Task switching and attention: An EEG study of distraction by food stimuli
Student ID: 610018120
PSY3250
University of Exeter
Ackwnoledgements
I would like to thank my supervisor, Dr. Aureliu Lavric, for the invaluable guidance he provided
throughout this project and for being a continuous source of inspiration. I would also like to
thank my parents, whose encouragement and support made all of this possible.
Abstract
We investigated the largely neglected subject of distractibility during task switching using
images of food as distractors. Participants were required to abstain from eating for 3h prior to the
session, since hunger levels were previously correlated with attentional bias for food stimuli.
They completed a computer task where they were required to switch between identifying colours
and shapes. On half of the trials, task-irrelevant images (food- and office-related images)
appeared on both sides of the relevant stimulus and subjects were asked to ignore them. We
acquired measures of performance (RT and error rate), brain activity (EEG) and food-related
motivation (food craving trait questionnaire and hunger measures during the session. Contrary to
our expectations, although the presence of a distractor did affect performance in a negative way,
participants were not more distracted on switch trials compared to repeat, as the distractor effect
was not modulated by switching; behavioural and EEG data show similar distractibility on
switch and repeat trials. Secondly, we found that motivationally salient stimuli (food image) are
processed more than neutral (office) items, reflected in ERP lateralization- an effect that was not
greater during task switches than during task repetitions. Lastly, we found evidence of a switch
cost, reflected in RTs and error rates, but not in ERPs and contrary to expectations, this switch
cost was not reduced by longer preparation times. This could be explained by the fact that
participants were busy preparing for the eventual appearance of the distractor but more research
will be needed to fully understand this effect.
Imagine the following scenario: You are driving on a busy motorway when the person sitting on
the passenger seat asks you a question. You turn the radio down and answer. Meanwhile, you
notice a billboard on the side of the road, advertising a new meal at your favourite restaurant so
you decide to stop on the way home and buy some food. Situations like this are not uncommon
and people often find themselves having to switch back and forth between various activities
(Monsell, 2003). In this particular case, the tasks are demanding enough that they engage one’s
cognitive functions but nevertheless, attention is still drawn to a distractor which is entirely
irrelevant to both the phone conversation and driving. Although, there is considerable literature
in the area of task-switching, not much is known about the interaction between task-switching
and attention, and even less about the role that motivationally salient stimuli, such as food items
might have in mediating this relationship (Engelmann & Pessoa, 2007). Thus, the present
experiment investigated these two largely neglected areas, by employing a task-cuing paradigm
with images of food and office items serving as distractors for participants who were in a hungry
state.
Responding to daily demands in a flexible way requires adopting a task set (Monsell, 1996), a set
of mental representations that allows people to identify relevant stimuli, select appropriate
responses and act in accordance with contextual requirements (Logan & Gordon, 2001). In
laboratory settings, regulation of these mechanisms of cognitive control has been studied using
task-switching experiments, initially run by Jersild (1927). In a task-switching experiment,
participants are usually presented with stimuli that afford two or more responses (Monsell, 1996)
and are trained on tasks which usually involve categorization or identification (Vandierendonck,
Liefooghe & Verbruggen, 2010). Subjects then perform the tasks which can either change from
trial to trial or remain the same on successive trials (Kiesel et al., 2010). Comparing the
performance in these two conditions (Nieuvenhuis & Monsell, 2002) has shown that switching
between tasks results in disrupted performance (Rogers & Monsell, 1995), expressed through
significantly longer response times (RT) and higher error rates on switch trials compared to
repeat trials (Meiran, 1996).
There are various methods in which the participant can be informed about which task needs to
be performed, but the most commonly used procedure is the task-cuing-paradigm (Monsell &
Mizon, 2006), where the required task is specified by an explicit cue which appears before or
with the stimulus (Altman, 2004). This paradigm makes it possible for the experimenters to
manipulate preparation times by altering the interval between cue and stimulus (CSI- cuestimulus-interval, (Meiran, 1996). Advance knowledge of the upcoming task in the form of
longer CSI has been linked to decreased switch costs, termed the ‘RISC’ effect (reduction in
switch cost with increased preparation time; Longman, Lavric & Monsell, (2013). However,
even with CSI as long as 1420ms (Longman, Lavric, Munteanu & Monsell, in press), a complete
elimination of the switch cost does not seem to be possible and two competing explanations have
been offered for the source of this asymptotic ‘residual’ switch cost: Task-Set Reconfiguration
and Task-Set Inertia.
Firstly, cognitive control theorists explain switch cost though Task-Set Reconfiguration (TSR;
Yeung & Monsell, 2003), which is the need to prepare a set of endogenous cognitive processes,
such as retrieving goal states, during the CSI (Nieuwenhuis & Monsell, 2002). Empirical
evidence for TSR was provided by Rogers and Monsell (1995), who employed a task-switch
categorization paradigm and varied the time participants had to prepare before the task. Their
results showed that completing TSR in advance reduces error rates and RTs, supporting the
RISC effect. An advantage of this model is that it accounts for the presence of residual switch
costs. According to Rogers and Monsell (1995), irrespective of the length of CSI, part of TSR
can only be done after stimulus onset. A more recent theory (De Jong, 2000), considers the
residual switch cost as an average of trials where TSR was successfully completed before the
stimulus (no residual switch cost) and trials where TSR “fails to engage” before the stimulus.
An alternative explanation of switch cost is provided by the carryover of stimulus-response
mappings of the previously relevant task, termed Task-Set-Inertia (TSI, Allport et al., 1994).
Recently, the term “attentional inertia” has been put forward (Longman et al., 2013), to suggest
that when the participant is cued to perform a task, the previous task-set continues to interfere
and thus switch cost arises because participants’ attention is drawn to properties of the stimuli
that were previously relevant (De Jong, 2000). The majority of distraction studies so far have
focused on dual-task interference (Strayer & Drews, 2007) and thus, there is a gap in research
investigating the interaction between attention and task switching. Among the few studies
conducted in this area, which support the idea of attentional inertia, is a study by Longman et al.
(2012). In this study, participants had respond using the keyboard to pictures representing one of
four faces that had one of four letters superimposed on the foreheard and each task was
associated with a location on the screen. Task-cuing was employed using an auditory cue and
participants had either 200ms or 800ms to prepare before stimulus presentation. Eye-tracking
analysis showed that switching tasks determined participants to delay their attention to task-
relevant attributes and wrongly allocate attention to the previously relevant location. A more
recent study (Longman, Lavric, Munteanu, & Monsell, in press), used the simultaneous
presentation of three digits associated with a location and a classification task to isolate
attentional inertia from general distractibility. Using eye-tracking, this study showed that people
have a tendency to wrongly allocate attention on the location that was relevant on the previous
trial, and not on other irrelevant locations. This effect was reduced by extending the CSI to over
one second, providing support for the contribution of attentional inertia to ‘residual’ switch cost.
Conflicting evidence comes from Lien et al (2010), who claimed that the contribution of
attentional processes to residual switch cost is not as significant as previously thought. Their
experiment used a contingent capture paradigm with uninformative cues, which either had the
same colour and location as the target stimulus or different ones. Results showed that these cues
only affected performance when the colour was relevant, with no significant evidence suggesting
attentional capture by stimuli whose colour was previously, but no longer relevant (i.e. on switch
trials).
In these cases, distraction was represented by a stimulus which recently required a response, and
thus, was relevant for the allocation of attentional resources. However, in daily life, people often
get distracted by stimuli which are entirely irrelevant to the task at hand and that should
theoretically be ignored (Forster & Lavie, 2008). Considering that not all perceived information
is relevant, selective attention is needed so that people can focus on stimuli which are needed for
the task at hand, while keeping less relevant information in the background (Lawo, Fels, Oberem
& Koch, 2014). Understanding these failures of selective attention during task switching is a
largely neglected area of study. In fact, a few experiments have shown that the presence of
irrelevant distractors during a single-task can affect performance to the same extent as responsecompeting distractors (Theuuves, 1991). As a task switch possibly resets attention and fully
engages cognitive abilities (Lavie, 2010), it would be interesting to gain a better understanding of
the effect of irrelevant distractors in this setting, so that ways to avoid their interfering effects
can be identified (Forster & Lavie, 2008). A good theoretical background is provided by Lavie’s
‘Load Theory’ (2004) which suggest that attentional control is worse under high levels of
cognitive load, and thus, it would be expected that people are more easily distracted by irrelevant
stimuli during switches. Another question logically follows: if attention is, in fact ‘grabbed’ by
external irrelevant events, is there any difference in the type of distractors that are presented?
