Analysis of Anxiety and Cognitive Impairment

This assignment requires a comprehensive analysis of the relationship between anxiety and cognitive impairment in older adults. It involves reviewing research studies on topics such as selective attention, visual search, working memory, and perception in elderly individuals. The studies reviewed are from reputable sources, including journals and academic papers, covering a range of relevant topics. This assignment aims to provide a deeper understanding of the complex interactions between anxiety, cognitive impairment, and various psychological processes in older adults.

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Does Anxiety influence Information Processing Speed and variability, Memory and attention in
ageing?

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ABSTRACT
INTRODUCTION: The main objective behind carry out study is to analyze the extent to
which anxiety level has high level of impact on information processing speed, memory and
attention in older adulthood.
METHODS: Tests of anxiety, including beck anxiety inventory, state and trait anxiety,
sleep quality, depression and subjective and objective cognitive functioning and visual search
task were administered to two groups of adults, one younger (n= 52; 18-25 years) and one older
(n= 52; 50-80 years).
RESULTS: From assessment, it has identified that detrimental effects of anxiety are not
limited to the clinical disorders. Hence, higher depression scores resulted in longer Visual
Search Effect times. All these were statistically significant, and occurred only in older males.
Prior studies have indicated that anxiety lengthens response times, and the Visual Search task
displayed impacts of anxiety levels.
DISCUSSION: From assessment, it has identified that results were consistent with prior
studies pertaining to anxiety lengthens response times. Along with this, it has found from visual
search task results that anxiety level has influence on response times.
Keywords: Anxiety, Attention, Information processing speed, Subjective memory
function, Objective cognitive function, Ageing.

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Introduction
Mild cognitive impairment implies for the situation of abnormality that is higher as
compared to expected aspects pertaining to age and education. From assessment, it has found
that situation of self-perceived cognitive decline pertaining to memory aspect usually occurred
among older adults having both MCI and dementia at early stage. Along with this, it has found
that memory function closely influences daily life and mental health of individual. In both
cognitively healthy ageing and ageing–related diseases states such as Alzheimer’s disease, a
large body of evidence supports an association between information processing speed and the
functional integrity of both cognitive and of neuroanatomical aspects of the mind. These impacts
include changes to the brain’s white matter, and alterations to components of information
processing system (Anstey et al., 2007; Haynes et al., 2017; Kennedy et al., 2013; Lövdén, Shing
& Lindberger, 2007; Voelker et al., 2017). Neuroanatomical studies have identified age-related
differences in the interhemispheric data transfer rates and activation as well as differences in
white matter integrity, with older adults demonstrating greater transfer rates than younger ones,
possibly as a compensatory strategy to overcome ageing-related cognitive declines (Anstey et al.,
2007; Haynes et al., 2017). These differences are identifiable using reaction time tasks as
fundamental measures of both interhemispheric transfer and cognitive changes due to ageing
(Antsey et al., 2007).
As defined in the American Psychiatric Association’s (APA’s) Diagnostic and Statistical
Manual of Mental Disorders (DSM-5) mild cognitive impairment (MCI) occurs when an
individual experiences memory and other core brain functions problems that do not interfere
with independent living and which are not due to delirium or another mental disorder (APA,

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2013). Brain studies of individuals with MCI demonstrate that as many as 10 to 15 percent of
those with MCI develop full dementia within a year (Fauzan & Amran, 2015).
Information processing speed is commonly measured as part of the diagnosis of dementia
and cognitive impairment. In normal aging, the brain functions slow while intelligence measures
stabilise, and recall ability decreases significantly. Research evidence indicates that information
processing speed can vary significantly with respect to methodological factors such as the task
used and thus areas of the brain recruited for performance and response demands (Rodrigues &
Pandeirada, 2014; Valeriani et al., 2003) and person-related factors such as sex (Commmodari &
Guanera, 2013), education (Tales & Basoudan, 2016), and possibly genetic factors (Luo et al.,
2017). Interactions between these factors can result in significantly different impacts on
processing speeds depending on specific brain areas required for performance of tasks (Tales et
al., 2010; Tales & Basoudan, 2016). Hence, older individuals often experience greater difficulty
remembering the name of people, places and other aspects. Mild cognitive impairment (MCI)
occurs when the individual experiences measureable problems in relation to memory and another
core brain functions. MCI is also associated with increased risk of developing Alzheimer’s
disease in the short term.
RT variability is impacted by a combination of three physiological effects: hemispheric
specialization, individual neuroanatomy, and transient functional fluctuations between trials
(Antonova et al., 2016), Thus, RT measures need to consider many aspects of typical behavior
and environmental interaction (Rodrigues & Pandeirada, 2014, Tales & Basoudan, 2016).
Results of information processing speed tests in clinical populations may need to be interpreted
with respect to such caveats, making it difficult to relate clinical to research findings. It is
included in DSM-5 for measurement specifically with respect to attention-related processing

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(Naveteur et.al., 2005). Research indicates that information processing speed can vary
significantly with respect to methodological factors such as the task used and thus areas of the
brain recruited for performance and response demands and person-related factors such as sex and
educational level (Tales, Bayer, Haworth & Snowden, 2010). Such research evidence indicates
that the results of information processing speed tests in clinical populations may need to be
interpreted with respect to such caveats, especially as there is a high degree in variability in the
tasks used to measure information processing speeds in clinical populations, making the
comparison of results within the clinical arena problematic and also making it difficult to relate
clinical to research findings.
It is important to measure information processing speed because slowing of information
processing can have a negative influence upon many aspects of real life [Increased RT in older
adults has been shown to be associated to increased antagonist muscle co-activation. In older
subjects, the RTs for both measures were significantly longer than for younger adults (Arnold et
al., 2015). From assessment, it has identified that three physiological effects namely hemispheric
specialization, individual neuroanatomy and transient functional fluctuations between trials has
an impact on RT variability (Antonova et al., 2016). Thus, RT measures need to be assessed
appropriately and in relation to many aspects of typical behaviour and environmental interaction
(Rodrigues & Pandeirada, 2014).
By doing research, it has identified that risk of dementia can be reduced significantly
through the means of resourcefulness training. From evaluation, it has asserted that RT training
provides high level of assistance in reducing the level of caregiver stress. It is possible that other
factors that are rarely researched and rarely tested in clinical conditions, may also affect
information processing speed. One such factor is anxiety. Salthouse (2011) highlighted the

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possibility that anxiety may affect RT performance and that although high levels of clinical
anxiety may be acknowledged to affect information processing speeds, lower levels of anxiety
are generally not considered to have any effect. In contrast, however, Tales and Basoudan (2016)
noted that anxiety has a high prevalence in older adults and that even non-clinical anxiety may
impact cognitive performance. One aim of this research is to determine the potential for anxiety
to affect the outcome of information processing speed in older adults.
Subjective Cognitive Function
Another factor that has rarely been considered in the investigation of information
processing speed in ageing and ageing-related disease is subjective cognitive function. Although
most studies examining information processing speed in ageing measure and factor in the
potential influence of objectively measured status of cognitive function it is rare for research
results to consider how subjective feelings or assessment of cognitive function may influence
information processing speed. Objective measures of poor general cognition are often found to
be related to slower information processing speeds compared to intact cognition in older adults
(Tales & Basoudan, 2016). It is possible that subjectively poor cognition may also affect
information processing speed (either via similar or different mechanisms as yet to be fully
identified).
If this is the case then both clinical and research findings would need to be interpreted
with respect to this caveat also. A further aim of this study is also to determine whether in older
adults either objectively measured or subjectively experienced cognitive function influences
information processing speed. Finally, the potential influence of sex (male/female) and
educational level upon information processing speed will also be investigated.

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Intra-Individual Variability
In the research arena in addition to measuring information processing speed it has been
common to measure also its intra-individual variability (IIVRT). IIVRT like information processing
speed is also related to cognitive and neuroanatomical factors, with increased variability
commonly but not always, related to disease related ageing such as Alzheimer’s disease, and in
some cases, has been found to be more sensitive to ageing and or disease than mean information
processing speed per se (Luo et al., 2017; Tales & Basoudan, 2016). Although previous research
has examined IIVRT with respect to objectively measured cognitive function very little research
has examined the influence of subjective cognitive function and anxiety. This measure will thus
also be investigated in the present also be investigated in the present study considering gender
and level of education.
Attention
Attention is one of the cognitive functions most affected by ageing. The loss of function
in attention can impact a wide variety of cognitive activities and daily living functions (Akimoto
et al., 2014). Valeriani, Ranghi and Giaquinto (2003) identified differences in somatosensory
evoked potentials (SEPs) to median nerve stimulation between tasks requiring neutral
stimulation conditions and selective attention conditions for both older (mean age 71.7) and
younger (mean age 26.9) adults and abnormal attention function found in MCI and various
aetiologies of dementia (Tales, Bayer, Haworth & Snowden, 2010). Of particular context for the
current study is the demand by DSM-5 that information processing speed should be measured
with respect to attentional function. In this study, therefore, these questions are investigated

