







































52

Effects of Anxiety on Attention-Based Tasks in a 
College Population

Jacob DeSoto1,2 & Jessica Gaby2

1Department of Psychology, Western Carolina University 
2Department of Psychology, Middle Tennessee State University

 The current study examines the potential rela-
tionship between anxiety as measured by the State-
Trait Anxiety Inventory (STAI) and performance on 
the Navon and Stroop tasks, reflecting different as-
pects of attention that are crucial for academic success. 
These cognitive tasks measure different aspects of at-
tention, including visual global and local processing, 
and executive inhibition (Miyake et al., 2000; Navon, 
1977; Stroop, 1935). We measured two types of anx-
iety: trait anxiety and state anxiety. Trait anxiety can 
be defined as the general level of anxiety a person feels 
on an everyday basis, whereas state anxiety is the lev-
el of anxiety they feel in the current moment (Shil-
ton et al., 2019; Spielberger et al., 1983). We aimed 
to examine how anxiety may impact a person’s atten-
tion, executive functioning, and visual processing. If 
there is indeed a difference in performance based on 
anxiety, this could affect future treatment consider-
ations for college students with anxiety diagnoses. 
 We hypothesized that individuals with high levels 
of trait anxiety would have a local processing bias in 
the Navon task, specifically demonstrated by slower 
reaction times on global processing than participants 
with low trait anxiety for the Navon task. Addition-
ally, we hypothesized that participants with high trait 
anxiety would have faster reaction times on local pro-
cessing than participants with low trait anxiety. For 
the Stroop task, we hypothesized that individuals 
high in state anxiety, those high in trait anxiety, and 
those who score high in both types of anxiety would 
have slower reaction times on the incongruent trials 

of the Stroop task than those low in both types of 
anxiety. This study further explores the relationship 
that may exist between anxiety and interference in 
each of these tasks, leading to a better understanding 
of how state and trait anxiety impact performance 
on attention-based tasks in a college population. 
Anxiety in College Students
 College can be an emotional experience for stu-
dents, with the potential for many first-time, unfamil-
iar experiences. While many of these experiences may 
be enjoyable and eye-opening, some aspects of college, 
such as academic expectations or financial concerns, 
may lead individuals to experience high stress and anx-
iety levels. In one study, 40% of undergraduate univer-
sity students displayed anxiety symptoms (Beiter et al., 
2015). Asher BlackDeer et al. (2021) collected a sample 
of 117,430 students from over 100 college institutions 
and found that 9.2% of the overall sample displayed 
symptoms of anxiety. These studies demonstrate how 
prevalent anxiety can be in college students across the 
United States and suggest that pressures regarding 
academic performance may be a contributing factor 
(e.g., Asher BlackDeer et al., 2021; Beiter et al., 2015). 
 Research frequently suggests an impact of 
state-level anxiety on performance in specific contexts, 
such as test anxiety or statistics anxiety (e.g., Cassady & 
Johnson, 2002; Chew & Dillon, 2014; Hoegler & Nel-
son, 2018). More broadly, literature on trait anxiety 
suggests an inconsistent impact on academic perfor-
mance. For example, Vitasari et al. (2010) found that 
in university engineering students, there was a small 

Previous literature suggests that trait anxiety may lead to diminished global processing, and therefore, a local pro-
cessing bias (Basso et al., 1996), which may contribute to a narrowed scope of attention and impaired cognitive 
flexibility. Additionally, there is conflicting data on how anxiety interacts with performance on the Stroop task 
(e.g., Ursache & Cybele Raver, 2014). To understand this relationship, the authors used the State-Trait Anxiety 
Inventory (STAI) to divide participants into groups based on their levels of anxiety. Specifically, the researchers 
explored the effects of state and trait anxiety on college students’ attention using the Navon task and the Stroop 
task. The Navon task was used to compare the performance of people with high and low trait anxiety, utilizing two 
t-tests to analyze local and global processing. Four groups were created for the Stroop task: high trait/low state, 
low state/high trait, high trait/high state, and low state/low trait, which were compared through an ANOVA. 
No statistically significant differences were found in performance on the Stroop and Navon tasks based on state 
or trait anxiety. This may be due to the age range of participants and the lack of clinical elevation of these factors. 
The findings suggest that moderate levels of anxiety may not impact attention drastically in a college population.
 Keywords: anxiety, attention, college, Stroop task, Navon task

Graduate Student Journal of Psychology
Spring 2025 - Vol. 24

Copyright 2025 by the Department of Counseling and Clinical Psychology
Teachers College, Columbia University



53

DESOTO & GABY

but significant correlation between high anxiety as 
measured by the STAI and low academic performance. 
Another study found a significant indirect correlation 
between low academic performance and trait anxiety in 
middle school students (Owens et al., 2012). However, 
Chaplin (1989) suggests that it may not be the anxiety 
itself but rather how an individual reacts to anxiety 
that determines the impact on academic achievement. 
Anxiety and Attention 
 Attention may have an important impact on aca-
demic achievement. In addition to academic achieve-
ment, anxiety appears to impact attention (Najmi et 
al., 2012; Pacheco-Unguetti et al., 2010). The execu-
tive control network, or executive attention, consists 
of problem-solving, working memory, and managing 
conflicts. Executive attention is a part of the broader 
category of executive functioning and can be inves-
tigated using the Stroop task, which requires execu-
tive control when managing conflicting information. 
 The scope of attention involves the ability to 
expand or narrow one’s visual focus relative to the 
size of an individual’s visual environment (Koss-
lyn et al., 1999; Najmi et al., 2012). One study sug-
gests that undergraduate students with high levels 
of trait anxiety had an impairment in expanding 
the scope of their attention compared to students 
with low levels of anxiety. The authors of this study 
suggest the scope of visual attention may be related 
to global and local processing (Najmi et al., 2012).
Global and Local Processing
 When someone looks at a painting, do they start 
by recognizing the entire picture or by focusing on the 
detail within the painting? This question concerns the 
global and local elements found within the painting. 
Regarding visual perception, local processing focuses 
on the individual elements that make up a scene (Nayar 
et al., 2015). Global processing involves seeing all the 
individual elements that create the scene and using this 
information to create a comprehensive global image 
(Navon, 1977; Nayar et al., 2015). The theory of global 
precedence states that visual perception tends to start 
from seeing the big picture (the forest) first and then 
noticing the finer details (the trees) (Navon, 1977). 
 Research indicates that young children may rely 
mainly on local-level strategies and develop global-lev-
el strategies similar to the average adult between the 
ages of 7 and 10 (Nayar et al., 2015). This demon-
strates that humans may naturally adapt global pro-