The second theme of this paper will attempt to answer this latter question by focusing on a
specific type of distractor – stimuli that have motivational significance for the participant, as they
seem to be more effective in affecting task-relevant responses (Vergoeven et al., 2010). For
example, pictures depicting food items have been shown to increase attentional bias for food
related-stimuli (Mogg, Bradley, Hyare & Lee, 1998) and were associated with enlarged positive
ERP potentials over posterior sites (Stockburger, Weike, Hamm & Schupp, 2008), with even
stronger effects when participants were in a hungry, rather than satiated state (Engelmann &
Pessoa, 2007). Although laboratory based, this method of measuring distraction has a high level
of external validity, as in daily life, distractors are usually more attractive than task-relevant
stimuli (Forster & Lavie, 2008)
Studying the effect of distracting food stimuli can have major implications for developing
interventions in treating obesity, as reactivity to food cues is potentially modifiable (Castellanos
et al., 2009). Even though genetics are crucial in the development of this issue, this dramatical
rise in the prevalence of obesity can only be explained by the involvement of a different factor,
in the form of environmental influences (Peters, Wyatt, Donahoo & Hill, 2002). The incentive
sensitization model of obesity (Nijs & Franken, 2012) hypothesizes that because of the
abundance of food cues in the environment, people become sensitized to food stimuli, as their
attentional processing is enhanced and automaticised (Nijs, Franken & Murris, 2010) and this, in
turn, contributes to excessive food intake (Castellanos et al., 2009). Evidence for this model was
provided by a longitudinal study conducted by Calitri et al (2010), which showed that weight
gain was positively correlated with earlier attentional bias for food and was successfully
predicted 1 year in advance.
The current project aims to link the areas of task-switching, attention and motivation by using a
task-cuing paradigm closely modelled on that of Lavric, Mizon and Monsell (2008) with images
of food items as lateralized distractors. Our first hypothesis, based on task-switching research,
predicts that task switches will be associated with greater distractibility than task repeats, as
switches would put greater load on WM, which, based on Lavie’s ‘load theory’ of attention
(Lavie, de Fockert & Viding, 2004), predicts larger vulnerability to distractors. Secondly, we
hypothesize that food, as motivationally salient stimuli, will be processed more and affect
performance on greater extent compared to neutral office items (larger ERPs) and that this
difference will be larger on switch trials. Lastly, based on previous findings in task-switching
literature, we predict that performance on the colour-shape task will be affected more on switch
trials compared to repeat trials (slower RT, larger error rates and larger ERPs) and that this
switch cost will be reduced by longer CSI.
Methods
Participants
The participants were 23 undergraduate students at the University of Exeter (11 men and 12
women), aged between 19 and 24 (M=20.82, SD=.936). Participants were a convenience sample
recruited personally by the experimenters. In exchange for their time, they were compensated
with up to £5 (a flat rate of £2 plus performance related bonus based on minimizing RT and error
rates). All participants gave informed consent and the study was approved by the University of
Exeter School of Psychology ethics committee. One participant was excluded from ERP analysis
due to large number of artifacts.
Design
The experiment used a within subjects design with task-relevant stimuli (coloured shapes)
presented centrally and irrelevant images of food or motivationally-neutral office items presented
concomitently to the left or right. Measures of Reaction Times and Error Rates were taken
alongside measures of preferential processing of motivationally-salient distractor in the form of
posterior lateralized ERPs (visual processing area). The independent variables were transition
(switch vs repeat), distractor (present vs absent), response congruence (congruent vs incongruent)
and CSI (short vs long).
Participants were approached by the experimenters and asked to take part in a study investigating
the effects of task switching on performance. They were informed that they will be required to
restrain from eating for 4 hours prior to the experimental session. Before the beginning of the
session, they were given consent forms, giving a brief overview of the study and specifying that
the information is anonymous and that they have the right to withdraw at any point (Appendix A).
During the EEG set-up, participants were asked to complete two questionnaires measuring
hunger levels and food-craving trait (Appendix B). Hunger levels were assessed before and after
the session with a short 3-item questionnaire (e.g. “How strong is your desire to eat”) and foodcraving was assessed once on a 6-point scale ranging from 1=never to 6=always (e.g. “I feel like
I have food on my mind all the time”).