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using a computer-based visual search task which allows measurement of attention-related choice
reaction time and attentional function (Therrien & Hunsley, 2011).
Anxiety
Anxiety prevalence and levels vary with respect to age and age-related conditions such as
subjective and objective cognitive impairment, and Alzheimer’s disease (AD). Estimates vary
from 1 to 28% depending on whether clinical or community-dwelling populations are used
(Bryant et al., 2008; Therrien & Hunsley, 2011; Wolitzky-Taylor, et al., 2010). Anxiety also
impact visuals search tasks, with generalized anxiety disorder patients showing slower RTs, but
not enhanced detection of threats (Rinck et al., 2003). There exists, however, a paucity of
evidence regarding the potential influence anxiety, especially non-clinical anxiety, may have
upon research outcome and therefore potentially clinical results and interpretation. Evidence
exists that associates anxiety with the ventral prefrontal cortex and the amygdala. These two
areas of the brain have greater activation in anxious situations as they are the main activation
sector to responses accorded by the brain (Guyer et al, 2008). Anxiety brings about an increase in
stimulus-driven processes that negatively impact efficient top-down control and consequently
have a detrimental effect on the ability to regulate emotional inhibitions (Ansari & Derakshan,
2011).
Anxiety also impact visuals search tasks, with generalized anxiety disorder patients
showing slower RTs, but not demonstrating enhanced detection of threats (Rinck et al., 2003).
Dennis, Scialfa and Ho (2004) found that older adults were no more reliant on bottom-up vs. top-
down learning in visual search tasks compared to younger adults, and were no more susceptible
to distractions.

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Subjective feelings of cognitive impairment
Subjective cognitive impairment (SCI), while not recognized as a mental disorder in the
DSM-V, may presage future decline in cognitive abilities. Symptoms of SCI reports are similar
to those for MCI: increasing level of forgetfulness, depression, overwhelmed feelings, etc.
(Stewart, 2012). SCI is considered subjective in nature because patients with SCI do not fail
objective tests of cognitive functioning, though they report cognitive lapses performing daily
cognitive tasks. Stewart (2012) noted that SCI is associated with some brain abnormalities
similar to those associated with dementia. Some underlying brain changes may be amenable to
interventions that slow or even stop further. An emerging area of research in ageing and
dementia is the characterization of subjective feelings of cognitive impairment and the
development of dementia and effects upon behaviour, daily living and social interaction
irrespective of aetiology (Torrens-Burton et al., 2017).
Methods
Participants
Participants included a group of younger (n =52) undergraduates chosen from the
psychology department at the University, with 31 females and 21 males (mean 19.9; s.d. 1.6;
range 18 to 25 years) and older adults (n =52) recruited from the general public, 32 females and
20 males (mean 66.5; s.d. 4.5; range 50 to 80 years). Exclusion criteria included severe
depression, poor self-reported general health; history of head injury or neurological, medical, or
psychological problems; medically diagnosed subjective cognitive impairment and self-reported
medications that impact cognitive functioning. All had normal or corrected to normal vision and
hearing.

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Table 1: Group Demographics
Younger Group Older Group
Age (s.d.; range) 19.9 (s.d.=1.6; 18–25) 66.5 (s.d.=4.5; 50–80)
No. Recruited 23 M; 31 F 21 M; 32 F
No. Excluded 2 M; 0 F 1 M; 0 F
Total Participants (N) 52 (21 M; 31 F) 52 (20 M; 32 F)
Years of Education 14.72 (s.d. = 2.698) 14.53 (s.d. = 4.320)
Data Collection and Instruments
In addition to providing demographic data, participants completed a battery of assessment
instruments and performed a computer-based Visual Search task. The battery of instruments
included the Beck Depression Inventory (BDI), which was used to exclude participants with
results indicating high levels of depression, i.e., scores >20 (Farinade, 2013). The BDI is a 21-
item instrument with responses rated on a 0 to 3 Likert scale, giving a possible range of scores
between 0 and 63, with scores greater than 21 associated with depression in the general
population (Farinade, 2013). Other instruments used included the Montreal Cognitive
Assessment (MoCA) v.7.1, the Prospective-Retrospective Memory Questionnaire (PRMQ),
Pittsburgh Sleep Quality Index (PSQI), Beck Anxiety Inventory (BAI) and the State Trait
Anxiety Inventory (STAI), including both the State and Trait subsections (STAI-S and STAI-T).
The MoCA is designed to objectively assess cognitive domains such as attention, concentration,
executive functions, memory, language, visuospatial skills, abstraction, calculation, and
orientation (Julayonont et al., 2013). The PRMQ is a self-reported instrument that measures
prospective and retrospective memory slips in ordinary living activities. The PSQI is a self-rated
questionnaire which assesses the quality and patterns of sleep in adults. The STAI is a commonly
used measure of the intensity of feelings of anxiety, differentiating between current-state anxiety