cessing strategies as their brains develop. This may be 
because a child’s prefrontal cortex is still developing 
(Tsujimoto, 2008). As humans develop, there are in-
stances where these global-level strategies may be ben-
eficial. For example, Woltin et al. (2012) found that 
participants from a college sample correctly under-
stood the communicative intent of a written message 
(sincere or sarcastic) more frequently when they were 
globally primed than when they were locally primed. 
Anxiety and Global/Local Processing
 High trait anxiety may impact an individu-
al’s scope of attention on global/local tasks, leading 
to a local-level processing bias (Basso et al., 1996; 
Becker et al., 2017; Najmi et al., 2012; Shilton et al., 
2019). Shilton et al. (2019) found that participants 
with high trait anxiety were biased toward process-
ing local-level visual stimuli in a global-local visual 
processing task, with less of a preference towards 
global-level processing. In contrast, their high state 
anxiety group displayed greater interference (slower 
reaction times) from global stimuli when attempting 
to process the local-level stimuli compared to their low 
state anxiety group. This would indicate that, unlike 
trait anxiety, no local processing preference appears 
to result from state anxiety (Shilton et al., 2019). 
 On the other hand, Basso et al. (1996) found that 
male participants exhibiting symptoms of depression 
or anxiety displayed a bias towards local processing, 
suggesting that these pathologies diminish global 
processing. Becker et al. (2017) used a Navon task to 
demonstrate that trait-level anxiety may lead to a local 
processing bias. According to Tyler and Tucker (1982), 
individuals with trait anxiety may rely more on their left 
hemisphere in visual perception, such as performing the 
Navon task. If this is true, this may explain some of the 
local processing bias. This detail-focused (local-level) 
perceptual bias may indirectly lead to maladaptive be-
haviors, such as an eating disorder (Becker et al., 2017).
Stroop Task and Executive Attention
 If a student were to shout out an answer without 
raising their hand during a lecture, that would be an 
example of failing to inhibit a response. The ability 
to restrain one’s unwanted responses is known as re-
sponse inhibition (Albert et al., 2013). In this study, 
one task we used to measure attention was the Stroop 
task. In this task, participants are shown multiple trials 
of the color names in various colors of ink (e.g., the 
word blue in the ink color red) (Stroop, 1935). The 



54

ANXIETY EFFECTS ON ATTENTION-BASED TASKS 

primary component measured in the Stroop task is 
managing the conflict of incongruency between color 
and word meaning through inhibition (Miyake et al., 
2000; Stroop, 1935). The primary area of the brain 
involved in the Stroop task appears to be the dorso-
lateral prefrontal cortex (DLPFC), which may have 
a role in executive functioning (Milham et al., 2003).
Anxiety and Stroop Task
 Some evidence suggests that state anxiety does 
not negatively affect but may actually improve per-
formance on the Stroop task (see Ursache & Cybele 
Raver, 2014). However, there is mixed evidence. Ro-
sa-Alcázar et al. (2021) found that higher scores on 
a generalized anxiety disorder (GAD) screening sur-
vey were associated with worse performance on the 
Stroop color-word test. Another study found that 
participants who met the criteria for GAD performed 
worse than the “healthy” control group (Hallion et 
al., 2017). The authors suggested that the presence 
of GAD predicted deficits in performance on the 
Stroop task. This study’s results could indicate how 
participants with high trait anxiety may perform on 
the Stroop task. Data suggests that trait anxiety may 
impair the DLPFC’s role in attentional control while 
processing conflicting information (Bishop, 2009). 
 Furthermore, Heller et al. (1997) found that in-
dividuals with high levels of trait anxiety (anxious 
apprehension) seem to have asymmetry in the fron-
tal lobes. These findings could be seen in decreased 
activity in the right frontal lobe, which may lead 
to possible deficits in executive functioning tasks 
(Heller et al.,1997; Milham et al., 2003). Finally, one 
study found a positive correlation between academic 
achievement and activation of brain areas responsible 
for strong performance on the Stroop task (Veroude 
et al., 2013), suggesting that if anxiety harms an indi-
vidual’s performance on the Stroop task, it may also 
adversely affect their academic performance (Hallion 
et al., 2017; Saviola et al., 2020; Veroude et al., 2013). 

Methods
Participants
 One hundred and seventeen undergraduate and 
graduate students from a large university in the south-
east United States completed our study. Participants 
were recruited through SONA, word of mouth, email, 
and social media (e.g., Facebook, Instagram, and Red-
dit). Golden and Freshwater (2002) suggest a shift in 

Stroop scores after the age of 25, so all participants were 
between 18-25. We also only included participants 
who completed every item of the anxiety scale and 
both attention tasks. This study was approved by Mid-
dle Tennessee State University’s institutional review 
board, and all participants provided informed consent. 
 Of our 117 participants, 24 identified as male, 81 
identified as female, 10 identified as non-binary or a 
third gender, and two preferred not to say. In the sam-
ple, 92 participants identified as white, 13 participants 
as Hispanic or Latino, 13 participants as Black or Af-
rican American, four participants as Asian, one par-
ticipant as Native Hawaiian or Pacific Islander, eight 
participants as mixed ethnicity, and four as Other 
Ethnicity. Participants also reported previous anxiety 
diagnoses. There were 27 GAD diagnoses, 13 unspec-
ified anxiety diagnoses, seven social anxiety disorder 
diagnoses, six panic disorder diagnoses, two preferred 
not to say, and one agoraphobia diagnosis reported. 
 Additionally, 35 participants reported being pre-
scribed medication for anxiety or attention-deficit/
hyperactivity disorder, and one preferred not to say. 
65 participants reported that they currently were or 
had previously received psychotherapy for anxiety or 
depression. Fifty-one participants reported having 
never received psychotherapy, and one preferred not 
to say. Lastly, 33 participants reported that they were 
currently taking prescribed or non-prescribed psycho-
tropic medications, 18 participants reported a history 
of severe head injury (e.g., concussion, TBI), and 20 
participants reported using recreational substances. 
Measures
 All data was collected using PsyToolkit, a brows-
er-based data collection tool with a large collection 
of psychological tests. The program allows research-
ers to assemble these individual tasks, along with de-
mographic questions, and distribute them virtually 
for cloud-based data collection (Stoet, 2010, 2017). 
Traditionally, reaction time data is collected via 
in-person testing methods; the gold standard for col-
lecting this type of data is a program called E-prime 
3.0, which does not have browser-based capabilities. 
 Kim et al. (2019) found a high degree of rep-
licability in reaction time measurement between 
E-prime and PsyToolkit. No significant differences 
were observed in the response time results between 
the two, indicating that PsyToolkit is comparable 
to E-prime 3.0 for measuring reaction times (Kim et 



