black for
all displays. In the xation display, a white cross bar
was presented in the center of the screen. In the search
display, six circles (1.4° diameter each) were presented
around an invisible circle (5° radius). Inside a target
object, a horizontal or vertical white line was presented,
and inside each distractor object, a white line tilted 45°
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 6
to the left or right was presented. One of the six circles
was a target color (yellow, red, green, or blue), and the
others were distractor colors (orange, purple, aqua,
white, and gray). In the search display of the test phase
(see Figure 3), the search target was a unique shape: a
circle among diamonds or a diamond among circles.
On half of the trials, all of the objects were white. On
the other half of trials one of non-target objects was
equiprobably either red or green. In the search display
in the training and test phases, the xation cross was
removed considering that there is a close coupling of
covert attention and overt attention in value-driven
attention (Anderson & Yantis, 2012;Le Pelley et
al., 2019), and in a pilot test participants reported
fatigue due to a requirement to use covert attention
(eyes had to remain on the xation cross during
search).
Design
The experiment consisted of 720 training trials
followed by 384 test trials. The independent variable
is the value of the test target colors. In the training
phase, correct responses earned 100 and 1000 points for
the test target colors, red and green, respectively (the
reverse association for half of the participants). For the
reference target colors, correct response earned 1 point
and 901 points for blue and yellow, respectively (the
reverse association for half of the participants). The
100-point test target and the 1-point reference target
were presented in blocks 1, 3, and 5 (in blocks 2, 4,
and 6 for half of the participants). The 1000-point test
target and the 901-point reference target were presented
in blocks 2, 4, and 6 (in blocks 1, 3, and 5 for half of the
participants). Within each block, each of the two target
colors (one test target color and one reference target
color) were presented on 50% of trials. The test target
colors (red or green), the reference target colors (yellow
or blue), and the block order (ABABAB or BABABA)
were fully counterbalanced across the participants.
Therefore, stimuli dierences were controlled between
the 100-point and 1000-point test targets, and proximity
(the last [sixth] block of the training phase was the
1-point and 100-point color context for half of the
participants and the 901-point and 1000-point color
context for the other half) from the training to test
phase was controlled between the test targets. Critically,
the test targets had a dierent reference target in the
context blocks (Kim & Beck, 2020b).
Procedure
In the training phase, participants were instructed
to nd a circle with one of the two target colors and
report the orientation of the line inside the circle by
pressing the N-key for a horizontal line or M-key for a
vertical line as quickly and accurately as possible. On
each trial, the xation display was presented for 400
ms, followed by the search display until a response was
made. After the response, there was a blank display
for 50 ms and then a feedback display for 900 ms. In
the feedback display, earned points (e.g., +100) were
presented when a correct response was made within
1500 ms. For incorrect responses, “+0 (wrong)” was
presented. For correct but slow responses (over 1500
ms), “+0 (too slow)” was presented.
Participants rst completed 40 practice trials during
the training phase. In the rst 20 practice trials, the
target colors were the same as the target colors in
the rst, third, and fth training blocks (e.g., red and
yellow). In the second 20 practice trials, the target
colors were the same as those of second, fourth, and
sixth training blocks (e.g., green and blue). Before
each practice, oral and written instructions regarding
the target colors were provided. Before each of the
six training blocks, written instruction regarding the
target colors was provided. Participants were informed
that they would receive points when fast and correct
responses were made, and the experiment would
nish earlier as they received more points. However,
unbeknownst to the participants, earned points did
not aect the number of trials in the experiment.
Non-monetary rewards have shown to be eective in
guiding human behaviors such as attention (Beck,
Goldstein, van Lamsweerde, & Ericson, 2018;Kim
& Beck, 2020b) and decision-making (Tversky &
Kahneman, 1981).
The test phase followed immediately after the
training phase. Participants were instructed to search
for a unique shape (a circle among diamonds or a
diamond among circles) and report the orientation
of the line in the unique shape; therefore, color was
task-irrelevant. Also, they were informed that reward
points were not given in the test phase. The timing of
each screen and required response was the same as in
the training phase, but the search display was replaced
with the shape singleton search display. During 20
practice trials, an experimenter checked and conrmed
that participants understood the singleton shape
detection instructions. Then, 384 randomly ordered
test trials were given. On 96 of the 384 trials, one of
non-target objects was green. On another 96 trials, one
of the non-target objects was red. On the remaining 192
trials, all objects were white.
Results
The dependent variables are accuracy and response
time (RT) recorded from the onset of the search display.
Only correct responses were included in analyses of
RTs (incorrect trials: 4.1% in training phase and 6.9%
in test phase). Also, trials in which RT was shorter
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 7
than 150 ms (<0.01% in the training phase, <0.01%
in the test phase) or longer than 1500 ms (1.1% in the
training phase, 4.6% in the test phase) were excluded
from the analysis. The rst three trials of each block
in the training phase and of the test phase were also
excluded from the analysis to allow some time to change
the attentional control settings.
Training phase
Given that the 100-point color and 1000-point color
were presented separately in the 1-point and 100-point
context and the 901-point and 1000-point context,
respectively, we examined if the contexts inuenced
task performance in the training phase. Two-way
within-subject analyses of variance (ANOVAs) were
conducted on RT and accuracy with two within-subject
variables: context (the 1-point and 100-point context
and the 901-point and 1000-point context) and
within-block value (low-value colors, 1-point color and
901-point color, reference target colors; high value
colors, 100-point color and 1000-point color, test target
colors).
For RT, the main eect of within-block value was
not signicant: low (M=674 ms, SE =8ms)and
high (M=678 ms, SE =9ms),F(1, 71) =2.01, p
=0.16, η2
p=0.028. The main eect of context was
also not signicant: the 1-point and 100-point context
block (M=681 ms, SE =9 ms) and the 901-point and
1000-point context block (M=673 ms, SE =9ms),
F(1, 71) =2.82, p=0.097, η2
p=0.038. The interaction
between context and within-block value was also not
signicant, F(1, 71) =0.96, p=0.33, η2
p=0.013. For
accuracy, the main eect of the within-block value was
signicant: low (M=95.5%, SE =0.3%) and high
(M=95.9%, SE =0.3%), F(1, 71) =4.74, p=0.033,
η2
p=0.063. However, given the direction of the RT
dierence, the main eect of within-block value seemed
to be due to a speed–accuracy tradeo. Therefore,
inverse eciency scores (RT/accuracy) were used as a
dependent variable. No dierence in inverse eciency
scores between low (M=708, SE =9) and high (M
=709, SE =9) color was found, t(71) =0.2, p=
0.84, suggesting that the main eect of within-block
value resulted from a speed–accuracy tradeo. The
main eect of context was also signicant: the 1-point
and 100-point context (M=95.4%, SE =0.3%) and
the 901-point and 1000-point context (M=95.9%,
SE =0.3%), F(1, 71) =5.42, p=0.023, η2
p=0.072.
Higher accuracy for the 901-point and 1000-point
colors may be due to participants being more motivated
in the 901-point and 1000-point context. If this
carried over to the test phase, it would increase the
strength of the 1000-point color distractor attracting
attention compared to the 100-point color distractor
attracting attention in the test phase. Note that the
direction of this carry-over eect is the opposite of the
prediction of the diminishing sensitivity hypothesis. The
interaction between the context and the within-block
value was not signicant, F(1, 71) =1.26, p=0.27,
η2
p=0.017.
To directly see whether an individual’s facilitation
dierence between the contexts in the training phase
inuenced an individual’s value-driven attention eect
in the test phase, correlation analyses were conducted
between a facilitation dierence and a value-driven
attention eect. A facilitation dierence was calculated
by subtracting RT, accuracy, and inverse eciency
for the 1000-point and 901-point context from RT,
accuracy, and inverse eciency for the 100-point and
1-point context. A value-driven attention eect was
calculated by subtracting RT for 1000-point color
distractor presence from RT for 100-point color
distractor presence. No signicant correlations were
found: r(70) =–0.181, p=0.128 for RT; r(70) =
–0.187, p=0.117 for accuracy; and r(70) =–0.180, p=
0.131 for inverse eciency. The results indicated that a
facilitation dierence between the 1-point and 100-point
color context and the 901-point and 1000-point color
context did not inuence a value-driven attention eect
of the 100-point color distractor and the 1000-point
color distractor.
To examine whether value-driven attention operates
based on proportional dierence or absolute dierence,
the 100-point target color and the 1000-point target
color were compared on RT and accuracy. Mean
accuracy during the training phase was not dierent
between when the target was the 100-point (compared
to 1-point) color (M=95.7%, SE =0.3%) and the
1000-point (compared to 901-point) color (M=96.0%,
SE =0.3%), t(71) =0.96, p=0.34, d=0.11. Mean
RT was not dierent between when the target was the
100-point (compared to 1-point) color (M=684 ms, SE
=9 ms) and the 1000-point (compared to 901-point)
color (M=673 ms, SE =10 ms), t(71) =1.90, p=
0.062, d=0.22. This lack of dierences is typical in the
training phase of the value-driven attention paradigm,
not a lack of dierence in learned associated value
between the colors (e.g., Anderson, 2015;Anderson et
al., 2011;Anderson, Leal, Hall, Yassa, & Yantis, 2014;
Kim & Beck, 2020b;Roper et al., 2014;Wang, Yu, &
Zhou, 2013).