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in the present moment and trait anxiety that is a general tendency to perceive situations as
threatening or anxiety-producing (McDowell, 2006). The full STAI has two scales, the STAI-S
Anxiety scale evaluates current state of anxiety, and the STAI-T Anxiety scale evaluates general,
long-lasting feelings of anxiety. The STAI is highly reliable in both scales, and across a variety
of samples, including older adults, working adults, general population, students, and others
(McDowell, 2006). The BAI measure is designed to distinguish between anxiety and depression
using a 21-item instrument with a 4-point Likert scale range, between 0 to 3. Total scores range
between 0 and 63, with scores between 0–9 indicating normal or no anxiety, 10–18 indicating
mild anxiety levels, 19–29 indicating moderate to severe anxiety, and 30 or greater indicating
severe anxiety (Julian, 2011).
Visual Search Task
For the purpose of computer-based visual search task, time which is taken to respond the
target in the context of isolation and irrelevant as well as distracting stimuli was determined. All
such investigation was conducted on a Dell Precision PC running Windows XP X86 CPU,
viewed at a distance of 57 cm. In order to meet the goals and objectives all trials were made
including black target. In this task, participants were shown a specific figure, either a left-
pointing or a right-pointing arrow. They were asked to determine whether the arrow pointed left
or right. The target appeared alone 8 times at each of the locations around the circle and 8 times
when it was surrounded by 7 distracting figures as shown in Figure 1. For recording time that is
taken for position the target when it is appeared alone and with 7 distracters clock face
configuration was used. In this, total number of 64 trials were made in which target is appearing
8 times at every clock face locations. Under half of the trials, distracters were presented, whereas

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as in the remaining case not. In the left image in the figure, the target arrow pointed left; in the
right image, the target arrow (in the lower left of the image) pointed right. Separate data was kept
for trials that included both the presence and absence of distracters to determine the size of the
impact of the distracters on the RTs. The central cross in the image appeared 1000 msec. prior to
the target arrows and distractors. Participants fixed their gaze on the central cross and responded
by pressing either the left < key or the right > key as quickly as possible while still maintaining
accuracy. Participants had a practice block of up to 10 trials to ensure they understood the task.
They received no feedback on the accuracy of their responses. About 64 trials were completed in
a single block unless the participant specifically requested a break.
Figure 1. An example of the visual search task with and without distracters
Incorrect responses, those obviously due to a lapse of concentration or disturbance, and
those with times less than 150 msec. (the “normal” RT expected) were excluded from the data.
The data analysis generated the participant’s median RTs for the target by itself and for the target
with distractors. Those individual RTs combined to create group mean RTs.

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RESULTS
Table 2 presents a brief summary of the basic results from the computer tasks. The results
are broken down by participant group (i.e., older or younger adults) and include the mean RT for
the group, the standard deviations and standard error of the mean for each aspect of the visual
search task (i.e., target presented alone, target plus distracters, and the visual search effect,
computed by subtracting target alone RTs from the target plus distracters RTs.
Table 2: Test Results from the Computer Tasks
Mean
RT (msec.) Std. Dev. Std. Error
Mean
Visual Target Alone (50-80) older adults 982.40 384.327 53.297
(18-25) Young group 798.81 262.420 36.391
Visual Target + Distracters (50-80) older adults 1618.96 394.688 54.733
(18-25) Young group 1344.21 223.309 30.967
Visual Search Effect (50-80) older adults 636.56 204.455 28.353
(18-25) Young group 545.40 192.234 26.658
Komogorov Smirnov test
Value of test is lower then 0.05 which indicate that variables are not normally distributed
and justified application of non-parametric test. From assessment, it has found that p value is
0.00 which shows that alternative hypothesis in true. Thus, it can be presented that statistical and
significant difference takes place in the dependent variables across the groups. As is often the
case with information speed studies the data is not normally distributed and hence a non-
parametric analysis approach will be used.
Visual search task: Information processing speed as per age group
Older Adults vs. Younger Adults
By applying descriptive statistics tool on the data set of older adults it has been assessed
that mean value of plus distracters (msec 1618.96) was greater than target alone (msec 982.40)

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and visual search effect (VSE) (msec 636.56). Hence, it can be depicted from such evaluation
that people of old age group takes more attention when they need to assess target plus distracters.
On the other side, in the case of young age group, mean value of plus distracters (msec 1344)
over other variables (msec, VTA 798.81, VSE 545.40). This aspect clearly exhibits that old age
people take more attention in comparison to the younger one.
Table 3. Mann-Whitney U Nonparametric Test Results All Adults
Target alone
Target Plus
Distracters VSE
All Adults Mann-Whitney U 892.000 770.500 876.500
Wilcoxon W 2270.000 2148.500 2254.500
Z -2.991 -3.781 -3.092
Asymp. Sig. (2-tailed) .003 .000 .002
Younger Adults Mann-Whitney U 131.000 118.000 248.000
Wilcoxon W 627.000 614.000 744.000
Z -3.628 -3.871 -1.446
Asymp. Sig. (2-tailed) .000 .000 .148
Older Adults Mann-Whitney U 217.000 226.000 298.500
Wilcoxon W 745.000 754.000 508.500
Z -1.937 -1.768 -.404
Asymp. Sig. (2-tailed) .053 .077 .686
Further analysis was conducted with a Mann-Whitney nonparametric test, based on all adults,
older adults, and younger adults. The results of these tests are presented in Table 3. For the target
alone condition, Mann Whitney u test analysis revealed significantly slower mean choice RT i.e.,
information processing speed in the older compared to the younger adults (U=131, p=.000).
Similarly when the same target was surrounded by distracting stimuli, choice RT was also
significantly slower for the older (U=226, p=.077) as compared to the younger adults (U=118,
p=.000). Along with this, by doing assessment, higher differences were identified that between
RT and measures without distracters. Moreover, statistical results shows in the context of older
adults (U=298.5, p=.686), whereas the same in younger adults imply for (U=248, p=.148)

