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ATTENTIONAL SELECTION
AND SUPPRESSION IN NON-
CLINICAL ADULTS
An Event-Related Potential Study
Bachelor Degree Project in Cognitive Neuroscience
Basic level 22.5 ECTS
Spring term 2020
Oscar Magnusson
Supervisor: Oskar MacGregor
Examiner: Pilleriin Sikka
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 2
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that manifests
as a developmentally inappropriate pattern of inattention, and hyperactivity or impulsivity.
ADHD is a multifactorial disorder with inter alia deficits in selective attention processing. The
current diagnosis of ADHD is error-prone as it relies on subjective descriptions and external
observations of behavior. Measures that are less reliant on subjective descriptions can enable
more accurate and informative diagnoses of ADHD. Wang et al. (2016) have identified two
event-related potential (ERP) components, posterior contralateral N2 (N2pc) and distractor
positivity (PD) as predictors of ADHD symptom severity in children. N2pc reflects target
selection and PD reflects distractor suppression during visual selective attention. The present
study aimed to examine how target-evoked N2pc and distractor-evoked PD related to attentional
capacity in non-clinical adults. Participants were presented with a visual search paradigm and a
self-report scale, the Everyday Life Attention Scale (ELAS). The amplitude of target-evoked
N2pc and distractor-evoked PD amplitude was compared to ELAS score in multiple linear
regression models. Results displayed that the peak amplitude of target-evoked N2pc was a
significant predictor of attentional capacity (as measured with ELAS), while the peak amplitude
of distractor-evoked PD was not associated with attentional capacity. Participants with higher
attentional capacity (ELAS score) displayed less negative peak amplitudes of target-evoked
N2pc. This seems to suggest that target selection, but not distractor suppression in nonclinical
adults can predict attentional capacity. However, due to a limited sample size, further research is
needed before drawing any major conclusions.
Keywords: attention deficit hyperactivity disorder, everyday life attention scale, selective
attention, target-evoked N2pc, distractor-evoked PD
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 3
Table of Contents
1. Introduction ................................................................................................................. 5
2. Background ................................................................................................................. 6
2.1 Attention Deficit Hyperactivity Disorder.................................................................. 6
2.1.1 Functional neuromarkers of attention deficit hyperactivity disorder. ................ 9
2.2 Spatial and Selective Visual Attention .................................................................... 10
2.2.1 The N2pc component. ...................................................................................... 12
2.2.2 The PD component. ......................................................................................... 15
2.3 Rationale for the Present Study............................................................................... 17
2.4 Aim and Hypothesis for the Present Study ............................................................. 21
3. Method ......................................................................................................................... 21
3.1 Participants .............................................................................................................. 21
3.2 Sampling Procedure ................................................................................................ 21
3.3 Stimuli ..................................................................................................................... 22
3.4 Visual Search Task .................................................................................................. 23
3.5 Everyday Life Attention Scale ................................................................................ 25
3.6 Experimental Procedure .......................................................................................... 27
3.7 Electroencephalography Recording Setup .............................................................. 27
3.8 Processing of Electroencephalography Data .......................................................... 28
3.9 Data Analysis .......................................................................................................... 30
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 4
4. Results .......................................................................................................................... 31
4.1 Behavioral Analysis ................................................................................................ 31
4.2 Target-Evoked N2pc and Distractor-Evoked PD .................................................... 32
4.3 Predicting ELAS Score from Target-Evoked N2pc and Distractor-Evoked PD
Amplitude ................................................................................................................................. 34
4.4 Peak and Mean Amplitude Analysis ....................................................................... 38
5. Discussion .................................................................................................................... 38
References ......................................................................................................................... 45
Appendix A ....................................................................................................................... 57
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 5
1. Introduction
In everyday life, we are constantly guided by the visual information we take in. When we
are looking for our missing keys, intercept a moving ball, or cross the road we rely on the
accuracy and speed of incoming sensory information to perform necessary behaviors and reach
our goal. Within our brains, an intricate system of processing mechanisms enables us to
selectively process relevant information from an otherwise overwhelming set of individual data
points. This system is commonly known as selective attention. Selective attention constitutes a
set of cognitive processes that enables the optimal direction of neurocognitive resources through
capacity-limited pathways (Burra & Kerzel, 2014).
In most people, this system is so effective that we do not even acknowledge its existence.
We only become aware of it once something malfunctions. There are a few different disorders
wherein attention processing has become impaired (Gitelman, 2003). The most common
attentional disorder is attention deficit hyperactivity disorder (ADHD). ADHD is a
neurodevelopmental disorder that manifests as a developmentally inappropriate pattern of
inattention and hyperactivity or impulsivity (Gallo & Posner, 2016). ADHD is diagnosed in 4%
of children and 0.05% of adults (age 40) (Sachdev, 1999).
Currently, the diagnosis of ADHD is heavily reliant on subjective descriptions and
external observations of behavior. Although the Diagnostic and Statistical Manual of Mental
Disorders (DSM-5) (American Psychiatric Association, 2013) and other diagnostic guidelines
provide accurate descriptions of symptoms, diagnosis is error-prone. There is an urgent need for
more sensitive and objective measures for the diagnosis of ADHD. Neuroscientists are currently
examining the sensitivity and accuracy of functional neuroimaging techniques for the diagnosis
of ADHD (Mller et al., 2019). The measurement of event-related potentials (ERP) appears
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 6
promising as a diagnostic method. It allows for tracking of cognitive processes non-invasively at
millisecond scales (Banaschewski & Brandeis 2007).
Recent findings by Cross-Villasana et al. (2015), Luo et al. (2019), and Wang et al.
(2016) have demonstrated that the peak amplitudes of posterior contralateral N2 (N2pc) and
distractor positivity (PD) display a significant difference in amplitude between individuals with
and without ADHD. However, findings in adults are inconsistent (Cross-Villasana et al., 2015;
Luo et al., 2019). Moreover, although the amplitudes of N2pc and PD have been shown to predict
ADHD symptom severity in children with ADHD (Wang et al., 2016), which has raised interest
in the components as potential functional neuromarkers of ADHD, no such studies have been
conducted on adults. The aim of the current experiment is to examine how target-evoked N2pc
and distractor-evoked PD relates to attentional capacity in non-clinical adults.
2. Background
This section provides a contextual background of relevant research and findings. First, a
brief introduction to ADHD, including its characteristics and diagnosis, is provided. Potential
functional neuromarkers for ADHD are also discussed. Next, processing characteristics of spatial
and selective visual attention are presented, including categories of attention, and two attentional
ERP components, N2pc and PD. Finally, research on the N2pc and PD in adults and children, as
well as in participants with and without ADHD is presented.
2.1 Attention Deficit Hyperactivity Disorder
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that
manifests as a developmentally inappropriate pattern of inattention and hyperactivity or
impulsivity (Gallo & Posner, 2016). ADHD is more common in children (4%), than in
adolescents aged 20 (0.8%), and adults aged 40 (0.05%) (Sachdev, 1999). ADHD is also two to
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 7
four times more prevalent in male children than in females (Davies, 2014). For a child to be
diagnosed with ADHD they must display six or more symptoms from either the inattention or
hyperactive and impulsive domains. For adults (age 17 and older) to be diagnosed with ADHD, a
person must display at least five symptoms of either of the two domains (American Psychiatric
Association, 2013).
Brodeur and Pond (2001) have demonstrated that children with ADHD are less efficient
in selective attention tasks and that selective attention appears to improve with age. Although
children with ADHD have also been shown to perform adequately on spatial tasks that require
focusing of attention, their selective deficits are more linked to an inability to successfully inhibit
irrelevant stimuli (Malone & Swanson, 1993). ADHD is a multifactorial disorder that appears to
be influenced by a variety of genes and environmental factors. Even though there are multiple
factors affecting the disorder, ADHD traits of attentional capacity have been shown to follow a
normal distribution, where clinical ADHD is considered an extreme attentional impairment
(Coghill & Sonuga-Barke, 2012; Willcutt, 2005). Currently, most measures of ADHD are binary
(present or absent) with no potential in generating meaningful information in between the two.
This greatly limits the analysis of attentional impairment and may lead to a loss of meaningful
variations across the spectrum (Fair, Bathula, Nikolas, & Nigg, 2012). Improved measures that
detect attentional variations have the potential of providing clinicians with more details of each
individual case, which can enable a more beneficial treatment based on the severity of symptoms
(Greven et al., 2016).
Self-report questionnaires are most often employed to measure attentional capacity in
clinical settings. They provide an effective and convenient tool to determine attentional
impairments, especially for ADHD, since behavioral symptoms of inattention are central for
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 8
diagnosing ADHD (American Psychiatric Association, 2013). The Everyday Life Attention Scale
(ELAS) is a newly developed self-report questionnaire created by Groen et al. (2018). In this
questionnaire people are instructed to report their level of focus on a task in percentages or the
temporal durations during which they can stay focused on a task, rather than base their answers
on ambiguous reference points (a prevalent problem in many self-report questionnaires).
ELAS also provides nine different situations where attentional capacity can be
compromised. The formation of ELAS provides three distinct advantages: (1) removes ambiguity
from questions; (2) examines attentional capacity in more specific everyday situations; (3) scores
can be compared to averages of a wider population (e.g., equivalent educational background,
age, and gender) (Groen et al, 2018). Groen et al. (2018) validated ELAS in a “healthy” (non-
ADHD) sample as ADHD is an extreme deviation on the attentional spectrum. Validation of an
ADHD scale in a non-ADHD sample is advantageous since ADHD symptoms can be examined
with a limited risk of comorbid disorders and it can provide improved statistical power compared
to categorical measures (Coghill & Sonuga-Barke, 2012). ELAS scores sampled from a large
group (n = 1874) of Dutch participants displayed an average score of 58.6% (Fuermaier et al.,
2019).
The definition of attention and question formations in ELAS has in large part been
influenced by the multicomponent model of attention formalized by van Zomeren and Brouwer
(1994). The multicomponent model of attention is divided into five interdependent components,
vigilance/sustained attention, focused/selective attention, alertness (tonic and phasic), divided
attention, and strategy/flexibility. Sustained attention is the ability to actively focus on stimuli or
activity over an extended period of time (Sarter, Givens, & Bruno, 2001). Focused and selective
attention refers to attention that can be focused when presented with distractors (van Zomeren &
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 9
Brouwer, 1994). Although this is the definition based on the multicomponent model of attention
(van Zomeren & Brouwer, 1994), it should be noted that the model is outdated. More recent
models such as Posner´s model of attention (Petersen & Posner, 2012) have found further
support. The model presents focused and selective attention as part of two separate systems, with
selective attention arising in the orienting network and focused attention arising in the executive
attention network (Petersen & Posner, 2012).
Alertness reflects a person's state of arousal which affects their cognitive resources for
sensory processing (Oken, Salinsky, & Elsas, 2006). Divided attention is employed when people
perform two or more subtasks simultaneously which requires allocation strategies towards each
task to carry them out accurately and quickly (Karatekin, White, & Bingham, 2008).
Strategy/flexibility is not a distinct feature of attention in itself but rather one´s ability to adapt to
different task demands and situations (van Zomeren and Brouwer, 1994). Although improved
methods employed in self-report questionnaires such as ELAS have the potential to improve
accuracy and reliability for diagnosis of ADHD, diagnosis based on subjective descriptions is
still error-prone and new methods are desperately needed (Mller et al., 2019). The next sections
will discuss functional neuromarkers and their potential applications in diagnosing psychiatric
disorders such as ADHD.
2.1.1 Functional neuromarkers of attention deficit hyperactivity disorder.
Neuromarkers are measurable indicators of biological processes, used to evaluate normal as well
as pathophysiological processes. Functional neuromarkers are identified using functional
neuroimaging methods that record electrical or hemodynamic activity during neurocognitive
processes (Jollans & Whelan, 2018). A variety of methods have been used to identify functional
neuromarkers, including functional magnetic resonance imaging (fMRI), ERP, and continuous
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 10
electroencephalography (EEG) (Banaschewski et al., 2008; Bramon et al., 2008; Hasler et al.,
2016; Luck et al., 2011; Mayer, Wyckoff, & Strehl, 2016). Functional neuromarkers can reflect
pathological processes in a variety of psychiatric conditions, including ADHD and schizophrenia
(Luck et al., 2011).
Rosenberg et al. (2015) have for instance developed a model for attention task-based
connectivity, based on whole-brain functional connectivity in fMRI (termed Sustained attention
network [SAN] model). Although SAN was originally researched and developed while
participants performed sustained attention tasks it has also been deemed accurate in identifying
participants with ADHD from resting-state activity. Characteristics and application of two new
potential functional neuromarkers (N2pc and PD) will be presented in the coming sections.
2.2 Spatial and Selective Visual Attention
In visual perception, the quantity of information that enters the visual system is greater
than what can be processed. Thus, relevant visual information must be appropriately selected
(Parkhurst, Law, & Niebur, 2002). Through selective visual attention, the brain sorts sensory
information for processing through capacity-limited pathways (Burra & Kerzel, 2014). Selective
attention constitutes a set of cognitive processes that enable the optimal direction of
neurocognitive resources. It is generally accepted that attention operates through two means of
processing, stimulus-driven attention or bottom-up mechanisms, and goal-driven attention or
top-down mechanisms (Gaspelin & Luck, 2018b).
According to stimulus-driven theories, salient items will capture attention regardless of
the observer intention (Theeuwes, 1992; Yantis & Hillstrom, 1994). Salience refers to how a
stimulus stands out from its surroundings because of some distinct physical property (Gaspelin &
Luck, 2018b). Bright colors or loud sounds are good examples of stimulus-driven features, as
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 11
they do not require an observer’s self-imposed mental effort. According to goal-driven theories,
attention will only be captured by stimuli that correspond to features of a pre-specified target
item (Bacon & Egeth, 1994; Folk, Remington, & Johnston, 1992). Goal-driven attention can, for
example, be elicited during a search of a program icon (e.g., Google) on a computer screen or a
tiny golf ball in thick green grass, where the mental effort has to be intentionally directed.
The filtering of visual stimuli is partly reliant upon the inner configuration of the eyes.
The size of an external object presented within the visual field corresponds to its visual angle,
measured in degrees of arc. Humans’ visual peripheral field encloses approximately 135° arc
from its center (Gutwin, Cockburn, & Coveney, 2017). However, the majority of one variety of
photoreceptors referred to as cones (essential for color vision) reside within a slim region known
as the fovea. Although the fovea only extends across 2° of the visual field, most of the sensory
information that enters the brain goes through the fovea (Fairchild, 2013). To effectively process
incoming sensory information the eyes must be oriented so that relevant information enters
through the fovea. Eyes are oriented via small eye movements known as saccades. Saccades are
quick movements of both eyes simultaneously between phases of fixation (Alvarez, 2013).
Saccades drive one category of attention known as overt attention. During overt attention
saccades orient the eyes towards targets so that sensory information can enter through the fovea.
Although sensory information is most effectively processed through overt attention,
attention can also be allocated through another mechanism known as covert attention (Kulke,
Atkinson, & Braddick, 2016). During covert attention, attention is oriented by mentally shifting
focus without directly fixating on a target or moving the eyes. Covert attention enables effective
localization and processing of information outside of the fovea. Activation of neurons in visual
area V4 whose receptive field receives sensory information from attended stimuli is enhanced by
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 12
covert selective attention without altering the content of information (Gregoriou, Gotts, Zhou, &
Desimone, 2009).
2.2.1 The N2pc component. The filtering of visual stimuli has an important role in what
information we attend to and ultimately what information we perceive. The N2pc component is
an attentional component that was detected by Luck and Hillyard (1990) at Hillyard lab at UC
San Diego together with their colleges (Heinze, Luck, Mangun, & Hillyard, 1990; Luck, Heinze,
Mangun, & Hillyard, 1990; Luck & Hillyard, 1994a, 1994b). N2pc emerges in posterior
electrode sites contralateral to the hemifield where a target item is presented (Luck &
Kappenman, 2012).
N2pc has been identified as a component of covert selective (or visual-spatial, depending
on the definition) attention where targeted sensory information is selected (Li, Liu, & Hu, 2018).
It has become a popular tool for spatial and selective attention research (Luck, 2014; Tay,
Harms, Hillyard, & McDonald, 2019). N2pc is an early ERP component emerging during
attentional filtering in a large set of visual items (Tay et al., 2019). It is calculated as a
contralateral-minus-ipsilateral difference wave, where activity ipsilateral to a target is subtracted
from activity contralateral to a target. This simple subtraction is meant to remove underlying
source waves and strengthen components that differ (Luck & Kappenman, 2012). N2pc
amplitude is strongest in electrode sites PO7/PO8 (Bacigalupo & Luck, 2019; Tay et al., 2019).
Activity associated with the N2pc component typically emerges in the N2 wave between
150-300 ms post-stimulus presentation (Luck & Kappenman, 2012) however, some researchers
specifically localize the activity of the component between 180-200 ms (Berggren & Eimer,
2020). Multiple experimental paradigms can be exploited to elicit and examine N2pc. One
example is the capture-probe paradigm, were participants are tasked with reporting letters
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 13
briefly presented in the position of a singleton item. Participants are less likely to report probe
letters in the position of a singleton-distractor (Gaspelin, Leonard, & Luck, 2015). A singleton is
an item with one distinct feature that makes it stand out from its surroundings (Gaspelin & Luck,
2018a). The most common paradigm for eliciting N2pc is the visual search paradigm (Woodman
& Luck, 2003). In this paradigm participants are presented with a large array of stimuli, typically
eight or more (Tay et al., 2019; Woodman, Arita, & Luck, 2009), and instructed to attend to a
target with a specific feature (e.g., red, square). For each array, participants must press one of
two buttons to indicate the presence or absence of a target stimulus, or distinct features of a
target (e.g., a gap in a Landolt square) (Luck et al., 2006).
Localizing the neural generator of N2pc is difficult because of the inverse problem in
EEG (Luck, 2014; Michel et al., 2004; Hopf et al., 2000; Hopf, Boelmans, Schoenfeld, and Luck,
2004). However, using a combination of magnetoencephalography and fMRI, Hopf et al. (2000,
2004) have localized N2pc to the occipitotemporal cortex. Because of its location in the
occipitotemporal cortex and apparent processing within early ERP wave formations it has been
suggested that N2pc is elicited in the intermediate to higher-level visual processing areas within
V4 (in the ventral pathway) (Bacigalupo & Luck, 2019). Hopf et al. (2000, 2004) have also
identified a second covert selective attention component in the parietal regions. However,
because of its orientation, strength, and partial overlaps with the occipitotemporal component, it
has not been observed in EEG recordings.
Research on N2pc has shown that its properties can be altered by changing task demands.
N2pc appears for targets and for non-target arrays that require effortful attention to distinguish
them from distractors. However, it is absent for nontargets with salient feature dissimilarities
(Luck & Hillyard, 1994b). There is a significant reduction in N2pc amplitude when attentional
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 14
demands are reduced by removing distractions from visual search arrays (Luck & Hillyard,
1994b) or by presenting an insufficient number of distractors (Luck et al., 1997). Two studies
have demonstrated that N2pc amplitude is significantly more negative when the number of
distractors increases from one to three (Luck et al., 1997) or from three to 19 (Mazza, Turatto, &
Caramazza, 2009). Furthermore, N2pc amplitude becomes less negative when targets are
presented in the upper visual field as compared to the lower visual field (Bacigalupo & Luck,
2019; Luck et al., 1997).
N2pc amplitude is less negative in visual search for salient singletons or pop-outs
compared to multi-feature targets that require more effort to discern targets from distractors and
thus elicit top-down control mechanisms (Kerzel, Barras, & Grubert, 2018; Theeuwes, 2010).
N2pc amplitude has also been shown to be more negative when distractors are presented in close
proximity to a target. It has been suggested that the more negative amplitude in N2pc is caused
by both items being present within the same V4 receptive field (Luck et al., 1997), thus requiring
increased processing to effectively distinguish targets from distractors. Burra and Kerzel (2014)
have identified interindividual variations in N2pc amplitude between groups of more and less
distractible individuals. Less distractible individuals display more negative N2pc to distractor
items than highly distractible individuals. Burra and Kerzel (2014) suggest that their results
demonstrate that less distractible individuals allocate more attention to distractor items. Although
these findings are counterintuitive, results from studies on action video game players (Chisholm,
Hickey, Theeuwes, & Kingstone, 2010) and a study reporting a correlation between self-report
everyday distractibility and gray matter volume in the focal region of left superior parietal cortex
(Kanai, Dong, Bahrami, & Rees, 2011) seem to support these results.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 15
N2pc has also been shown to be diminished and delayed in special populations. N2pc
amplitude and onset are reduced and delayed in older individuals (Mage = 68) (Lorenzo-Lpez,
Amenedo, & Cadaveira, 2008). Similar results have been demonstrated in patients with hepatic
encephalopathy (a neurodegenerative disorder caused by liver failure) (Schiff et al., 2006).
However, no effects been observed in participants with schizophrenia (Luck et al., 2006),
Parkinson's disease (Praamstra & Plat, 2001), or athletes with a history of multiple concussions
(De Beaumont, Brisson, Lassonde, & Jolicoeur, 2007).
Selection history has been shown to influence the temporal emergence of the N2pc.
When an antecedent target is presented alongside a new target N2pc becomes significantly
delayed. The effect has been shown to remain over multiple blocks in a visual-search paradigm
(Kadel, Feldmann-Wstefeld, & Schub, 2017). N2pc is also affected by item eccentricity.
Papaioannou and Luck (2020) have demonstrated that N2pc remains robust up to 4° of visual
angle. When a stimulus appears at 8° N2pc amplitude becomes less negative. Similar findings
have been obtained by Parkhurst et al. (2002), with an apparent limit of 5° visual angle. Another
ERP component PD has also been identified as a covert selective attention component,
suppressing sensory information from distractor items. PD and its attentional processing
characteristics are presented in the next section.
2.2.2 The PD component. While N2pc emerges during selection of visual target items, a
separate ERP component the PD emerges during inhibitory processing of distractor items. The PD
component is a positive amplitude deflection that appears when task demands require
participants to effectively suppress distracting items in a visual search array (Luck, 2014). PD was
first observed by Hickey, Di Lollo, and McDonald (2009). Similar to N2pc, PD is calculated as a
contra-minus-ipsilateral difference wave, but directed by the location of a distractor rather than
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 16
by a target (Sawaki & Luck, 2010; Sawaki, Geng, & Luck, 2012). PD amplitude is largest in
electrodes sites PO7 and PO8 (Sawaki et al., 2012).
According to Sawaki and Luck (2010), there are two major findings that suggest that PD
reflects a suppressive or inhibitory process directed towards distractors. First, PD is lateralized in
respect to distractors and not targets, which implies that PD operates on distractors (Luck, 2014;
Sawaki & Luck, 2010). Second, in their original study, Hickey et al. (2009) observed a less
positive PD when participants were only requested to identify the presence of targets, instead of
evaluating its properties. This is thought to have reduced demands to actively suppress
distractors and therefore effectively eliminate PD (Luck, 2014). It has also been demonstrated that
PD but not N2pc is elicited if participants are shown highly salient distractors (e.g., color pop-
outs) (Burra & Kerzel, 2014). In contrast, when attentional priority demands are high, such as for
target processing in an identification task, target-evoked N2pc is elicited (Luck & Ford, 1998).
This seems to suggest that PD and N2pc are elicited during different attentional task demands.
When the activity associated with PD emerges has been inconsistent between studies,
within early (Sawaki et al., 2012), intermediate (Hilimire, Hickey, & Corballis, 2012), as well as
late time windows (Kiss, Grubert, Petersen, & Eimer, 2012; Sawaki & Luck, 2011). The
emergence of PD seems to vary across a broad range (100–400 ms) depending on stimuli and task
demands. An inconsistent onset may generate some problems for researchers examining PD, as
narrow measure windows may miss PD and wide time widows are unable to filter out the noise
and other components (Sawaki et al., 2012), something to take into consideration in PD studies.
As both N2pc and PD are measured at electrode sites PO7 and PO8, ideas have emerged for how
the components could be coupled within the neural circuitry. Some have suggested that N2pc
and PD emerge from the same dipole as both appear in the same region and have opposing
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 17
polarities (Luck, 2014). However, at present, there is little evidence to support this theory.
Another idea is that the components reflect processes occurring in two separate visual pathways.
N2pc is processed in the ventral pathway and PD in the dorsal pathway. There is more substantial
support for this theory as N2pc appears to be elicited in area V4 (and lateral occipital complex)
(Hopf et al., 2000, 2004), while PD has been localized in parietal regions (Hickey et al., 2009).
Besides suppressing distractor processing, Sawaki et al. (2012) have found evidence that
PD is one of the main mechanisms which terminate attention processing once attention is no
longer required. This would suggest that termination of attention is an active process indexed (at
least in part) by PD. Similar widespread suppression mechanism have been observed in memory-
driven attentional biases, wherein sensory inputs that match the information held within working
memory are automatically detected, but, signaling can be overcome by an active suppression
mechanism (PD) that prevents attentional capture (Sawaki & Luck, 2011).
