Manipulating perceptual parameters in a continuous performance task
Manipulating perceptual parameters in a continuous performance task
Nir Shalev 0
Glyn Humphreys 0
Nele Demeyere 0
0 Cognitive Neuropsychology Centre, Department of Experimental Psychology, University of Oxford , Oxford OX1 3UD , UK
Sustained attention (SA) is among the most studied faculties of human cognition, and thought to be crucial for many aspects of behavior. Measuring SA often relies on performance on a continuous, low-demanding task. Such continuous performance tasks (CPTs) have many variations, and sustained attention is typically estimated based on variability in reaction times. While relying on reaction times may be useful in some cases, it can pose a challenge when working with clinical populations. To increase interpersonal variability in task parameters that do not rely on speed, researchers have increased demands for memory and response inhibition. These approaches, however, may be confounded when used to assess populations that suffer from multiple cognitive deficits. In the current study, we propose a new approach for increasing task variability by increasing the attentional demands. In order to do so, we created a new variation of a CPT - a masked version, where inattention is more likely to cause misidentifying a target. After establishing that masking indeed decreases target detection, we further investigated which task parameter may influence response biases. To do so, we contrasted two versions of the CPT with different target/distractor ratio. We then established how perceptual parameters can be controlled independently in a CPT. Following the experimental manipulations, we tested the MCCPT with aging controls and chronic stroke patients to assure the task can be used with target populations. The results confirm the Following the first submission of this paper, Prof. Glyn Humphreys tragically passed away. This paper is dedicated to his memory.
Sustained attention; Alertness; Continuous performance task; CPT; Stroke; Temporal attention; Attention; Ageing; Chronic stroke; Methods
As our environment is constantly changing over time, we must
detect those changes and act accordingly. This is why the ability
to remain vigilant, or sustain attention over time, is a
prerequisi te for almost every aspect of adaptive behavior.
Acknowledging the importance of the ability to sustain
attention, researchers have studied individual performance over time
from the earliest days of contemporary cognitive research (e.g.,
Mackworth, 1948). There is ample evidence demonstrating
how sustained attention is crucial across the life span.
Difficulties in sustaining attention are correlated with learning,
behavioral, and emotional difficulties in adolescence (e.g.,
Shalev et al., 2015), with professional development (e.g.,
Kalechstein, Newton, & van Gorp, 2003), driving (e.g.,
Schmidt et al., 2009), and more. Sustained attention failures
are also affiliated with various psychiatric disorders, such as
sub-groups of attentional deficit hyperactive disorder (e.g.,
Tsal et al., 2005), autism (Garretson, 1990), learning difficulties
(Richards et al., 1990), and schizophrenia and affective
disorders (Liu et al., 2002). Another clinical group who often suffer
from impaired sustained attention are patients with brain lesions
(e.g., Hyndman & Ashburn, 2003; Robertson et al., 1997a).
Within this group, beyond the everyday implications we
discussed so far, sustained attention has been found to be an
important factor in recovering from other cognitive syndromes
caused by brain injuries, such as motor problems (Robertson
et al., 1997a) or unilateral neglect (Robertson et al., 1995).
Typically, sustained attention is assessed based on
variations of the continuous performance test (CPT). In a CPT,
participants are required to maintain their concentration level
over time during a relatively simple (and inherently boring)
task: the identification of a pre-specified target within a
continuous stream of distractors (e.g., Conners & Staff, 2000). One
type of outcome measure is based on accuracy: the number of
omission (Bmiss^) and commission (Bfalse-alarm^) errors.
However, in most tasks these variables do not provide enough
variability between individuals due to a ceiling effect in
accuracy (Halperin et al., 1991; Robertson et al., 1997b; Sarter
et al., 2001). Therefore, the more typical outcome measure of
most CPT variations is the variability of reaction time (RT),
supposed to represent the overall stability of performance. The
lack of discrete outcome measures, however, such as the
number of omission errors as an index for task performance, may
pose a problem when applied to some clinical populations due
to possible motor confounds when assessing RTs with such
groups (e.g., Ada et al., 1996; McCrea & Eng, 2005).
One way to address the issue of ceiling performance was
suggested by Robertson and his colleagues (1997b),
introducing the SART task. In the SART task, participants have to
respond to all distractors by pressing a button ? and to withhold
their response whenever identifying a target. Indeed, this task
configuration significantly increased the number of
commission errors (false alarms), allowing high sensitivity in a
nonRT based measurement. A different approach is based on
increasing memory demands, such as in the case of the CPT-AX
(e.g., Chen & Faraone, 2000). In the CPT-AX participants have
to respond to a target ? the letter BX^ ? only if it appeared after
the letter BA^. Therefore, they have to store in their memory the
identity of the target and the pre-target stimulus. Other
examples of manipulations researchers have used in order to increase
task sensitivity include stimulus degradation (e.g.,
Parasuraman, Mutter, & Molloy, 1991) and attentional load
(Shalev et al., 2011). Nevertheless, it seems like all the different
variations suffer from the same trade-off: either the task relies
on RT-based outcome measurements, or it is at risk of being
confounded by unrelated cognitive constructs. For instance,
many patients with brain lesions suffer from response inhibition
problems (e.g., Aron et al., 2003); since the outcome measure in
the SART is based on failing to inhibit responses to targets, it is
difficult to imagine how one can differentiate between poor
attention and poor inhibition when applied to patients (also
see Ballard, 2001). A similar argument can be made with poor
memory and performance on the CPT-AX (for more about the
involvement of memory in CPT-AX see Lee & Park, 2006).