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respectively. Hence, considering the overall evaluation it can be depicted that level of RT was
longer or higher in the case of older adults as compared to the younger one. Thus, it can be
mentioned from the study that distracting information level or aspect has significant impact on
the information processing speed of older adults rather than younger.
Attentional function [the search effect] (measured by subtracting the target alone choice RT from
the target plus distracters RT) was also significantly greater (i.e., the distracters made RT much
longer in the old than for the young, i.e., distracting information had a far greater detrimental
effect upon information processing speed for the old compared to the young).
Visual search task: information processing speed and Anxiety (Age groups)
Older Adults
Non-parametric correlation, Spearman RHO test, revealed that for the older adult group
there was a negative correlation between the target alone choice RT and state anxiety [r=
–.111p=.434]. and trait anxiety (r = –.122, p =.391). Further, target plus distracters choice RT
and State [r= –.029, p=.837] as well as Trait anxiety [r= –.050, p=.724]. This aspect shows that if
anxiety level will be higher then information processing speed of older adults is lower.
Moreover, from assessment, it has identified that anxiety level interferes with verbal tasks and
activities that require more attention as well as co-ordination. Due to the declining memory level
and processing resource older adults are recognized as more vulnerable over younger adults
because anxiety has greater influence on multiple cognitive domains.
For the visual search effect [ie measure of attention and detrimental effect of distracting
information on information processing speed) was positively correlation with state measure (r =
0.182, p=0.05). Hence, considering the overall evaluation, it can be depicted that greater anxiety
level has high level of impact on information distraction and RT’s. Hence, in the category of
older adults, anxiety level has significant impact or influence on RT (target alone) in against to
target plus distracters.

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Young adults
The below mentioned table shows significant (p>0.05) as well as negative correlation (r =
-.336) between the variable BAI and target-alone. Further, negative correlation (r= -339) has also
found among BAI and target plus distracters to the significant level (P>0.05). Hence, by taking
into account such aspect, it can be depicted that if one variable will increase then other moves in
downward direction. This aspect shows that in the case of younger adult’s anxiety level has
significant impact on response time and target plus distracters in a negative manner.
Table 4 summarizes the correlations between information processing speed and anxiety
by age groups, i.e., older adults and younger adults.
Table 4. Information Processing Speed and Anxiety
BAI STAI-S STAI-T
Older Adults Target Alone Correlation –.051 –.111 –.122
Sig. 0.721 0.434 0.391
Target + Distracters Correlation –.007 –.029 –.050
Sig. 0.960 0.837 0.724
VSE Correlation 0.069 0.182 0.147
Sig. 0.625 0.050 0.297
Younger Adults Target Alone Correlation –.336 –.097 –.121
Sig. 0.015 0.494 0.392
Target + Distracters Correlation –.339 –.108 –.128
Sig. 0.014 0.445 0.364
VSE Correlation –.109 –.078 –.109
Sig. 0.442 0.584 0.443

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Visual search task: information processing speed (Gender differences)
Visual search task: information processing speed and Anxiety (Gender differences)
The visual search task data were also analysed comparing anxiety and gender for both
older and younger adult groups. Table 5 summarises the results of that analysis for each of the
BAI, STAI-S and STAI-T instruments.
Table 5. Spearman’s Rho for Anxiety and RT by Gender
BAI STAI-S STAI-T
Older Males Target Alone –0.94 (p=.695) –.430 (p=.050) –.243 (p=.302)
Target + Distracters .076 (p=.749) –.248 (p=.292) –.134 (p=.574)
VSE .205 (p= .387) –.434 (p=.050) .325 (p=.162)
Older Females Target Alone .051 (p=.781) .093 (p=.614) –.092 (p=.617)
Target + Distracters .010 (p=.959) .126 (p=.492) .052 (p=.776)
VSE .013 (p=.944) .028 (p=.879) .065 (p=.723)
Younger Males Target Alone .238 (p=.298) .091 (p=.696) .139 (p=.548)
Target + Distracters .231 (p=.315) .143 (p=.536) .231 (p=.314)
VSE –.072 (p=.756) –.118 (p=.610) .071 (p=.759)
Younger Females Target Alone –.285 (p=.120) –.077 (p=.679) –.100 (p=.591)
Target + Distracters –.248 (p=.178) –.120 (p=.519) –.140 (p=.453)
VSE –.064 (p=.731) –.094 (p=.615) –.153 (p=.411)
Young males and females:
Statistical output shows that correlation found in younger adults differ from the older one.
In the case of young males, positive relationship has found RT and state as well as trait anxiety
but to the lower level that highly closer to zero. This aspect shows that younger males take more
time for giving response when anxiety level increases. However, exceptionally, in younger
males, negative correlation takes place between VSE and state anxiety. On the other side, in the
case of young females all of such factors having lower and negative relationship with each other.
As, result of Spearman’s Rho, it can be stated that younger adults yielded different results over
the older one. Thus, referring correlation analysis, it has found that responses time and manner
of individuals differ as per gender.