2.3 Rationale for the Present Study
Identifying variations in neurocognitive processes between various groups can provide
researchers with both an improved theoretical understanding of specific processes, but also
information that can be applied in a larger context. In the last five years N2pc and PD research
has identified amplitude and onset latency variations between children and adults (Sun et al.,
2018) as well as between people with and without ADHD (Cross-Villasana et al., 2015; Luo et
al., 2019; Wang et al., 2016). The currently ongoing research regarding connections between
N2pc/PD and ADHD can prove beneficial in the understanding and assessment of ADHD
symptoms.
Sun et al. (2018) examined the neurophysiological bases of covert selective attention
during normal human development. They sampled a group of young adults (Mage = 27, female =
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 18
12) and children (Mage = 11, female = 8) for participation in two experiments. In the first
experiment participants were tasked with reporting the position (upper or lower field) of one
variant item among multiple items. In the second experiment, a salient-but-irrelevant color
singleton distractor was added to give rise to opposition between distractor’s bottom-up saliency
and an observer’s top-down goal. Results from Sun et al. (2018) demonstrated that the peak
amplitude of target-evoked N2pc was significantly smaller and prolonged in children compared
to adults during the first experiment. When distractors were presented laterally (in the search
array), peak amplitude of distractor-evoked PD was significantly larger in children than in adults.
These results appear to demonstrate a more pronounced selection of targets in adults and a more
pronounced suppression of distractors in children. Notably, although children displayed a more
distinct PD (indexing increased processing of distractors), they were less accurate in the
experiment task.
To further explore the relationship between PD amplitude and task accuracy Sun et al.
(2018) divided each age group into two sub-groups, one for participants with the highest 35%
accuracy on the experimental task and one for the lowest 35%. Comparisons between groups
demonstrated that PD was more positive in low-accuracy children as compared to high-accuracy
children. This seems to suggest that low-accuracy children require more inhibitory filtering of
distractor items than high-accuracy children during selective attention. Moreover, the high-
accuracy adults and high-accuracy children did not display a significant difference in PD
amplitude, suggesting that children with high accuracy exhibit similar active suppression during
selective attention as adults.
Wang et al. (2016) examined variations in N2pc and PD peak amplitude in children (9-15
years of age, female = 32), between a group with ADHD and typically developing children (TD).
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 19
Participants were presented with a fixed-feature singleton paradigm (analogous to Sun et al.,
2018). Results demonstrated that children with ADHD displayed a smaller target-evoked N2pc
compared to their TD counterparts during a low saliency visual-spatial task, which seems to
suggest that target selection is impaired in children with ADHD. Furthermore, children with
ADHD displayed a smaller distractor-evoked PD compared to TD children when presented with a
salient-but-irrelevant color singleton distractor. Wang et al. (2016) also demonstrated that
children with ADHD only showed less positive amplitudes in PD compared to TD children
during short reaction time trials but not during longer reaction times. These results seem to
suggest that active suppression of distractors is impaired in children with ADHD and that they
direct less attention to target items when they are unable to actively suppress distractors.
Wang et al. (2016) also demonstrated that the peak amplitudes of target-evoked N2pc and
distractor-evoked PD were associated with ADHD symptom severity. During low-saliency trials,
target-evoked N2pc amplitude predicted symptom severity in children with ADHD but not TD
children. Similarly, during trials with an added salient-but-irrelevant color singleton distractor-
evoked PD amplitude was shown to predict symptom severity in children with ADHD, as well as
decreased accuracy on the experiment task. Less negative target-evoked N2pc peak amplitude
and less positive target-evoked PD peak amplitude predicted higher symptom severity. This
seems to suggest that attentional problems in children with ADHD are at least partly related to
impairments in covert selective attention which potentially arises from deficits in the attentional
selection of targets (indexed by N2pc) and active suppression of distractors (indexed by PD).
Wang et al. (2016) have further suggested that their findings may lay the framework for
implementing N2pc and PD as prospective functional neuromarkers of ADHD.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 20
Similar to Wang et al. (2016), Cross-Villasana et al. (2015) examined for variations in
N2pc amplitude across participants with ADHD and those without ADHD. Unlike Wang et al.
(2016) they sampled adult participants (Mage = 33, female = 9). The component was also referred
to as posterior contractor negativity (PCN) (which they maintain is the same as N2pc). They
were presented with a compound search paradigm, wherein eight items were displayed in a
circular array. A target was defined by a unique color-item (red circle) or shape-item (square)
among seven distractors (yellow circle). All items contained a grating oriented either
horizontally or vertically. Participants were tasked with reporting the orientation of targets as
quickly as they could. Results demonstrated that target-evoked N2pc onset was significantly
delayed in the ADHD group compared to the non-ADHD group, but N2pc peak amplitude in
both groups was not significantly different. This stands in contrast to recent findings by Luo et
al. (2019) (adults, Mage = 26) where N2pc peak amplitude was less negative in adults with
ADHD as compared to adults without ADHD.
At present, target-evoked N2pc and distractor-evoked PD amplitude variability have been
observed between age groups, as well as between groups with and without ADHD. However,
some results (most notably target-evoked N2pc activity in adults with ADHD) have been
inconsistent. More research is needed in adults and other sub-groups to examine correlations
between target-evoked N2pc, distractor-evoked PD, and interindividual variability in attentional
processes within the population. The present study aims to provide new information for how
target-evoked N2pc and distractor-evoked PD activity relates to attentional capacity in non-
clinical adults.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 21
2.4 Aim and Hypothesis for the Present Study
The aim of the current experiment is to examine how target-evoked N2pc and distractor-
evoked PD relates to attentional capacity (as measured with ELAS) in non-clinical adults. My
hypothesis is that target-evoked N2pc and distractor-evoked PD amplitude will predict self-
reported attentional capacity as measured through ELAS. My hypothesis is confirmed if target-
evoked N2pc and distractor-evoked PD predicts participant’ self-reported ELAS score as
examined in a multiple linear regression model.
3. Method
3.1 Participants
Participants (N = 10, female = 5, 10 right-handed, Mage = 23.9, SDage = 2.8) consisted of
undergraduate students at University of Skvde, including both Swedish and international
students. Prior to the experiment, I was aiming at collecting at least 25 participants for data
analysis, however, due to COVID-19, the lab had to be closed after just seven days. Inclusion
criteria required participants to: (a) be between 18-40 years old; (b) have normal or corrected-to-
normal vision (with glasses or contact lenses) in order to view a computer screen at a distance of
1 m; (c) have no impairments associated with color vision; (d) not suffer from epilepsy; (e) not
suffer from dyslexia; (f) not have any current (ongoing) psychiatric or neurological illness; (g) be
able to look at a computer screen for longer periods of time; (h) be able to read in English. In
addition, they were asked to refrain from caffeinated beverages prior to the experiment.
3.2 Sampling Procedure
Sampling of participants was carried out by distributing scripted invitations that were
either posted on social media or sent in-person to students who showed an interest in the
experiment. Participants were offered cookies, tea or coffee after their participation. Participants
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 22
received no monetary compensation. Data collection was carried out over a seven day period.
Prior to participation, all participants received information about the experiment in accordance
with the Declaration of Helsinki (World Medical Association Declaration of Helsinki, 2013) and
consented to participation.
3.3 Stimuli
Each visual search array contained 12 items (0.92° x 0.92°), evenly distributed in 30°
increments (15°, 45°, 75°... 345°) on an imaginary circle (arc radius 4.0°) around a fixation cross
presented in the center of screen. All items were created in Microsoft Windows Enterprise
(Paint) version 6.1. All 12 items consisted of two distinct features: (a) a Landolt square (with a
70% gap on one length); (b) an inner configuration consisting of two intersected lines oriented
into either a plus sign (+) or an x-shape (x) of equal dimensions. 10 items were gray ([Red,
Green, Blue]: 95, 95, 95) equiluminant (89 cd/m2) non-salient distractors which could have their
outer a feature oriented in any of four directions (90°) (gap on one length), and their inner b
feature oriented in a plus sign or an x-shape.
Two color-items in each search array could either represent a task-based target or a
difficult distractor. These color-items contained three distinct features. The b feature could either
contain a plus sign or an x-shape of equal proportions. Second unlike non-salient distractors their
a feature could be oriented in one of two directions (gap facing right or left). Third, unlike non-
salient distractors, they could consist of any of three feature colors: (1) “true” green (RGB: 0,
191, 0); (2) light green (RGB: 150, 191, 0); (3) blue-ish green (RGB: 0, 191, 150), all three
color-items where equiluminant (90 cd/m2). An example of the stimuli is presented in Figure 1.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 23
3.4 Visual Search Task
Our visual search paradigm was presented on an HP Compaq LA2306x 23 inch monitor
(native screen resolution 1920 x 1080 refresh rate: 60 hz), run on E-prime (E-run 2.0 script).