The study used a task-cuing paradigm, presented using the software E-Prime, with participants
being required to switch between identifying colours and shapes, while ignoring irrelevant
pictures (food and office items) which appeared bilaterally at unpredictable times on half of the
trials. The task changed with a probability of 0.33 and the cue-stimulus interval (CSI) was either
long or short and distractors appeared either relative to cue onset (long CSI: 240 ms, 360 ms, 480
ms, 600 ms and short CSI: 40 ms, 160 ms, 280 ms, 400 ms) or relative to stimulus onset (40 ms,
160 ms, 280 ms, 400 ms). There was a filler trial at the start of each block in order to create a
transition (switch or repeat) for the first analyzed trial. No distractors were presented on these
and filler trials were not considered for analysis.Before starting the main experiment, participants
completed three practice blocks. First, they practiced the colour-task and shape-task separately,
then did a practice block on switching between the two and finally did a shorter block where they
got familiarized with the main procedure, which involved distracting pictures.
The main session, which lasted approximately 50 minutes, consisted of a total of 1164 trials,
divided in 12 blocks with 96 trials each, plus a start up filler trial before each block.
Information appearing on the monitor instructed participants to keep their gaze on a fixation
cross located in the middle of the screen. On each trial, a cue preceding the onset of the stimulus
indicated whether they should attend to the shape (SHAPE or FORM) or colour of the stimulus
(COLOUR or PAINT) (See Figure 1).
Figure 1. Example of cue, stimulus and distractors.
The stimulus was represented by one of four shapes in one of four colours, resulting in 16
possible combinations. Participants were asked to keep their left and right index and middle
fingers on the keys (V, B, N, M) of a standard QWERTY keyboard and to answer according to
the learned responses (See Table 1)
Table 1. Key-press responses in the two tasks.
On 1/3 of the trials, distracting pictures appeared bilaterally and the participant was instructed to
ignore them. The pictures represented office items (IT and non-IT) and food items (sweet and
savory), matched for size, complexity and colouring, to avoid bias. At the end of the 12 blocks,
the participant was asked to complete the hunger-level questionnaire once again. Finally,
participants were fully debriefed (Appendix C) and appropriately compensated for their
participation.
EEG procedure
The EEG was recorded (sampling rate, 1000Hz; bandpass, 0.016-100Hz, reference: Cz; ground:
AFz) from 62 10-10-configured scalp electrodes plus two earlobe-electrodes (ActiCap,
BrainProducts, Munich, Germany), then 20 Hz lowpass-filtered offline and re-referenced to the
averaged earlobes. Ocular artefacts were corrected using Independent Component Analysis
(Infomax ICA). To ensure optimal performance of the ICA algorithm, stretches of the EEG
corresponding to breaks between the experimental blocks, as well as the beginning and end of
testing, were removed from processing because they contained large head movement artifact that
is not characteristic of the EEG recorded during the experimental blocks. Following artifact
correction, the EEG from the long CSI condition was segmented into 340 ms epochs (300 ms
following the distractor onset plus a 40 ms pre-distractor baseline); this segmentation was done
separately for the distractors preceding the stimulus (CSI distractor) and following the CSI
(stimulus distractor). Segments containing amplitudes exceeding plus/minus 60 mV (likely
containing residual artifacts associated with head movements and skin potentials) were discarded
automatically; the remainder were averaged to yield the ERP in response to distractor onset for
every subject, condition (switch vs repeat) and time interval (CSI distractor vs stimulus
distractor). To examine the preferential processing of the food-related images relative to the
neutral (office-related) images, differences were computed between the electrodes contralateral
to the food-related image and the electrodes ipsilateral to the food-related image (which was also
contralateral to the office-related image). This difference was computed for three pairs of
occipital electrodes: PO7/PO8, PO5/PO6 and O1/O2, and submitted to two kinds of analysis.
First, one-sample t-tests were run on the average of these three pairs of electrodes (and averaging
over switch and repeat trials), to ascertain whether there was indeed preferential processing of
the food distractor, whatever the transition (switch or repeat). The t-tests were run for ERPs
averaged for a 75 ms time-window (100-175 ms following the onset of the distractor) comprising
the P1 peak of the ERP for the CSI and the stimulus distractor conditions separately. For the CSI
condition, there was some suggestion in the ERPs of non-trivial amplitudes of the lateralisation
difference preceding the P1 peak- so an additional t-test was run for the time-window 50-100 ms
following distractor onset. Second, ANOVAs with factors transition (switch, repeat) and
electrode pair (PO7/PO8, PO5/PO6, O1/O2) were run for each of the three time-windows above.
Results
Behavioural results
Two analyses were conducted on performance data (RT and Error rates). Firstly, trials from both
CSIs were analyzed with the following factors: transition (switch vs repeat), distractor (present
vs absent), response congruence (congruent vs incongruent) and CSI (short vs long, see Table 2
for relevant means).