55

DESOTO & GABY

al., 2019). Using PsyToolkit allowed participants to 
complete the study without any in-person interac-
tion, minimizing any chance of spreading COVID-19 
and allowing us to distribute the study more wide-
ly. PsyToolkit meets the standards of data protec-
tion laws in Europe and is supported by SONA. 
 There is currently no available reliability and 
validity on the Navon and Stroop task as measured 
through PsyToolkit. However, other studies have 
used PsyToolkit to measure reaction times and in-
hibitory control (e.g., Invernizzi et al., 2022; Uta-
matanin & Pariwatcharakul, 2022). Other theses 
and dissertations have utilized PsyToolkit in studies 
measuring the Stroop task (e.g., Ackerman, 2022; 
Anjomshoae, 2022; Bertleff, 2022). While there is 
limited data on these tasks as measured on PsyTool-
kit, PsyToolkit was the best option for collecting the 
data needed, considering our available resources.
 The State-Trait Anxiety Inventory (STAI) form 
Y was used to measure state anxiety (in the present 
moment) and trait anxiety (general sense of anxiety). 
The STAI is a 4-point Likert scale ranging from “not 
at all” to “very much so” (Spielberger et al., 1983). 
STAI-S is the state anxiety subscale, consisting of 
20 items about “how you feel right now, at this mo-
ment,” and STAI-T is the trait anxiety subscale also 
consisting of 20 items about “how you generally feel.” 
 In a sample of undergraduate students, the internal 
consistency reliability for males on the STAI-S was α = 
0.91, and on the STAI-T, α = 0.90. For females, STAI-S 
was α = 0.93, and for STAI-T, α = 0.91 (Spielberger, 
1983). In a meta-analysis, Barnes et al. (2002) reported 
an average internal consistency for STAI Form Y of α 
= .92. In a sample of undergraduate students, Cream-
er et al. (1995) found moderate test-retest correlation 
coefficients between the STAI-T and the Beck Anxi-
ety Inventory (r = .57 and .68), as well as the STAI-S 
with the Beck Anxiety Inventory (r = .56 and .64).
 The Navon task measures participants’ global and 
local processing. It was chosen because it has been used 
in previous literature on anxiety and global/local pro-
cessing (Becker et al., 2017; Shilton et al., 2019). The 
Navon task measures response times and errors in pro-
cessing global and local visual elements (Navon, 1977; 
Nayar et al., 2015). This task presents the participant 
with a global stimulus (i.e., a large letter). This global 
stimulus shape comprises many local stimuli (i.e., small 
letters). Participants are asked to decide if they see the 

target letters (H or O) on either the global level or the lo-
cal level of the stimuli (Stoet, 2010, 2017; Navon, 1977). 
 The PsyToolkit version of the task consists of 50 
trials: 12-13 global congruent trials, 12-13 local con-
gruent trials, and 24-26 trials that have neither H nor 
O in either the global or local elements and consist 
of only other letters. Each global-level figure is seven 
local-element letters tall and five letters wide. For the 
Navon task, one study on undergraduate students 
had an average test-retest reliability using Pearson 
correlation coefficients r = .66 for global-level pro-
cessing and r = .73 for local-level processing, which 
suggests acceptable reliability (Dale & Arnell, 2013).
 First, participants viewed a screen with instructions 
and examples of congruent and incongruent trials, with 
no practice trials. Upon clicking through the instruc-
tions, the task began. During each trial, participants 
viewed a large letter composed of small letters. Partici-
pants had to indicate whether the figure contained the 
letters H or O by pressing a key. Participants pressed 
the “b” key if either of these letters were present, and 
the “n” key if neither letter was included in the figure. 
 Participants had 4000 milliseconds to respond. 
A green smiling face would flash on the screen to 
alert participants if they correctly identified an H 
or O appearing on either the global or local lev-
el of the figure, and a red frowning face would ap-
pear if the participant incorrectly identified an H 
or O appearing. If participants exceeded the time 
limit without a response, the word “slow” would 
appear on the screen, leading to the next trial. 
 The Stroop task was chosen for this study because 
it is widely used to measure executive skills and func-
tioning (Rueda et al., 2016). The Stroop color-word 
task provided by PsyToolkit measures inhibition in 
executive control through response times. For this 
study, we exclusively used the color-word trials where 
the participant was asked to only respond to the col-
or of the ink the word is in while ignoring its mean-
ing. Participants were expected to ignore the word’s 
meaning in this task and respond only to its color. 
 There were congruent (e.g., the word blue in 
the ink color blue) and incongruent trials (e.g., the 
word red in the ink color blue). The PsyToolkit ver-
sion of the Stroop color-word task consists of 40 
trials of the color-word Stroop task while ignoring 
the word’s meaning, with 11-12 congruent trials 
and 28-29 incongruent trials. The task began with 



56

ANXIETY EFFECTS ON ATTENTION-BASED TASKS 

instructions and examples of congruent and incon-
gruent trials, but there were no practice trials. Upon 
clicking through the instructions, the task began. 
 A fixation cross flashed on the screen for 250 milli-
seconds in these trials to direct the participant’s atten-
tion. This was followed by the name of a color (blue, 
red, yellow, or green) in blue, red, yellow, or green ink 
flashing on the screen for 2000 milliseconds, during 
which the participants had to identify the color of the 
ink with a key press. The participant had to press the 
key on their keyboard that matched the corresponding 
color: b for blue, r for red, y for yellow, and g for green. 
 In each trial, the participant had to respond within 
2000 milliseconds. If they chose the incorrect key or ex-
ceeded the time limit, the word “wrong” would appear 
on the screen, leading to the next trial. If they chose 
the correct key, the word “correct” would appear. One 
study using undergraduate students found retest reli-
ability for the standard Stroop color-word task’s con-
gruent color-word α = .71 (p < .001) and an incongruent 
color-word of α = .79 (p < .001) (Strauss et al., 2005).
Procedures
 To begin, the participants visited the URL and read 
the informed consent. After agreeing to participate, 
participants completed the state anxiety index of the 
STAI. Participants then completed both the Navon and 
Stroop tasks. We chose to put the state anxiety subscale 
before the two tasks to get as accurate a measurement 
of their current state as possible. We also chose to put 
the trait subscale after the tasks in an attempt to avoid 
inducing any additional anxiety in the participant. 
 The study was counterbalanced so that equal 
numbers of participants started with the Navon or 
the Stroop task. There were no breaks between the 
two tasks, with the next task following the completion 
of the first task. Upon completion of both tasks, the 
participants then completed the trait anxiety index 
of the STAI. This was followed by the collection of 
demographic information. Lastly, a debriefing state-
ment appeared on screen thanking the participants 
for their participation and ending the experiment.