During the experiment, participants were instructed to report the orientation of the target items
gap only when items that were displayed matched pre-specified target features. Features that
represented a target (color and inner feature orientation [x or +]) were displayed at the start of
each block (see Figure 2). Landolt square orientation of an item was correctly reported when a
participant responded with either a right arrow button-press for a right-facing target, a left arrow
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 24
button-press for a left-facing target, or withheld a button-press when there was no target present
during that trial (20% of trials).
The orientation of target items was random (50%). Depending on task demands each
search array could either contain one task-based target and one difficult distractor, or two
difficult distractors. In addition to being instructed to report the orientation of targets,
participants were also instructed to ignore difficult distractors. Each search array was separated
into four quadrants with three items within each quadrant. Color-items were displayed quasi-
randomly and in any position within each quadrant. However, the two color-items could not be
positioned within the same quadrant nor adjacent to each other when they were in different
quadrants. The positioning of non-salient distractors was always arranged randomly around
color-items and their features were also randomly displayed.
The visual search task consisted of 24 blocks with 30 trials per block (~45 minutes). The
first trial in each block displayed a 1000 ms fixation cross and all following trials were initiated
with a 500 ms (± 100 ms, jitter) fixation cross. At the start, search arrays were present for 200
ms. If participants had more than 83% correct responses on the previous block the duration
decreased to 83 ms. If participants’ response accuracy subsequently fell below 60%, stimulus
duration once again became 200 ms. Stimulus durations varied to keep the task at a similar
difficulty for all participants, for equivalent response accuracies.
The search array was followed by a post-stimulus fixation cross during which participants
had time to respond to the orientation of a target item (if present). The duration from when
stimuli were displayed until when responses would no longer be registered was kept constant for
both 83 ms and 200 ms trials (1150 ms), with post-stimulus fixation cross matching stimulus
durations (1067 ms or 950 ms). After the post-stimulus fixation cross, a blank screen appeared
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 25
(100 ms) to indicate that responses for the trial would no longer be registered (SOA = 1700 ms)
Each trial was followed by a follow-up question or initiation of the next trial. Across all 24
blocks, follow- up questions appeared randomly for 0, 10, 25, 33, 50, 100% of trials. Each level
of follow-up questions were presented four times to each participant. Follow-up questions are
part of a separate thesis and will therefore not be discussed in further detail.
3.5 Everyday Life Attention Scale
The Everyday Life Attention Scale (ELAS) (Groen et al., 2018) (see Appendix A)
incorporates nine situations that adults may encounter in everyday life: (1) reading; (2)
movie/documentary; (3) [physical] activity; (4) lecture; (5) conversation; (6) assignment; (7)
cooking, (8) cleaning; (9) driving. These specific situations are presented because they take a
substantial amount of time and thereby require sustained attention. They also require attentional
effort and strategy. Certain situations can be categorized as receptive situations (reading,
movie/documentary, lecture, and conversation), others can be categorized as productive
situations (activity, assignment, cooking, cleaning, driving [and conversation]). During the
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 26
experiment, ELAS was presented to participants in English. Participants are instructed to
imagine an average day or week wherein they experience these situations. Mental visualization is
encouraged for each situation and participants are asked to respond even if they do not
experience the situation regularly (Groen et al., 2018).
For each situation, five questions are asked in order to survey all five attention
components in the multicomponent model of attention (van Zomeren & Brouwer, 1994).
Focused attention is assessed with the item "How well can you focus on this?". Selective
attention is assessed with the item: "How well can you focus on this if there is distraction around
you (e.g., children playing)?". Two questions have been deemed unfit in the situations where
they are asked, thus, “divided attention” questions have been removed from the reading situation,
and “sustained attention” questions have been removed from the cooking situation. ELAS
internal consistency was examined with Cronbach’s α, where the overall scale reliability was
shown to be high (α = 0.93). Unfortunately Cronbach’s α was not presented for the selective
attention component.
Participants report their level of attention on a Likert scale ranging from 0 to 100% with
11 increment steps (±10%). References are provided for each question in the beginning (0%),
middle (50%), and end (100%). Sustained attention is measured as participants’ unbroken
attention in a situation, reported on a sliding scale ranging from 0 to 120 minutes (e.g., how long
can you read a book, or perform in an outdoor activity without taking a break). Participants’
responses to the sustained attention questions are added together and divided with the number of
questions in order to generate an average time across all sustained attention questions. This is
subsequently converted ((score/120) x 100) to percent in order to enable comparisons between
all attentional components in ELAS. Participants’ answers are added together and divided by the
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 27
number of components to generate a final attention score ranging from 0% to 100%. The final
scores themselves have no comparative or diagnostic values. They only become meaningful once
compared to scores reported in a larger sample, wherein scores can be compared to people with
equivalent educational background, age, and gender (Groen et al., 2018).
3.6 Experimental Procedure
The experiment consisted of two separate tasks an initial questionnaire task during which
participants carried out with ELAS, and a visual search task performed while the participants’
brain activity was recorded with EEG. Participants began the study by filling in ELAS and the
informed consent form. Once they had finished the paperwork they were asked to be seated in
the EEG testing room. They were seated ~100 cm away from a computer screen, in a room with
dim lighting and temperature of 21.7-23.8°C with constant air conditioning switched on to
sustain air temperature and quality.
A stretchable EEG electrode cap (g.GAMMAcap3) was fitted to each participant’s heads,
external electrooculogram (EOG), and mastoid electrodes were attached. Conductive electrode
gel (g.tec) was inserted into the electrodes. During gelling a practice module was presented to
participants and Matlab (Simulink 1.4) was activated in order to verify recording reliability prior
to experiment initiation. The practice module was designed to systematically present participants
with experiment task demands through distinct levels of increasing difficulty. Once participants
had finished the practice module and all their questions had been answered the visual search task
was initiated.
3.7 Electroencephalography Recording Setup
The EEG signals were recorded with 27 Ag/AgCl electrodes (g.LADYbird electrodes,
manufactured by g.tec) positioned at standard 10/20 sites (AF3, AF4, Cz, C3, C4, CPz, CP1,
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 28
CP2, Fz, FCz, F3, F4, F5, F6, F7, F8, Oz, O1, O2, POz, PO3, PO4, PO7, PO8, Pz, P3,P4),
sampled at a 512 Hz frequency. Horizontal eye movements (saccades) were measured with two
horizontal EOG electrodes mounted ~1 cm external to the right and left canthi. Two EOG
electrodes mounted ~2 cm above and below the right eye measured vertical eye movements (eye
blinks and saccades). Two electrodes were mounted on the right and left mastoids. All six
external electrodes were attached with adhesive tape. All electrodes, including external EOGs
and mastoids, were carefully prepared by injecting conductive electrode gel (g.tec).
Online reference was recorded from electrode site Cz and ground from AFz. EEG signal
was recorded through two g.GAMMAbox interface/driver boxes and two g.USBamp amplifiers
(g.tec). Amplifiers had an input range of ± 250mV, which allows for recording on DC without
saturation. The amplifier had a 24-bit resolution with a concurrent sampling of 16 DC-coupled
wide-range input channels with up to 38.4 kHz, connected via USB 2.0 (g.tec). Active electrode
impedances were transformed by the system to output impedances of approximately 1kOhm.
Data was filtered online with a 6th order Butterworth band-pass filter at 0.01-100 Hz. EEG data
were acquired in MATLAB R2015a (8.5.1.28.12.78).
3.8 Processing of Electroencephalography Data
Offline analysis was conducted using the programs, EEGLAB version v19.1 (Delorme &
Makeig, 2004) and ERPLAB version 7.0 (Lopez-Calderon & Luck, 2014) in MATLAB R2019b
(9.7.0.1296695). Continuous EEG data were first re-referenced to the average of the two
mastoids. The data were then filtered offline through a 180th band-stop notch filter at 50Hz (to
remove line noise) and downsampled from 512 Hz to 256 Hz. Data were filtered with a second-
order Butterworth bandpass filter with a half-power (-3dB) and cut off at 1 and 30 Hz. An
eventlist was added to specify the items position within the quadrants of the visual search task.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 29
Data was segmented into epochs of 800 ms (200 ms pre-stimulus baseline and 600 ms post-
stimulus).
A pre-processing step was added to remove artifacts using Infomax independent
component analysis (ICA). ICA identifies and subsequently extract components that represent
ocular artifacts. This is especially important in visual-spatial attention where ocular artifacts can
have a major impact on ERPs (Drisdelle, Aubin, & Jolicoeur, 2016). ICLabel was used to
automatically identify and remove independent components that were classified as artifacts.
