Angry faces hold attention: Evidence of attentional adhesion in two paradigms

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Angry faces hold attention: Evidence of attentional adhesion in two paradigms D. Vaughn Becker*, Hansol Rheem, Cari M. Pick, Ahra Ko, Stacie R. Lafko Arizona State University, Tempe, AZ, United States *Corresponding author: Tel.: +1 480-775-1151, e-mail address: [email protected]

Abstract Growing evidence suggests that angry faces do not “pop-out” of crowds, and that the evidence for such effects has tended to arise from methodological issues and stimulus confounds. In contrast, evidence that angry faces exert special influence at later stages of information processing is accumulating. Here we use two common paradigms to show that participants have difficulty disengaging attention from angry faces relative to happy faces. Experiment 1 used a visual search task to show that angry crowds took longer to search. Experiment 2 used an exogenous cueing paradigm to show that brief onset angry faces held attention and delayed responses on a primary task. This suggests that when seen, they engage attention for longer time, but they do not have the preattentive features that would allow them to pop-out. Together, these two different experimental paradigms and realistic stimulus sets suggest that angry faces resist attentional disengagement.

Keywords Face perception, Attention, Anger, Emotional expressions, Ecological psychology

1 Angry faces demand attention: Evidence of attentional adhesion in two paradigms What is the function of attention? Certainly it allows more controlled processes to focus information gathering in ways consistent with our conscious goals. But it is equally obvious that there are more automatic processes that guide and constrain the movement of attention, and that some things seem to have the inherent ability to grab and/or hold onto our attention regardless of our intentions. From a functional perspective, these processes also serve goals, but goals that are rooted in long-standing behavior patterns of the individual as shaped by the regularities of the ancestral environment, rather than the more arbitrary and Progress in Brain Research, ISSN 0079-6123, https://doi.org/10.1016/bs.pbr.2019.03.033 © 2019 Elsevier B.V. All rights reserved.

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proximate goals imposed by the typical cognitive experiment. For instance, the goal of avoiding physical injury is ancient. A modern consequence is that ancestrally-recurrent signs of threat, like rapidly approaching objects, automatically seize attentional resources because the full information-processing capacity of the organism needs to be rapidly mobilized to avoid the potential harm. Other signs of threat, however, are more ambiguous in the regularity with which they have been associated with immediate danger. Such stimuli may instead elicit enhanced cognitive scrutiny, resisting efforts to disengage attention from them while further information about the intent is discerned. The expression of anger is an expression of threat, and so should elicit extra cognitive processing. But does it grab attention, forcing our gaze to find it even in noisy environments, or does it hold onto attention once it is seen?

1.1 Selective attention “Selective attention” can mean a number of things. One can select a particular sensory modality at the exclusion of another (e.g., vision vs. hearing). Within that modality one can focus attention in particular ways. For example, one can focus on a particular source or location (e.g., Posner, 1980), one can search among objects for those with a specific feature (e.g., a red object; Treisman and Gelade, 1980), or within an object, one can select a particular feature or dimension while ignoring other features (e.g., Garner, 1974). Furthermore, when researchers talk about an object receiving selective attention, they often mean that the object draws attention to itself automatically and in a way that can work with or resist the more controlled aspects € of attention (e.g., Ohman et al., 2001). This is really a preattentive phenomenon, in which the stimulus selects or grabs attention for itself. The present investigation will be concerned with selective visual attention. According to one popular model, three distinct brain areas control different aspects of the movement of visual attention, with one area disengaging attention from the current focus, another moving the “spotlight” to a new location, and a third locking attention in at the new location (Petersen and Posner, 2012; Posner, 1992). Each one of these mechanisms could be differentially affected by threatening, relative to nonthreatening, stimuli. Thus, even if a stimulus cannot preattentively seize attention, it might hold on to attention and make it more difficult for an observer to disengage from the object and move on to something else. Why might selective visual attention be selective for facial displays of anger? Because anger can signal the intention to do physical harm, it is plausible that adult cognition developed to find anger preattentively, i.e., to find an angry face in a scene quite rapidly, regardless of the complexity of that scene. This implies that the presence of anger automatically disengages attention from its current focus, moves attention to the location of the angry face, and locks attention onto this location. While this would be adaptive, we should keep in mind that neither evolution nor developmental factors must necessarily produce such an outcome. This selective attention may be particularly likely if the angry facial expression has more than one meaning.

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Indeed, if the angry expression is also used as a social regulator, without the threat of explicit physical violence, this other meaning may give rise to a difficulty disengaging attention from the angry face.

1.2 Evidence that angry faces grab attention Several lines of research suggest that angry faces may be automatically and preattentively detected. For example, studies in classical conditioning have shown that € angry faces are capable of being subliminally perceived (Dimberg and Ohman, € 1996; Esteves et al., 1994; Ohman and Dimberg, 1978). This finding provides a nice precondition for either a preattentive grabbing by, or a difficulty disengaging from the angry face, though the fact that participants gaze directly at the faces in such studies precludes strong claims about an angry face automatically drawing attention to its location prior to a directed search. The bulk of the evidence that angry faces grab attention has arisen from the visual search paradigm (Treisman, 1986; Treisman and Gelade, 1980), in which participants must find a target stimulus in an array of distractors. Several widely cited studies using the visual search paradigm report that angry faces are detected faster in crowds of faces, relative to happy faces. Hansen and Hansen (1988) first used the visual search paradigm to demonstrate this, and moreover found no significant difference between the anger detection latencies in four and nine face crowds, evidence that anger could be preattentively detected—that is, that anger “popped out,” drawing attention to its location automatically regardless of array size. However, a flaw in this research was identified by Purcell et al. (1996), who demonstrated that there were artifacts in the two angry stimuli in the critical third study, and that it was these confounds that were “popping out.” As we will see, confounds such as these are typical in demonstrations of what has been come to be known as the Anger Superiority Effect (ASE). More significantly, there have been several demonstrations in which schematic line drawings of faces were used to show that negative faces are more efficiently € detected (Eastwood et al., 2001; Fox et al., 2000; Ohman et al., 2001; White, 1995). Unfortunately, these schematic faces are sometimes more sad than angry. € Ohman et al. (2001) used the most unambiguously angry schematic faces, and found evidence of pop-out for angry (and happy) stimuli in neutral crowds, and a general superiority for anger detection when the crowd was expressive. It is important to note that schematic stimuli have one problem at a theoretical level: their simplicity makes it very easy for low-level visual features, such as the basic shapes of facial features, to drive detection effects (see Horstmann, 2007; Purcell and Stewart, 2002). And not all attempts to use schematic displays of facial anger have yielded positive evidence for the ASE. Hunt et al. (2007) have used eye-tracking technology to investigate this effect and have failed to find that angry schematic stimuli grab attention any faster than other (emotionally expressive and neutral) stimuli. Due to possible stimulus effects with schematic faces, it is crucial to look for evidence that angry faces grab attention when more realistic stimuli are used.