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Older males and females:
From Spearman’s RHO test negative correlation has identified between state and trait
anxiety while doing assessment of older males. However, exceptionally it has assessed and stated
that state anxiety has negative relationship with target alone and VSE measures. On the other
side, in the case of older females, it has found that positive as well as lower relationship takes
place between anxiety measures and RT’s. Thus, referring the results it can be inferred that, in
older adults, greater anxiety level resulted into faster RT’s. In other words, it can be depicted that
in the case of having high anxiety level older males will response quickly. However, the same
situation or outcome is reverse in the case of older females.
Information Processing Speed with Other Variables by Age (BDI, MOCA, PSQI and
PRMQ)
Table 6 presents the Information processing speed correlations with other measures in
this study, including the BDI, the MoCA, the PRMQ, and the PSQI instruments.
Table 6. Information Processing Speed Correlations with Other Measures by Age
BDI MoCA PRMQ PSQI
Older Adults Target alone Correlation .054 –.170 –.250 .041
Sig. (2-tailed) .702 .227 .037 .774
Target + Distracters Correlation .183 –.258 –.264 –.012
Sig. (2-tailed) .195 .032 .029 .933
VSE Correlation .231 –.198 .012 .018
Sig. (2-tailed) .050 .160 .934 .901
Younger Adults Target alone Correlation –.148 .199 –.329 –.197
Sig. (2-tailed) .294 .157 .017 .161
Target + Distracters Correlation –.157 .141 –.253 –265
Sig. (2-tailed) .266 .318 .071 .029

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VSE Correlation –.043 –.013 .045 –.048
Sig. (2-tailed) .763 .928 .752 .734
Older Adults vs. Younger Adults.
Interesting differences appeared in the various measures between the two age groups.
With respect to the BDI, older adults consistently had positive correlations for each of target
only, target plus distracters, and VSE. Older adults had statistically significant positive
correlations to the BDI for Visual Search Effect (0.231, p=0.050). Younger adults were
consistently negatively correlated with those three RT measures. This implies that in older
adults, greater levels of depression resulted in longer RTs and slower information processing
speeds, while in younger adults, greater levels of depression shortened RTs and resulted in faster
information processing speeds.
Measures of objective and subjective cognitive functioning also differed between older
and younger adults. The MoCA results were negatively correlated with RTs and with VSE for
older adults, but where positively correlated with RTs (but not with VSE) for younger adults.
This implies that greater cognitive impairments in older adults resulted in shorter RTs while
greater cognitive impairment in younger adults resulted in longer RTs.
The PRMQ measure of subjective cognitive functioning again was negatively correlated
with RTs in older adults, but also negatively correlated with RTs in younger adults. Older adults
had statistically significant negative correlations to the PRMQ for target alone (0.250, p=0.037)
and the target plus distracters (–0.264, p=0.029). Younger adults’ times negatively correlated
PRMQ with the target alone (–0.329, p=0.017). This implies that an individual’s sense of SCI
tended to decrease overall RTs for both age groups.