A recent solution for the trade-off between retrieving a
discrete outcome measure based on accuracy while avoiding
confounds was recently suggested by O?Connell and his
colleagues (2009). In their task, the continuous temporal
expectation task (CTET), participants observed a continuous
stream of squared patterned stimuli flickering on the screen
and alternating its orientation every 800 ms or 1,120 ms.
When participants observed a stimulus which remained for
1,120 ms (the longer exposure time), this was the target to
respond to. In this task, the accuracy rate was very low
compared to other paradigms, and reached an average of 67%
(O?Connell et al., 2009) in healthy young participants.
Another key benefit of the CTET, besides its evident
sensitivity, is that the task is made up from a continuous stream of
information in the same spatial location. The presence of a
constant stimulus may help to prevent a possible confound
of spatial orienting of attention by target onset. When
performing a normal CPT, arguably several attentional
processes are involved in the target discrimination: attentional
shift and engagement, followed by target selection. This is
because of the way most CPTs were designed, with an abrupt
onset of targets and distractors over a blank screen (or a screen
with a fixation point). This way, the mere appearance of either
a target or a distractor acts as an exogenous cue, causing
attentional capturing in a bottom-up manner. In other words,
when the target or distractor suddenly appears, the abrupt
onset attracts attention. Only after attention is oriented and
engaged to the stimulus, the processes of target discrimination
are initiated. The CTET paradigm avoids this confound with a
continuous presentation at the center of the screen throughout
the task. This way there is no abrupt onset, and therefore less
involvement of orienting mechanisms of any kind.
Although the CTET hereby resolved some important issues
in measuring sustained attention, when it comes to clinical
populations there are some shortcomings. First, the low
accuracy rate received with neurologically normal participants
may mean that this task is too difficult for some populations.
Second, and more important, when moving from the
wellestablished method of visual target discrimination to temporal
judgment, there is a risk of recruiting other unrelated
mechanisms that could be impaired independently. Some studies
have already observed specific problems in temporal
judgment after brain injury (e.g., Lackner & Teuber, 1973;
Robin, Tranel, & Damasio (1990). Therefore, in the case of
the CTET, some performances may be relying on several
mechanisms that are not necessarily related to sustained
attention, and may confound any observation in cases of working
The following study aimed to establish a novel approach
for assessing sustained attention. We modified a CPT task to
resolve some of the major challenges we raised hitherto: while
maintaining the well-established goal of a CPT task, it should
facilitate a higher error rate outcome measure, even in a
healthy population. This will allow the assessment of
sustained attention without relying solely on RT. At the same
time, the task should be suitable for various populations
(simple, not too difficult); it should not require a high memory
capacity; it should not be dependent on unimpaired inhibitory
control mechanisms; and it should be continuous, in order to
avoid the abrupt onset of targets and distractors. With these
constraints in mind, we aimed to increase task performance
variability based on individual differences in attention, rather
than memory, motor skills, or inhibitory control. In particular,
when working with clinical populations, there is the danger
that other cognitive impairments, unrelated to attention, could
camouflage the individual sustained attention capacity when
assessed using a cognitive task. Importantly, our aim in this
particular study was to develop an assessment tool ? and not to
test individual differences in sustained attention. Therefore,
our analysis does not focus on individual profiles of
performance, and instead aims solely to establish increasing
variability and sensitivity in error rates.
Experiment 1: Continuous performance task
Experiment 1 employed a novel CPT paradigm, which was
designed to derive interpersonal variability in accuracy-based
measures, even in a young, healthy population. The new test
aimed to create a more inclusive assessment, avoiding the use
of speeded responses, which in various clinical populations
may confound the measure (e.g., Ada et al., 1996; McCrea
& Eng, 2005). Typically, young healthy participants perform
at ceiling in CPT tasks, making almost no errors (Halperin
et al., 1991), and the standard deviation of RTs (RTSD) is used
to derive a more sensitive measure of sustained attention in
these high-performing groups (e.g., Shalev et al., 2011). Our
paradigm was aimed to enhance the number of task errors,
bypassing the need to rely on RT-based outcome measures
to assess sustained attention performance.
In order to achieve this, in our version the CPT the targets
and distractors are continually masked (pre- and post-mask).