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While there have been some such demonstrations (Horstmann and Bauland, 2006; Pinkham et al., n.d.; Williams et al., 2005), alternative explanations can account for almost all of these effects (an issue to which we will return in the discussion). One confounding factor is that the rate of search through a crowd of angry faces may be slower than that through other types of faces, and so target detection effects may reflect the degree to which attention drags through the crowd. Indeed, Horstmann and colleagues (e.g., 2007; Horstmann and Bauland, 2006) have subsequently identified this as a key problem in earlier studies and generally conclude that there are no anger pop-out effects when real faces are used. Moreover, other published attempts to show that photographs of angry faces grab attention have failed to find this effect at all (Juth et al., 2005), or have emphasized the fact that uncontrolled stimulus-level features can drive the effects (Calvo and Nummenmaa, 2008). When we look across these studies, attempts to meta-analytically explore these issues also reach varying conclusions. Frischen et al. (2008) concluded that there was evidence for superior detection of anger, but they failed to distinguish between studies that used schematic stimuli and photographic stimuli. Becker et al. (2011) surveyed the extant schematic and photographic designs and concluded that there was little evidence that angry faces grabbed attention once studies involving schematic faces were excluded. They further presented several new experiments that suggested that if anything, happy faces are the more efficiently detected (see also, Becker and Srinivasan, 2014; Becker et al., 2012). In summary, while the idea that angry faces grab attention is widespread, the evidence may be less compelling than is typically thought. All varieties of stimuli generate inconsistent effects, but schematic stimuli have additional difficulty evading the criticism that detection may be capitalizing on low-level stimulus features that have nothing to do with emotion perception. For example, one angry schematic face often used in these demonstrations has very salient angry eyebrows that on their own can drive an efficient search (see the Becker et al., 2011, appendix for a full account and demonstration of this confound). No emotion needs to be perceived at all. Crucially, however, this criticism of schematic angry faces only applies to paradigms in which detection is the dependent variable, for it is only in a detection task that preattentively-available low-level stimulus features could drive a parallel search that might be confused for emotional expression effects. When we turn to the evidence that attention is held by angry faces once it has been allocated to them, low-level stimulus features present will not present a confound—if anything, they would speed up attentional disengagement.

1.3 Evidence that angry faces resist attentional disengagement There is indeed good evidence that negatively–valenced information cannot be ignored. For instance, participants take longer to name the color of negative trait words relative to positive words, suggesting that a word’s negativity is automatically processed and draws on cognitive resources even when the participant tries to ignore it (Pratto and John, 1991). In this vein, Bargh and Pratto (1986) found that any

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chronically accessible traits slowed down Stroop performance, such that participants showed delayed color naming responses when the colored stimulus was a traitrelevant word. Consistent with this, there is substantial evidence that threat-related stimuli hold attention in participants with high trait-anxiety. The meta-analysis by BarHaim et al. (2006) found a reliable threat-related attentional bias in anxious individuals, but concluded that the evidence was weak for non-anxious participants. Included experiments fell into two broad categories—Stroop tasks and exogenous cueing paradigms like the dot-probe—and included both word and pictorial stimuli. Looking only at the pictorial stimuli (which are, for the most, part expressive faces), the results do appear to suggest that both Stroop and dot-probe effects are most robust for high trait-anxiety populations, consistent with the authors’ more general conclusions. Two noteworthy demonstrations that angry facial expressions cannot be ignored use the emotional Stroop paradigm. Van Honk et al. (2001) used colored expressive photographs and found that, whereas color-naming latencies were faster for angry expressions relative to neutral expressions overall, this was not the case for participants high in trait-anger. In other words, participants with a greater tendency to anger seemed to have a relatively more difficult time ignoring the anger on a face. Another suggestive demonstration used a masked version of the Stroop task with similar stimuli. Putman et al. (2004) showed expressive faces for either 25 ms (followed by a similarly colored pattern mask that disrupted visual processing) or a full second, and found an 8 ms delay in the color-naming of angry relative to neutral faces, but only in their masked condition. This small effect is intriguing, but the failure to find a delay at supraliminal exposures means that a general attentional adhesion effect for angry faces remains to be seen using the emotional Stroop task. Exogenous cueing paradigms also provide some evidence that angry faces hold onto attention. Typically, participants are required to respond to a target stimulus that appears in one of several locations on a computer screen. Prior to the target, a cue (for example, a dot) appears in one of the locations, and this cue reliably, though not perfectly, predicts where the target will appear. Participants very quickly learn that if the dot is 80% likely to predict the targets location, they can perform the task faster by shifting their attention to the dot-probe’s location. However, on cue-invalid trials, performance is impaired, because participants must shift their attention away from the cued location to attend and respond to the target appearing in a different location. Fox et al. (2001) used this paradigm, but substituted expressive faces for the cues. On cue-invalid trials, high trait-anxious individuals showed difficulty disengaging their attention from angry faces, relative to happy and neutral faces. This occurred for both schematic and pictorial faces, but only for high-anxiety participants. In follow-up research, Fox et al. (2002) again used schematic faces in an exogenous cueing paradigm and produced some evidence that even low-anxious participants continued to dwell on angry schematic faces. However, this research also produced delayed disengagement from happy schematic faces and schematic faces with jumbled features, so while the results are suggestive, strong evidence that angry faces hold attention for non-anxious participants is yet to be provided by exogenous cueing methods.