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Sleep quality as measured by the PSQI instrument tended to have near-zero correlations
with older adults (maximum magnitude was only 0.041), but had substantially larger and
negative correlations for younger adults. Young adults had statistically significant negative
correlations to the PSQI for target plus distracters (–0.265, p=0.029). This implies that poorer
sleep quality (i.e., higher PSQI scores) correlated with shorter RTs and faster information
processing speeds.
Information Processing Speed with Other Variables by Gender (BDI, MOCA, PSQI and
PRMQ)
Table 7 presents the Information processing speed correlations with other measures in
this study by gender, including the BDI, the MoCA, the PRMQ, and the PSQI instruments.
Table 7. Information Processing Speed Correlations with Other Measures by Gender
BDI MoCA PRMQ PSQI
Older Males Target Alone .003
(p=.990)
–.469
(p=.037)
–.128
(p=.590)
–.111
(p=.642)
Target +
Distracters
.246
(p=.296)
–.434
(p=.050)
–.287
(p=.221)
–.053
(p=.823)
VSE .464
(p=.039)
–.116
(p=.626)
–.126
(p=.597)
–.031
(p=.898)
Older Females Target Alone .053
(p=.773)
.133
(p=.467)
–.238
(p=.190)
.176
(p=.336)
Target +
Distracters
.108
(p=.557)
–.077
(p=.677)
–.079
(p=.669)
.032
(p=.863)
VSE .046
(p=.801)
–.240
(p=.187)
.107
(p=.559)
.062
(p=.735)
Younger Males Target Alone .215
(p=.350)
.211
(p=.358)
–.360
(p=.109)
–.053
(p=.863)
Target +
Distracters
.366
(p=.103)
.253
(p=.268)
–.175
(p=.448)
.076
(p=.743)
VSE .111
(p=.631)
–.018
(p=.938)
.196
(p=.394)
.122
(p=.600)
Younger Females Target Alone –.075
(p=.687)
.155
(p=.406)
–.328
(p=.036)
.014
(p=.940)

21
Target +
Distracters
–.155
(p=.406)
.126
(p=.500)
–.322
(p=.039)
–.176
(p=.344)
VSE –.702
(p=.702)
.002
(p=.992)
–.078
(p=.676)
–.069
(p=.712)
Older Males vs. Older Females and Other Measures
Both older males and older females showed positive correlations of similar magnitude
between BDI and each of the visual search task measures, including the VSE. Older males’ BDI
scores positively correlated with VSE times (0.464, p=0.039). No such statistically significant
correlations appeared for older females.
The MoCA measures were not as consistent across gender for the older group, with males
showing strong negative and generally statistically significant correlations for both target alone (r
= –.469, p = .037) and target plus distracters (r = –.434, p = .050), but older females had positve
correlation with the target alone, and negative correlations with both target plus distracters and
VSE. The MoCA correlation in older males was also statistically significant with the target alone
task (–0.469, p=0.037) and the target plus distracters task (–0.434, p=0.050). In essence, these
results imply that greater cognitive dysfunction implies shorter RTs in general for the older
adults.
The SCI measure (i.e., PRMQ) were consistently negatively correlated for both older
males and older females, and with approximately equivalent magnitudes. This implies again that
greater SCI perception was associated with faster RTs.
In sleep quality, however, major gender differences were found. Older males had small
negative correlations with PSQI and RTs across the board, implying greater sleep dysfunction
resulted in faster RTs. In contrast, older females had positive correlations with the PSQI scores,
implying that more sleep dysfunction tended to increase the RTs for older females.

22
Younger Males vs. Younger Females and Other Measures
The BDI showed sharp differences between younger males and younger females.
Younger males showed positive correlations for both target alone, target plus distracters, and
only slightly negative correlation with VSE, implying that greater depression led to slower RTs
for younger males. In contrast, younger females had negative correlations for all three values,
implying greater depression resulted in faster RTs for younger females.
For cognitive measures, both males and females in the younger group had generally
positive correlations, implying greater cognitive dysfunction resulted in longer (slower) RTs.
However, greater SCI dysfunction (as measured by the PRMQ) resulted in negative correlations,
implying SCI was associated with faster RTs in both males and females. These correlations were
statistically significant for younger females only for both target only (r = –.328, p=.036) and
target plus distracters (r = –.322, p=.039).
In terms of sleep quality measures, younger males and younger females both had small
correlations, with younger males having negative correlations only for target alone, and younger
females having negative correlations for both target plus distracters and for VSE.
Discussion
The main motive behind conducting present study is to assess the extent to which anxiety
level influences information processing speed and variability, memory & attention in the context
of younger as well as older adults. The results show that not only does anxiety impact
information processing speeds, but it does so in varying ways based on both age and gender.
Furthermore, objective measures of cognitive functioning and subjective measures of cognitive
functioning also impacted the information processing speed of the participants, again in varying