Test phase
One-way within-subject ANOVAs on mean RT and
mean accuracy were conducted to explore how the
three distractor conditions (100-point color distractors,
1000-point color distractors, no color distractors)
inuenced search.
For accuracy, the main eect of distractor condition
was not signicant, F(2, 142) =2.55, p=0.08,
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 8
Figure 4. Response times of the test phase in Experiment 1.
Error bars represent standard error of the mean (*p<0.05;
**p<0.001).
η2
p=0.035: 100-point color distractors condition (M
=92.9%, SE =0.5%), 1000-point color distractors
condition (M=93.1%, SE =0.4%), and no color
distractors condition (M=93.8%, SE =0.4%).
For RT, there was a main eect of type of distractor,
F(2, 142) =193.91, p<0.001, η2
p=0.73 (see Figure 4).
Planned comparisons revealed that mean RT was slower
when the 100-point color distractors (M=865 ms, SE
=10 ms) were presented than when no color singleton
distractors (M=783 ms, SE =9 ms) were presented,
t(71) =18.21, p<0.001. Mean RT was slower when
the 1000-point color distractors (M=855 ms, SE =10
ms) were presented than when no color distractors were
presented (M=783 ms, SE =10 ms), t(71) =15.61, p<
0.001. These slower RTs reect that the singleton color
distractors slow processing of the target due to the
distractors’ physical saliency and associated rewards.
Most importantly, mean RT was slower when the
100-point color distractors were presented than when
the 1000-point color distractors were presented, t(71) =
2.27, p=0.026, d=0.27 (see Figure 4). The ndings
imply that the 100-point color distractor attracted
attention more than the 1000-point color distractor.
This delay is further supported by Bayes analysis
(Cauchy of 0.5): Bayes factor (BF+0)=3.54 (van
Doorn et al., 2020) indicated that the prediction of
more attention to the 100-point (compared to 1-point)
color distractor than the 1000-point (compared to
901-point) color distractor was 3.54 times more favored
than the null.
Discussion
Search was delayed more so when the 100-point color
distractors were presented than when the 1000-point
color distractors were presented, suggesting that the
100-point color distractors attracted attention more
than the 1000-point color distractors. This nding is
in line with the previous ndings of Thaler (1980)
and Tversky and Kahneman (1981). The proportional
distance between 100 and its reference point 1 is
larger than the proportional distance between 1000
and its reference point 901 (100:1 >1000:901).
Therefore, according to diminishing sensitivity, the
dierence between 1 and 100 should loom larger than
the dierence between 901 and 1000, although the
absolute dierence (100 – 1 =1000 – 901) was the same.
Accordingly, the results of Experiment 1 demonstrated
that the diminishing sensitivity principle of prospect
theory operates in selective attention.
In this experiment, the absolute dierence hypothesis
predicted comparable attraction eects between the
100-point and 1000-point color distractors. Therefore,
it was possible that the nding that the 100-point color
distractor attracted attention more than the 1000-point
color distractor was due to both the proportional
dierence eect (the 100-point than the 1000-point
color distractors attract more attention) and absolute
dierence eect (the 100-point and the 1000-point color
distractors attract attention with similar strength).
Experiment 2 resolves the question by varying the
absolute dierences while controlling the impact of the
diminishing sensitivity (proportional dierence).
Experiment 2
Experiment 2 investigated whether the absolute
dierence or the proportion inuences attentional
selection when the impact of diminishing sensitivity is
controlled. In line with the ndings of more sensitivity
to the dierence between $10 and $15 than $120 and
$125 (Tversky & Kahneman, 1981) and more sensitivity
to the dierence between $20 and $25 than $495 and
$500 (Thaler, 1980), Tversky and Kahneman (1981)
and Thaler (1980) suggested that the eort to save $5
on a $50 purchase would be similar to the eort to save
$15 on a $150 purchase due to the same proportion
(50:5 =150:15). In addition, Kahneman and Tversky
(1982) suggested that the amount of money required
for someone to forego a 50% chance of winning $100,
$200, $500, $1000, and $2000 are roughly proportional
to the size of the bet. For example, to forego a 50%
chance of $100 someone would need roughly $35, and
to forego a $1000 bet that person would need roughly
$350. Thus, as the size of the stake has increased by
a factor of 10, the amount needed to forego the bet
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 9
increases by almost the same factor. The proportional
nature of value seen in prospect theory is consistent
with the Weber–Fechner law (Thaler, 1980,1999). These
ndings regarding the same proportion (Kahneman
& Tversky, 1982;Thaler, 1980;Tversky & Kahneman,
1981) were subsequently veried in various scenarios
empirically and computationally in monetary and
non-monetary domains (Azar, 2011;González-Vallejo,
Harman, Mullet, & Sastre, 2012; for a review, see
González-Vallejo, 2002).