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Fox and colleagues have also provided evidence for attentional adhesion to anger using an endogenous cuing paradigm (Fox et al., 2007). In this study, participants were required to respond to a cue that appeared on the left or the right of the screen. Prior to this cue, however, a face appeared at the central fixation point. This face bore either a neutral, happy, angry or fearful expression, and its eyes were directed to the left, right, or directly at the participant. The investigators found that participants scoring high in trait anxiety showed a significant delay of about 10 ms in responding to the peripheral cue when the face was angry and looking directly at the participant. Additional evidence can be found in studies that examined inhibition of return (IOR). This is an inhibitory effect observed at a previously-attended location approximately 300 ms after the location is attended (Posner and Cohen, 1984). Contrary to typical cue-valid trials, participants’ responses are slowed down when the stimulus onset asynchrony (SOA) is longer than 300 ms. Previous studies have pointed out that attentional disengagement from the initially attended location is the primary cause behind the IOR. As a result, an inhibitory effect is elicited at that location which seems to reflect a strategy to enhance performance by preventing unnecessary attentional revisits to the previously searched locations (Klein, 2000; Posner et al., 1985). Therefore, one could verify if an angry face cue resisted attentional disengagement by examining the IOR effect in experimental designs wherein angry face cues are presented >300 ms before the target object. Whereas a reversed cueing effect or an IOR effect would indicate that an angry face could not resist attentional disengagement, a positive cueing effect or a reduced IOR effect would suggest that attention was maintained at the location cued by the angry face. Fox et al. (2002) did indeed demonstrate that the IOR effect is significantly reduced in response to targets cued by angry faces, especially for high-anxious participants. In contrast, Lange et al. (2008) found no indication of IOR modulation by face emotion. In their study, participants were instructed to make a response as soon as they detected a target dot which was presented up to 550 ms after a drawing of a spider, butterfly or a cross in Experiments 1A and 3, or an angry, neutral or a happy face in Experiments 1B and 4. The result revealed consistent IOR effects across different cue types or face emotions, and regardless of whether participants were highly spider-fearful or high in more general anxiety. Other lines of study also suggested that threatening faces (Hu et al., 2014) and fearful faces (Stoyanova et al., 2007) used as cues do not yield significantly reduced IOR effects compared to neutral or scrambled face cues. Moreover, faces with neutral expressions do not hold attention longer than nonfacial cues when the face expresses a neutral emotion (Stoyanova et al., 2007; Taylor and Therrien, 2005). Collectively, such results may argue against the idea that angry faces can resist attentional disengagement. But are all negative facial expressions equivalent? Or does anger pose a distinct signal of threat that isn’t signaled by other cues of negativity? Fear, for example, may suggest that a threat is present, but contains uncertain information about a potential threat (Stoyanova et al., 2007; Whalen et al., 1998), which may account for the different response tendencies observed between responses to a fearful face and an

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angry face (Marsh et al., 2005). Moreover, it is also possible that the sensitivity of the observer may influence the magnitude of the face cue’s resistance from attentional disengagement. This possibility is in line with the finding by Fox et al. (2002) that stronger emotional modulation of IOR from high-anxious participants was observed, after a mood induction procedure was applied to elevate state anxiety of participants, thereby increasing the sensitivity to threat-related cues. In summary, there is some evidence that angry faces hold attention in participants high in trait-anger or anxiety. However, unambiguous evidence that angry faces hold attention in the general population has yet to be provided, and persuasive evidence should come from more realistic faces that are unambiguously angry.

1.3.1 Overview of the present research Existing research suggests that angry faces may both grab attention and resist attentional disengagement, though this evidence is somewhat inconclusive (in the former case) and limited to a specific subpopulation (in the latter case). The present experiments will provide new evidence that angry faces resist attentional disengagement, and will do so using realistic computer-generated and photographic images in two different experimental paradigms. We will first demonstrate that a conventional visual search using realistic schematic faces produces no evidence that angry faces grab attention but does suggest that they resist attentional disengagement.

2 Experiment 1 We begin this investigation using the task most often used to show that angry faces preattentively grab attention: the visual search task. This paradigm requires the participant to search for a particular target in an array of distractor stimuli. The arrays come in varying sizes, which allows the calculation of the mean target detection time as a function of the number of distractors. Furthermore, since the target is not present on some of the trials, one can also calculate correct rejection times as a function of array size. These target-absent searches are necessarily exhaustive, because participants must search through the entire array to accurately report that the target is not present. Such target-absent searches can be used to assess the degree to which faces of a particular type resist the disengagement of attention—reflected in steeper slopes relative to other stimulus types. Turning to the time it takes to detect a target, if the slopes across arrays of differing sizes are about one-half of the target-absent, exhaustive search trials, this has traditionally been taken to suggest that the search is serial and self-terminating (because the target can be found halfway through the array on average, at which point the search process terminates). Both the exhaustive and the self-terminating searches presuppose a serial search process, in which the targets are attended to and evaluated in a sequential order. It is important to note that there is the additional possibility:

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the observer could search all items in the array in a parallel fashion, which would entail that targets could be detected in the same amount of time regardless of the number of distractors. Most current researchers admit that limited capacity parallel searches are likely, which would give rise to non-zero slopes, but which nevertheless do not require a systematic, item-by-item search through the array. Thornton and Gilden (2007) have recently developed methods for distinguishing such searches from truly serial searches, but these need not concern the present investigations (see however, Becker et al., 2011, for a use of their multi target search method). In light of these theoretical concerns, it is perhaps more useful to acknowledge that search asymmetries (Wolfe, 2001) may be all that is necessary to reveal an ASE. For example, an attention-grabbing effect could be demonstrated if the search for angry faces were relatively faster relative to the search for happy faces. It is crucial, however, to interpret target detection functions relative to exhaustive, target-absent search functions. This can reveal more or less efficient searches because the targetabsent searches also allow one to partial out the time it takes to process and reject each additional distractor. Indeed, such an analytic strategy is critical, because otherwise, what ostensibly looks like an efficient target detection function may merely be efficient processing and disengagement from a particular kind of distractor. € In the present experiment, we heed Ohman et al.’ (2001) concerns that photographs of real faces introduce too much variance in the expressions, which clouds the interpretability of the results. However, we are also mindful of the shortcomings of simple schematic stimuli and the attendant danger that pop-out effects could arise from low-level visual feature detectors. Consequently, we used a special set of computer generated expressive faces (see Fig. 1) that were decidedly more realistic than simple line drawings but still allowed a great degree of control. Specifically, precise control over expressions was exercised such that all of the expressive intensities were parametrically identical across the different exemplars, and the angry and happy expressions were equivalently different (i.e., had the same number and degree of changes) from the neutrally expressive prototypes from which they were derived. These stimuli expressions have been identified with 100% accuracy by 28 undergraduate participants, and reflect a balance of equivalent discriminability, experimental control, and identifiability. If angry faces hold onto attention, then the slopes for anger detection should be significantly shallower than the slopes for happy detection, controlling for the targetabsent search rates. If angry faces resist attentional disengagement, then these exhaustive target-absent search rates themselves should be significantly different, with the steepest slopes for angry crowds.

2.1 Method: Experiment 1 2.1.1 Participants Thirty-seven (15 men and 22 women) introductory psychology students participated in exchange for partial course credit, and were treated in accordance with APA standards for human subjects.

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FIG. 1 A sample array from Experiment 1.

2.1.2 Materials Five male and five female stimulus faces were created using Poser 4 (Curious Labs), a software package widely used in the graphic arts for its ability to create lifelike figures (see Hugenberg and Bodenhausen, 2004, 2003, for other uses of Poser stimuli in psychological experimentation). Independent controls allowed the deflection of the mouth and brows such that the expressions were clearly identifiable as angry and happy, and the magnitude and number of changes (from neutral) needed to create the two expressions were identical (see Becker et al., 2007, for examples of these faces).

2.1.3 Design In this and the subsequent experiment, E-prime was used to display experimental stimuli on Pentium computers with identical hardware specifications. There were four crowd sizes (4, 6, 8 and 10) with faces randomly assigned to one of 18 possible positions on the screen (constructed to fall within the largest oval that could be displayed on the screen). Each face in the visual display filled a closely cropped

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rectangle approximately 1 in. by 1 1/3 in. Crowds consisted of faces of the same emotion, with equal numbers of trials for each expression (angry, happy and neutral). On half of each of these crowd-expression trials, a target face expressing one of the other two emotions (relative to the crowd expression) was assigned to a random location in the array. Note also that each array was constructed so that none of the component faces were of the same individual, in order to increase the ecological validity of these crowds. Fig. 1 illustrates an example of an angry crowd with one happy target present.

2.1.4 Procedure Participants were greeted and directed to a computer. The basic task was explained, and they were told that their continued participation would constitute their consent that the data could be used in published research. Half of the participants were told that they would see groups of faces on the computer screen, and that they were to strike the “5” key on the numeric keypad if a discrepant emotional expression was in the crowd, and the “A” key if the faces all had the same expression. The other half of the participants were told that they would be given a text cue indicating which specific target to search for on each trial (i.e., look for an angry face), and the same keys were used to register their decision. There was no effect of these instructional differences, so they were collapsed across and are not considered further. Participants were instructed to make their decisions as quickly as possible, but to maintain accuracy above 85%. Once the computer program was started, there were additional text instructions that reiterated the directions. There were 240 trials in all. Each trial began with a fixation point, a cross in the middle of the screen, presented for 1 s. Then the array was displayed, and remained visible until the response was made or 10 s elapsed. The participants then received feedback regarding their accuracy and speed, which remained visible for one second at which point the next trial began. At the end of the session, the purpose of the study was explained, and the participants were dismissed.

2.2 Results: Experiment 1 For each participant, mean accuracy and correct reaction times were calculated as a function of whether the target was present or absent, the emotion of the crowd, the crowd size, and, when the target was present, the expression of the target. These were subjected to multivariate repeated measures ANOVAs with crowd size always treated as a linear contrast. Table 1 contains summary measures for these results.

2.2.1 Target-absent (exhaustive search) trials For reaction times, the expected linear effect of crowd size was significant, F(1,36) ¼ 335.82, P < 0.001, partial η2 ¼ 0.908, with longer reaction times for larger crowd sizes. There was a main effect of crowd emotion, F(2,35) ¼ 5.13, P ¼ 0.001, partial η2 ¼ 0.227, with angry crowds taking significantly longer to search through

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Table 1 Exhaustive searches in Experiment 1. Exhaustive, target-absent searches Neutral