23
ways depending on age and gender. Sleep quality and depression were also shown to impact
information processing functions.
In this, findings clearly support hypothesis which in turn shows that response time varies
in the case of younger and older adults when variables like target alone as well as plus distracters
have been considered. Statistical output presents that older adults were more negatively affected
from distracting info as compared to younger adults having larger VSE scores. The response
times (RT) for older adults were universally longer than for younger adults on the same task, in
accord with prior studies such as that by Arnold et al. (2015).
Despite prior studies by Valeriani et al (2003), Commodari, and Guarnera (2013), in
which women were found to have slower response times in visual search times, no such gender-
based impairment was identified in this study. No statistically different response times were
identified by gender on the computer-based task. With that said, older males only were found to
experience performance impairments with higher State Anxiety scores indicating improved
Visual Search task in both Target Alone and Search Effect measures.
Higher measures of objective cognitive impairment also impacted the Visual Search tasks
to a great extent. From assessment, it has identified that cognitive impairment implies for the
decline which takes place in the abilities of individual pertaining to memory, thinking skills etc.
Further, it has found that risk is relation to developing Alzheimer’s or another dementia
increased significantly when level of cognitive impairment is high. On the other side, in the case
of visual search task, active attention is required to scan the environment. Considering such
aspect, it can be stated that cognitive impairment has high level of influence on visual search task
(Torrens-Burton, Basoudan, Bayer & Tales, 2017). From evaluation, it has identified that
cognitive impairment has greater impact on response time. Moreover, for performing visual

24
search task, high attention is required to scan the environment. Considering such aspect, it can be
depicted that cognitive impairment level closely impacts visual search task (Tales, Bayer,
Haworth & Snowden, 2010). Further, results reveal that VSE time is longer when depression
scores higher. Hence, all such aspects were statistically significant and occurred only in older
males. Prior studies have indicated that anxiety lengthens response times, and the Visual Search
task displayed impacts of anxiety levels.
Anxiety and response time are the main two variables that highly associated with each
other but it varies on the basis of age factor. Visual Search tasks had correlations with anxiety
scores, as measured by the STAI-S, and STAI-T instruments. The impact of anxiety levels on
response times was also strongly age-dependent. Older adults had significantly negative
correlations between state anxiety (as measured by STAI-S) and the Visual Search scores, with
higher anxiety correlating to lower response times.
Cognitive impairment has been identified as a detriment to both attention and to response
times in some studies, while Naveteur et al. (2005) found that mild anxiety may improve overall
response times particularly in visual search tasks, although results on this are somewhat
inconsistent in the literature (Derakshan & Koster 2010; Phillips & Takeda, 2010). Dennis et al.
(2004) found that older adults were less susceptible to distractions than younger adults were. In
this study, that inconsistency in result was evident.
In terms of objective cognitive impairment (the MoCA), increased objective cognitive
impairment in older adults led to significantly shorter response times in the Visual Target plus
Distractors task. Increased subjective cognitive impairment scores in older adults also led to
shorter response times in both the Visual Target Alone and the Visual Target plus Distractors

25
tasks. In contrast, increased objective cognitive impairment in younger adults had no statistically
significant impact on response times for any of the visual tasks. The subjective cognitive
impairment scores did impact both the Visual Target Alone and the Visual Target plus
Distractors tasks in younger adults, with the correlation with the Visual Target plus Distracters
task just short of statistical significance. It means that, in the case of target plus distracters,
cognitive impairment impact younger adults but not with the higher rate in the context of
response time.
From both statistical evaluation and secondary data set it has found that response time
moves in an unpredictable manner in the context of both older and younger adults. Moreover,
sometimes situation of cognitive impairment improves response time and vice versa. Further, it
has assessed that subjective measure of cognitive impairment affects Visual Search tasks
significantly. Such findings can also be explained through results of younger adults which in turn
present that response time decreases when SCI increase. On the other side, in the case of older
adults, objective cognitive impairment has greater influence on response times in a negative
manner.
From overall evaluation, it can be presented that aspects in relation to anxiety, cognitive
impairment and attention differently affects responses of both younger & older adults. Hence, by
using such system relatively new discipline can be maintained within medicine, neuroscience
and psychology. Furthermore, the responses are inconsistent, with some tasks negatively
impacted and others with performance improvements. With no significant gender differences
appearing in either older or younger age groups, no support was found for Valeriani et al (2003)
and Commodari and Guarnera (2013) results.

26
Study Limitations
The key limitation of this study is the relatively small scale of the investigation with only
(n=52) participants in each of the two age groups. In addition, this study used only cognitively
healthy adults, even for the older adult group. It is unclear if these results would be replicated in
a more cognitively mixed group of participants. Neuroimaging is also known as brain imaging
which in turn lays high level of emphasis on using various techniques through either directly or
indirectly. Such system provides high level of assistance in imaging the structure and functions
of nervous system. Thus, by using such system relatively new discipline can be maintained
within medicine, neuroscience and psychology. Hence, it is recommended that, in the further
research, high level of emphasis should be placed on including more respondent. Along with
this, in the future investigation researcher needs to lay focus on evaluating aspects related to
neuroimaging for presenting the better view of nervous system.
ACKNOWLEDGMENTS
<TO BE ADDED LATER>
I would like to impart my sincere thanks to Mr. Gareth who provided assistance to me in
recruiting older adults for the concerned study. From his support and experience I have
completes dissertation prominently. Finally, I want to extend my sincere thanks to everyone who
have directly or indirectly supported me in completion of my dissertation.

27
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