Following previous work showing greater task sensitivity with
a conjunctive set of stimuli (e.g., Shalev et al., 2011; Tsal et al.,
2005), the target is defined by a conjunction of features (color
and shape), and the distractors could share these features. We
introduced the new masked conjunctive continuous
performance test (MCCPT) to a young healthy control sample.
Experiment 1 compared performance in our new MCCPT task
with a non-masked version of the same task (CCPT).
We had two main reasons to believe that adding a mask to
the CPT paradigm would help us create a clearer and more
inclusive measure of sustained attention. First, by using a
mask in our newly developed task, we avoid the abrupt onset
of targets and distractors. During the MCCPT task, there is a
continuous stream of visual stimuli which decreases the
spatial cuing to a minimum and requires a continuous
engagement at the same location on the screen in a goal-directed
Second, by using a mask we degraded the stimuli. This
meant that if participants failed to attend to the shape while
it was presented, they would simply miss it and make an
omission error. The perceptual degradation of the stimulus
could be compensated by attention, based on the familiar
notion that engaged attention enhances perception (e.g., Muller
& Humphreys, 1991; Posner, 1980). Hence, our MCCPT
allows us to ensure participants will use their attentional system
to identify the target and discriminate it from the distractors.
Importantly, as opposed to previous paradigms where
perceptual degradation was used (e.g., Parasuraman, Mutter, &
Molloy, 1991), in our task we do not interfere with the stimuli
itself. Therefore, our degradation can be compensated by
using attention, as opposed to cases where targets are blurred
and may be missed due to perceptual limitations.
The interplay between visual masking and attention has
been extensively studied in the past. Within the attention
literature, attending a stimulus is thought to reduce the effect of
masking (Enns & Di Lollo, 1997). Another experimental
tradition, closely related to attention, in which masking is often
used is in the study of visual short term memory (VSTM)
consolidation. Here a visual mask is deployed to interfere with
iconic memory representation (e.g., Gegenfurtner & Sperling,
1993; Shibuya & Bundesen, 1988). Within this context,
researchers have found that the process of consolidating items
from their fragile iconic representation into VSTM occurs
within an early timeframe following stimulus exposure,
ranging between 30 ms (Shibuya & Bundesen, 1988) and 50 ms
(e.g., Vogel, Woodman, & Luck, 2006). An integrative
perspective of VSTM encoding and visual attention has been
described as part of the theory of visual attention (TVA).
The TVA is a mathematical formalization of the Bbiased
competition^ account of visual attention (Duncan &
Desimone, 1995), where visual categorizations ascribing
features to objects compete to be encoded into a limited capacity
VSTM. The categorization of a visual element is
accomplished once it has been encoded to VSTM. In line with this
perspective, we consider attention to be the mechanism that
can prioritize elements to be stored in VSTM, and in the
context of our current study, attention would be the mechanism
which needs to be deployed efficiently as the target appears, in
order to be encoded before the masking will appear and erase
iconic traces. In addition to this, we also ensured a sufficient
stimulus exposure time to formulate a VSTM representation.
If indeed adding the mask increases error rate, this will lead
to a clear hypothesis about the relation between the outcome
measures: RTSD and number of omission errors should be
correlated. The reason for this hypothesis is our notion that they
both reflect the same construct: Battentional disconnections,^ or
Btemporal inattention.^ Importantly, these variables reflect
mathematically independent values: omission errors total the
number of errors where participants did not respond to the
target, whereas RTSD relies on RTs for correct target
identification only. As opposed to omission errors, the case of
comissions (Bfalse alarms^) is a little bit trickier. While
comissions can result from an Battentional slippage,^ they
may also result from a failure in inhibiting prepotent responses,
as often observed in go/no-go tasks (e.g., Nieuwenhuis et al.,
2003). Therefore, any correlation we might find between
commissions and RTSD should be smaller compared to omissions
One way to incorporate the two error types while
controlling for the involvement of response inhibition mechanisms is
by using parameters derived from the signal detection theory
(SDT; Green & Swets, 1966). In SDT, the perceptual
sensitivity parameter (d?) incorporates the two error types: omissions
and commissions. Another parameter derived from SDT is the
criteria parameter (?), which indicates the bias towards a
perceptual decision: participants can be biased either towards
missing targets (omissions) or towards responding to
distractors (false alarms, or commissions). Here, we suggest
using the SDT parameters as two different task indices: d? as
the marker of task performance, and ? as a control for the
dominant error type. By maintaining the ? value either at zero
or at a positive value, we assure that most of the errors
committed were omission errors. As we will further verify in
Experiment 2, the main characteristic of a task demanding
response inhibition is a negative ? value.