Results
Analysis Summary
 All the data was analyzed using Jamovi (Ver-
sion 2.4.1). Participants were split into low and high 
anxiety groups by their mean scores on the STAI-S 
and STAI-T subscales for each task. In the Navon 

task, we divided participants into a low trait anxiety 
group and a high trait anxiety group using a mean 
split of the STAI-T scores. We conducted a t-test to 
compare median response times on global congru-
ent trials between participants with high and low 
trait-anxiety. We used the same method to compare 
the two groups’ response times for local congruent 
trials. For the Stroop task, we divided participants 
into four groups based on state and trait anxiety. 
We conducted an ANOVA to compare response 
times on incongruent trials across the four groups.
State-Trait Anxiety Inventory
 We calculated each participant’s state anxiety 
scores (STAI-S) and trait anxiety scores (STAI-T). 
Scores were calculated by summing the Likert respons-
es (valued one to four) on each 20-item subscale, with 
some items being reverse-coded per the STAI manual 
(Spielberger et al., 1983). For each subscale (STAI-T, 
STAI-S), scores can range from 20 to 80. In the original 
normative data, their college sample was split into male 
and female groups. The male group’s average score on 
the state subscale was (M = 36.47, SD = 10.52), and 
the female group’s average score on the state subscale 
was (M = 38.76, SD = 11.07). The average male score 
on the trait subscale was (M = 38.30, SD = 8.88), 
and the average female score on the trait subscale was 
(M = 40.40, SD =9.31) (Spielberger et al., 1983). 
 In our study, participants’ scores on each subscale 
of the STAI were used to create a mean split to divide 
participants into high and low trait anxiety and high 
and low state anxiety groups as indicated. Participants’ 
state anxiety subscale scores (N = 117, M = 45.56, SD = 
11.78) were normally distributed, slightly skewed right 
with a .20 skewness, and platykurtic with kurtosis of 
-.73. Trait anxiety (N = 117, M = 51.74, SD = 11.91) 
was normally distributed, slightly skewed left with a 
-.21 skewness, and platykurtic with kurtosis of -.42.
Navon Task
 Reaction times in each trial were measured in mil-
liseconds, with a notation of whether the trial was a 
local or global congruent task. We calculated the me-
dian reaction time for each participant for global and 
local trials. We then divided participants into high-
trait and low-trait anxiety groups using a mean split on 
their STAI-T scores. We calculated a mean global and 
local reaction time based on the median scores for each 
group and compared them using one-tailed t-tests.
 We conducted a t-test on median global pro-



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DESOTO & GABY

cessing response times in milliseconds, comparing 
low and high trait anxiety groups to address our first 
hypothesis. We hypothesized that participants with 
high trait anxiety would have slower reaction times 
on global processing than those with low trait anxi-
ety. Equality of variance was assumed F(1,115) = 0.979. 
We did not find a statistically significant difference, 
t(115) = -0.09, p = .464, d = -0.02 between our low 
trait anxiety (N = 58, M = 828.67, SD = 171.97) 
and high trait anxiety (N = 59, M = 831.60, SD = 
180.98) groups in reaction times on global trials. 
 We also conducted a t-test on median local pro-
cessing response times in milliseconds, comparing 
low trait and high trait anxiety groups to address 
our second hypothesis. We hypothesized that partic-
ipants with high trait anxiety would have faster reac-
tion times on local processing than low trait anxiety 
participants. Equality of variance was assumed, F(1,115) 
= .660. We did not find a statistically significant dif-
ference, t(115) = -1.15, p = .127, d = -0.21 between our 
low trait anxiety (N =58, M = 815.78, SD = 179.60) 
and our high trait anxiety (N = 59, M = 861.09, SD 
= 242.95) groups in reaction times on local trials. 
Stroop Task
 Lastly, we conducted a one-way ANOVA be-
tween the low state/low trait group (LL) (N = 43, 
M = 927.70, SD = 180.87), high state/low trait (HL) 
(N = 15, M = 1021.40, SD =225.63), low state/
high trait (LH) (N = 22, M = 923.91, SD =177.71), 
and high state high trait group (HH) (N = 37, M = 
942.38, SD = 162.84) on median Stroop incongruent 
trials. We did not find a statistically significant differ-
ence between groups, F (3, 113) = 1.12, p = .344. This 
addressed our last hypothesis, that the three groups 
of anxiety (HL, LH, and HH) would have slower re-
action times on the incongruent trials of the Stroop 
task when compared to the control group (LL).

Discussion
 This study explored the possible interactions be-
tween state and trait anxiety and attention in college 
students through performance on versions of the 
Navon and Stroop tasks available on PsyToolkit (Stoet, 
2010, 2017). We measured the relationship of trait anx-
iety with global and local processing using the Navon 
task. We also measured the relationship between state 
anxiety and trait anxiety on cognitive inhibition of ex-
ecutive control through the Stroop task. We did not 

find statistically significant results to support our three 
hypotheses: there was no association found between 
anxiety and performance on either task in this study. 
Anxiety and Global/Local Processing
 We hypothesized that participants with high trait 
anxiety would have slower reaction times on glob-
al processing trials than those with low trait anxiety. 
Additionally, based on previous findings, we hypoth-
esized that participants with high trait anxiety would 
have faster reaction times on local processing trials 
than low-anxiety participants. However, our results 
showed no significant differences between groups 
on either task. Some evidence suggests the lack of 
a finding in our global processing t-test is not sur-
prising (Shilton et al., 2019). There is considerable 
evidence from previous literature for our local pro-
cessing hypothesis (Basso et al., 1996; Becker et al., 
2017; Derryberry & Reed, 1998; Shilton et al., 2019). 
 Interestingly, although not statistically signifi-
cant, our results went in the opposite direction of 
what was hypothesized for local processing: on aver-
age, for global trials, low trait anxiety participants (M 
= 828.67) had slightly faster reaction times than high 
trait anxiety participants (M = 831.60). For local tri-
als, the high trait anxiety participants (M = 861.09) 
had slightly slower reaction times than those with 
low trait anxiety (M = 815.78). Our high trait anxi-
ety participants did not display a local processing bias. 
 Variations between our design and previous 
studies may explain our findings (e.g., online study 
or having specific target letters). Some of the pre-
vious literature used different global and local pro-
cessing paradigms (Basso et al., 1996; Shilton et al., 
2019). Other variables could also explain the ob-
served results, including variability due to uncon-
trolled testing environments, as participants com-
pleted the study online with no restrictions about 
the type of location where they completed the tasks.
 There is a need to further investigate global and 
local processing in college samples. In a sample of un-
dergraduate students, Tan et al. (2017) found evidence 
that their participants were more willing to take aca-
demic risks when globally primed than participants 
who were locally primed. This willingness to take 
more significant risks in an educational setting could 
lead to greater academic achievement. To our knowl-
edge, the literature on global and local processing and 
academic success is sparse. One study used a global and 