ICLabel is an algorithm that has been trained on hundreds of data-sets to identify independent
components within EEG data, inter alia, artifacts (Pion-Tonachini, Kreutz-Delgado, & Makeig,
2019).
After ICLabel, ICA weights were transferred back into the pre-processed data.
Subsequently, data used for analysis were filtered with a second-order Butterworth high pass
filter with a half-power (-3dB) cutoff at 0.1 Hz. A binlist was added for positions and features of
targets and difficult distractors. The data were epoched to the same latencies as during ICA
analysis (200 ms pre-stimulus baseline and 600 ms post-stimulus). Step-wise artifact rejection
was performed in ERPLAB 7.0 where all epochs containing step-wise activity greater than 150
μV within a 100 ms moving window with a step size of 20 ms were rejected.
After offline processing, two of the 10 processed ERP data-sets were shown to have a
high artifact detection rate, they were therefore further examined for removal from the
experiment. One participant had a 100% artifact detection rate. However, this participant was not
removed as after interpolation of one noisy channel (F6) the artifact detection rate fell to 2.5%.
The other participant had a 63.4% artifact detection rate (mainly affected by noise in O2 and
PO7). Unfortunately, as one of the affected channels (PO7) was vital for ERP analysis in the
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 30
current experiment the participant had to be removed. The remaining participants’ (N = 9)
epochs were averaged and filtered using a low-pass filter at 30Hz.
3.9 Data Analysis
ERP data were analyzed as contra-minus-ipsilateral difference waves measured from
electrode sites PO7 and PO8, analogous to Tay et al. (2019) for N2pc, and Burra and Kerzel
(2014) for PD. Two paired-samples t-tests (two-tailed) were conducted to evaluate the presence
or absence of target-evoked N2pc and distractor-evoked PD denoted by a significant difference
between contralateral and ipsilateral waveforms. Target-evoked N2pc was analyzed for mean
amplitude in reference to the target, measured within 200-300 ms. Distractor-evoked PD was
measured as the mean amplitude in reference to a difficult distractor within 300-400 ms.
How well the amplitude of target-evoked N2pc and distractor-evoked PD could predict
attentional capacity (as measured with ELAS) was examined in three multiple linear regression
models. Two of the regression models used peak amplitude as its measure for a quantitative
value of the target-evoked N2pc and the distractor-evoked PD. Peak onset latency was measured
to identify when the ERP wave formations reached 50% peak amplitude between two time-
points, which was 200-300 ms for target-evoked N2pc and 300-400 ms for distractor-evoked PD.
Within these time windows 50% peak amplitude was reached after 242 ms for N2pc and 396 ms
for PD. These time points were used to set a 20 ms measure window were peak amplitude was
recorded (analogous to Wang et al., 2016; Sun et al., 2018).
One multiple linear regression analysis was conducted to examine whether the peak
amplitudes of target-evoked N2pc and distractor-evoked PD could predict total ELAS score. A
second multiple linear regression analysis was conducted to examine whether the peak
amplitudes of target-evoked N2pc and distractor-evoked PD could predict ELAS score for the
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 31
selective attention component. A third regression model used mean amplitude as its measure for
a quantitative value of the target-evoked N2pc and distractor-evoked PD. Fractional area onset
latency was measured to identify when 50% area under the curve amplitude was reached
between two time-points, which was 200-300 ms for target-evoked N2pc and 300-400 ms for
distractor-evoked PD. Within these time windows, 50% mean amplitude was reached after 267
ms för N2pc and 363 ms for PD. These measures were then used to set a 20 ms measure window
where mean amplitude was recorded, which is analogous to Luck and Gaspelin (2016).
According to Luck and Gaspelin (2016), analyzing ERP components with peak amplitude
may lead to several methodological complications. These include that peak amplitude is more
affected by noise and other components in the waveform. The timing of peaks during component
measures is also more inconsistent. Overall peak amplitude measures are less versatile and less
reliable than mean amplitude. Luck and Gaspelin (2016) therefore advocate using mean
amplitude when measuring ERP wave formations. Differences between target-evoked N2pc and
distractor-evoked PD peak and mean amplitudes were analyzed in two paired-samples t-tests
(two-tailed), one between peak amplitude N2pc and mean amplitude N2pc. The other examined
differences between peak amplitude PD and mean amplitude PD. All statistical analyses were
carried out in SPSS v.25.
4. Results
4.1 Behavioral Analysis
Participants’ mean accuracy on the visual search task was 73.1% (SD = 0.08). They were
significantly more accurate on shorter 83 ms stimulus durations (M = 75.3%, SD = 0.32), as
compared to longer 200 ms stimulus durations (M = 70.4%, SD = 0.27), t(6751) = 4.64, p < .001.
Participants’ mean reaction time on the visual search task was 629.9 ms (SD = 155.8). They had
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 32
a significantly faster reaction time during longer 200 ms stimulus durations (M = 623.2, SD =
163.7), as compared to shorter 83 ms stimulus durations (M = 635.4, SD = 149), t(5096) = 2.87,
p = .004. Participants’ mean overall score on ELAS was 62.8% (SD = 11.63) and their mean
selective attention score on ELAS was 55.3% (SD = 15.07).
4.2 Target-Evoked N2pc and Distractor-Evoked PD
For both target-evoked N2pc and distractor-evoked PD, the mean amplitudes in the
contralateral and ipsilateral electrode sites (PO7/8) were compared using paired-samples t-tests.
Results indicated that there was a significant difference in the mean amplitude of target-evoked
N2pc between contralateral (M = 0.66 μV, SD = 1.87 μV ) and ipsilateral (M = 1.66 μV, SD =
1.96 μV) electrode sites, t(8) = 10.26, p < .001 (see Figure 3a). Similarly, there was a significant
difference in the mean amplitude of distractor-evoked PD between the contralateral (M = 4.95
μV, SD = 2.58 μV) and ipsilateral (M = 4.44 μV, SD = 2.20 μV) electrode sites, t(8) = 3.01, p
= .017 (see Figure 3b).
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 33
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 34
4.3 Predicting ELAS Score from Target-Evoked N2pc and Distractor-Evoked PD Amplitude
A multiple linear regression was carried out to investigate whether the peak amplitude of
target-evoked N2pc and distractor-evoked PD could significantly predict participants’ overall
ELAS score. Assumptions required for performing multiple linear regression analysis (normal
distribution of residuals, multicollinearity, and presence of outliers) were examined. One outlier
with more than 3 interquartile range (IQR) outside the third quartile (Q3) was observed within
the target-evoked N2pc sample. The other assumptions were not violated. The results of the
regression analysis indicated that the model explained 21% of the variance and that the model
was not a significant predictor of overall ELAS score, F(2, 6) = 0.82, p = .49. Both target-evoked
N2pc (t = 0.44, p = .68, R2 = .02) (see Figure 4a) and distractor-evoked PD (t = 1.28, p = .25, R2 =
.19) (see Figure 4b) did not significantly contribute to the model. As the sample contained a
significant outlier, post-hoc analysis was performed after the exclusion of the outlier. The results
of the regression analysis indicated that the model explained 57% of the variance and that the
model was not a significant predictor of overall ELAS score, F(2, 5) = 3.37, p = .12. Target-
evoked N2pc was shown to be a significant contributor to the model (t = 2.60, p < .05, R2 = .51)
(see Figure 4c), while distractor-evoked PD was not (t = 0.89, p = .42, R2 = .07) (see Figure 4d).