Performance in our new task was compared to performance
in a variation of the CPT: the conjunctive CPT (CCPT; Tsal,
Shalev, & Mevorach, 2005). In this variation, participants are
required to identify a target shape and ignore distractors, some
of which have conjunctive features (either the same color or
same shape). The use of conjunctive features for distractors
was found to increase demands for attention while
maintaining high task reliability (Shalev et al., 2011). In our case, the
use of the CCPT as a control allowed us to investigate the
influence of only one task factor ? adding the mask inbetween
stimuli. In order to make it suitable for various populations,
other than adding a mask, the properties of the CCPT were
preserved as in Tsal et al. (2005): it is based on shapes and not
letters or numbers, and does not require holding more than one
target in memory.
A PC with Intel i7 processor and a dedicated 2GB AMD
video card was used for displaying stimuli and recording
data. The task was generated using NBS presentation
software (Neurobehavioral systems, Albany, CA, USA). The
stimuli were presented on a ViewSonic V3D245 LED
monitor, with screen resolution of 1,080X1,920 and a
screen refresh rate set at 100 Hz allowing display times
varied in gaps of 10 ms. All stimuli were preloaded to
memory using the presentation software, to guarantee
minimal temporal noise.
Task 1: MCCPT sustained attention
A colored mask (Mask), comprised of four superimposed
figures in different colors (square, triangle, circle, and hexagon)
appeared at the center of the screen. The total size of the mask
occupied 3? visual angle. In order to avoid habituation effects,
we generated minor movements to the Mask. The movement
was generated by alternating every 10?20 ms between two
mask-images, one of which had thicker outlines for the
superimposed figures (the two alternating mask images are
illustrated in Fig. 1a). The mask appeared at the center of the
screen and disappeared only when it was replaced by either a
target or a distractor shape for 100 ms; the mask then
reappeared immediately, generating pre- and post-masking
of each target or distractor. The target shape was a red circle,
and distractor stimuli were either similar in color to the target
(red hexagon and red triangle), similar in shape (blue circle
and red circle), or completely different (yellow and blue
hexagon). All distractor types appeared in an equal distribution. All
distractors and target shapes appeared at the center of the
screen and circumscribed a square of 3? visual angle. The
inter-stimulus interval was jittered, randomly between 2,000
and 5,000 ms (see Fig. 1b for a schematic outline of the
experimental procedure). Participants were told that the static
shape that appeared at the center of the screen (the mask)
would be replaced every few seconds with another shape
which would appear only briefly. The task was to press as fast
as possible whenever they recognized a red circle at the center
of the screen. They were instructed to do nothing when they
saw any other shape.
Twenty-two naive volunteers participated in this experiment
(ten female). They were recruited through an online research
participation system at the University of Oxford. All had
normal/corrected-to-normal eyesight and were right-handed
(mean age 28.4 years, SD 4.95). They were compensated for
their time (payment of ?10 per hour, inclusive of travel
Task 2: Non-masked CCPT
Task 2 was identical to Task 1, apart from removing the
masking condition. There was no mask present at any time.
Participants simply focused on the center of the screen and
indicated when they identified a target. All other parameters
remained the same.
Mask, created from all distractors
superimposed at the same loca on
Non-conjunc ve distractor
Fig. 1 (a) The two alternating masks; (b) the Masked CCPT sustained attention schematic outline. Values are in ms
All participants performed both tasks, with a short break
inbetween. The order of administration was balanced across the
group. Each task started with a short practice block (15 trials),
and the experimenter monitored participants? responses at this
stage to ensure the instructions were clear. For each task, after
finishing the practice, the participants performed the whole
session without any break until the task terminated after
approximately 13 min. Each task was comprised of 240 trials.
The target appeared on 80 trials (33% target); and there were
160 distractor trials (66%) in which one of six possible
distractors appeared on the screen in a randomized order. A
distractor was either a color-conjunctive-distractor, where it
had the same color as the target (22%); a
shape-conjunctivedistractor, where it had the same shape (22%); or a
non-conjunctive-distractor where it differed from the target in both
shape and color (22%).
For each participant, we extracted data about the correct
reports of targets, the number of omission errors and the number
of commission errors, as well as RTs. These measures allowed
us to calculate individual performance according to multiple
indices : (a) the standard deviation of reaction time; (b)
sensitivity, or the discriminability of the target from distractors (d?),
in accordance with the signal detection theory (SDT); and (c)
the criterion for the perceptual decision (?) (also based on
Whereas the ability to discriminate target from distractor
(d?) incorporates the two error types ? commissions and
omissions, the criteria (?) provides a measure of the balance
between error types: a positive value means a higher tendency
towards omission errors, and vice versa (when ? value is zero,
there is no bias towards any particular error type). The ?
parameter will be used as a measure for understanding
whether the task facilitates inattention-based errors or
inhibitionbased errors. As we suggested earlier, commission errors can
be resulted not only from instances of temporal inattention,
but also from a failure in response inhibition. By measuring
the ? parameter, we attempt to control for this by estimating
which error type is more dominant: presumably, when
stressing one?s sustained attention, we expect to see either
no bias or a bias towards omission errors. Conversely, if we
discover a bias towards commission errors, perhaps our task
also involves high requirements for response inhibition. This
working assumption will be tested separately in Experiment 2.