Angry

Happy

Crowd Size

RT

ACC

RT

ACC

RT

ACC

4 6 8 10 Slope Mean

1867 2274 2652 3047 196 2460

0.94 0.94 0.92 0.92

1901 2343 2820 3259 228 2581

0.89 0.84 0.87 0.84

1670 2185 2566 2921 207 2335

0.95 0.95 0.93 0.92

0.93

0.86

0.94

than happy (P ¼ 0.003) crowds, and marginally longer than neutral (P ¼ 0.096) crowds. The interaction of the linear contrast of crowd size and expression-type was significant, F(1,36) ¼ 5.41, p ¼ 0.025, partial η2 ¼ 0.132, such that the slope for searches through angry crowds was significantly steeper than for neutral crowds, F(1,36) ¼ 4.97, P ¼ 0.032, partial η2 ¼ 0.121, and trended toward being steeper than in happy crowds, F(1,36) ¼ 2.35, P ¼ 0.13, partial η2 ¼ 0.061, an indication that attention lingers longer on angry face distractors. For accuracy, the effect for crowd size was not significant, F(3,34) ¼ 2.05, P ¼ 0.13, but there was a significant effect for crowd expression, F(2,35) ¼ 10.19, P < 0.001, partial η2 ¼ 0.368, with angry crowds eliciting the lowest accuracy, and happy crowds eliciting the highest accuracy. There was no interaction of crowd size and expression, F < 1. If we correct reaction times for the accuracy differences (by multiplying the participant’s reaction times by 1 plus the error rate for that cell), the results suggest that speed-accuracy tradeoffs in no way alter the conclusion that attention seems to move more slowly through the angry crowd (see Fig. 2). Angry crowds took significantly longer to search through than happy crowds, F(1,36)¼ 15.77, P < 0.001, partial η2 ¼ 0.301, and relative to neutral crowds, F(1,36) ¼ 9.79, P ¼ 0.003, partial η2 ¼ 0.214. Furthermore, the slope for searches through angry crowds was significantly steeper than for neutral crowds, F(1,36) ¼ 6.51, P ¼ 0.016, partial η2 ¼ 0.153, and marginally higher than for happy crowds, F(1,36) ¼ 3.94, P ¼ 0.055, partial η2 ¼ 0.099.

2.2.2 Target-present trials For reaction times to angry and happy targets in the neutral crowds, there was a significant linear effect of crowd size, F(1,36) ¼ 51.04, P < 0.001, partial η2 ¼ 0.602, but no difference in reaction time as a function of target expression, F < 1, and no slope difference, F < 1. Furthermore, there were no significant differences in accuracy, all **ps > 0.20. A parallel analysis of the reaction times for angry targets in happy crowds vs. happy targets in angry crowds revealed a significant linear effect of crowd size,

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Reaction Time Corrected for Accuracy

12

4000 3500 neutral

3000

angry 2500

happy

2000 1500 four

six

eight

ten

Crowd Size

FIG. 2 Exhaustive (target absent) search rates in Experiment 1, with correct reaction times adjusted for differences in accuracy (Corrected RT ¼ RT* (1 + error rate)).

F(1,36) ¼ 57.64, P < 0.001, partial η2 ¼ 0.620, but again, angry targets were not detected faster than happy targets, and the slope for the happy target detections in angry crowds was not significantly different from that for angry detections in happy crowds, both Fs < 1. Accuracy revealed little evidence of an effect of crowd size, F(1,36) ¼ 2.01, P ¼ 0.16, nor of target emotion, F(1,36) ¼ 1.7, P ¼ 0.20, and the interaction was not significant, F < 1. Finally, an analysis of the reaction times for angry targets in happy crowds vs. happy targets in angry crowds controlling for the exhaustive search rates (detection-speed/rejection-speed at each level of crowd size) revealed a significant effect of crowd emotion, F(1,36) ¼ 16.03, P < 0.001, partial η2 ¼ 0.308, with more efficient detection of happy faces. In other words, once we take into account the fact that angry crowds take longer to search through, it appears that it is the happy face in the crowd that really stands out.

2.3 Discussion: Experiment 1 This experiment provides new evidence that people have difficulty disengaging their attention from an angry face. Specifically, the exhaustive search rate through computer-generated angry faces was significantly higher than searches through neutral crowds, and correcting for speed-accuracy trade-offs enhanced this tendency and revealed a similar pattern relative to happy crowds as well. Note that because the faces were computer generated and the expressions were precisely controlled so as to be consistent across all faces with the same expression type, it cannot be argued that the variability in the angry expressions was greater than for happy or neutral € expressions (as Ohman et al., 2001, correctly contend is a danger with realistic stimuli), and thus that longer times identifying anger on each face accounted for the slower search rate. Indeed, other research with these same stimuli using a single face emotion identification task (see experiment 4 of Becker et al., 2007) showed that

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anger was identified on average as rapidly as happiness (expression did interact with face’s gender but the present design used crowds with equal numbers of male and female exemplars). These effects are not unusual. The finding that it takes longer to exhaustively search through an angry crowd compared to a happy crowd has been shown before in studies using schematic line drawings of faces (e.g., Fox et al., 2000; White, 1995). Other research using photographic stimuli has found angry crowds take significantly longer to search through relative to happy, neutral, and fearful crowds (Becker et al., 2011). Furthermore, in the latter research, neither photographic nor computergenerated stimulus sets ever produced evidence that angry faces preattentively guided attention to their location—on the contrary, happy faces were more rapidly and accurately detected, and they generated shallower slopes, even when the exposed teeth (a high contrast confound that can drive efficient parallel searches) were eliminated (Becker et al., 2011). The results suggest that angry faces resist attentional disengagement, but they do not preattentively draw attention to their location. Of course, the evidence of attentional-holding in the visual search task is dependent on searches though crowds where the target is absent and the task itself demands expression processing. Other paradigms offer the potential for stronger complementary inferences.

3 Experiment 2 The first experiment involved controlled search processes. Even stronger evidence would be provided if angry faces could be shown to resist attentional disengagement automatically. Exogenous cueing paradigms (Posner, 1980) provide an excellent methodological option to show this effect. Accordingly, the next study used a method in which no controlled shifts of attention were required. Participants had to decide whether a centrally presented target was the number 1 or the number 0 by pressing a key rapidly. Distracting images, including angry and happy faces, appeared on either side of the screen 50 milliseconds prior to the target onset, and participants were explicitly instructed to ignore these. In this time range, such brief onset cues have been shown to reliably elicit a reflexive orienting of attention regardless of stimulus content (Muller and Rabbit, 1989). Therefore, if reaction times to the central stimuli are delayed when angry faces are the brief onset distractor (relative to happy faces or other distracting images), this indicates an automatic resistance to attentional disengagement.

3.1 Method: Experiment 2 3.1.1 Participants Fifty-seven (57, 38 female) introductory psychology students participated in exchange for partial course credit. All were treated in accordance with the APA guidelines for ethical treatment of human subjects.

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3.1.2 Materials Six male and six female faces from the Ekman and Friesen Pictures of Facial Affect (1976) stimulus set were used. Each individual portrayed both emotions (anger and happiness). For a comparison stimulus type, images of abstract art pre-rated as either positively- or negatively-valenced (culled from Duckworth and Bargh, 2002) were also used. E-Prime was used to create the experimental displays and record reaction times, and the experiments were run on Dell desktop computers with Pentium 4 processors and identical hardware specifications.