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ANXIETY EFFECTS ON ATTENTION-BASED TASKS 

local processing paradigm to predict academic achieve-
ment. However, this study focuses on meta-motiva-
tion, and it was unclear how global and local process-
ing affect academic achievement (Nguyen et al., 2023). 
 Another study found that in children with at-
tention-deficit/hyperactivity disorder, the ability to 
shift between global and local stimuli was predictive 
of academic achievement.  (Sjöwall & Thorell, 2014). 
In that same study, inhibition was also predictive of 
academic achievement (Sjöwall & Thorell, 2014). 
Additionally, the ability to efficiently process one’s 
visual environment on the global level appears to be 
beneficial in understanding written communication 
(Woltin et al., 2012). This may translate to the abili-
ty to understand the information in a textbook while 
studying, suggesting that if anxiety does decrease 
global processing, high trait anxiety could impair 
reading ability while studying (Basso et al., 1996). 
Anxiety and Stroop Task 
 We hypothesized that the HL group (M = 
1021.40), LH group (M = 923.91), and the HH group 
(M = 942.38) would have slower reaction times on the 
incongruent trials of the Stroop task when compared 
to the LL group (M = 927.70). We found no signif-
icant impact of anxiety on response inhibition. Our 
groups’ distributions may have contributed to our ob-
served results (see Limitations for a more in-depth dis-
cussion). It is important to continue investigating how 
various factors influence executive attention. Studies 
have found a correlation between executive function-
ing and academic achievement from early childhood 
through college (Baars et al., 2015; Best et al., 2011).
Limitations 
 This study had several limitations, one of which 
was group dispersion. For our ANOVA, we would 
have preferred to have roughly 25 participants in 
each of our four groups. Instead, we had 43 par-
ticipants in our LL group, 15 participants in our 
HL group, 22 participants in our LH group, and 
37 participants in our HH group. This lack of dis-
persion may have prevented our groups from vary-
ing enough to observe any notable differences. 
 Collectively, our average STAI scores were rough-
ly a standard deviation above the original normative 
sample (Spielberger et al., 1983). This may have con-
tributed to a lack of lower levels of anxiety. Another 
limitation is the fact that we measured state anxiety 
without any stress induction or any way to control 

it. This caused us to rely on participants already be-
ing in an anxious state when creating our groups. 
 The average age of participants in our study was 
only 19.85. Previous studies had participants rang-
ing in age from 17 to 71. Many previous studies used 
participants with a wider range of ages; it is possible 
that age may contribute to the discrepancies between 
those studies and our results (Hallion et al., 2017; 
Pacheco-Unguetti et al., 2010; Rosa-Alcázar et al., 
2021). Our study may better represent anxiety’s ef-
fect on predominantly white college students between 
the ages of 18-25, but it may not be generalizable to 
other demographics or the general population. Pref-
erably, we would have recruited more participants 
to allow us to exclude additional factors that may 
have affected their performance, such as the use of 
psychotropic medication, psychotherapy, and past 
head injury (e.g., traumatic brain injury, concussion). 
 As previously mentioned, the study was conducted 
entirely online, which limits our control of participants’ 
machines and testing environments. Additionally, Psy-
Toolkit versions of both tasks required the use of key 
inputs. This motor input might have unintentionally 
activated areas of the brain (Cramer et al., 1999), which 
would ultimately add additional variability. Despite 
this, Hallion et al. (2017) also used a computer-adapt-
ed version of the Stroop task and found evidence sug-
gesting that the presence of GAD predicted Stroop 
task results. Previous studies that used the traditional 
Stroop Color Word Test design with word trials and 
color trials may also contribute to the observed differ-
ences (Hallion et al., 2017; Rosa-Alcázar et al., 2021). 
 The PsyToolkit version of the Stroop task required 
four different keystrokes, which might have made the 
task more confusing and possibly measured more 
than response inhibition. Additionally, we eliminated 
timed-out and incorrect trials, where the traditional 
Stroop Color Word Test asks the test taker to retry the 
item as the timer goes down. Our method requires the 
test taker to inhibit their response to every trial correct-
ly. Differences between versions of the task may influ-
ence observed performance. We also used versions of 
these tasks that do not have normative data. Finally, we 
had more women in our high anxiety group than our 
low anxiety group, which could possibly impact scores.
 Additionally, having a higher percentage of males 
in our low anxiety groups may have had an impact on 
the observed results. Males and females are known to 



DESOTO & GABY

have neural structural differences (Ingalhalikar et al., 
2013). Specifically, findings in other visual percep-
tion tasks that target the right parietal lobe show a 
difference in performance between males and females 
(Kalichman, 1988; Linn & Petersen, 1985). Given 
this, our findings on global processing, which targets 
the right parietal lobe, may be affected by gender dif-
ferences between groups (Kimchi & Merhav, 1991).
Future Directions
 In the future, studies should examine other ar-
eas we did not address, such as socioeconomic sta-
tus, culture, or gender-related differences that may 
contribute to individual differences on these tasks. 
Future studies examining how emotions, mood, 
and anxiety may contribute to deficits in attention 
should consider a more general and comprehensive 
battery, such as tasks designed to measure the oth-
er two neural networks of attention, proposed by 
Posner and Petersen (1990) (alerting and orienting). 
In this study, only one aspect of executive atten-
tion was measured, which was response inhibition. 
 There are other parts of executive functioning and 
other tasks that measure these aspects (e.g., Tower of 
London, Trail Making Test, and Wisconsin Card Sort-
ing Test) that could be used for future research (Etnier 
& Chang, 2009). Future studies comparing all-male 
to all-female participants may be beneficial in observ-
ing whether there are any gender differences in these 
tasks as measured by PsyToolkit. PsyToolKit is a free 
and readily available resource for psychological testing. 
This makes it ideal for use by students and others with 
limited funding and resources. Therefore, it is crucial 
to establish normative values and ecological validity for 
these specific versions of each task, which would en-
able future research and possibly clinical applications.
Conclusion
 Our three hypotheses that global processing, local 
processing, and response inhibition would be impacted 
by elevated levels of anxiety were not supported. The re-
sults of this project are inconsistent with previous find-
ings (Basso et al., 1996; Becker et al., 2017; Derryberry 
& Reed, 1998; Hallion et al., 2017; Pacheco-Unguetti 
et al., 2010; Rosa-Alcázar et al., 2021; Shilton et al., 
2019). Anxiety symptoms appear prevalent in college 
students, and academic achievement has been observed 
as one of the most significant stressors in a student’s 
life (Asher BlackDeer et al., 2021; Beiter et al., 2015). 
 Additionally, while not replicated in this study, 