We compared all the outcome measures to test for
consistency within and between the masking and no-masking
versions of the tasks, and in order to determine whether the use of
masking indeed increases the sensitivity of our measurements,
as we predicted.
First, we used multiple methods to confirm the reliability of
both the MCCPT and CCPT task. Prior to the analysis, we
removed one participant whose accuracy performance in both
tasks was below three standard deviations compared to the rest
of the group.
Based on previous studies using CPT, and considering the fact
that we tested normal young participants with no motor
limitations, we started the process of task validation by assessing
reaction time related variables. In order to test for internal
consistency in RTs and the standard deviation of reaction time
(RTSD), we split the data into four quartiles and calculated for
each the RT and RTSD for correct target identifications. Then,
we used Cronbach?s alpha test for the four quartiles which
yielded a high consistency of .948 for RT and .843 for RTSD.
After verifying internal consistency, we tested for
correlations between error types and RT related measures. Because
our group was smaller than 30 and our calculations are based
on discrete variables, we calculated a correlation using
Spearman?s rho test for non-parametric correlation. In
accordance with our initial hypothesis, the number of omission
errors was significantly correlated with RTSD (Spearman?s
rho (21)=.74; p<.001). A similar high correlation was found
between our main construct ? target detection d? ? and RTSD
(Spearman?s rho (21)=-.69; p=.001), demonstrating that a
lower ability to discriminate target was linearly related to a
high variability in reaction times. The correlation between the
number of commission errors and RTSD was not significant
(Spearman?s rho (21)=.30; p=.18).
The analysis of CCPT data was carried out following the same
procedures as for the MCCPT task. When testing for task
consistency, as we did for the MCCPT task, we split the
responses into four quartiles and averaged RT and RTSD for
each. Cronbach?s alpha test for the four quartiles yielded a
high consistency of .897 for RT and .794 for RTSD.
We also tested for correlations between RTSD and error
type: once again, as we hypothesized, omission errors were
significantly correlated with RTSD (r(21)=.62; p=.002), and
no significant correlation was found between commission
errors and RTSD (r(21)=.03; p=.99)
Comparing and cross-validating CCPT and MCCPT
For the purpose of cross-validating the tasks, we assessed the
correlation between individual performance on each task. We
found a significant correlation between the RTSD measure of
the masked and unmasked CPT (r(21)=.75; p<.001). A similar
high correlation was observed for sensitivity (d?) (Spearman?s
rho (21)=.68; p=.001) and for the criteria (?) (Spearman?s rho
After this confirmation that our new MCCPT indeed
reliably assessed the same parameters as the CCPT, we carried
out a series of direct comparisons between the two. A repeated
measures t-test revealed a significantly higher RTSD in
MCCPT (141 ms) compared to CCPT (114 ms) (t(20)=2.18;
p=.04; 95% CI [1.17?50.74]). We also found that sensitivity to
target (d?) was significantly higher in CCPT (d?=4.1)
compared to MCCPT (d?=3.64) (t(20)=-2.76; p=.012; 95% CI
[-.80 to -.11]), and suggests that participants had more
difficulty in differentiating the target from the distractors in the
masking condition. We performed a further analysis of target
discriminability by comparing the number of commission
errors for conjunctive distractors (e.g., distractors sharing the
same color or shape as the target) compared to
nonconjunctive distractors. Our comparison showed a
significantly higher percentage of errors for conjunctive distractors
(t(21)=4.75; p<.001). The criteria variable (?) was
significantly higher in MCCPT (?=0.274) compared to CCPT
(?=0.089) (t(20)=4.46; p<.001), 95% CI [.098?.270])
demonstrating a higher bias towards omission errors in the masked
condition. More important, in the masked condition the bias
parameter was significantly larger than zero (t(20)=3.98;
p=.001; 95% CI [.130?.417]), and in the non-masked it was
not significantly different from zero (p=.12). Therefore, in
accordance with our hypothesis, in these particular task
settings participants had either a dominancy of omission errors
(masked task)? or no dominancy of any specific error type
Our results clearly demonstrated how adding a mask in
between targets on a sustained attention task increased the
general task sensitivity: participants had a higher RTSD,
showed an increased bias towards omissions, and had a
lowered target discriminability (d?). Earlier we argued that
the bias parameter could be used to control for the
involvement of inhibitory mechanisms. This is based on
the assumption that in a task that has high demands for
inhibitory control, we should observe a higher proportion
of commission targets (resulting from failure to inhibit
prepotent responses; on measuring response inhibition
see e.g., Aron & Poldrack, 2005).
One potential variable affecting the proportion of
commission errors, and therefore also the bias direction, is the
target frequency: increasing the target frequency should
increase the number of responses, which may therefore
increase the demands for cognitive control to withhold the
prepotent responses in case of irrelevant distractors (see also
e.g., Swick, Ashley, & Turken, 2008). Furthermore, we
demonstrated how the masked sustained attention task
configuration creates a positive ? value, which reflects a general
bias towards missing targets. If increasing the number of
targets increases the requirement for inhibitory control, we
should observe an increase in commission errors resulting
in a modulation of the bias parameters towards negative
values (i.e., bias towards false alarms).