In Experiment 2, in the training phase, the 100-point
and 1000-point target colors have 1-point and 10-point
reference target colors, respectively. The ratio between
1 and 100 is the same as the ratio between 10 and 1000
(100:1 =1000:10), allowing control of the impact of
diminishing sensitivity between the 100-point and the
1000-point color distractors. Thus, if the ratios but
not the absolute dierences matter, the strength of
attention to the 100-point color distractors should be
similar to the strength of attention to the 1000-point
color distractors. However, the absolute dierence is
larger for the 1000-point distractor (990) versus the
100-point distractor (99). Thus, if absolute dierences
inuence attentional selection, then the 1000-point
color distractor should attract attention more than the
100-point color distractor, delaying search time when
the 1000-point color distractors are present compared
to when the 100-point color distractors are present.
Method
Participants
Based on the same power analysis for Experiment
1, 72 undergraduate students (mean age =19.5 years;
61 females) with normal or corrected-to-normal vision
participated for course credit.
Apparatus, stimuli, design, and procedure
The apparatus, stimuli, design, and procedure were
identical to those for Experiment 1. The only dierence
was the reward point allocation during training. In the
training phase, the 100-point and 1000-point test color
targets had the 1-point and 10-point reference color
targets, respectively. Like Experiment 1, the test target
colors (red or green), the reference target colors (yellow
or blue), and the block order were fully counterbalanced
across the participants. Thus, both stimuli dierences
and proximity from the training to test phase were
controlled between the test target colors.
Results
As in Experiment 1, incorrect trials (5.4% in the
training phase, 8.5% in the test phase), trials in which
RT was shorter than 150 ms (<0.01% in the training
phase, <0.01% in the test phase), and trials in which RT
was longer than 1500 ms (1.2% in the training phase,
5.0% in the test phase) were excluded from the analysis.
Training phase
To check if the contexts inuenced task performance
in the training phase, two-way within-subject ANOVAs
were conducted on RT and accuracy with two
within-subject variables: context (the 1-point and
100-point context and the 901-point and 1000-point
context) and the within-block value, either low-value
color (1-point color and 901-point color, reference
target colors) or high-value color (100-point color and
1000-point color, test target colors).
For RT, the main eect of the context was not
signicant: 1-point and 100-point context (M=685 ms,
SE =9 ms) and the 901-point and 1000-point context
(M=678 ms, SE =9ms),F(1, 71) =1.99, p=0.16,
η2
p=0.027. The main eect of the within-block value
was also not signicant: low (M=683 ms, SE =8ms)
andhigh(M=680 ms, SE =9ms),F(1, 71) =1.20, p
=0.28, η2
p=0.017. The interaction between the context
and the within-block value was also not signicant, F(1,
71) =0.31, p=0.58, η2
p<0.01. For accuracy, the main
eect of the context was not signicant: 1-point and
100-point context (M=94.3%, SE =0.5%) and the
901-point and 1000-point context (M=94.4%, SE =
0.5%), F(1, 71) =0.2, p=0.65, η2
p<0.01. However, the
main eect of the within-block value was signicant:
low (M=94.1%, SE =0.5%) and high (M=94.5%, SE
=0.5%), F(1, 71) =5.01, p=0.028, η2
p=0.066. This
higher performance in the high (test target colors) than
in the low (reference target colors) color would be due
to visually dierent colors used and/or the high colors
being more valuable than the low color. The interaction
between the context and the within-block value was not
signicant, F(1, 71) =0.4, p=0.53, η2
p<0.01.
To check whether an individual’s facilitation
dierence between the contexts inuenced an
individual’s value-driven attention eect in the test
phase, correlation analyses were conducted between
a facilitation dierence and a value-driven attention
eect. No signicant correlations were found: r(70) =
0.049, p=0.68 for RT, r(70) =–0.026, p=0.83 for
accuracy; r(70) =0.051, p=0.67 for inverse eciency.
These results suggest that a facilitation dierence did
not inuence the value-driven attention eect.
To examine whether value-driven attention operates
based on proportional dierence or absolute dierence,
the 100-point target color and the 1000-point target
color were compared on RT and accuracy. Mean
accuracy was not dierent between when the target
color was the 100-point color (compared with 1-point,
M=93.1%, SE =0.4%) or the 1000-point color
(compared with 10-point, M=93.8%, SE =0.4%),
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 10
Figure 5. Response times of the test phase in Experiment 2.