there is previous literature that suggests anxiety can 
harm not only attention but academic achievement, 
as well (Basso et al., 1996; Cassady & Johnson, 2002; 
Hoegler & Nelson, 2018; Pacheco-Unguetti et al., 
2010; Rosa-Alcázar et al., 2021; Vitasari et al., 2010). 
Literature suggests that better performance on the tasks 
used in this study can predict academic achievement. 
 Future studies are needed to address the uncertainty 
of the effects state anxiety may have on the Stroop task, as 
there are mixed findings and conflicting theories about 
whether state anxiety is beneficial or detrimental to ex-
ecutive functioning (Eysenck et al., 2007; Pacheco-Un-
guetti et al., 2010; Ursache & Cybele Raver, 2014). 
Furthermore, future studies should continue to an-
alyze the potential benefits of using software similar 
to PsyToolkit as a substitute for collecting data in 
a laboratory setting. Similarly, the clinical utility of 
online-based data collection of inhibitory control, 
global processing, and local processing should also 
be addressed in future research. Overall, our study 
did not find any statistically significant differenc-
es in the effect of anxiety on attention as measured 
by these two tasks. This study provides a valuable 
understanding of how state and trait anxiety affect 
performance on these two tasks as measured by Psy-
Toolkit (Stoet, 2010, 2017) in this specific sample 
of students at Middle Tennessee State University.

References
Ackerman, S. (2022). COVID-19 and Executive Func-

tions. [Bachelor’s thesis, Eastern Kentucky Uni-
versity]

Albert, J., López-Martín, S., Hinojosa, J. A., & Car-
retié, L. (2013). Spatiotemporal characteriza-
tion of response inhibition. NeuroImage, 76, 
272-281. https://doi.org/10.1016/j.neuroim-
age.2013.03.011

Anjomshoae, F. (2022). Testing Effects of Bilingual-
ism on Inhibition, Shifting and Working Memory 
Ability in Adults. [Master’s thesis, University of 
Alberta] 

Asher BlackDeer A., Patterson Silver Wolf D.A., Ma-
guin E., & Beeler-Stinn S. (2023). Depression and 
anxiety among college students: Understanding 
the impact on grade average and differences in 
gender and ethnicity. Journal of American College 
Health, 71(4), 1091-1102. https://doi.org/10.108
0/07448481.2021.1920954

59



60

ANXIETY EFFECTS ON ATTENTION-BASED TASKS 

Baars, M. A., Nije Bijvank, M., Tonnaer, G. H., & 
Jolles, J. (2015). Self-report measures of executive 
functioning are a determinant of academic per-
formance in first-year students at a university of 
applied sciences. Frontiers in Psychology, 6, 1131. 
https://doi.org/10.3389/fpsyg.2015.01131  

Banich, M. T., Milham, M. P., Atchley, R., Cohen, 
N. J., Webb, A., Wszalek, T., Kramer, A., Li-
ang, Z., Wright, A., Shenker, J., & Magin, R. 
(2000). fMRI studies of Stroop tasks reveal 
unique roles of anterior and posterior brain sys-
tems in attentional selection. Journal of Cogni-
tive Neuroscience, 12(6), 988-1000. https://doi.
org/10.1162/08989290051137521 

Barnes, L. L., Harp, D., & Jung, W. S. (2002). Reli-
ability generalization of scores on the Spielberger 
state-trait anxiety inventory. Educational and Os-
ychological Measurement, 62(4), 603-618. https://
doi.org/10.1177/0013164402062004005

Basso, M., Schefft, B., Ris, M., & Dember, W. (1996). 
Mood and global-local visual processing. Jour-
nal of the International Neuropsychological So-
ciety, 2(3), 249-255. https://doi.org/10.1017/
S1355617700001193

Becker, K. R., Plessow, F., Coniglio, K. A., Tabri, N., 
Franko, D. L., Zayas, L. V., Germine, L., Thomas, 
J. J., & Eddy, K. T. (2017). Global/local process-
ing style: Explaining the relationship between trait 
anxiety and binge eating. International Journal of 
Eating Disorders, 50(11), 1264–1272. https://
doi.org/10.1002/eat.22772

Beiter, R., Nash, R., McCrady, M., Rhoades, D., Lin-
scomb, M., Clarahan, M., & Sammut, S. (2015). 
The prevalence and correlates of depression, anxi-
ety, and stress in a sample of college students. Jour-
nal of Affective Disorders, 173, 90–96. https://doi.
org/10.1016/j.jad.2014.10.054

Bertleff, A. J. (2022). Automaticity in Musicians as 
Demonstrated by a Modified Stroop Task [Doctor-
al dissertation, Kent State University].

Best, J. R., Miller, P. H., & Naglieri, J. A. (2011). 
Relations between executive function and aca-
demic achievement from ages 5 to 17 in a large, 
representative national sample. Learning and In-
dividual Differences, 21(4), 327-336. https://doi.
org/10.1016/j.lindif.2011.01.007

Bishop, S. J. (2009). Trait anxiety and impoverished 
prefrontal control of attention. Nature Neuro-

science, 12(1), 92-98. https://doi.org/10.1038/
nn.2242

Cassady, J. C., & Johnson, R. E. (2002). Cognitive test 
anxiety and academic performance. Contemporary 
Educational Psychology, 27(2), 270-295. https://
doi.org/10.1006/ceps.2001.1094

Chaplin, T., (1989). The relationship of trait anxiety 
and academic performance to achievement anx-
iety: students at risk.  Journal of College Student 
Development, 30, 229-236. 