A better understanding of continuous performance
tasks is crucial for improved diagnostics of attentional
disorders. There is currently no single convention for an
optimal outcome measure for sustained attention, and in
many cases commission errors, omission errors, and
RTSD are used interchangeably. Here we attempt to
incorporate the different error types using d?, while assuring
that there is no bias towards commissions. Experiment 2
aimed to investigate if there is a difference in the pattern
of performance when we re-design the sustained attention
task to act as a go/no-go task. The only difference
between this Experiment 1 and Experiment 2 is the
targetdistractor ratio: in a go/no-go task, it is customary to
challenge participants with a high frequency of targets,
to encourage more responses and more false alarms
(Simmonds, Pekar, & Mostofsky, 2008). We used the
same task design as in Experiment 1, but increased the
target probability from 33% to 66%. We hypothesized this
change will influence mostly the bias SDT parameter
reflecting the more probable type of error (commissions
We hypothesize that calibrating the CCPT and MCCPT into a
go-no/go task only by increasing the proportion of targets will
influence only the decisional criteria (?), which will become
negative comparing to the sustained attention settings. Such a
pattern of performance may reflect a greater involvement of
inhibitory mechanisms, as false alarm errors are considered to
be indices for lapses of inhibitory control in go/no-go tasks
(e.g., Aron, Trevor, & Russell, 2004).
Twenty-two naive volunteers participated (nine female). All
had normal/corrected-to-normal eyesight and were
righthanded (mean age 27.8 years, SD 4.6). They were recruited
and compensated for their time in the same way as in
The apparatus was the same as in Experiment 1.
Task 3: MCCPT Go/No-go
We repeated the same task configuration as in Task 1:
MCCPT, only this time we inverted the target/distractor ratio,
with 33% distractors and 66% targets.
Task 4: CCPT Go/No-go
We repeated the same task configuration as in Task 2: CCPT,
only this time we inverted the target/distractor ratio, with 33%
distractors and 66% targets.
First, in order to compare the task to our findings from
Experiment 1, we repeated the same procedure of task
validation for the various outcome measures. Following this, we
performed a direct comparison between the two experiments
in order to pinpoint how performance on the different
measures was influenced by the manipulation of the
Consistent with our procedure from Experiment 1, task
reliability was verified based on RT and RTSD over four
quartiles. In the masked version (MCCPT), Cronbach?s alpha test
for the four quartiles yielded a high consistency of .942 for RT
and .731 for RTSD. In the non-masked version, Cronbach?s
alpha test for the four quartiles yielded a high consistency of
.935 for RT and .826 for RTSD,
Comparing sustained attention and go/no-go tasks
Descriptive statistics appear in Table 1.
Next, we assessed which measures of task performance
were influenced by changing the target-distractor ratio. We
performed a series of direct comparisons between the two
Descriptive statistics ? performance in masked and non-masked variations of the CCPT
groups (Exp. 1 vs. Exp. 2, or the sustained attention and go/
no-go manipulations), on each of the two conditions (masked
vs. unmasked). For each of the critical variables, we carried
out a mixed model 2x2 ANOVA, with the masked versus
unmasked conditions as the within subjects factor and the
low versus high target ratio as the grouping factor.
When applying the 2X2 ANOVA to the percentage of
omission errors, we found a significant main effect for
masking, with a higher percentage of omission errors
whenever the stimuli were masked (F(1,39)=21.937,
p<.001, ?2=.360). We also observed a significant main
effect for masking in the perceptual sensitivity parameter
(d?) with higher perceptual sensitivity in the non-masked
conditions (F(1,39)=22.817, p<.001, ?2=.369). These
observations are in line with our motivation for increasing
task sensitivity by adding a mask. For the percentage of
commission errors, we found a significant main effect for
experimental condition (go/no-go vs. sustained attention),
with more commission errors when we increased the
number of targets (F(1,39)=14.766, p=.001, ?2=.275). This is
in line with our hypothesis that commission errors are
sensitive to the involvement of inhibitory control (by
increasing proportion of targets), whereas omission errors
are not. For the criteria or response bias (?), we found a
main effect for masking (F(1,39)=27.957, p<.001,
?2=.418) with greater bias towards omitting targets in
the masked condition (this is a replication of what we
found in experiment 1). We also found a main effect for
condition (F(1,39)=22.433, p<.001, ?2=.365) with a
greater bias towards omitting targets when distractors?
proportion increases. We did not observe any interaction
between the proportion of targets and the use of masking
(all p >.24). This is in line with our argument that the two
task parameters influence the perceptual parameters
In Experiment 2 we successfully established that
increasing demands for response inhibition, by adjusting task
parameters to a go/no-go task, changes the response bias
towards a higher proportion of commissions errors. In
other words, the response bias is the main task property
that changes between a sustained attention task and a
response inhibition task. Therefore, it can be considered as
a control variable for the dominancy of inhibitory
At this point, we clarified that the perceptual sensitivity
measure (d?) is decreased when we add a mask, and in
Experiment 1 we demonstrated it also correlates with the more
prevalent index of sustained attention ? the RTSD. We can
carefully hypothesize that d? may be somehow influenced by
sustained attention: potentially, a higher variability of
attention may eventually lead to misidentification of targets.
Indeed, it seems that with our perceptual manipulation (i.e.,
adding a mask and increasing perceptual demands) we caused
these two correlated performance indices to change. We also
noticed that the bias parameter (?) alternated between a bias
for missing a target (in the sustained attention task settings)
and committing a false alarm (in the Go/No-go task settings).
The change in this parameter reflects the change in the pattern
of errors: it is a way of representing the more frequent error
type on each condition.
After establishing that the MCCPT indeed increased
sensitivity in accuracy-based measures, and that
performance patterns (as reflected in the bias parameter) were
influenced by adjusting the appropriate target-distractor
ratio, we aimed to investigate whether the task is feasible
and informative in the target population. In Experiment
3, we used an adjusted version of the MCCPT with both
a group of ageing individuals and chronic stroke
Experiment 3: Testing older adults and clinical patients
In Experiment 3 we describe the accuracy based outcome
measures of a sample of patients and older adults who
performed the MCCPT (with a slight variation of the
exposure times). A subset of this sample and the associated
MCCPT data was previously described in an experimental
paper focusing on the functional outcomes of impaired
sustained attention (Shalev, Humphreys, & Demeyere,
The experimental group consisted of 75 participants, 42 of
which were neurotypical adults (26 females; mean age 68.3
years; SD=8.1) and 33 were chronic stroke patients (11
females; mean age 63 years; SD=13.7). The groups did not
differ significantly in their age or level of education. The
clinical group consisted only of chronic stroke survivors,
who had their stroke more than a year ago, and came as
volunteers to the Oxford Cognitive Neuropsychology
Centre; the patients varied in their lesion site and volume,
and we did not exclude any of the available volunteers from
taking part. (For a detailed description of the experimental
settings, recruitment process, and the lesion sites of the
majority of the clinical group, see Shalev, Humphreys, &
The apparatus was the same as in Experiments 1 and 2.
Task: MCCPT austained-attention
We repeated the same task configuration as in Task 1:
MCCPT, only this time we extended the stimulus exposure
time to 150 ms. We did so in order to ensure the task would
be simple enough for all of the older participants and stroke
survivors. We assessed in this experiment whether this
relatively long exposure time still produces meaningful variability
in our accuracy based outcome measures.
Task performance indices are described in Table 2. The
distribution of individual detection rates in both groups is shown in
Evidently, as with the younger individuals we have tested,
the majority of the individuals did not reach ceiling
performance even though we extended the stimulus exposure time to
150 ms. In line with our previous findings, the mean ? value
was higher than zero in both groups, suggesting that at the
group level there was no bias towards committing
falsealarm errors. When inspecting individual performance, we
observed two stroke patients who had performed at chance
level. Both patients were unable to maintain fixation while
seated due to motor limitations.
The frequency table depicted in Fig. 2 also shows that the
distribution of performance seems to be somewhat skewed.
Indeed, we calculated the skewness of the d? parameter among
the control group and we found a negative skew of ?1.3
(SE=.374). Nevertheless, there is a possibility that this is a
result of a relatively small sample size of aging adults, and this
observation should be further tested with a larger sample. We
encourage future studies to collect more data in order to better
reflect the distribution among normal population. Such studies
should also account for age-related changes in sustained
attention that might influence our performance parameters. A
negatively skewed distribution was also observed among patients
(skewness = ?1.5; SE = .409), although such an observation
could be explained by the small, non-homogeneous, clinical
sample. As opposed to the skewed distribution of the d?
parameter, the distribution of the bias (?) parameter was closer to
symmetrical among controls (skewness = ?.041; SE = .374)
as well as among patients (skewness = ?.243; SE = .409).
We described the performance distribution of the two
experimental groups, with an emphasis on accuracy-based
outcome measures. Our goal was to make the task simple
enough for the clinical population, while at the same time
avoiding ceiling or floor effects in performance. We also
compared the groups to see if they could be distinguished
based on the task.
We compared the group detection rate (d?) and bias (?) using a
t-test for independent samples (equality of variance not
assumed). The group differed significantly in their d? (t=3.082;
p=.004; 95% CI [0.24?1.19]) with an overall higher detection
rate among controls. The ? did not differ between the groups
(t=.234; p=.816). To make sure that the significant group
difference is not driven by the two patients with performance at
% miss (mean; median; SD)
% false alarms (mean; median; SD)
Individual target detec on (d')
Control Pa ents
Fig. 2 Individual target detection (patients and controls)
chance, we repeated the analysis excluding them. The results
remained significant (t=2.756; p=.008; 95% CI [0.13?0.84]).