Table 2 .Mean Reaction Times, Error Rates and Switch Cost .
A repeated measures ANOVA on all CSI found evidence of a switch cost, with participants
being significantly slower (F(1,22)=42.93, p<.001) and making more errors (F(1,22)=42.60,
p<.001_ on switch trials compared to repeat. Performance was also affected by the incongruence
effect, worse reaction times (F(1,22)=41.97, p<.001) and higher error rates (F(1,22)=61.13,
p<.001) in congruent trials compared to incongruent, supporting the 3rd hypothesis. However,
there was no evidence of a reduction of switch cost with increased preparation time (eg. RT
F(1,22)=0.20, p=.655 n.s).
As far as the effect of the distractor is concerned, participants took significantly longer to
respond when distractors were present, compared to when they were not (Figure 2), reflected in
RT (F(1,22)=5,56, p=.028), but there was no significant difference in terms of error rates
(F(1,22)-1.396, p=.250).
Figure 2. Effect of distractor on switch cost.
A second repeated measures ANOVA was used to contrast the effects of presenting distractors
during the CSI (before stimulus) or after the stimulus, and thus, for the purpose of this analysis,
short CSI trials were excluded, as they did not include distractors before the stimulus onset. The
factors submitted for analysis were CSI, switch, congruence and distractor (absent, before
stimulus, after stimulus). The results indicated the presence of a switch cost, visible in both RTs
(F(1,22)=56.48, p<.001) and error rates (F(1,22)=47.95, p<.001) supporting the first hypothesis
regarding performance being affected more on switches. Also, similar to the first analysis, we
found significant evidence of an incongruence effect in RT (F(1,22)=51.48, p<.001), but not
error rates (F(1,22)=.22, p=.79, n.s)
The main effect of distractor was significant (F(1,22)=11,68, p<.001) so we conducted a paired
sample t-test on the RT means for each distractor onset timing (See Figure 3). We found that
performance is disrupted significantly more by distractors that appear after stimulus onset
(M=821,88, SD=155,86), compared to no distractors (M=719.46, SD=138,82; t(1,91)=-4.27,
p<.001), or to distractors that appear before the stimulus (M= 784.45, SD=146.80; t(1,91)=-4.71,
p<.001).
Figure 3. Effect of distractor onset time.
Hunger level scales
To explore participants’ hunger levels, we conducted a one-sample t-test on the first scale
(t=11.201, p<.001) and second scale (t=11.108, p<.001), and both results achieved high levels of
significance, showing that the subjects had a desire to eat and reported hunger that as
significantly different from 0. To explore whether the first and third scale reflect the same
phenomenon, we ran a correlation analysis using scale values as covariates. High correlations
were found between hunger and desire to eat, for both before testing (r(22)=0.72, p<.05) and
after testing (r(22)=0.87, p<.05.Thus, we decided to use the third scale (desire to eat) as a
continuous independent variable in re-running the RT and error rates analysis for both the 4factor and the 3-factor analysis. Results showed that desire to eat did not interact significantly
with the distractor in any of the analyses (eg. RT first analysis: F(22, 1)=.129, p=.72). There was
a significant effect for a 5-way interaction between distractor, CSI, congruence and desire to eat
in terms of RT (F(22,1)=6.330, p=.02) but, its meaning is difficult to infer.
Elecrophysiological data
We tested the hypothesis that the food-related images are processed more than office-related
images by conducting a one-sample t-test on the average of lateralization differences of three
pairs of occipital electodes (PO7/PO8, PO5/PO6, O1/O2),averaged over electrode pairs and
switch/repeat. A significant effect was found for the CSI distractor condition(See Figure 4, 100175ms after the onset of the distractor (t=4.058, p=.001), but not for the time-window 50-100ms
following distractor onset (t=2.020, p=.056)
Figure 4. ERPs for distractors appearing during the CSI
Also, a significant effect was also found for distractors appearing during stimulus presentation
(t=4.267, p<.001), supporting the idea that motivationally salient stimuli are processed more
compared to neutral ones.
Figure 5. ERPs for distractors appearing during the stimulus presentation
A repeated measures ANOVA with factors electrode pair (PO7/PO8, PO5/PO6, O1/O2) and
transition (Switch/Repeat) was run for each of timewindows. No significant effects were found
for the presence of switch cost or for the distractor effect being modulated by switch, in either of
the timewindows (CSI 50-100, CSI 100-175, Stimulus 100-175). Contrary to expectations, the
lateralization effect was actually smaller on switch trials, compared to repeat, but the effect was
not statistically significant.
