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.