The Face in the Crowd Revisited: A Threat Advantage With Schematic Stimuli

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1 Journal of Personality and Social Psychology 2001, Vol. 80, No. 3, Copyright 2001 by the American Psychological Association, Inc. O /0l/$5.00 DO1: // The Face in the Crowd Revisited: A Threat Advantage With Schematic Stimuli Arne Ohman, Daniel Lundqvist, and Francisco Esteves Karolinska Institutet Schematic threatening, friendly, and neutral faces were used to test the hypothesis that humans preferentially orient their attention toward threat. Using a visual search paradigm, participants searched for discrepant faces in matrices of otherwise identical faces. Across 5 experiments, results consistently showed faster and more accurate detection of threatening than friendly targets. The threat advantage was obvious regardless of whether the conditions favored parallel or serial search (i.e., involved neutral or emotional distractors), and it was valid for inverted faces. Threatening angry faces were more quickly and accurately detected than were other negative faces (sad or "scheming"), which suggests that the threat advantage can be attributed to threat rather than to the negative valence or the uniqueness of the target display. Unlike most other musculature, the facial muscles are designed to move skin tissue rather than bones (Fridlund, 1994). In combination with functional, comparative, and developmental considerations (e.g., Ohman & Dimberg, 1984), this anatomical fact suggests that the face has evolved as a specialized module to serve nonverbal social interchange (an idea pioneered by Darwin, 1872/ 1998). Inspired by this evolutionary premise and by a previous article by Hansen and Hansen (1988), the research reported in this article examines whether people preferentially direct their attention toward a threatening face in a crowd of faces. Facial threat is typically conveyed by a set of gestures suggesting an emotional expression of anger: pronounced frowning brows, intensely staring eyes, and a shut mouth with lowered corners (Ekman & Friesen, 1975). This is similar to the facial display shown by confidently dominant primates when they assert their position in social hierarchies (e.g., Hinde, 1975). Furthermore, these features figure prominently among ceremonial masks that are understood as evil or threatening in diverse cultural contexts (Aronoff, Barclay, & Stevenson, 1988). Because our focus is on the signaling rather than the expressive role of facial displays, we prefer to discuss threatening rather than angry faces, even though in many contexts the terms can be used interchangeably. For the same reason, we refer to friendly rather than to happy faces. In agreement with the evolutionary scenario, pictorially depicted facial threat is an efficient cue for human fear conditioning (e.g., Ohman & Dimberg, 1978). Furthermore, these effects are not dependent on conscious identification of stimuli, because they can Arne Ohman, Daniel Lundqvist, and Francisco Esteves, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. The research reported in this article was supported by a grant from the Bank of Sweden Tercentennial Foundation. We gratefully acknowledge the technical assistance of Jan-Eric Litton and the assistance of Jorge Patraquim and Fredrik Palm in collecting parts of the data. Correspondence concerning this article should be addressed to Arne Ohman, Section of Psychology, Department of Clinical Neuroscience, Karolinska Institutet and Hospital, Z6, S Stockholm, Sweden. Electronic mail may be sent to be observed in reaction to masked facial stimuli (e.g., Esteves, Dimberg, & Ohman, 1994; see Dimberg & Ohman, 1996, for a review of conditioning to facial stimuli). Recent brain imaging studies show that nonconscious activation of regional cerebral blood flow to masked angry faces centers on the right amygdala (Morris, Ohman, & Dolan, 1998), to which information is conveyed through subcortical visual pathways (Morris, Ohman, & Dolan, 1999). Neuropsychological studies of patients with bilateral amygdala damage suggest that these patients overestimate the trustworthiness and approachability of those faces that are rated as most negative by normal participants (Adolphs, Tranel, & Damasio, 1998). In concert, these findings demonstrate that humans can decode, learn, and emotionally respond to threatening facial stimuli that they do not consciously perceive and that these effects are adaptive and mediated by specialized neural circuitry. Thus, this set of findings may reflect an evolved, specialized behavior module for responding to emotional facial expressions (Ohman & Mineka, in press; Tooby & Cosmides, 1992). Because facial threat provides a warning that aversive consequences are likely, the evolved module should be biased for orienting attention to salient facial gestures that convey threat. This hypothesis was tested in a pioneering study by Hansen and Hansen (1988). Using a visual search methodology to separate capacityindependent parallel search from effort-demanding serial search for target faces (see Wolfe, 1998), Hansen and Hansen exposed their research participants to "crowds" of people composed of matrices of individual faces. The participants' task was to detect whether all the faces in a crowd showed the same emotional expression or whether there was a face with a discrepant expression present in the crowd. In support of the hypothesis, Hansen and Hansen's first experiment showed that participants found threatening faces in friendly crowds significantly faster and with fewer errors than they found friendly faces in threatening crowds. However, other aspects of the data were in less agreement with the hypothesis. For example, participants found threatening faces no faster than neutral faces in friendly crowds. Because the apparently larger between-subjects variability in threatening than in friendly or neutral expressions complicated the 381

2 382 OHMAN, LUNDQVIST, AND ESTEVES interpretation of the effect of expression in their first experiment, Hansen and Hansen (1988) changed the composition of the stimuli in their second experiment. Rather than involving different individuals, the crowds in the following experiments consisted of identical pictures (i.e., the same individual expressing the same emotion) with targets of the same individual expressing a different emotion. With these stimuli, Hansen and Hansen again found faster detection of threatening targets in friendly crowds than vice versa. Furthermore, whereas the detection of threatening faces was unaffected by crowd size, it took significantly longer to detect a friendly target (in a threatening crowd) when the matrix was composed of nine than of four faces. This was interpreted as a "pop-out" effect indicating a parallel, preattentive search (see, e.g., Treisman, 1988) for threatening targets but a serial postattentive search for friendly targets. Thus, the studies reported by Hansen and Hansen (1988) appeared to provide support for the hypotheses that threatening facial gestures effectively command attention and that the effect was mediated by automatic, preattentive perceptual processes. However, subsequent research has cast considerable doubt on the validity of these conclusions. For example, in support of serial rather than parallel search for threatening targets, significant effects of target location in the matrix have been reported both for threatening and for friendly targets (Hampton, Purcell, Bersine, Hansen, & Hansen, 1989). Because the latencies Hansen and Hansen (1988) found for deciding about target absence were shorter for friendly than for threatening crowds, finding threatening targets faster may simply depend on discarding friendly distractors more quickly (Hampton et al., 1989). Accordingly, more efficient processing of friendly than of threatening facial expressions is the common finding in the literature (e.g., Esteves & Ohman, 1993; Harrison, Gorelczenko, & Cook, 1990; Kirouac & Dore, 1984; Wagner, MacDonald, & Manstead, 1986). Finally, and most important, the pop-out effect for threatening targets appears to be due to a stimulus confound (Purcell, Stewart & Skov, 1996). Hansen and Hansen (1988) used high-contrast black-andwhite pictures produced by thresholding (white above the threshold, black below it) the gray scale of Ekman and Friesen's (1975) facial photographs. As a result, conspicuous dark areas that were not apparent in the friendly faces accidentally appeared on both of their threatening faces, thus introducing a low-level perceptual confound that provided an alternative explanation of the findings. Purcell et al. (1996) showed that the original faces with the full gray scale (Ekman & Friesen, 1975) did not produce any threat pop out. However, the thresholded threatening faces did, but only for participants who reported that they had discovered the confounding dark areas. The more efficient processing of friendly than of threatening facial expressions (e.g., Esteves & Ohman, 1993; Kirouac & Dore, 1984; Wagner et al., 1986) may be due to the fact that friendly faces are simply more familiar than threatening faces are. Bond and Siddle (1996) collected several sets of data in which college students reported the frequency with which they encountered individuals exhibiting different prototypical facial expressions in their environment. It is not surprising (although it is reassuring) that students most frequently encountered happy expressions, whereas they saw angry expressions more seldom. Thus, cognitive representations of friendly expressions should be more or less permanently primed, which facilitates many types of cognitive operations, including some types of attention. Indeed, extensive pilot work in our laboratory as well as some published work (Byrne & Eysenck, 1995) show that friendly targets are more quickly located in neutral crowds than are threatening targets, at least when several exemplars are used for each category (i. e., when the individual faces are relatively unfamiliar). Another problem may be that threatening and neutral faces are more similar to each other than are friendly and neutral faces. Hansen and Hansen (1988) commented in a footnote on the confusability of angry and neutral faces in their Experiment 1. Indeed, a truly neutral face, lacking any invitation to interact, is easily interpreted as slightly hostile. A third problem concerns individual variability in posing different types of emotional facial expressions. Virtually everyone can provide a reasonably convincing friendly smile, but fewer persons can produce a convincing threatening, angry expression on command. As a result, a threatening crowd is necessarily more heterogeneous than a friendly or a neutral crowd is. Because distractor homogeneity is an important determinant of visual search efficiency (Duncan & Humphreys, 1989; Wolfe, 1998), the larger variability of threatening faces runs the risk of confounding comparisons between threatening and friendly crowds when several stimulus individuals are used (Hansen & Hansen, 1988). However, to avoid this problem by using the same individual for all positions in a crowd (Hansen & Hansen, 1988) may introduce other problems. First, idiosyncrasies of the particular individuals chosen may introduce confounds (see Purcell et al., 1996), and, second, it provides a loss of ecological validity, because crowds of clones so far are exceedingly rare in real life. These problems (except that pertaining to ecological validity) could be avoided if schematic facial stimuli such as those shown in Figure 1 were used. Because these stimuli are abstractions from real faces, they are all unfamiliar, and thus the differences in priming real faces of different expressions may be less important. Second, the physical features of the faces can be tightly controlled. In Figure 1, it is obvious that the physical differences between the threatening and the friendly face, on the one hand, and the neutral face, on the other hand, are identical. Third, the two emotional faces contain identical physical components that are organized in different ways. For example, the two eyebrows have changed places, and the mouths and eyes have been turned upside down when comparing friendly and threatening expressions. Fourth, because these stimuli are abstract and schematical, they are prototypical rather than individual instantiations of a particular facial expression, and thus all crowds are by necessity homogenous. The prototypical nature of schematic faces may be important, because signal evolution is likely to result in stereotypical, exag- Neutral Friendly Threatening Figure I. Schematic faces controlled for physical differences between emotional and neutral expressions.

3 THE FACE IN THE CROWD REVISITED 383 gerated signaling gestures (Krebs & Davies, 1993). Whether the particular faces shown in Figure 1 capture the essential features of each expression is an empirical question. Lundqvist, Esteves, and Ohman (1999) performed a series of rating studies of schematic faces of this type. They interpreted their result to show that V-shaped eyebrows allocated a face to a "threat area" within a three-dimensional emotional space defined by orthogonal dimensions of valence, arousal, and dominance. The mouth and the eyes provided further differentiation within this threat area. The friendly face, on the other hand, was placed at the opposite end of the emotional space, defined by positive valence, low arousal, and low dominance. Thus, research participants appeared to provide a reasonable emotional differentiation between the two emotional faces depicted in Figure 1. The purpose of the five experiments reported in this article is to use schematic facial stimuli to examine whether threatening and friendly facial expressions are differentially effective in capturing attention. Experiment 1 The first experiment essentially replicates the design of Hansen and Hansen's (1988) Experiment 1 with schematic faces as stimuli. Thus, we asked research participants to search for discrepant faces in friendly, threatening, and neutral crowds. To allow assessment of the effectiveness of the search, we set the exposure time of the display at either short (1 s) or long (2 s). Method Participants. The research participants were 20 students at the University of Stockholm. They were recruited through advertisements on the Stockholm University campus, and they were paid 80 kronor (approximately US $10) for their participation. There were 12 men and 8 women, and the age range was years (with a positively skewed distribution). The participants agreed to participate in the experiment on an informed consent basis. Apparatus. Visual stimuli were presented on the 20 in. (50.80 cm) screen of a Macintosh 8100/100 computer that was activated by a 486 Personal Computer, programmed in the Microexperimental Laboratory software (Schneider, 1988) to initiate trials and to measure reaction times (RTs). The participants responded by pressing two different keys on the computer keyboard with their left and right index fingers. Except for timing, the experiment was programmed and presented on the Macintosh computer using the Macromedia Director 5 software. Stimuli. The stimuli were matrices composed of nine individual schematic faces arranged in 3 X 3 matrices. The displays with a target are shown in Figure 2. The faces were drawn in black against a white background. The outline of the face and the nose were drawn with 1-pixel lines, and the eyebrows, eyes, and mouth were drawn with lines of 2 pixels. The individual faces were 84 X 98 pixels. Half of the matrices were composed of faces that all showed the same emotional expression (i.e., neutral, friendly, or threatening). In the other half of the matrices, one of the faces was designated as the target and had a different emotional expression from that of the background distractors (see Figure 2). All distractor expressions were combined with all target expressions, making six different target-distractor combinations (Figure 2). Thus, to assure varied mapping of targets and distractors (i.e., all stimuli serving in both roles, which promotes effortful serial search; Schneider & Shiffrin, 1977), we made sure that neutral faces occurred as targets, even though the hypothesis only concerned the difference between friendly and hostile targets. The target could occur at any of the nine positions in the matrix. Thus, there were 54 different matrices containing a target and three different distractor matrices without targets (neutral, happy, angry). Procedure. The participants were tested individually. They were seated approximately 1 m from the computer screen in a comfortable chair whose height could be adjusted so that the participant's eyes were positioned at the center of the screen. The keyboard was placed to allow the two responding fingers to be held on the two response keys with the participant's arms comfortably rested on the table. The individual face had a size on the screen of approximately 3 X 3.5, and the outline of the stimulus matrix on the screen gave visual angles of approximately 10 X The general nature of the experiment was first orally outlined to the participants, and then more detailed written instructions were presented on the computer screen. The instructions explained that the task was to detect a discrepant face in a matrix of faces. It was also explained that half of the matrices contained a target and that the participant was expected to press different keys depending on whether a discrepant target was present in a matrix. Before the task began, the participants were taken through a series of self-paced training trials on the computer, which showed and explained the stimuli and the nature of the participant's task, stressing the need to decide quickly whether a target was present in a matrix or not. A positive decision (target present) was always indicated by the right index finger, and a negative decision (target absent) was indicated with the left index finger. A trial was initiated by the appearance of a fixation point (0.4 cm diameter) at the center of the screen, located where the center face of a matrix would later appear. The fixation point was on for 2 s and was immediately replaced by the matrix. The duration of the matrix was either 1 or 2 s. With 54 target matrices, 54 matrices with only distractors, and 2 matrix durations, each participant was exposed to 216 randomly ordered trials. A trial was terminated by the response, and then there was a 4-s intertrial interval before the fixation point reappeared on the screen, initiating a new trial. Design and statistical analysis. The RTs for trials on which participants pressed the wrong button (missing a target or falsely perceiving a target in a target-absent display) were replaced by the participant's mean for the condition. Individual RTs deviating by more than three standard deviations from the participant's mean for the general condition (e.g., target-no target) were replaced by the mean plus or minus three standard deviations (given the skewed distribution of RTs, this almost exclusively happened for long RTs). The design of the study involved three independent variables: distractor expression, target expression, and matrix duration. Neutral targets were not incorporated in the analysis, because they were not explicitly part of the hypothesis. Furthermore, they could only be analyzed against emotional (friendly and threatening) backgrounds. Finally, because they had the unique feature of horizontal lines, they could be very efficiently picked up by a parallel search (Treisman & Gelade, 1980). Thus, neutral targets among emotional distractors always had the shortest search times of all conditions. To allow an overall analysis incorporating threatening and friendly targets, we rearranged the distractor to include two levels, neutral and emotional, in which the latter comprised friendly (with threatening targets) and threatening (with friendly targets) expressions. Thus, the statistical design was a (threatening vs. friendly) X Distractor (neutral vs. emotional) X Exposure Time (1 s vs. 2 s) factorial with repeated measurements in all factors. Statistical analyses of RTs for correct responses and proportion of correct responses were accomplished by analysis of variance (ANOVA), using Tukey's honestly significant difference (HSD) tests as follow-up tests when appropriate. Tests for normality in the distribution of RT data suggest that a logarithmic transformation was warranted (Ratcliff, 1993). The figures, however, show mean RTs. Results Mean RTs for all conditions of the Experiment are shown in Figure 3a, and accuracy scores are given in Figure 3b. It appears in Figure 3a that the participants found the threatening target faster than they found the friendly one in all conditions except at the

4 384 OHMAN, LUNDQVIST, AND ESTEVES I 10 fl ft A SO O J ft fl & O O 10 J 6 Figure 2. Examples of 3 X 3 matrices with targets used in Experiment 1. short exposure with emotional distractors. Identification of targets was more accurate when the targets were threatening than when they were friendly and with long rather than with short exposure (Figure 3b). According to the statistics, the participants were, overall, faster, F(l, 19) = 14.90, p <.001 (Figure 3a), and more accurate, F(l, 19) = 12.66, p <.002 (Figure 3b), when finding threatening than friendly target faces. The search times were considerably longer, F(l, 19) = , p <.0001, and less accurate, F(l, 19) = 40.79, p <.0001, when the distractors were emotional than when they were neutral. The third factor, exposure time, did not produce any main effect for RT, but accuracy was better with longer exposure, F(l, 19) = 8.21, p <.01. According to a series of significant interactions, the experimental factors modulated the effects of each other. Most important, there was a reliable three-way interaction between target, distractors, and exposure time, F(l, 19) = 11.77, p <.003, for RTs that subsumed two two-way interactions. As is clear in Figure 3a, the advantage for a threatening target was obvious for both exposure times for the neutral distractors, but in the more difficult condition with emotional distractors, the threat advantage was obvious only with the long stimulus exposure (p <.01; with even a tendency in the opposite direction with the short exposure). As a result of this three-way interaction, the participants found threatening targets more quickly than friendly targets when the distractors were neutral (p <.0002; Figure 3a, left panel) but not when they were

5 THE FACE IN THE CROWD REVISITED 385 (a) (b) Neutral 1 2 Exposure Time (sec.) Neutral 65 t 1 2 Exposure Time (sec.) Emotional 1 2 Exposure Time (sec.) Emotional S S s Exposure Time (sec] Friendly Threatening Figure 3. Reaction times (a) and accuracy (b) for detecting friendly and threatening targets among neutral and emotional distractors as a function of exposure time in Experiment 1. emotional (Figure 3a, right panel), F(l, 19) = 8.55, p <.01, for the interaction between targets and distractors. Furthermore, whereas the participants were, overall, no faster in finding threatening than friendly targets with 1 s exposure, they were clearly faster (p <.0007) with threatening faces in the 2 s exposure, F(l, 19) = 11.45, p <.004, for the interaction between target and exposure time. In contrast to the high (and equal) accuracy for both targets with neutral distractors (Figure 3b, left panel), the participants were considerably more accurate in finding the threatening than the friendly (p <.0002) face with the emotional background (Figure 3b, right panel), F(l, 19) = 20.72, p <.0003, for the interaction between target and distractors in correct responses. RTs for target absent conditions (Af = 1,511 ms) were longer than for target present conditions (see Figure 3a) but did not differ between threatening and friendly crowds, F(l, 19) < 1. They were faster with the shorter exposure time (1,421 vs. 1,612 ms for 1 and 2 s exposure times, respectively), F(l, 19) = 24.20, p < Response accuracy in deciding that a target was not present was lower with the short (.91) than with the long (.96) exposure. Discussion The results from this experiment show an advantage for finding a threatening face in a crowd of faces, as expected from the evolutionary perspective. This was particularly obvious when the facial expression of the distractor faces in the crowd was neutral. In this condition, the advantage for the threatening face was evident even when the matrix was exposed for only 1 s. With emotional distractors (i.e., friendly distractor faces for threatening targets or threatening distractors for friendly targets), the advantage for the threatening target required an exposure of 2 s to be manifested as a statistically significant effect. The strong effect of the emotionality of the background crowd is at least partly interpretable in terms of Treisman's (1988; Treisman & Gelade, 1980) feature integration theory. As shown by the overall shorter RTs (Figure 3a) and higher accuracy (Figure 3b), search for the target faces appeared considerably easier with neutral than with emotional distractors. It is plausible that the targets in this condition were located by a parallel search process, because there are unique features that distinguish between the target and the distractors. The neutral faces (see Figure 1) contain only horizontal lines for eyebrows and mouth, whereas the emotional faces have angular lines for eyebrows, curved mouths, and half circles rather then ellipses for eyes. Thus, to find the target in this condition, the participants merely had to notice any of these features somewhere in the display. Essentially, therefore, with neutral distractors, targets could pop out from the crowd, making target detection independent of exposure time. This interpretation is further supported by the short latencies for finding neutral targets among emotional distractors (M = 1,061 ms), which were faster than those for finding both threatening and friendly targets against emotional distractors (1,172 and 1,300 ms, respectively). It is interesting to note that participants were quicker to find neutral targets among threatening distractors (M = 1,030 ms) than to find neutral targets among friendly distractors (M = 1,092 ms), F(l, 19) = 8.61, p <.01. Nevertheless, even though both emotional targets appeared to pop out from among neutral distractors, there was an advantage for the threatening face. This finding is in accordance with theoretical notions suggesting that negative affect is activated at an early, automatic level of stimulus analysis (e.g., Niedenthal, 1992; Ohman, 1993; Zajonc, 1980). With emotional distractors, target search apparently was more effortful and time consuming, and, therefore, the exposure time of the stimuli became important. Because there is no single, simple feature that differentiates between threatening and friendly faces, conjoining of features was required to distinguish the target from the distractors, and postattentive serial search was necessary (Treisman & Gelade, 1980). Again, however, given that the display was available for a sufficient time, participants were faster to find threatening than friendly targets. In this condition, therefore, a search asymmetry was apparent according to which threatening faces were more quickly found among friendly ones than were friendly faces among threatening ones. Such an asymmetry suggests that the cognitive system interpreted friendly faces as the background or the standard, whereas the threatening face was given more weight as discrepant, interesting, or important (Treisman & Gormican, 1988; Treisman & Souther, 1985). Thus, the threatening face is likely to have some critical feature that captures attention more efficiently than do the features that make up the friendly face. The long latency of finding threatening targets among friendly distractors at the short exposure (Figure 3a, right panel) is puzzling, but as the difference between the two types of targets was not significant, it is probably a chance effect.

6 386 OHMAN, LUNDQVIST, AND ESTEVES The threat advantage obtained in this experiment is consistent with the results from a parallel series of studies that used pictures of threatening animals (snakes and spiders) as target stimuli among neutral distractors (flowers and mushrooms) and vice versa (Ohman, Flykt & Esteves, in press). In further support of a threat interpretation, the threat advantage was specifically enhanced in participants selected as highly fearful of either snakes or spiders (but not of both). For example, snake-fearful participants were reliably faster to detect snake than spider targets and were faster to detect spiders than neutral targets. In summary, the results from Experiment 1 suggest that threatening facial targets are more quickly found in a crowd of distractor faces than are friendly faces both when the conditions favor a parallel automatic search and when they favor a slow serial search (provided a sufficient exposure time in the latter condition). However, to further delineate the processes governing the visual search for emotional faces, it is necessary to manipulate the most important variable for inferring parallel search, the number of distractor stimuli. We did this in Experiments 2 and 3. Experiment 2 The purpose of Experiment 2 was to examine the search for threatening and friendly faces in crowds of neutral faces over a wide range of crowd sizes. Thus, we wanted to look at the effect of the number of distractors under conditions (i.e., neutral distractors) that, according to the results of Experiment 1, favor parallel search. Furthermore, by only using neutral distractors, we could study a wider range of display sizes within a reasonable number of total trials for each participant. A pop-out effect, indicative of parallel search, would be revealed by a near-zero slope of RT on the number of distractors. In practice, parallel search involves an increase of less than 5 ms per added distractor in the display, whereas serial search involves an increase of more than 10 ms (and typically much more) per searched distractor (e.g., Treisman and Souther, 1985; Wolfe, 1998). Method Participants. Sixteen new participants, 10 men and 6 women, were recruited from the same populations and were treated identically as those in Experiment 1 were. Procedure. The equipment and the general procedures were almost identical to those used in Experiment 1. However, in this experiment, the exposure was terminated by the RT response. The experiment examined the participants' search for threatening and friendly target faces in different sized crowds of neutral faces. The crowds were arranged as 2 X 2, 3 X 3, 4X4, and 5X5 matrices. Thus, the number of distractors varied between 3 and 24. The physical size of the matrix was not controlled. Thus, the smallest matrix was 7 X 7.5, and the largest was 19 X To roughly equalize the proportion of the different matrix sizes of the total number of trials, we used more replications of small than of large matrices. For the 2X2 matrices, there were four replications of each target-position combination. This resulted in 16 matrices with threatening and 16 with friendly targets, which required thirty-two 2X2 matrices without targets to achieve equal likelihood of matrices with and without targets. For the 3X3 matrices, there were two replications of each target-position combination, which gave a total of 36 matrices with targets and 36 without. For the 4X4 and the 5X5 matrices, there was only one presentation of each target-position combination, resulting in a total of 64 and 100 matrices for these conditions, respectively. Thus, each participant was exposed to a total of 300 trials in the experiment, presented in several random orders for different participants. Statistical analyses. Replacement of RTs for error trials and the control of outliers was handled in the same way as in Experiment 1, with extreme values being replaced by the mean plus or minus three standard deviations for the particular matrix size. The data were analyzed in a (threatening vs. friendly) X Matrix Size (2 X 2, 3 X 3, 4 X 4, 5 X 5) repeated measures ANOVA on log-transformed RTs. We performed follow-up tests using Tukey's HSDs. Results The RT data for both the target present and the target absent conditions are given in Figure 4a, and the accuracy data are given in Figure 4b. Because the distractors were always neutral, the overall RT for finding targets was considerably faster in this experiment than in Experiment 1. It is obvious in Figure 4a that the responses were faster to threatening than to friendly targets and that it took longer to decide that there was no target in a display. Furthermore, whereas the RTs were relatively stable across matrix sizes with a target, there was, as we expected, a large increase in RTs to decide that a target was not present as the number of distractors increased. (a) ids] 8.52 I CO c o Q. (B DC (b) DC x2 3x3 4x4 Matrix Size (items) 5x5 2x2 3x3 4x4 5x5 Matrix Size (items) Absent Friendly Threatening Figure 4. Reaction times (a) and accuracy (b) for detecting friendly and threatening targets among neutral distractors and matrices without targets as a function of matrix size in Experiment 2.

7 THE FACE IN THE CROWD REVISITED 387 Participants found threatening target faces faster than they found happy target faces, independent of crowd size, F(l, 15) = 12.41, p <.003, for the main effect (Figure 4a). Accuracy was again better for threatening than for friendly faces, F(l, 15) = 6.62, p <.02 (Figure 4b). Compared with the conditions without a discrepant target face (Figure 4a), the increase in RTs for targets in bigger crowds was small. Nevertheless, the effect of matrix size in the analyses of targets only was highly significant, F(3, 45) = 50.17, p < Follow-up tests showed that for both types of targets, the 5 X 5 matrix had slower RTs than did all the smaller matrices. Similarly, the 4 X 4 matrix was slower than the 3 X 3 and the 2X2 matrices (p <.03), but the latter two did not differ from each other. Accuracy in detecting targets showed an overall decrease with increased matrix size, F(l, 19) = 13.40, p <.0001 (Figure 4b), primarily because of poor performance with the 5 X 5 matrix, which was significantly poorer than all the others (p <.003). According to the interaction between target emotion and matrix size, F(l, 19) = 10.71, p <.0001, the overall low level of performance with the 5 X 5 matrices could be attributed to the friendly targets (p <.006 for comparison between the 5 X 5 matrix and all the others; Figure 4b), whereas for threatening targets, the decrement was smaller and more regular across matrix sizes (the 4X4 and 5X5 matrices differed from the 2 X 2 matrix, p <.04). RTs to decide that a matrix lacked a target were clearly longer than for deciding that a target was present, and they increased with increasing matrix size, F(3, 45) = 27.00, p <.0001 (Figure 4a). Contrary to the deteriorating accuracy for larger matrices with target present, the accuracy in deciding that there was no target in a matrix increased with increasing matrix size, F(3, 45) = 14.25, p < (Figure 4b). Discussion Consistent with the findings reported in Experiment 1, the present results show a reliable, albeit relatively small, advantage for search for threatening over friendly faces in crowds of neutral faces. This advantage was remarkably stable across crowd sizes. Thus, it pertained to intercept rather than to slope of the regression of RT on the number of distractors. This result is contrary to the effect of display size in the study using small animals as threatening stimuli (Ohman et al., in press). In two independent experiments, we found no effect of display size for threatening targets (snakes and spiders among flowers and mushrooms) but a significant effect of display size for neutral targets (flowers and mushrooms among snakes and spiders; Ohman et al., in press). Search times were somewhat shorter for the 3 X 3 than for the 2X2 matrix, then increased progressively for the 4 X 4 and 5X5 matrices. As a result, the effect of crowd size on search for both types of targets was statistically highly significant, and, except for the two smallest crowds, the individual conditions were statistically separated from each other. Nevertheless, the total increase in search times across crowd sizes is modest and within the minimum search time per item that has been required for the search to qualify as parallel (e.g., less than 5 ms; Treisman & Souther, 1985). Indeed, as judged from Figure 4a, search times remained relatively stable from the 2 X 2 to the 4 X 4 matrix, and then there was a clear increase up to the 5X5 matrix. Because we did not control the actual size of the display, this latter increase may have been due to the increasing need for eye movements when the display became very large. The important point here, however, is that the effect was very similar for the two targets. Thus, regardless of whether we want to denote the search as parallel or merely as very efficient (see Wolfe, 1998), it could be attributed to the fact that both the threatening and the friendly faces had unique, simple features that distinguished them from the distractors. The most notable aspect of these findings is that we again found the detection latencies to be shorter for the threatening face, even though the physical features of the display favored efficient search for both types of targets. Experiment 3 Experiment 2 showed a very efficient search for both threatening and friendly faces among neutral distractors. The purpose of Experiment 3 is to examine the more difficult search condition in which threatening faces were targets against a background of friendly faces and vice versa. To allow direct comparison with the data from Experiment 2, we incorporated a condition with neutral distractors into the design. Method Participants. We recruited 16 new paid volunteers, 11 men and 5 women, from the pool of participants used in Experiments 1 and 2. Procedure. The equipment and the general procedures were identical to those used in Experiment 2. The experimental design involved three crowd sizes (2 X 2, 3 X 3, and 4 X 4), three crowd expressions (neutral, friendly, and threatening), and three target expressions (neutral, friendly, and threatening). Thus, as in Experiment 1, to assure genuinely varied mapping of the target and distractors (Schneider & Shiffrin, 1977), we used neutral targets with friendly and threatening distractors, but we did not include this condition in the statistical analysis. For the 2 X 2 matrices, there were two replications of each of the four target-position combinations, which, with three types of targets (neutral, threatening, and friendly) against two types of distractors (threatening and friendly, neutral and friendly, and neutral and threatening, respectively), made a total of 48 trials with targets for this matrix size and 48 without targets. The 3X3 matrices had one presentation target-position combination, which made 54 matrices with targets and 54 without. For the 4 X 4 matrices, however, each participant was exposed to targets at only half of the positions, with the particular positions counterbalanced across participants for equal representation across the whole group. This made 48 matrices with targets and 48 without targets. Thus, there were a total of 300 trials presented in several randomized orders. Statistical analysis. RTs and accuracy scores were analyzed using a 3X2X2 (Crowd Size X Crowd Expression X Expression) ANOVA (on log RTs) in which the three crowd expressions of the design were collapsed into two (neutral and emotional) and only two of the three target expressions (friendly and threatening) were analyzed. Results RT data are shown in Figure 5a, and the accuracy scores are given in Figure 5b. It is clear from Figure 5a that the participants again were faster in finding threatening than friendly targets and that the slope of RT on crowd size was steeper with emotional than with neutral crowds and for target absent than for target present decisions. The statistical analyses showed that, overall, participants found the threatening target faces faster, F(l, 15) = 16.67, p <. 001

8 388 OHMAN, LUNDQVIST, AND ESTEVES (a) Neutral 2x2 3x3 4x4 Matrix Size (items) Emotional 2x2 3x3 4x4 Matrix Size (items) Absent Friendly Threatening.005) with emotional distractors. Finally, the interaction between matrix size and distractor emotion, F(2, 30) = 3.26, p <.05, could be attributed to the contrast between the lack of slope with neutral distractors and the overall poorer performance with the largest matrix with emotional distractors (Figure 5b). For matrices without targets, the RTs were, overall, slower for emotional matrices, F(l, 15) = 95.06,p < , and increased substantially from the 2 X 2 to the 4 X 4 matrices, F(2, 30) = 85.65, p < According to the interaction between matrix size and distractor emotion, F(2,15) = 11.69, p <.001, the increase across matrix sizes was more pronounced for emotional than for neutral distractors (Figure 5a). Accuracy in deciding that the matrix lacked a target showed an overall increase with increased matrix size (Figure 5b), F(2, 30) = 6.07, p <.01. (b) 5 o Neutral -a 2x2 3x3 4x4 Matrix Size (items) Emotional 2x2 3x3 4x4 Matrix Size (items) Figure 5. Reaction times (a) and accuracy (b) for detecting friendly and threatening targets among neutral and emotional distractors and for matrices without targets as a function of matrix size in Experiment 3. (Figure 5a), and more accurately, F(l, 15) = 10.19, p <.006 (Figure 5b), than they found the friendly target faces. The search was much slower, F(l, 15) = , p <.0001, and less accurate, F(l, 15) = 17.19, p <.001, with an emotional as opposed to a neutral crowd. Larger crowds produced an overall increase in RTs, F(2, 30) = 43.46, p <.0001, and an overall decrease in accuracy, F(2, 30) = 5.95, p <.01. According to the interaction between crowd expression and crowd size, F(2, 30) = 34.05, p <.0001, the RT increase was steeper for emotional than for neutral crowds (Figure 5a). With emotional crowds, all crowd sizes were significantly different from each other (p <.0002), whereas with neutral crowds, only the 2 X 2 and 4X4 crowds differed (p <.05), with the 3 X 3 matrix falling in between, significantly different from neither of the other two. For accuracy, there was no slope difference across matrices for crowds of different emotions. However, response accuracy for target emotion interacted both with matrix size, F(2, 15) = 7.84, p <.002, and with distractor emotion, F(l, 15) = 6.50, p <.05. According to the former, there was no effect of matrix size for threatening targets, but for friendly targets, the performance was poorer for the 4X4 matrix than for the 2X2 and 3x3 matrices (with no difference between the latter two; see Figure 5b). The accuracy interaction between distractor and target emotion could be attributed to participants having similarly high levels of performance for both targets with neutral distractors but poorer performance for friendly than for threatening targets (p < Discussion As in the previous experiments, there was a highly significant, albeit quantitatively small, effect of emotional expression of the target, with faster detection of threatening than of friendly faces, irrespective of crowd expression or crowd size. In support of the preliminary conclusion about differential efficiencies for searches through neutral and emotional crowds, the search function was essentially flat for neutral crowds but showed a positive slope (about 35 ms/item) for emotional crowds. As in Experiment 1, however, the advantage of threatening over friendly target expressions was valid for both levels of search efficiency. Whereas the accuracy of locating threatening targets showed modest effects of crowd sizes both in this and in the previous experiment, performance for friendly targets fell dramatically with the largest matrices in both experiments (cf. Figures 4b and 5b). In contrast to these falling trends over matrix sizes for the accuracy of target detection, the accuracy of deciding that a target was not present increased with matrix size in both Experiment 2 and Experiment 3 (cf. Figures 4b and 5b). This combination of effects suggests that the participants adopted a bias for perceiving that the largest matrices lacked a target. This bias, however, did not compensate for the deteriorating RT performance with increased matrix size (Figure 4a and 5a). It is particularly interesting to note that the bias for deciding that a target was absent in the largest matrices resulted in poor accuracy of target detection only for friendly faces, whereas the performance level was relatively intact with threatening targets (see Figures 4b and 5b). Thus, in contrast to friendly targets, threatening targets appeared effective, albeit slower, in accurately capturing attention even during the most difficult search conditions in these experiments. Experiment 4 There was a consistent advantage for threatening over friendly targets both for neutral and emotional distractors across the previous three experiments. Regardless of whether the conditions favored very efficient, perhaps parallel (cf. Wolfe, 1998) search (neutral distractors) or less efficient, serial search (emotional distractors; e.g., friendly distractors with threatening targets), participants were faster and more accurate in detecting threatening than friendly targets. In spite of the apparent pop out with neutral distractors, the threatening face was consistently found faster than the friendly

9 THE FACE IN THE CROWD REVISITED 389 face. Both types of emotional targets can be distinguished from neutral distractors at the level of simple features (lines in an angle for eyebrows, curved mouths, half circles rather than ellipses for eyes). But if these simple features were the decisive determinants of target detection, one would not expect a difference between threatening and friendly targets. The fact that such differences emerged, therefore, implies that emotion made a difference at this very early stage of perceptual processing. This is consistent with the theoretical views of Zajonc (1980) and Ohman (1993; see also Robinson, 1998). The threatening face cannot be distinguished from the friendly face if we adhere to a rigorous definition of features in simple physical terms (lines, curves, forms), but if we allow conjoining of simple features such as form and direction, this can be done. One candidate for a discriminating feature is the mouth, because the direction of its arc (up or down) provides a unique difference between the threatening and friendly faces we used. Furthermore, if we allow the two lines constituting the eyebrows to be combined, then their angle (up or down) is another candidate for a relatively simple discriminator between the two emotional targets. Indeed, Aronoff et al. (1988; Aronoff, Woike, & Hyman, 1992) has argued that these types of stimuli in isolation may be critical for activating emotional responses, both to faces (Aronoff et al., 1988) and to more general stimulus patterns (Aronoff et al., 1992). If this hypothesis is correct, the threat advantage would be turned into a friendly advantage if the faces were inverted, because an inverted happy face would have the characteristic V-shaped eyebrows and the upward arc for the mouth that characterize a normal angry face, even though the mouth is placed above the eyebrows. However, it is a well-established finding that face inversion takes away many of the holistic characteristics of normal face perception (e.g., Bruce, 1988; Farah, Wilson, Drain, & Tanaka, 1998), even though it is less clear whether this also is true for recognition of facial emotion. If configurations are important, therefore, it is likely that inverting our stimulus faces will eliminate the difference between threatening and friendly schematic faces. Experiment 4 was designed to test whether the advantage for threatening faces can be attributed to isolated features such as the eyebrows or the mouth. Thus, we compared one group of participants who were presented with the standard procedure we introduced in Experiment 1 (but with exposure time determined by RTs) with another group who were presented with exactly the same procedure but with the stimulus displays inverted. Method Participants. We recruited 36 new participants (18 women and 18 men) from the same pool of potential participants from which we drew participants in the previous experiments, and we paid them the same amount as in the previous experiments. They were randomly allocated to two gender-balanced groups. Procedure. The setting, equipment, and general procedure were identical to those used in the previous experiments. As in Experiment 1, the participants were exposed to three target (neutral, friendly, threatening) and three distractor (neutral, friendly, threatening) conditions in which nine faces were organized in 3 X 3 matrices. For 18 participants (9 women and 9 men), the displays were presented in the normal orientation, and for the remaining 18 participants, the displays were inverted. Statistical analysis. RTs and accuracy scores were analyzed using a 2X2X2 (Orientation X Crowd Expression X Expression) ANOVA in which orientation was treated as a between-subjects variable and the two other variables were treated as within-subject variables. As in the previous experiments, the three crowd expressions of the design were collapsed into two (neutral and emotional), and only two of the three target expressions (friendly and threatening) were analyzed. Results RT data are shown in Figure 6a, and the accuracy scores are given in Figure 6b. It is somewhat surprising to note that the advantage for threatening targets neither disappeared nor was inverted to a friendly advantage with inverted matrices but was obvious for both orientations in both RTs and accuracy. As before, there was a strong effect of distractor type. Consistent with previous findings, the participants were, overall, faster, F(l, 34) = 54.97, p <.0001, and more accurate, F(l, 34) = 18.81, p <.0002, in detecting threatening than friendly targets. Neither for RT, F(l, 34) = 2.27, ns, nor for accuracy, F(l, 32) < 1, was there an overall effect of matrix orientation. The participants were much faster, F(l, 34) = , p <.0001, and more accurate, F(l, 34) = 33.54,/? <.0001, with neutral than with emotional distractors. According to the significant interaction between orientation and distractors for RTs, F(l, 34) = 15.30, p <.001, inverting the matrix made little difference with neutral distractors (p =.856 for the contrast between orientations) but resulted in deteriorating performance when the distractors were (a) (b) 100 r Neutral Upright Inverted Orientation Neutral Upright Inverted Orientation Emotional Upright Inverted Orientation Upright Inverted Orientation Friendly Threatening Figure 6. Reaction times (a) and accuracy (b) for detecting friendly and threatening targets among neutral and emotional distractors as a function of matrix orientation in Experiment 4.

10 390 OHMAN, LUNDQVIST, AND ESTEVES emotional (p =.0003 for the upright-inverted contrast; Figure 6a). The interaction between target and distractors was significant for accuracy, F(l, 34) = 8.69, p <.01. Performance was at a high level both for threatening and for friendly targets when the distractors were neutral {p =.653 for the threatening-friendly contrast), whereas accuracy decreased, particularly for friendly targets, when the distractors were emotional (p =.0001 for the threatening-friendly contrast; Figure 6b). Discussion This experiment showed a strong effect of target emotion both with normal and with inverted faces. In fact, in terms of the magnitude of the F ratio for the main effect of target emotion, the advantage of a threatening over a friendly target appeared at least as strong in this experiment as in any of the previous ones. These data clearly refute the hypothesis that the threat advantage would be turned into a friendly advantage with inverted faces, assuming that the participants used eyebrows and mouths in isolation to determine the emotional valence of the targets. Clearly, the upward arc of a threatening mouth did not retain its threat value when it occurred at the top of an inverted friendly face. Similarly, the V-shaped eyebrows that provided such a strong determinant of rated negative valence of threatening faces (Lundqvist et al., 1999) completely lost their threatening power when placed in the context of an inverted friendly face. Thus, it was not the physical shapes of the eyebrows and mouths in isolation that determined rapid target detection but the. context or configuration in which they appeared. It is quite clear, therefore, that the perceptual systems of the participants were able to extract more than mere physical features from the display even during the very efficient parallel search with neutral distractors. If anything, the configural effect was stronger than we anticipated, because rather than being lost in the inverted faces, the advantage of threatening over friendly faces was retained. In spite of the inversion, the participants seemed able to treat these faces as normal ones, because there were surprisingly few effects of the inversion. Indeed, with regard to accuracy, the participants' performance with inverted faces was indistinguishable from that with normal ones (see Figure 6b). For RTs, the search was slowed, particularly in the more difficult condition with emotional distractors. Given the sensitivity of face perception to face orientation (e.g., Bruce, 1988; Farah et al., 1998; Tanaka & Farah, 1993), this is a surprising finding. Indeed, because the threat advantage survived inversion, one could argue that it must be due to some low-level confounding factor that is specific to angry faces and that is unrelated to facial configuration, such as the dark area of the threatening targets used by Hansen and Hansen (1988; see Purcell et al., 1996, and Fox et al., 2000). Because of the design of our faces (see Figure 1), however, it is very hard to see what such a confounding factor might be, because the features defining a threatening face were changed into those defining a friendly face when inverted, and vice versa. Thus, the fact that the threat advantage survived inversion must imply that inverted threatening faces indeed were perceived as faces in the sense that the relation between mouth, eyes, and eyebrows, rather than these features per se, turned out to be critical. With one exception (Fox et al., 2000), previous visual search studies using schematic faces (Nothdurft, 1993; White, 1995, Experiments 1 and 2) have failed to observe differences in search times for discrepant emotion targets presented in upright or inverted displays. Thus, one possibility is that inverting the stimulus display is less detrimental to schematic than to real faces, perhaps because the former are less complex than the latter. Tong and Nakayama (1999) showed that the search advantage for a highly familiar real face (one's own) among unfamiliar distractors was retained with inverted displays, even though, as for the emotional distractors in the present experiment, the overall search times were increased with inversion. This was interpreted in terms of the robustness of the representation of highly overlearned faces (Tong & Nakayama, 1999), and it is conceivable that schematic faces, because of their simplicity, may become robustly represented more easily than may real faces. Another possibility is that the modest effect of inversion is specific to the visual search paradigm, because other studies of face inversion typically have used paradigms that are more dependent on memory (e.g., Tanaka & Farah, 1993). From the literature, it appears that the effect of inversion on search for real faces has also been somewhat variable (e.g., Kuehn & Jolicoeur, 1994; Tong & Nakayama, 1999). