Introduction
Processing human faces spontaneously and accurately is a vital skill for
humans (Chen, 2014) because faces are considered as the most socially
and emotionally significant visual stimuli in the environment (Gupta,
Hur, & Lavie, 2016). They can convey different social information with
others to express happiness, sadness, anger, fear, surprise, and disgust
(Keltner, Ekman, Gonzaga, & Beer, 2003). Being competent in this skill
facilitates individuals to interact appropriately with others and the
surrounding environment. However, the environment is often filled with
other forms of distractions which are difficult to ignore. Nonetheless,
human faces are processed differently and show processing advantage
compared to other non-social stimuli such as vehicles and buildings. For
example, only a short duration of eye movements (also refer to as
saccades) is required for processing facial stimuli (around 100 ms)
compared to an average of 140 ms for processing non-face stimuli such as
vehicle (Crouzet, Kirchner, & Thorpe, 2010). Besides, individuals only
need as little as 360 ms to discriminate between unfamiliar and familiar
faces (Barragan-Jason, Besson, Ceccaldi, & Barbeau, 2013). This
indicates the uniquenss of facial stimuli and the extent of faces in
capturing attention and cognitive demands.
To compare processing speed of animal faces, human faces and non-facial
stimuli, Crouzet, Kirchner, and Thorpe (2010) have conducted experiments
using the Saccadic Choice Task with the predictions that human faces are
quicker and more salient in capturing attention than the other stimuli.
The results of the first study showed that rapid saccadic responses were
initiated when processing human faces (around 110 ms) following the
onset of the
stimuli. A
followed-up experiment from this showed that even when participants were
instructed to saccade toward other non-facial stimuli like vehicles,
they still showed faster saccades directed toward faces. Both findings
suggest that attentional bias towards facial processing is relatively
difficult to suppress which underlie the processing advantage and unique
characteristics of human faces from other stimuli.
According to the mood congruency hypothesis (Bower, 1981), a theory that
explains how emotional information is more easily retrieved when it has
the same emotional content as the current emotional state of the
individual (Bower, 1981). Consistent with this hypothesis, studies
reported that positive emotional state was repeatedly found to
facilitate processing of happy faces relative to neutral faces
(D’Argembeau, Van der Linden, Comblain, & Etienne, 2003). Aside from
the impact of current emotional state, the intensity of emotional facial
expressions could also enhance performance on facial recall (Bate,
Parris, Haslam, & Kay, 2010). This is apparent in findings (e.g.,
Craig, Becker, & Lipp, 2014) supporting happiness superiority effect,
wherein positive but not negative emotions of the stimuli are generally
processed more quickly than neutral ones. It is explained that
attentional bias towards emotional stimuli (especially threatening and
aversive ones) can be useful for survival reason, and is regarded as a
result from adaptation to environmental danger (Öhman, 2002).
Although there is general attentional bias towards emotional faces
relative to other non-facial stimuli due to adaptation advantage and
survival (Öhman, 2002), there is limited capacity of attentional
resources which means that when two or more incoming stimuli need to be
processed, priority must be assigned to one or the other at a given
time. Working memory can be conceptualized as the interface between
internal executive control and external attentional control. It is also
used to actively manipulate visual stimuli in the environment and to
direct attention to the goal-relevant stimuli (Chun et al., 2011). An
example would be to try and do a mathematical calculation while
processing emotional facial stimuli. Theories of emotional interference
and selective attention (Lavie, 2005; Pessoa, 2009) suggest that the
amount of attentional resources allocated to certain stimuli are
determined by the trade-off between bottom-up (or stimuli-driven)
influences such as emotional salience of the faces and top-down (or
goal-directed) influences like doing a mathematical calculation.
Research has shown that attentional control can help to select stimuli
in the visual environment via top-down or bottom-up mechanism (Hu, Xu,
& Hitch, 2011). Working memory plays an important role in this process
by actively retaining the information while information is processed
(Chun, Golomb, & Turk-Browne, 2011; Vogel, Woodman, & Luck, 2005).
Evidence for the tradeoff between top-down and bottom-up processing were
evident by an increased interference for emotional stimuli in cognitive
tasks (as reflected by lower accuracy and slower response time). For
example, in an emotional Stroop task, the emotional faces were the
bottom-up processing whereas the color naming was the top-down
processing. Emotional facial processing involves automatic (bottom-up)
processing and colour naming involves controlled (top-down) processing.