Error bars represent standard error of the mean. (**p<0.001;
ns, p>0.05).
t(71) =0.76, p=0.45, d=0.09. Mean RT was not
dierent between when the target color was a 100-point
color (M=684 ms, SE =9 ms) or a 1000-point color (M
=676 ms, SE =9ms),t(71) =1.42, p=0.17, d=0.17.
Test phase
As in Experiment 1, one-way within-subject
ANOVAs on mean RT and mean accuracy were
conducted to explore how the three distractors (100-
point color distractors, 1000-point color distractors, no
color distractors) inuenced search.
For accuracy, the type of distractor aected search
accuracy, F(2,142) =7.0, p=0.001, η2
p=0.09. Post
hoc comparisons revealed that accuracy was lower
when the 100-point distractors appeared (M=91.1%,
SE =0.6%) than when no distractors appeared (M=
92.7%, SE =0.5%), t(71) =4.16, p<0.001. Accuracy
was lower when the 1000-point distractors appeared
(M=91.7%, SE =0.6%) than when no distractors
appeared (M=92.7%, SE =0.5%), t(71) =2.42, p=
0.018. Mean accuracy was not dierent between when
the 100-point distractors appeared (M=91.1%, SE =
0.6%) and when the 1000-point distractors appeared (M
=91.7%, SE =0.6%), t(71) =1.11, p=0.27, d=0.13.
For RT (see Figure 5), the type of distractor
inuenced RT of search, F(2, 142) =192.2, p<0.001,
η2
p=0.73. Planned comparisons revealed that mean
RT was slower when the 100-point color distractors
were presented (M=867 ms, SE =10 ms) than when
no color distractors were presented (M=789 ms,
SE =9ms),t(71) =18.44, p<0.001. Mean RT was
slower when the 1000-point color distractors were
presented (M=865 ms, SE =10 ms) than when no
color distractors were presented (M=789 ms, SE =9
ms), t(71) =16.04, p<0.001. Importantly, mean RT
was not dierent between when the 100-point color
distractors were presented (M=867 ms, SE =10
ms) and when the 1000-point color distractors were
presented (M=865 ms, SE =10 ms), t(71) =0.61,
p=0.54, d=0.07 (see Figure 5), suggesting that the
strength of the attention to the two color distractors
was similar. In addition, the Bayes factor (BF0–)=8.24
(van Doorn et al., 2020) indicates that the prediction
of more attention to the 1000-point color distractor
than the 100-point color distractor was 8.24 times less
favoredthanthenull.
Discussion
In this experiment, the impact of the diminishing
sensitivity was controlled because the ratio between
1 and 100 and the ratio between 10 and 1000 were
the same (Kahneman & Tversky, 1982;Thaler, 1980;
Tversky & Kahneman, 1981). For the 1000-point
distractor, the absolute dierence from the training
reference was higher than for the 100-point distractor.
However, search speed was not delayed when the
1000-point color distractors were presented compared
with when the 100-point color distractors were
presented. This nding suggests that the attentional
capture eect of the distractors was little inuenced by
absolute value dierences, inconsistent with economic
theory.
General discussion
The current study examined whether value-driven
attention reects proportional dierences or absolute
dierences by applying the foundational ndings for
diminishing sensitivity from Thaler (1980) and Tversky
and Kahneman (1981) in the modied value-driven
attention paradigm (Kim & Beck, 2020b). Experiment
1showed that when the 100-point color and the
1000-point color were compared to the 1-point color
and the 901-point color, respectively, in the training
phase, the 100-point color distractors attracted
attention more than the 1000-point color distractors
in the test phase. The results of Experiment 1 are
consistent with previous ndings (Thaler, 1980;Tversky
& Kahneman, 1981) indicating that the dierence
between $20 and $25 is psychologically larger than the
dierence between $495 and $500 although the absolute
dierence is the same (Thaler, 1980). Therefore, the
former comparison (between 20 and 25) inuenced
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 11
decision-making more than the latter comparison
(between 495 and 500). In line with this, in Experiment
1, although the absolute dierence between 1 and 100
was the same as the absolute dierence between 901 and
1000, the psychological dierence was larger between 1
and 100 than 901 and 1000. The former comparison
(between 1 and 100) inuenced selective attention more
than the latter comparison (between 901 and 1000);
accordingly, the 100-point color distractors attracted
attention more than the 1000-point color distractors,
demonstrating the diminishing sensitivity principle in
selective attention.