A multiple linear regression was carried out to investigate whether the mean amplitude of
target-evoked N2pc and distractor-evoked PD could significantly predict participants’ overall
ELAS score. Assumptions required for performing multiple linear regression analysis were all
met in the sample. The results of the regression analysis indicated that the model explained 37%
of the variance and that the model was not a significant predictor of overall ELAS score, F(2, 6)
= 0.46, p = .65. Both target-evoked N2pc (t = 0.46, p = .66, R2 = .05) and distractor-evoked PD (t
= 0.96, p = .38, R2 = .32) did not significantly contribute to the model.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 35
A multiple linear regression was carried out to investigate whether the peak amplitude of
target-evoked N2pc and distractor-evoked PD could significantly predict participants’ selective
attention ELAS score. One outlier with more than 3 interquartile range (IQR) outside the third
quartile (Q3) was observed within the target-evoked N2pc sample. The other assumptions were
not violated. The results of the regression analysis indicated that the model explained 36% of the
variance and that the model was not a significant predictor of overall ELAS score, F(2, 6) = 1.69,
p = .26. Both target-evoked N2pc (t = 0.60, p = .57, R2 = .04) and distractor-evoked PD (t = 1.84,
p = .12, R2 = .32) did not significantly contribute to the model. As the sample contained a
significant outlier, post-hoc analysis was performed after the exclusion of the outlier. The results
of the regression analysis indicated that the model explained 54% of the variance and that the
model was not a significant predictor of overall ELAS score, F(2, 5) = 2.92, p = .15. Both target-
evoked N2pc (t = 2.27, p = .07, R2 = .54) and distractor-evoked PD (t = 0.10, p = .92, R2 = .01) did
not significantly contribute to the model.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 36
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 37
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 38
4.4 Peak and Mean Amplitude Analysis
Paired-samples t-tests were conducted to compare amplitude variability between peak
amplitude and mean amplitude measures, used to quantify the components target-evoked N2pc
and distractor-evoked PD. Results displayed a significant difference in target-evoked N2pc
amplitude when comparing peak amplitude (M = -1.43 μV, SD = 0.41 μV) and mean amplitude
(M = -1.20 μV, SD = 0.44 μV) measures, t(8) = 3.34, p = .01. Moreover, results displayed a
significant difference in distractor-evoked PD amplitude when comparing peak amplitude (M = -
0.23 μV, SD = 0.46 μV) and mean amplitude (M = -0.46 μV, SD = 0.45 μV) measures, t(8) =
2.91, p = .02.
5. Discussion
The aim of the present study was to examine how target-evoked N2pc and distractor-
evoked PD related to attentional capacity (as measured with ELAS) in non-clinical adults. I was
specifically interested in examining if target-evoked N2pc and distractor-evoked PD could predict
attentional capacity in non-clinical adults, since attentional capacity has been demonstrated to be
normally distributed (Coghill & Sonuga-Barke, 2012). My hypothesis was that target-evoked
N2pc and distractor-evoked PD amplitude would predict self-reported attentional capacity as
measured with ELAS. The hypothesis would be confirmed if target-evoked N2pc and distractor-
evoked PD could predict participants’ self-reported ELAS score as examined in multiple linear
regression models. In addition I examined if the method utilized to quantify ERP components
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 39
resulted in significant differences in values for amplitude. Peak amplitude measures and mean
amplitude measures were compared in paired-samples t-tests.
Behavioral results in the current study displayed an overall ELAS scores at 62.8%, which
is analogous to that in a large sample of (n = 1874) Dutch participants’ (58.6%) (Fuermaier et al.,
2019). This suggest that the average ELAS score in the current study is representative of that in a
larger sample. Selective attention ELAS score were on avarage lower at 55.3%. Results from the
visual search task demonstrated that participants were more accurate during the shorter 83 ms
stimulus duration as compared to during longer 200 ms stimulus duration. Moreover,
participants’ reaction times were faster during longer 200 ms stimulus duration as compared to
during shorter 83 ms stimulus duration.
Differences in mean amplitude at contralateral and ipsilateral electrode sites (PO7/8)
were compared using paired-samples t-tests for target-evoked N2pc and distractor-evoked PD.
Results demonstrated a significant difference in mean amplitude between contralateral and
ipsilateral electrode sites (PO7/8) for both target-evoked N2pc and distractor-evoked PD, with a
more negative mean amplitude at contralateral electrode sites compared to ipsilateral electrode
sites in reference to the target. This suggest that the experimental paradigm used in the study
elicited target-evoked N2pc. Furthermore, a more positive mean amplitude was observed at
contralateral electrode sites compared to ipsilateral electrode sites in reference to the difficult
distractor, suggesting that the experimental paradigm used in the study also elicited distractor-
evoked PD.
Results from the multiple linear regression models did not support my hypothesis that
target-evoked N2pc and distractor-evoked PD amplitude could predict ELAS score. Target-
evoked N2pc peak amplitude was shown to be a independent predictor in the overall ELAS score
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 40
regression model. However, these results were obtained after removal of a outlier and target-
evoked N2pc and distractor-evoked PD together did not predict overall ELAS score. No other
individual predictor displayed a significant relationship between the ERP components and ELAS
score. Results from comparing the quantitative amplitude variability in peak amplitude and mean
amplitude measures demonstrated that for both target-evoked N2pc and distractor-evoked PD
there was a significant difference between quantative peak and mean amplitude values. Target-
evoked N2pc peak amplitude was less negative on average than mean amplitude, while
distractor-evoked PD peak amplitude was less positive on average than mean amplitude.
Comparisons for mean amplitude and peak amplitude measures themselves do not
demonstrate any evidence regarding the reliability or applicability of the measures. However, it
does show how components measured in the same wave formation can result in different
quantitative component values. This is important as previous studies that have examined the
relationship between N2pc/PD and ADHD (Cross-Villasana et al., 2015; Luo et al., 2019; Wang
et al., 2016) have all utilized peak amplitude measures, even though the currently held view in
ERP research is that peak amplitude is less versatile and less reliable measure than mean
amplitude for analysing ERP components. Problems associated with peak amplitude include a
discrepancy for being more affected by noise and other ERP components, as well as a temporal
inconsistency for measures of ERP components. These potential problems are especially
important if in the future N2pc and PD are to be applied as functional neuromarkers of ADHD.
Then the versatility, reliability, and temporal depends are crucial for the applicability of the
components. Because of the potential problems associated with peak amplitude, I maintain that
future studies that aim to examine the relationship between N2pc/PD and ADHD should employ
mean amplitude measures.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 41
Based on the current results it appears that target-evoked N2pc can predict attentional
capacity in non-clinical adults. This is the first results were a linear relationship has been
observed between target-evoked N2pc and attentional capacity in non-clinical participants.
However, as these results were obtained after a outlier was removed from the analysis, the results
may be circumstantial. Although this is certanly possible, we may still examine the results and
their potetial implications. Results form the multiple linear regression of the current study stand
in contrast to that of Wang et al. (2016) where no relationship was observed between the ERP
components and ADHD symptom severity in non-clinical participants.
The direction of the relationship in the current study was also unpredicted. Results by
Wang et al. (2016) observed that target-evoked N2pc amplitude became less negative with more
severe ADHD symptoms. They also observed that distractor-evoked PD became less positive
with more severe ADHD symptoms. Results in the current study observed that target-evoked
N2pc became more negative with lower attentional capacity, the opposite of what was observed
by Wang et al (2016). These results could imply that the directionality of the relationship
between target-evoked N2pc amplitude and attentional capacity in non-clinical adults are in the
opposite direction compared to children, participants’ with ADHD, or children with ADHD.
However, it is difficult to make any major conclusions for these results. More research is needed
in order to examine if the results can be replicated and what the potential source may be.
Another interesting finding in the current study is that target-evoked N2pc was shown to
have a relationship with attentional capacity, while PD did not. The age of participants in the
current study could have affected why N2pc was shown to have a relationship with attentional
capacity, while PD was not shown to have a relationship. Sun et al. (2018) have demonstrated
higher N2pc amplitude and lower PD amplitude in adults compared to children. This may have
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 42
influenced my results wherein a more negative amplitude in N2pc and a less positive amplitude
in PD could have affected the relative activation of the two components, generating a decreased
sensitivity of PD and an increased sensitivity of N2pc in the current study relative to the children
present in Wang et al. (2016). Lorenzo-Lpez et al. (2008) have also observed age-related effects
on selective attention, with a less pronounced and delayed N2pc in older individuals. However, it
seems unlikely that this would be an explanation for the current findings as Lorenzo-Lpez et al.
(2008) findings were observed in N2pc and not PD. Furthermore, Lorenzo-Lpez et al. (2008)
participants were significantly older (Mage = 68) compared to my participants (Mage = 24). This
also makes me hesitant that age can have affected the results. Lastly, as prior findings for a
relationship between N2pc/PD and ADHD in adults by Cross-Villasana et al. (2015) and Luo et
al. (2019) have been inconsistent it is difficult to determine if age has an effect on the amplitude
of the components and the relationship between amplitude and self-reported attentional capacity,
more research is needed to examine age-related effects.
There are a few potential limitations in the experimental design of the current study that
could also have influenced the results. First, although my results appear to have observed the
presence of both a target-evoked N2pc and a distractor-evoked PD designing an experiment to
examine both components in the same experiment is complex and rarely performed. It is more
common to perform two separate experiments where one experiment is designed to evoke
neurocognitive processes reflecting target enhancement (meant to evoke activity associated with
target-evoked N2pc) and one experiment is designed to reflect distractor suppression (indexing
distractor-evoked PD) (e.g., Sun et al., 2018; Wang et al., 2016).
Second, the experiment was designed so that depending on participants’ performances on
the visual search task the stimulus duration could become longer or shorter between two set
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 43
durations, 83 ms and 200 ms. Depending on how well a participant performed the task, the time
could either increase to 200 ms if a participant accuracy was below a certain level, or decrease to
83 ms if a participants’accuracy was above a certain level. This design was chosen as one of my
lab partners required a certain accuracy in the visual serch task to perform his analysis. The
drawback of this design is that the sensory information may have varied between participants.
This means that the sensory processing in each participant could have been slightly different
which may have affected the neurocognitive processes measured with the EEG.
The final potential problem with the experimental design is how targets and distractors
were located in trials. Most N2pc and PD experiments present targets and distractors to different
hemifields in order to control for possible sensory confounds between the two hemifields. In our
design we presented targets and difficult distractors in any of twelve positions on the screen (in
any position except next to one another). We choose this experimental design because one of my
lab partners examined how the vertical and horizontal position of targets and difficult distractors
(in reference to each other), as well as the proximity of the two items affected the target-evoked
N2pc and distractor-evoked PD amplitude. Unfortunately, this may have once again affected the
sensory information presented to participants in my study, which affected the neurocognitive
processes measured with EEG between participants.
The sample size in current experiment was also limited due to the current spread of
COVID-19. Only 10 participants were able to partake in the study before our lab had to be
closed. When data processing had been finalized and outliers had been removed I ended up with
eight participants for statistical analysis. Even though my sample is similar to other N2pc and PD
studies (e.g., Mazza et al., 2009; Woodman & Luck, 2003) the sample size is much lower than I
initially intended. This limits the likelihood that my results reflected a true effect.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 44
Future studies examining the relationship between target-evoked N2pc, distractor-evoked
PD and attentional capacity in non-clinical adults should take the following into consideration
before designing and performing the experiments. First, I maintain that future studies should
apply mean amplitude measures for the examination of a relationship between N2pc/PD and
ADHD to limit methodological complications associated with peak amplitude, especially the
temporal inconsistency if the components are to be applied as functional neural markers of
ADHD. Second, if researchers aim to analyze both N2pc and PD in the same study they should
have participants partake in two separate experiments, one designed to evoke target enhancement
and the other distractor suppression. This will increase the chance that any neurocognitive
activity measured reflects only the intended process (i.e. target enhancement or distractor
suppression). Third, researchers should be careful when designing selective and spatial visual
attention experiments. Designing an experiment where the sensory information presented has any
potetial of varying between participants (when this is not intended) may have serious
implications for the neurocognitive processes measured. Thus, researchers should be cautious
when choosing stimuli, position, durations, etc. Forth, researchers need to sample a larger group
of participants to ensure that any results obtained is not partly affected by an insufficient sample
size.
In summary, the current results demonstrate that target-evoked N2pc is a significant
predictor of self-reported attentional capacity in non-clinical adult participants, wheras PD is not.
However, due to a restricted sample size and outliers within the data further research is needed
before drawing any major conclusions for how target-evoked N2pc and distractor-evoked PD
relates to attentional capacity in non-clinical adults.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 45
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ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 57
Appendix A
The Everyday Life Attention Scale
Everyday Life Attention Scale
This questionnaire sketches nine situations in everyday life: reading a book, watching a movie or
documentary, performing an indoor activity, attending a lecture or open evening, having a
conversation, doing an assignment/ administration, preparing a meal, tidying up the house, and
driving a car. When reading the description of the situation, please imagine an average week or
day on which you come across a similar situation. The questions beneath each described
situation are about that specific situation. Whenever a new situation is described, all the
questions pertain to the new situation. We ask you to mentally visualize the situations as much as
you can and to fill out an answer even if you do not regularly experience a situation.
___________________________________________________________________
Situation A: Reading a book
You are reading a book of average interest (if you never read a book imagine reading
something else like an abstract, manual or guidelines) and have two hours to do some
reading.
A1. How long can you carry this out without having a break (so without a break or mind
wandering)? (Please mark the correct number of minutes)
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 58
A2. How well can you focus on this?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
A3. How well can you focus on this if there is distraction around you (e.g., children playing)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
A5. How motivated are you to perform the task well (so to take in all details)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
Situation B: Watching a movie/documentary
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 59
You want to see a movie or documentary of average interest that lasts for two hours.
B1. How long can you carry this out without having a break (so without a break or mind
wandering)? (Please mark the correct number of minutes.)
B2. How well can you focus on this?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
B3. How well can you focus on this if there is distraction around you (e.g., children playing)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
B4. How well can you concentrate if you have to do something else at the same time (e.g.,
talking to a friend about a different subject)?
0 = no focus on the task
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 60
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
B5. How motivated are you to perform the task well (so to take in all details)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
Situation C: Performing an indoor activity
You have two hours to perform an indoor activity of average interest (e.g., board game,
handcrafting, solving a puzzle).
C1. How long can you carry this out without having a break (so without a break or mind
wandering)? (Please mark the correct number of minutes.)
C2. How well can you focus on this?
0 = no focus on the task
50 = 50% of your focus on the task
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 61
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
C3. How well can you focus on this if there is distraction around you (e.g., children playing)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
C4. How well can you concentrate if you have to do something else at the same time (e.g.,
talking to a friend about a different subject)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
C5. How motivated are you to perform the task well (so to take in all details)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 62
0 10 20 30 40 50 60 70 80 90 100
Situation D: Attending lecture or open evening
You are attending a lecture or open evening of average interest which lasts for two hours.
D1. How long can you carry this out without having a break (so without mind wandering or
doing something else)? (Please mark the correct number of minutes.)
D2. How well can you focus on this?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
D3. How well can you focus on this if there is distraction around you (e.g., other people talking
to each other)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 63
D4. How well can you concentrate if you have to do something else at the same time (e.g.,
texting a friend)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
D5. How motivated are you to perform the task well (so to take in all details)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
Situation E: Having a conversation
You are having a conversation with a person of average interest for which you have two
hours of time.
E1. How long can you carry this out without having a break (so without a break or mind
wandering)? (Please mark the correct number of minutes.)
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 64
E2. How well can you focus on this?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
E3. How well can you focus on this if there is distraction around you (e.g., children playing)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
E4. How well can you concentrate if you have to do something else at the same time (e.g.,
texting a friend)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
E5. How motivated are you to perform the task well (so to take in all details)?
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 65
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
Situation F: doing an assignment/administration
You have two hours to work on an assignment of average interest, consisting of several
steps and for which you have to think (e.g., administration or an assignment for a training).
F1. How long can you carry this out without having a break (so without a break or mind
wandering)? (Please mark the correct number of minutes.)
F2.
How well can you focus on this?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
F3. How well can you focus on this if there is distraction around you (e.g., children playing)?
0 = no focus on the task
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 66
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
F4. How well can you concentrate if you have to do something else at the same time (e.g.,
texting a friend)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
F5. How motivated are you to perform the task well (so to take in all details)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
Situation G: Preparing a meal
You are preparing a meal for some people visiting you (meat/vegetables/potatoes).
G2. How well can you focus on this?
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 67
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
G3. How well can you focus on this if there is distraction around you (e.g., children playing)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
G4. How well can you concentrate if you have to do something else at the same time (e.g.,
talking to a friend about a different subject)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
G5. How motivated are you to perform the task well (so to take in all details)?
0 = no focus on the task
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 68
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
Situation H: Cleaning up
Your home is a mess and you decide it’s time to start cleaning up. You have two hours.
H1. How long can you carry this out without having a break (so without a break or mind
wandering)? (Please mark the correct number of minutes.)
H2. How well can you focus on this?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
H3. How well can you focus on this if there is distraction around you (e.g., children playing)?
0 = no focus on the task
50 = 50% of your focus on the task
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 69
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
H4. How well can you concentrate if you have to do something else at the same time (e.g.,
texting a friend)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
H5. How motivated are you to perform the task well (so to take in all details)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
Situation I: Driving a car
You are driving a car and are on your way to a destination where you have never been
before. The drive takes two hours.
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 70
I1. How long can you carry this out without having a break (so without a break or mind
wandering)? (Please mark the correct number of minutes.)
I2. How well can you focus on this?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
I3. How well can you focus on this if there is distraction around you (e.g., people talking to each
other in the back of the car)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
I4. How well can you concentrate if you have to do something else at the same time (e.g., talking
to your passenger about a different subject)?
0 = no focus on the task
ATTENTIONAL CAPACITY: SELECTION AND SUPPRESSION 71
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
I5. How motivated are you to perform the task well (so to take in all details)?
0 = no focus on the task
50 = 50% of your focus on the task
100 = 100% of your focus on the task
0 10 20 30 40 50 60 70 80 90 100
___________________________________________________________________________