3.1.3 Design The primary task was to identify a central target as a number “1” or a number “0” as rapidly as possible by pressing the corresponding key on the keyboard. On each trial, an image appeared 50 ms prior to the target onset, on either the left or the right side of a central cue (where the target would soon appear). Half of the time this image was a face, and half of the time it was an abstract art image, with equal numbers of angry and happy, or positively- and negatively-valenced images (there were also equal numbers of male and female faces, but this factor did not significantly affect the results, so we collapsed across it in the analyses). The dependent variable was the reaction time to identify the central stimulus, and participants were explicitly instructed to ignore the distracting images.

3.1.4 Procedure Participants were greeted and directed to a computer. After receiving verbal and written instructions emphasizing that the only task was to identify the centrallypresented digit and that this was best accomplished by ignoring the distracting images, the experiment commenced. On each of 160 trials, a fixation point appeared in the center of the screen for 500 ms, which was then replaced by either a “1” or a “0” in black Arial font, sized to be approximately ½ inch in height. Immediately (50 ms) prior to the target onset, one of the distractor images appeared either 2 or 3 in. to the left or right of the central fixation point (distance and side were randomly determined). Brief-onset stimuli have been reliably shown to elicit reflexive attentional orientation using this timing, suggesting that all of the distractor stimuli would grab attention to some extent. Following their decision, participants then received feedback regarding their accuracy and speed, which remained visible for 1 s, followed by the next trial. At the end of the experiment, its purpose was explained, and participants were allowed to ask questions and then dismissed.

3.2 Results: Experiment 2 Mean reaction times and accuracies were calculated for each participant as a function of distractor image type (art vs. face), valence (positive/happy or negative/angry), distance from the central cue (2 or 3 in.) and side (left or right). These means were entered into a four-way repeated measures ANOVA.

ARTICLE IN PRESS 3 Experiment 2

Neither distance, side, nor participant gender produced significant effects or significant interactions with other variables, so the following analyses focus on distractor type and valence. There were no significant accuracy effects, and mean accuracy was 97%. For reaction times, there was no main effect of distractor type, F < 1, nor was the main effect of valence significant, F(1,56) ¼ 1.26, P > 0.2, but the interaction between these factors was significant, F(1,56) ¼ 4.34, P ¼ 0.042, partial η2 ¼ 0.072. As can be seen in Fig. 3, this interaction was driven by slower reaction times for angry faces. This interaction is driven by slower responses to angry faces relative to happy faces, F(1,56) ¼ 5.41, P ¼ 0.024, partial η2 ¼ 0.088. Angry faces also slowed down responses relative to all other distractors combined, F(1,56) ¼ 4.66, P ¼ 0.035, partial η2 ¼ 0.077, suggesting a relatively unique effect of anger rather than a more general negative valence effect.. While this latter effect was statistically significant, the average difference across participants was only 10 ms, and such a small number may appear to have little practical significance (note, however, that this size is entirely consistent with the findings of Fox et al., 2001). However, this difference score was calculated across 160 trials, and since other authors have pointed out that the biggest attentional effects for negative stimuli occur for the first few stimuli (using words as stimuli: Harris and Pashler, 2004), a secondary analysis was conducted on the faces occurring in the first 10 trials. Here, angry faces produced latencies 112 ms longer than happy faces, F(1,49) ¼ 5.86, P ¼ 0.019, partial η2 ¼ 0.107. In other words, it initially took one tenth of a second longer to disengage attention from an angry face relative to a happy face, though repeated exposures attenuated this effect (note, however, that even in the final 60 trials the effect was marginally significant, P ¼ 0.084).

Reaction Time (msec)

450.00 440.00 430.00 negative

420.00

positive

410.00 400.00 390.00 Abstract Art

Faces, Angry and Happy

FIG. 3 Correct reaction times for in Experiment 2. Note that standard error bars are calculated across participants, but effects are all tested within-participants.

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3.3 Discussion: Experiment 2 Whereas the first experiment provided circumstantial evidence that angry faces held rather than grabbed attention in a task explicitly involving attention to expressive faces, experiment 2 suggested that even under conditions of passive attention, newly appearing angry faces held attention longer did than happy faces or images of abstract art. It is unlikely that these effects could also arise from more efficient “grabbing” of attention by the angry faces, because all brief-onset stimuli that appear 50 ms prior to the target elicit equivalent reflexive attentional engagement (Muller and Rabbit, 1989). While participants may be able to learn to ignore these brief-onset stimuli over the course of the study, few would be able to do this on the earliest trials, and it is here that we see the most robust evidence of delayed disengagement from angry faces. In other words, on the first 10 trials, happy faces and art could also grab attention, and yet it is angry faces that produce the 100 ms delay in responding to the central cue. One could still argue that attention reflexively orients to angry faces more rapidly than the other stimuli, but it is important to note that this would then require an even longer disengagement from anger to yield the effects reported here. It is also important to note that this experiment required the participants to engage in a task irrelevant to the angry faces that appeared—indeed, they were instructed to ignore them. This method is thus the first demonstration that this effect occurs automatically, without using a task requiring controlled shifts of attention to the area in which the angry face appears. Furthermore, while the task is similar to previous demonstrations with schematic faces (e.g., of Fox et al., 2000, 2001), it provides new evidence it is difficult to disengage attention from angry faces using real photographs of faces (a step better than schematic faces in terms of ecological validity). It is also a demonstration that this effect can be shown in participants who are not selected for high trait-anger or anxiety.

4 General discussion Two experiments, using two different paradigms and two different stimulus sets, provided consistent and converging evidence that angry faces resist attentional disengagement. However, there was little evidence that angry faces automatically draw attention. The first experiment used the visual search method, which has typically been used to show that angry faces are detected more efficiently than other faces. The focus here was instead on the exhaustive searches through crowds of homogenous expressions when the target was absent. There was a significant effect of crowd emotion, with angry crowds generating the steepest slopes, evidence that they took the longest to search through. It is important to note that the faces were computer generated and the expressions were precisely controlled such that were consistent across all faces with the same expression type. Furthermore, for the happy and angry expressions, the number and degree of changes from a neutral expression were

ARTICLE IN PRESS 5 Do angry faces grab or hold attention?

equated. Thus, it cannot be argued that the variability in the angry expressions was greater than for happy or neutral expressions and that this accounted for the slower search rate. The first experiment also had the potential to reveal that angry faces grabbed attention, or even that they “popped-out” of crowds, but no significant differences in the speed or accuracy for angry and happy target detections were obtained. In fact, once exhaustive search rates were taken into account, happy targets showed a significant advantage in grabbing attention. The second experiment used an exogenous cueing paradigm in which participants had to rapidly identify a central target, but distracting images appeared immediately before the target’s onset, which has been shown to elicit reflexive orienting of attention (Muller and Rabbit, 1989). When these distracting images were angry faces, reaction times to the central target were significantly delayed, evidence that attention clung to these angry faces relative to both happy faces and to positivelyand negatively-valenced images of abstract art. Furthermore, consistent with other research (Harris and Pashler, 2004), the earliest trials revealed the greatest inability to disengage attention from angry distractors. Because it is these trials that would be most likely to elicit reflexive orienting to all of the brief onset stimuli, the delay is unlikely to reflect the grabbing of attention by angry faces, but instead reflects a relatively pure measure of delayed attentional disengagement. It should also be noted that this is the first demonstration that photographs of real angry faces hold attention in non-anxious participants. In addition, while previous exogenous cueing paradigms (using schematic faces) involved controlled movements of attention, this reveals that reflexive, automatic orienting of attention leads to difficulty disengaging attention from an angry face in non-anxious participants.

5 Do angry faces grab or hold attention? The notion that angry faces grab attention, and even “pop-out,” is widespread, but we find no support for it across these studies. Indeed, the evidence in the larger literature is not as strong as people believe. Why? We believe that it is only because this effect fits nicely with intuitions about adaptive design that the effect continues to receive so much attention. Indeed, despite Purcell et al.’s (1996) repudiation of the original Hansen and Hansen (1988) finding, it continues to be cited far more often than the repudiation—clearly a case of a beautiful idea persisting in the face of an ugly fact. € Even more cited is the demonstration by Ohman et al. (2001) using schematic stimuli. These researchers offered evidence of pop-out for angry (and happy) faces in neutral crowds, and a general superiority for angry detections. It should be noted that they also found these schematic angry faces were better detected even when upside-down, a manipulation that ordinarily disrupts facial processing. This is consistent with the possibility that simple visual features that have nothing to do with the expression of anger were driving these results. Such features would not be expected to be compromised by inversion. Indeed, Purcell and Stewart (2002) have provided

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evidence that the way that the eyebrows and corners of the mouths radiate outward from the center of the schematic angry face actually accounts for these effects. This “line-out” configuration would allow very simple feature detectors in the visual cortex to detect these expressions, and do so even when the faces are presented € upside-down. Unfortunately, despite the very real possibility that the Ohman et al. (2001) stimuli have low-level visual confounds that account for the effects, they have been reused in a growing number of studies. Many of these purport to find that threat detection is unimpaired in a particular demographic group that might be expected not to show robust threat detection (e.g., in participants with Aspberger’s syndrome; Ashwin et al., 2006; and in the elderly; Mather and Knight, 2006). Of course, if Purcell and Stewart are correct, then these demonstrations may merely reflect intact feature detectors for “line-out” stimuli, rather than threat detection per se (see also the appendix of Becker et al., 2011). Such findings if spurious may be leading researchers in other fields to devote time and resources to avenues that will in the end prove equally spurious, rather than pursuing stimuli and methods with more promise. It is this danger that demands that we take a closer look at anger detectability using more realistic stimulus sets. Admittedly, there are demonstrations that angry faces are better detected using more realistic/ecologically valid stimuli, but all have one or more problems with the design. In the first place, many use angry targets that have exposed teeth, a simple visual feature that would be expected to drive efficient search (e.g., Williams et al., 2005, Experiment 3). In the second place, many fail to take into account the exhaustive search rates through crowds in which the target is absent—if angry targets yield shallower search rates relative to happy targets, but the exhaustive search rates are also higher for happy faces, this suggests that the results have more to do with ease of rejecting distractors rather than finding targets. For example, in Horstmann and Bauland’s (2006) demonstration that angry detection slopes are shallower, once these slopes are converted into slope ratios, 3 out of the first 4 experiments actually show more efficient detections (i.e., shallower slope ratios) for happy targets. Finally, we should be wary of any finding that arises from the same stimulus target being used over and over again (e.g., Experiments 1 & 2 of Williams et al., 2005). Research in the visual search paradigm has shown that almost anything can come to be detected efficiently if enough trials are administered (Cave and Baty, 2006; Wolfe, 1992). Thus, any convincing demonstration that angry faces hold attention using the visual search must also use a variety of targets, so that we can have some confidence that the effect is not due to learning the idiosyncrasies of a particular stimulus target, and this remains to be seen in the literature. In summary, it appears that the case for angry faces grabbing attention has been overstated, and it may be completely erroneous. For every seemingly unconfounded demonstration of the effect, there is another study that fails to find it. It is still possible that a legitimate grabbing effect might be demonstrated with, for example, enraged faces (as appear to be used in Pinkham et al., n.d.), but even here, individual attributes—like exposed teeth, an open mouth, flared nostrils, bulging veins and ruddy coloration—provide simple features that can drive the visual search in a