In Experiment 2, the 100-point color and 1000-point
color were compared to the 1-point color and 10-point
color, respectively, in the training phase. Therefore, the
diminishing sensitivity impacts were controlled; for
example, the psychological value dierence between
5 and 50 is similar to that between 15 and 150 due to
the same proportion (Thaler, 1980,1999). Also, the
absolute value and the absolute value dierence were
highest for the 1000-point color distractor. However,
the 1000-point color distractor did not attract attention
more so than the 100-point color distractor in the
test phase. This, combined with the results from
Experiment 1, further supports the conclusion that
absolute value and the absolute dierence are not used
to prioritize stimuli for selective attention. Additionally,
Experiment 2 reduces the possibility that the 100-point
color distractor attracting attention more than the
1000-point color distractor in Experiment 1 is due to
using the specic high relative value of 100 and specic
low relative value of 1000 rather than diminishing
sensitivity. If so, the 100-point color distractor should
have attracted attention more than the 1000-point color
distractor in Experiment 2.
Implications for prospect theory
Previous studies (Kim & Beck, 2020b;Vincent, 2011)
have shown that prospect theory extends to selective
attention. Kim and Beck (2020b) demonstrated that
reference dependence operates in selective attention.
The authors found that the value aecting the
allocation of attention relied on a reference point of
the value, suggesting that relative value, not absolute
value, is critical in selective attention. Vincent (2011)
demonstrated that the weighting function is applied
in selective attention. According to the weighting
function (Kahneman & Tversky, 1979), decision-makers
overweight low expectation levels and underweight high
expectation levels. Vincent (2011) found that, during
visual search, participants overweighted the probability
of a search target appearing at a particular location
when the probability was low but underweighted the
probability when it was high. The patterns of the bias
t with the weighting function of prospect theory. In
line with the previous studies, the current study further
shows the extendibility of prospect theory to selective
attention.
The current study demonstrated that the foundational
ndings for the diminishing sensitivity principle from
Kahneman and Tversky (1979) and Thaler (1980) are
also found with the modied value-driven attention
paradigm (Kim & Beck, 2020b), suggesting that the
diminishing sensitivity principle operates in selective
attention. Also, the value of the reference target color
played a critical role in determining the value of the
test color, suggesting that the reference dependence
principle operates in selective attention (Kim & Beck,
2020b). These ndings suggest that prospect theory
extends to selective attention.
Implications for value-driven attention
literature
The previous studies using the classic value-driven
attention paradigm showed that a more valuable item
attracts more attention (for reviews, see Anderson,
2016;Failing & Theeuwes, 2018;Rusz, Le Pelley,
Kompier, Mait, & Bijleveld, 2020). These ndings are
in line with the reference dependence and diminishing
sensitivity principles. In the classic paradigm (e.g.,
Anderson et al., 2011), associative learning between
stimuli and reward for both a more valuable stimulus
and a less valuable stimulus occurs in the same context
(e.g., a training phase); accordingly, the two stimuli
become reference points for one another. For example,
when the more and less valuable stimuli are associated
with 15 and 10 points, respectively, the objective values
of the two will be subjectively represented as shown
in Figure 1. Therefore, the ndings in the previous
literature indicating that the more valuable stimulus
(15) attracts attention more than the less valuable
stimulus (10) do not violate the reference dependence
and diminishing sensitivity principles.
The previous ndings with the classic paradigm,
however, could not demonstrate the principles of
prospect theory. In the classic paradigm, the more
valuable stimulus (15) is both absolutely and relatively
high-valued compared to the less valuable one (10)
because they are reference points for one another.
Therefore, it is unclear if relative value (reference
dependent), not absolute value, is critical to capturing
attention (Anderson, 2016). Also, it is unclear if the
psychological sensitivity is constant (linear), increases
(convex), or decreases (concave) as objective value
increases. Therefore, although the previous ndings
in the classic paradigm are in line with the principles
of prospect theory, it was unknown whether the
psychological value is distorted according to the
principles of prospect theory.
In the value-driven attention literature, the lack of
exploration as to whether the value of items is distorted
by the psychological principles in prospect theory may
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 12
be because prospect theory was researched largely in
the economic literature (e.g., behavioral economics)
(Barberis, 2013); Kahneman and Thaler received
Nobel Prizes in economics. Moreover, most studies in
the value-driven attention literature used the classic
value-driven attention paradigm, which does not allow
the independent manipulation of relative and absolute
value (Anderson, 2016). However, reference dependence
plays a critical role (e.g., each item having separate
reference points) in testing the diminishing sensitivity
principle, such as in Kahneman and Tversky (1982),
Tversky and Kahneman (1981),Thaler (1980),andthe
current study. The present study used the modied
value-driven attention paradigm (Kim & Beck, 2020b)
because a reference point of each stimulus (color) can
be manipulated independently, as in Kahneman and
Tversky (1982),Tversky and Kahneman (1981),and
Thaler (1980). Thus, the use of the modied paradigm
allowed verication that value-driven attention occurs
on the basis of reference dependence and diminishing
sensitivity.
Inuences of top–down task goals (Bacon & Egeth,
1994) and bottom–up physical saliency (Theeuwes,
1992) on attentional allocation were controlled between
two test target colors to measure value-driven attention
eects in the present study, as was done in previous
value-driven attention research (Anderson et al.,
2011). In the training, the test target colors (red and
green), reference target colors (yellow and blue),
and block order (ABABAB and BABABA) were
fully counterbalanced across participants. Therefore,
top–down task goals and bottom–up physical saliency
could not account for the value-driven attention eect
in the present study, allowing for the test of diminishing
sensitivity on value-driven attention.
An important methodological component of the
current design is the ability to establish dierent
reference points within blocks during training. This
method is consistent with previous diminishing
sensitivity research (Azar, 2011;Kahneman & Tversky,
1982;Thaler, 1980;Tversky & Kahneman, 1981),
value-driven attention research (Anderson, 2015), and
neuroimaging research on relative value (Elliott et al.,
2008;Tremblay & Schultz, 1999). In the current study,
participants were explicitly informed of two target
colors in each context in the training. For example,
participants would know that target colors were red
andyellowincontext1andgreenandblueincontext
2. Therefore, the reference point for red became yellow,
which was an available color in context 1, rather than
green and blue which were available colors only in
context 2. The 100-point stimulus had a reference
point of a 1-point stimulus because these stimuli
(but not the 901-point nor 1000-point stimuli) were
available targets in the 1-point and 100-point block. A
reference point was established within a block rather
than between blocks because a reference point is made
among concurrently available alternatives (Kahneman
& Tversky, 1979;Tremblay & Schultz, 1999). The
exibility of changing a reference point (Kahneman &
Tversky, 1979) is consistent with the ndings of a prior
behavioral study (Anderson, 2015) and imaging studies
(Elliott et al., 2008;Tremblay & Schultz, 1999). For
example, value-driven attention relies on the context in
which the stimuli–reward associative learning occurs,
indicating context dependence of value-driven attention
(Anderson, 2015). Brain imaging studies have shown
that reference points can be set within a single trial
based on available alternatives, indicating that reference
points can be adjusted rapidly and exibly (Elliott et
al., 2008;Tremblay & Schultz, 1999). Furthermore,
data in the current study also supports little inuence
of between-block reference points. If reference points
were set between blocks, 1000-point color distractors
should have attracted more attention than the 100-point
color distractors in Experiments 1 and 2. Therefore,
consistent with Kim and Beck (2020b) the current study
oers support for the ability to establish two separate
value-driven contexts within a training session.
The present study extends on the research of Kim
and Beck (2020b) by showing the importance of
relative dierence rather than absolute dierence in
value-driven attention. In Experiment 2 in Kim and
Beck (2020b), the relative value of two test colors
was higher, as in Experiment 1 in the current study.
However, in the previous experiment, the relative
dierence (7.6 vs. 1.9) and absolute dierence (39.2
vs. 45) of the two higher values were both not
systematically controlled. In Experiment 1 of the
current study, the absolute dierence (99 vs. 99) was
the same, whereas the relative dierence varied (100 vs.
1.1). However, given the current nding that relative
dierence rather than absolute dierence is critical,
in Kim and Beck (2020b) the 7.6 relative dierence
color might attract attention to some degree more
than the 1.9 relative dierence color regardless of
absolute dierence. Although value-driven attention
eects of the two colors were not statistically dierent,
the direction of the slight numerical dierence was
consistent with the proportional dierence hypothesis.
The lack of evidence might be because the magnitude
of the dierence of relative dierences (7.6 vs. 1.9) was
insucient to produce dierent value-driven attention
eects. However, the eect was evident in the current
study when the magnitude of the dierence of two
relative dierences (100 vs. 1.1) was much larger.
The current study used a non-probabilistic schedule
(e.g., always 100 points for red targets) unlike Kim
and Beck (2020b), where a probabilistic schedule was
used, such as 110 points (50%) or 90 points (50%)
for red targets. A non-probabilistic schedule was
used here because the expected value (absolute value)
was 100 points regardless of task performance in
the non-probabilistic schedule, whereas it might be
slightly higher or lower than 100 points depending
on task performance in the probabilistic schedule.
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 13
Therefore, the non-probabilistic schedule was applied
to strengthen the manipulation.
Limitations
Reaction time from manual responses is commonly
used to measure the attentional capture of singleton
distractors (Anderson, 2016;Anderson et al., 2011).