It was found that participants responded slower to colour naming of
emotional words compared to neutral ones due to the emotional
interference of facial stimuli with the task demand of colour naming
(Dresler, Mériau, Heekeren, & Van der Meer, 2009). Similarly, in an
n-back task that measures working memory, emotions of the stimuli was
found to impede task performance wherein participants responded slower
for emotional but not neutral stimuli (Bowling, 2015). It was found that
emotional faces yielded lower accuracy and there was longer response
time for emotional words when compared with their neutral stimuli.
Although facial expressions appear to be automatically processed and
showed processing advantage compared to other non-facial stimuli,
emerging evidence by Lavie (2005) suggest that the processing is vastly
dependent on the cognitive load of the competing task. He used varying
levels of cognitive load (by using short and long number of letter
strings in the task) as interfering distractors for a visual attentional
task. The top-down goal of the task was match the object or face with
the name presented on the screen. Results showed that the higher
cognitive load impaired WM ability more than low cognitive load ones.
This is possibly due to the difficulty in trying to actively retain
prioritized processing for stimulus-driven (bottom-up) goals. If both
the goal-relevant tasks require bottom-up processing, participants would
direct cognitive resources toward the more salient stimuli with less
distraction. That is, bottom-up stimuli are largely dependent on the
cognitive load of the distractors, which again reflect the limited
capacity of the WM span. Followed from this, O’toole, DeCicco, Hong, and
Dennis (2011) examined whether task difficulty might influence attention
performance of emotional stimuli. They have used attention task that
measures three aspects of attention performance: alerting, orienting,
and executive attention. Results showed that emotional faces
consistently facilitate to direct attentional resources in the easy task
but not the difficult task. These findings suggested that emotions may
exert effect on attentional control and working memory (WM) performance
but the complexity of the task (i.e., difficult versus easy) must be
accounted for in the investigation.
More recently, Allen et al. (2017) examined the interaction of
attentional control and WM. In a series of sever experiment using
distractions of different shapes, colors, positions timing etc, and
examined how WM was influenced by these factors. Their results suggested
that the type of distraction is independent to the WM performance.
However, Allen et al. (2017) concerned whether the results can be
replicated to other forms of cognitive tasks such as complex span
paradigm, a dual task that requires memorizing a list of items (e.g.,
words) while performing other task such as verifying a mathematical
equation. This combination of short-term storage and processing
requirements implements the basic definition of WM as simultaneous
storage and processing (Baddeley, 2012).
Based on the current scope of literature, it is evident that the
assessments of WM capacity varies greatly with the difference in
contents and measuring methods (Kane et al., 2004). Assessment of WM
began with the earliest measures using reading span task (Daneman &
Carpenter, 1980) towards the later development of a more complex-span
paradigm such as automated version of operation span (Asopan; Unsworth,
Heitz, Schrock, & Engle, 2005). The complex-span paradigm measures WM
span requires participants to pay attention and retrieve information
which is not currently available in the environment. For example,i n the current study, WM is not only used to actively
manipulate visual stimuli but also intentionally engage in solving
distracting tasks. In this way, there are two types of goal-relevant
stimuli: emotional faces and resolving math questions in which the
attention should be directed to.
According to the Time-based Resource-sharing model (TBRS; Barrouillet et
al., 2004), it is proposed that individual’s attention can quickly
switch back and forth from the processing to storage WM component to
prevent loss of memory trace. There is the assumption that the cognitive
load corresponds to the proportion of time required for the processing
but predicted that the storage of WM component is independent of the
processing component. Although previous research has examined the how
emotional face processing or cognitive load may separately impact WM, it
is unclear whether emotional faces can possess significant interference
on WM task when the complexity of the task is manipulated.
The primary hypothesis of the current study was to examine whether
distracting tasks at varying levels of cognitive load may influence the
WM performance of emotional facial processing. Manipulation check of
distracting tasks was conducted. The second hypothesis of this research
was to examine the effect of emotional faces on WM performance. It was
predicted that emotional faces would result in better WM performance.
Therefore, the current study investigated how emotional faces and
distractions may interact to affect WM performance.