ARTICLE IN PRESS 6 Conclusions

way that is entirely independent of expression processing. The visual search for expressive faces is a paradigm that will always have potential confounds muddying the waters. Things are much clearer when we consider the evidence that angry faces resist attentional disengagement. For here, low-level confounds that might grab attention via simple feature detectors are unlikely to account for delays in attentional disengagement. Previous demonstrations (Fox et al., 2001, 2002) do fall short of demonstrating that participants who are not high in trait-anxiety reliably show these effects, but they are more likely to reflect actual expression processing effects and not low-level confounds. The research reported here thus provides new evidence for the claim that facial displays of anger resist attentional disengagement, or rather, that anger “holds” attention once seen. In fact, these results demonstrate that anger holds attention even when we are trying to ignore the stimuli. Furthermore, these results suggest that both automatic and controlled processes can lead to difficulty disengaging attention from angry faces, linking the research to a larger body documenting delayed disengagement from threat (Bar Haim et al., 2006; Williams et al., 1996), and greater processing of negative information in general (Ito et al., 1998; Pratto and John, 1991). In summary, the present research provides no support for the notion that angry faces grab attention, but gives consistent support to the idea that it is difficult to disengage attention from an angry faces once it is seen. If angry faces hold onto attention but do not preattentively seize it, what does this entail for the meaning of an angry face? Perhaps the prototypical angry face used in psychological research is not that of an enraged individual bent on imminent attack, but rather the anger that a peer, parent, romantic partner or coalition member might display to express goal frustration. It would thus be reasonable that such an expression would have the effect of arresting one’s attention so that amends could be made (but not the effect of grabbing one’s attention to prevent imminent attack). It is also plausible that the angry facial expression expresses many possible intentions, and that this polysemy demands further processing. Perhaps there are some variations of the angry facial expression that do not hold attention. The mildest look of displeasure may permit one to look away, usually in a feeble attempt to feign unawareness (and perhaps the only easy direction to look away from an angry face is down, as a gesture of shame and supplication). In general, however, it seems to be the case that an angry face of any variety makes a demand on our attention at a deep and ancient level, such that even when passively viewing pictorial stimuli irrelevant to us in every way, we can’t quite disengage our attention from them as quickly we look away from other faces and things.

6 Conclusions The ecological psychologist James Gibson (1986) believed that perception is direct and unmediated, reflecting deep evolved affordances of signals and receivers. Paraphrasing the Gestalt psychologist Kohler, he wrote “the apple says ‘eat me’,

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the beautiful woman says ‘love me’….” The results of two experiments suggest that we can add to this list that the angry face says “keep paying attention to me.” Anger places demands on attention once it has been seen, but despite the larger literature, it does not appear to demand attention before it has been looked at. Anger doesn’t grab, it holds. Future research into the difficulties we have in disengaging our attention from an angry face will doubtlessly reveal many nuances to this effect that are only hinted at in the present literature. Researchers seeking to use the visual search task to explore whether anger grabs attention, on the other hand, should perhaps look to other paradigms, for this effect may be nothing more than a variety of easy-toreplicate confounds and errors.

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Posner, M.I., 1992. Attention as a cognitive and neural system. Curr. Dir. Psychol. Sci. 1, 12–14. Posner, M.I., Cohen, Y., 1984. Components of Visual Orienting. Attention and Performance X: Control of Language Processes. vol. 32, pp. Lawrence Erlbaum, Hillsdale, NJ, 531–556. Posner, M.I., Rafal, R.D., Choate, L.S., Vaughan, J., 1985. Inhibition of return: neural basis and function. Cogn. Neuropsychol. 2, 211–228. Pratto, F., John, O.P., 1991. Automatic vigilance: the attention-grabbing power of negative social information. J. Pers. Soc. Psychol. 61, 380–391. Purcell, D.G., Stewart, A.L., 2002. The face in the crowd: yet another confound. In: Poster Presented at the Annual Meeting of the Psychonomic Society, Kansas City, MO. Purcell, D.G., Stewart, A.L., Skov, R.B., 1996. It takes a confounded face to pop out of a crowd. Perception 25, 1091–1108. Putman, P., Hermans, E., van Honk, J., 2004. Emotional stroop performance for masked angry faces: it’s BAS, not BIS. Emotion 4 (3), 305–311. Stoyanova, R.S., Pratt, J., Anderson, A.K., 2007. Inhibition of return to social signals of fear. Emotion 7, 49–56. Taylor, T.L., Therrien, M.E., 2005. Inhibition of return for faces. Percept. Psychophys. 67, 1414–1422. Thornton, T.L., Gilden, D.L., 2007. Parallel and serial processes in visual search. Psychol. Rev. 114 (1), 71–103. Treisman, A., 1986. Features and objects in visual processing. Sci. Am. 255, 114–125. Treisman, A., Gelade, G., 1980. A feature integration theory of attention. Cogn. Psychol. 12, 97–136. Van Honk, J., Tuiten, A., de Haan, E., van den Hout, M., Stam, H., 2001. Attentional biases for angry faces: relationships to trait anger and anxiety. Cognit. Emot. 15, 279–297. Whalen, P.J., Rauch, S.L., Etcoff, N.L., McInerney, S.C., Lee, M.B., Jenike, M.A., 1998. Masked presentations of emotional facial expressions modulate amygdala activity without explicit knowledge. J. Neurosci. 18, 411–418. White, M., 1995. Preattentive analysis of facial expressions of emotion. Cognit. Emot. 9, 439–460. Williams, J.M.G., Mathews, A., MacLeod, C., 1996. The emotional Stroop task and psychopathology. Psychol. Bull. 120, 3–24. Williams, M.A., Moss, S.A., Bradshaw, J.L., Mattingley, J.B., 2005. Look at me, I’m smiling: searching for threatening and non-threatening facial expressions. Vis. Cogn. 12 (1), 29–50. Wolfe, J.M., 1992. The parallel guidance of visual attention. Curr. Dir. Psychol. Sci. 1, 124–130. Wolfe, J.M., 2001. Asymmetries in visual search: an introduction. Percept. Psychophys. 63, 381–389.

Further reading Byrne and Eysenck, 1995. Trait anxiety, mood and threat detection. Cognit. Emot. 9, 549–562. Eastwood, J.D., Smilek, D., Merikle, P.M., 2003. Negative facial expression captures attention and disrupts performance. Percept. Psychophys. 65, 352–358. Posner, M.I., Petersen, S.E., 1990. The attention system of the human brain. Annu. Rev. Neurosci. 13, 25–42. Sternberg, S., 1967. High-speed scanning in human memory. Science 153, 652–654.