However, it is possible that the reaction time reects
costs other than attention capture (cf. Folk, 2013;
Folk & Remington, 1998). A more sensitive measure
could be used to further validate the current ndings.
For example, further research using eye-tracking
(Le Pelley, Pearson, Griths, & Beesley, 2015)and
electrophysiological approaches (Hickey, McDonald, &
Theeuwes, 2006;seealsoMcDonald, Green, Jannati, &
Di Lollo, 2013) would help specify the nature of the
diminishing sensitivity of the value-driven attention
eect.
Using eye movements to measure the impact of
diminishing sensitivity on value-driven attention will
be important to further validate the current ndings.
In the current study, we measured the eect using RT,
and the eect in Experiment 1 was small (d=0.27). It
was too small to nd an interaction across Experiments
1and2,F(1, 142) =1.32, p=0.25, η2
p=0.01, without
increasing the sample size substantially (G*Power
estimates that a sample size of 782 is needed). Given
that the eect is small when measuring RT, measuring
eye movements may be more suitable for a sensitive
measure of the eect (Anderson & Kim, 2019). Eye
movements are a more direct measure of attention than
RT which is aected by factors other than attention
(e.g., Goldstein & Beck, 2018;Zelinsky & Sheinberg,
1995). For example, the time to rst xate the distractor
with the higher relative value should be shorter and/or
the frequency of xating the distractor should be
higher.
The ndings of the present study provide behavioral
evidence supporting the diminishing sensitivity
principle (proportional dierences) rather than linear
function (absolute dierences). However, exact metrics
of the diminishing sensitivity value function cannot
be described with only the behavioral evidence from
the two relative values tested here. Therefore, further
behavioral evidence and computational research
should be conducted to identify exact metrics of the
diminishing sensitivity.
Future directions
Prospect theory has contributed considerably to
understanding the actual decision-making processes in
various elds of society, such as marketing, economics,
and politics: mental accounting (Thaler, 1999)and
nudge eects (Thaler & Sunstein, 2009). Accordingly,
the investigation of whether prospect theory extends to
dierent cognitive stages could be similarly impactful.
In line with this, the outstanding question is how the
loss aversion principle is applied in selective attention.
Although there are value-driven attention studies
(Barbaro, Peelen, & Hickey, 2017;Wentura, Müller, &
Rothermund, 2014) where reward-associated stimuli
and loss-associated stimuli are presented, they have
failed to nd evidence that loss-associated stimuli attract
attention more than reward-associated stimuli. This
may be because loss and reward are based on dierent
cognitive systems due to their being linked to negative
and positive emotional states, respectively (Breiter,
Aharon, Kahneman, Dale, & Shizgal, 2001). Thus, an
attentional priority of loss- over reward-associated
stimuli may not be general; instead, it may require
certain task situations. Accordingly, whether the loss
aversion principle is applied in selective attention may
have to be further investigated in other attention task
paradigms.
Another outstanding question is how the mechanism
of selective attention reecting the principles of
prospect theory is implemented in the brain. One
potential way involves the dopamine system in the
midbrain. The dopaminergic processes modulate the
attentional allocation to reward-associated stimuli
(Anderson, 2017,2019;Anderson et al., 2016,2017;
Hickey et al., 2010) and reect subjective value in
decision-making (Lak, Stauer, & Schultz, 2014;Tre p el ,
Fox, & Poldrack, 2005). That is, the dopaminergic
system is closely associated with value-driven attention
and value-based decision making and could provide
further insight.
Conclusion
The current study showed reference dependence and
diminishing sensitivity in selective attention. This aligns
with the idea that the basic principles of perception
are the foundation of prospect theory (Kahneman &
Tversky, 1979;Thaler, 1980,1999). Furthermore, the
present study contributes to the understanding of the
mechanism of value-driven attention, showing that the
psychological value reecting reference dependence and
diminishing sensitivity, not objective value, plays a role
in attentional allocation.
Keywords: prospect theory, diminishing sensitivity,
reference dependence, value-driven attention, selective
attention
Acknowledgments
Supported by the Basic Science Research Program
through the National Research Foundation of
Korea funded by the Ministry of Education (NRF-
Journal of Vision (2022) 22(1):12, 1–16 Kim, Harman, & Beck 14
2021R1I1A1A01055027). Open Practices Statement:
The data and the preregistered document (As-Predicted)
for Experiments 1 and 2 are available at the Center for
Open Science (https://osf.io/bg2mh/).
Commercial relationships: none.
Corresponding author: Sunghyun Kim.
Email: sunghyunk58@gmail.com.
Address: School of Psychology, Korea University,
Seoul, Korea.
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