Chew, P. K. H., & Dillon, D. B. (2014). Statistics 
anxiety update: Refining the construct and rec-
ommendations for a new research agenda. Per-
spectives on Psychological Science, 9(2), 196–208. 
https://doi.org/10.1177/1745691613518077  

Cramer, S. C., Finklestein, S. P., Schaechter, J. D., 
Bush, G., & Rosen, B. R. (1999). Activation of 
distinct motor cortex regions during ipsilater-
al and contralateral finger movements. Journal 
of Neurophysiology, 81(1), 383-387. https://doi.
org/10.1152/jn.1999.81.1.383

Creamer, M., Foran, J., & Bell, R. (1995). The Beck 
Anxiety Inventory in a non-clinical sample. Be-
haviour Research and Therapy, 33(4), 477-485. 
https://doi.org/10.1016/0005-7967(94)00082-U 

Dale, G., & Arnell, K. M. (2013). Investigating the 
stability of and relationships among global/local 
processing measures. Attention, Perception, & Psy-
chophysics, 75, 394-406. https://doi.org/10.3758/
s13414-012-0416-7

Derryberry, D., & Reed, M. A. (1998). Anxiety and 
attentional focusing: Trait, state and hemispher-
ic influences. Personality and Individual Differ-
ences, 25(4), 745-761. https://doi.org/10.1016/
S0191-8869(98)00117-2  

Etnier, J. L., & Chang, Y. K. (2009). The effect of phys-
ical activity on executive function: a brief com-
mentary on definitions, measurement issues, and 
the current state of the literature. Journal of Sport 
and Exercise Psychology, 31(4), 469-483. https://
doi.org/10.1123/jsep.31.4.469

Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, 
M. G. (2007). Anxiety and cognitive performance: 
attentional control theory. Emotion, 7(2), 336. 
https://doi-org.ezproxy.mtsu.edu/10.1037/1528-
3542.7.2.336

Hallion, L. S., Tolin, D. F., Assaf, M., Goethe, J., & 
Diefenbach, G. J. (2017). Cognitive control in 



DESOTO & GABY

generalized anxiety disorder: Relation of inhibi-
tion impairments to worry and anxiety severity. 
Cognitive Therapy and Research, 41(4), 610-618. 
https://doi.org/10.1007/s10608-017-9832-2

Heller, W., Nitschke, J. B., Etienne, M. A., & Mill-
er, G. A. (1997). Patterns of regional brain ac-
tivity differentiate types of anxiety. Journal of 
Abnormal Psychology, 106(3), 376. https://doi.
org/10.1037/0021-843X.106.3.376

Hoegler, S., & Nelson, M. (2018). The Influence of 
anxiety and self-efficacy on statistics performance: 
A path analysis. Psi Chi Journal of Psychological 
Research, 23(5). https://doi.org/10.24839/2325-
7342.JN23.5.364

Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, 
T. D., Elliott, M. A., Ruparel, K., Hakonarson, H., 
Gur, R., Gur, R., & Verma, R. (2014). Sex differ-
ences in the structural connectome of the human 
brain. Proceedings of the National Academy of Sci-
ences, 111(2), 823-828. https://doi.org/10.1073/
pnas.1316909110

Invernizzi, P. L., Rigon, M., Signorini, G., Colella, D., 
Trecroci, A., Formenti, D., & Scurati, R. (2022). 
Effects of varied practice approach in physical edu-
cation teaching on inhibitory control and reaction 
time in preadolescents. Sustainability, 14(11), 
6455. https://doi.org/10.3390/su14116455

Kalichman, S. C. (1988). Individual differences in wa-
ter-level task performance: A component-skills 
analysis. Developmental Review, 8(3), 273-295. 
https://doi.org/10.1016/0273-2297(88)90007-X

Kim, J., Gabriel, U., & Gygax, P. (2019). Testing the 
effectiveness of the Internet-based instrument 
PsyToolkit: A comparison between web-based 
(PsyToolkit) and lab-based (E-Prime 3.0) mea-
surements of response choice and response time in 
a complex psycholinguistic task. PloS one, 14(9), 
e0221802. https://doi.org/10.1371/journal.
pone.0221802

Kimchi, R., & Merhav, I. (1991). Hemispheric pro-
cessing of global form, local form, and texture. 
Acta Psychologica, 76(2), 133–147. https://doi.
org/10.1016/0001-6918(91)90042-x

Kosslyn, S. M., Brown, H. D., & Dror, I. E. (1999). Ag-
ing and the scope of visual attention. Gerontology, 
45(2), 102-9. https://doi.org/10.1159/000022071

Linn, M. C., & Petersen, A. C. (1985). Emergence and 
characterization of sex differences in spatial abil-

ity: A meta-analysis. Child Development, 56(6), 
1479-1498. https://doi.org/10.2307/1130467

Milham, M. P., Banich, M. T., Claus, E. D., & Cohen, 
N. J. (2003). Practice-related effects demonstrate 
complementary roles of anterior cingulate and 
prefrontal cortices in attentional control. Neuro-
image, 18(2), 483-493. https://doi.org/10.1016/
S1053-8119(02)00050-2

Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, 
A. H., Howerter, A., & Wager, T. D. (2000). The 
unity and diversity of executive functions and 
their contributions to complex "Frontal Lobe" 
tasks: A latent variable analysis. Cognitive Psy-
chology, 41(1), 49–100. https://doi.org/10.1006/
cogp.1999.0734

Najmi, S., Kuckertz, J. M., & Amir, N. (2012). At-
tentional impairment in anxiety: inefficiency in 
expanding the scope of attention. Depression and 
Anxiety, 29(3), 243-249. https://doi.org/10.1002/
da.20900

Navon, D. (1977). Forest before trees: The prece-
dence of global features in visual perception. 
Cognitive Psychology, 9(3), 353–383. https://doi.
org/10.1016/0010-0285(77)90012-3

Nayar, K., Franchak, J., Adolph, K., & Kiorpes, L. 
(2015). From local to global processing: the devel-
opment of illusory contour perception. Journal 
of Experimental Child Psychology, 131, 38–55. 
https://doi.org/10.1016/j.jecp.2014.11.001

Nguyen, T., Scholer, A. A., Miele, D. B., Edwards, 
M. C., & Fujita, K. (2023). Predicting academ-
ic performance with an assessment of students’ 
knowledge of the benefits of high-level and 
low-level construal. Social Psychological and Per-
sonality Science, 14(2), 195-206. https://doi.
org/10.1177/19485506221090051

Owens, M., Stevenson, J., Hadwin, J. A., & Norgate, 
R. (2012). Anxiety and depression in academic 
performance: An exploration of the mediating 
factors of worry and working memory. School Psy-
chology International, 33(4), 433-449. https://doi.
org/10.1177/0143034311427433

Pacheco-Unguetti, A. P., Acosta, A., Callejas, A., & 
Lupiáñez, J. (2010). Attention and anxiety: Dif-
ferent attentional functioning under state and 
trait anxiety. Psychological science, 21(2), 298-304. 
https://doi.org/10.1177/0956797609359624

Posner, M. I., & Petersen, S. E. (1990). The attention 

61



62

ANXIETY EFFECTS ON ATTENTION-BASED TASKS 

system of the human brain. Annual Review of Neu-
roscience, 13(1), 25–42. https://doi.org/10.1146/
annurev.ne.13.030190.000325

Posner, M.I., Rueda, M.R., & Kanske, P.  (2007).  
Probing the mechanisms of attention.  In J.T. 
Cacioppo, J.G. Tassinary & G.G. Berntson (eds), 
Handbook of Psychophysiology, Third Edition. 
Cambridge U.K.: Cambridge University Press (pp 
410-432).

Rosa-Alcázar, A. I., Rosa-Alcázar, Á., Martínez-Espar-
za, I. C., Storch, E. A., & Olivares-Olivares, P. J. 
(2021). Response Inhibition, Cognitive Flexibil-
ity and Working Memory in Obsessive-Compul-
sive Disorder, Generalized Anxiety Disorder and 
Social Anxiety Disorder. International Journal of 
Environmental Research and Public Health, 18(7), 
3642. https://doi.org/10.3390/ijerph18073642

Rueda, M. R., Posner, M. I., & Rothbart, M. K. 
(2016). The development of executive attention: 
Contributions to the emergence of self-regula-
tion. In Measurement of Executive Function in 
Early Childhood (pp. 573-594). Psychology Press.

Saviola, F., Pappaianni, E., Monti, A., Grecucci, A., 
Jovicich, J., & De Pisapia, N. (2020). Trait and 
state anxiety are mapped differently in the human 
brain. Scientific Reports, 10(1), 1-11. https://doi.
org/10.1038/s41598-020-68008-z

Shilton, A. L., Laycock, R., & Crewther, S. G. (2019). 
Different effects of trait and state anxiety on 
global-local visual processing following acute 
stress. Cognition, Brain, Behavior. An Interdis-
ciplinary Journal, 23(3), 155–170. https://doi.
org/10.24193/cbb.2019.23.09

Sjöwall, D., & Thorell, L. B. (2014). Functional im-
pairments in attention deficit hyperactivity dis-
order: the mediating role of neuropsychological 
functioning. Developmental Neuropsychology, 
39(3), 187-204. https://doi.org/10.1080/875656
41.2014.886691

Spielberger, C. D., Gorsuch, R. L., Lushene, R., 
Vagg, P. R., & Jacobs, G. A. (1983). Manual for 
the State-Trait Anxiety Inventory. Palo Alto, CA: 
Consulting Psychologists Press.

Stoet, G. (2010). PsyToolkit - A software package for 
programming psychological experiments using 
Linux. Behavior Research Methods, 42(4), 1096-
1104. https://doi.org/10.3758/BRM.42.4.1096

Stoet, G. (2017). PsyToolkit: A novel web-

based method for running online question-
naires and reaction-time experiments. Teach-
ing of Psychology, 44(1), 24-31. https://doi.
org/10.1177/0098628316677643

Strauss, G. P., Allen, D. N., Jorgensen, M. L., & 
Cramer, S. L. (2005). Test-retest reliability of 
standard and emotional Stroop tasks: an inves-
tigation of color-word and picture-word ver-
sions. Assessment, 12(3), 330-337. https://doi.
org/10.1177/1073191105276375

Stroop, J. R. (1935). Studies of interference in serial 
verbal reactions. Journal of Experimental Psychol-
ogy, 18(6), 643-662. https://doi.org/10.1037/
h0054651   

Tan, E. W., Lim, S. W. H., & Manalo, E. (2017). Glob-
al-local processing impacts academic risk taking. 
Quarterly Journal of Experimental Psychology, 
70(12), 2434-2444. https://doi.org/10.1080/174
70218.2016.1240815

Tsujimoto, S. (2008). The prefrontal cortex: Func-
tional neural development during early child-
hood. The Neuroscientist, 14(4), 345-358. https://
doi.org/10.1177/1073858408316002

Tyler, S. K., & Tucker, D. M. (1982). Anxiety and per-
ceptual structure: individual differences in neuro-
psychological function. Journal of Abnormal Psy-
chology, 91(3), 210. https://doi-org.ezproxy.mtsu.
edu/10.1037//0021-843x.91.3.210

Ursache, A., & Raver, C. C. (2014). Trait and state 
anxiety: Relations to executive functioning in an 
at-risk sample. Cognition & Emotion, 28(5), 845-
855. https://doi.org/10.1080/02699931.2013.85
5173  

Utamatanin, N., & Pariwatcharakul, P. (2022). The 
Effect of Caffeine and Sleep Quality on Military 
Pilot Students’ Flight Performance-Related Cog-
nitive Function. The International Journal of 
Aerospace Psychology, 32(2-3), 152-164. https://
doi.org/10.1080/24721840.2022.2034505

Veroude, K., Jolles, J., Knežević, M., Vos, C. M., 
Croiset, G., & Krabbendam, L. (2013). Anterior 
cingulate activation during cognitive control re-
lates to academic performance in medical students. 
Trends in Neuroscience and Education, 2(3-4), 100-
106. https://doi.org/10.1016/j.tine.2013.10.001

Vitasari, P., Wahab, M. N. A., Othman, A., Herawan, 
T., & Sinnadurai, S. K. (2010). The relationship 
between study anxiety and academic performance 



63

DESOTO & GABY

among engineering students. Procedia-Social 
and Behavioral Sciences, 8, 490-497. https://doi.
org/10.1016/j.sbspro.2010.12.067

Woltin, K. A., Corneille, O., & Yzerbyt, V. Y. (2012). 
Improving communicative understanding: The 
benefits of Global Processing. Journal of Exper-
imental Social Psychology, 48(5), 1179–1182. 
https://doi.org/10.1016/j.jesp.2012.03.004  

 
 