These two comparisons show that the test can in principle
differentiate a group of clinical participants from non-clinical,
and that their overall pattern of performance is similar as
reflected in the bias parameter.
We successfully managed to establish a new reliable method
for increasing variability in accuracy in a CPT. By using a
mask, we degraded the perceptual sensitivity to target (d?)
and demonstrated how this parameter is correlated with a
well-established construct of sustained attention ? the RTSD.
We also demonstrated that a change in the criteria parameter
(?) characterizes the difference between a response inhibition
task and a sustained attention task. Finally, we demonstrated
the feasibility of the task for older adults and stroke survivors.
The increased sensitivity of the task is attributed to the use
of a visual mask. According to our theoretical view, the use of
masking increases the demand for attention. This view is in
line with findings demonstrating that attention can reduce the
effect of masking (Enns & Di Lollo, 1997), and enhance
perception (e.g., Muller & Humphreys, 1991; Posner, 1980).
Nevertheless, the deployment of visual masking also
eliminates iconic memory representations in the process of
encoding into VSTM (Smith, Ratcliff, & Wolfgang, 2004).
In this respect, while the MCCPT paradigm aims to avoid load
on memory components as in the case of the CPT-AX, there is
a potential that the masking still increases working memory
demands. Indeed, the exposure time used in our paradigm is
significantly shorter than the mean reaction time, and
therefore it is likely that the perceptual decision relies on the
maintenance of the target in VSTM. However, in our view the
involvement of memory mechanisms in the MCCPT does
not overshadow the attentional requirements and is not
comparable to the CPT-AX. First, while the CPT-AX requires the
active maintenance of two objects in memory, the MCCPT
requires only one item. In that respect, a memory capacity
for a single item seems to be a prerequisite for nearly any
visual discrimination task where participants need to
remember a predefined target. Second, when it comes to the process
of encoding the items from iconic memory to VSTM, we
would argue that this is exactly where attention plays a major
part: attention is the cognitive components which Btransfer^
visual objects into VSTM (e.g., Bundesen, 1990, 2005;
Desimone & Duncan, 1995) within a much shorter timeframe
(e.g., Vogel, Woodman, & Luck, 2006) than the one employed
in the exposure times used in the MCCPT. In particular, the
MCCPT relies on the visual presentation of repeating,
overlearned, simple stimuli; in such cases, it is likely that early
visual processes occur even faster due to the effects of learning
(e.g., Ahissar & Hochstein, 1997). More compelling so, our
empirical findings clearly support our view with the majority
of our participants, even within the clinical group, adequately
performing the task.
The current research has its own limitations. At this point,
we highlighted how the different outcome measures respond
to various parameters, and how they are related to each other.
Nevertheless, there is still the question of the ecological
validity of the task that should be further tested by trying to relate
the task to how people can actually sustain their attention
outside the laboratory settings (for example by looking into
self-reported distractibility). Another possible limitation is
related to the increased variability among healthy individuals
performing the MCCPT. Potentially, a task that facilitates a
higher error rate among non-impaired, might be too difficult
for a clinical population. We recently presented an
experimental study of the correlation between performance in our
MCCPT task and self-reports of cognitive difficulties among
chronic stroke patients and older adults where both
populations performed a variation of the MCCPT with a relatively
comparable, overall high performance (Shalev, Humphreys,
& Demeyere, 2016). We now expand on this data in
Experiment 3 where we demonstrated feasibility and
distributions in this clinical population, evidencing that the task can be
used (with a minor alternation of the exposure times) with
target populations such as aging individuals and stroke
survivors. We provide evidence that the MCCPT allows the
assessment of performance on a CPT without relying on RTs.
Nevertheless, one should keep in mind that some clinical
populations at acute stages may not be able to perform a
computerized cognitive task lasting 10 min.
Importantly, none of the task parameters should be set in
stone: our main conclusion from Experiments 1 and 2 is that
using a mask can be beneficial in increasing the number of
errors in a CPT without interfering with the overall reliability.
We also show that the target-distractor ratio should be
considered carefully. Parameters such as SOAs, exposure times, and
overall task length should be further manipulated in the future.
Finally, future studies should focus on collecting data from
larger samples of varying populations, in order to establish
reliable normative data. While we acknowledge that the
relatively small sample size we used in this study is an evident
limitation, we believe that the converging evidence supports
the use of the MCCPT as an alternative for standard CPT with
a minimum exposure to confounds unrelated to attention.
Acknowledgements This work was supported by a European Union
FP7 Marie Curie ITN Grant (606901), the NIHR Clinical Research
Facility (Oxford Cognitive Health), and the Stroke Association (TSA
Open Access This article is distributed under the terms of the Creative
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