Discussion
The current study sought to investigate the largely neglected subject of distractibility during task
switching and the way in which irrelevant but motivationally salient stimuli affect attentional
capacities in this setting.
A primary aim of this research was to investigate the effect that the presence of a completely
irrelevant distractor will have on performance during task switching. Our results showed that
participants seemed to take significantly longer to reply on trials where distractors were present,
compared to when they were not, and this effect seemed to be larger if the distractor was
presented after the stimulus. In accordance with Lavie’s (2004) Load Theory, these effects
suggest that the task was demanding enough so that no spare cognitive resources were available
for the participant to be able to actively reject the distractor. Previous studies in this area
(Longman et al, 2012; Longman et al, in press) have obtained similar results but the major
difference is that their studies have used distractors that were relevant on previous trials, whereas
in our paradigm, the food and office items presented laterally were completely irrelevant and
unrelated to the task. For example, Nijs et al. (2009) investigated the attentional processing of
food related words that were previously required for task-performance and found that people
were distracted by these stimuli even though they were now irrelevant to the task at hand. By
finding similar results using completely irrelevant distractors, the present paper suggests that
attentional processes are not as robust as previously thought (Lien et al.,2010) and that attention
can be ‘grabbed’ by external stimuli even when this is completely counterproductive to task
performance. Also, it allows us to infer that distraction during task switching can not be
accounted for by attentional inertia alone, but might suggest that other processes might be
involved. Further research would be needed to support this claim.
Our analysis also focused on whether or not distraction was affected by transition. Although,
based on Lavie’s cognitive load theory (2004), it was expected that participants will be more
distracted on switch trials compared to repeat trials, our results did not support this prediction
and, in fact, we found that reaction times were longer (although not significantly so) on repeat
trials. An explanation could be provided by the ‘late selection’ model of perception (Deutsch &
Deutsch, 1963), which states that everything is perceived, whether relevant or irrelevant,
regardless of cognitive load. Future research could investigate later components (P200 and P300),
which would show whether irrelevant stimuli are just a perceptual distraction or are, in fact,
processed on a more conscious and controlled level. Nijs’s paper (2009) suggested that obese
individuals were able to consciously suppress irrelevant food distractors, as they perceived food
related stimuli as threatening and thus, it would be interesting to see whether the same would
happen when a more complex processes, such as task switching is involved.
A second major theme of this paper was to investigate the extent to which different types of
distractors would influence performance and, to our knowledge, this is the first study
investigating the mediating role of motivation in task-switching distractibility. We found that
motivationally salient food stimuli were processed more compared to office items, effect which
was reflected in the ERP lateralization. Finding that hungry participants were more distracted by
food than by neutral items is in line with Mogg & Bradley’s findings (2002), which suggested
that smokers who are deprived of nicotine will be unable to ignore smoking-related cues.
However, the difference in processing between food and office items was not significant for
distractors appearing during the short CSI. An explanation could be provided by referring to
Lavie’s cognitive load theory (2004). As task-switching is a cognitively demanding process,
participants would use the CSI to prepare for the upcoming task and so it could be argued that
distractors that appear during short CSIs are not processed as much, due to time constraints.
Moreover, we found that this differential processing was not affected by whether the trial was a
switch or a repeat, effect which is in contradiction to our expectations.
Lastly, our results provided support for the presence of switch cost, which is a typical
phenomena associated with task switching literature (Meiran, 1996). More interestingly,
however, we found no evidence for the presence of a RISC effect, as the switch cost was not
reduced by longer CSI. This unexpected finding could be explained by noting that the presence
of distractors might have acted like a concurrent load, by affecting paricipants’ ability to
successfully prepare for the following trial.
In summary, the present study has contributed to the task switching literature, by providing
evidence for two major premises, namely, that irrelevant distractors can negatively affect
performance even in conditions of high cognitive load, such as task switching and that
motivationally salient stimuli are processed more compared to neutral ones.
.
Strenghts – laterally presented distractors – confidently infer . As the two distracting pictures
were the only objects presented lateral to the centrally located task features, any lateralization
effect can be attributed to the fact that more attention was allocated to one of the distractors,
compared to the other.
- distractors – entirely irrelevant (not merely att. Inertia )
EEG data- more direct measure of attention.
The current findings set stage for future research …..
An important goal for future research could be to unravel the exact mechanisms of…
Further explorations of……… could help……..
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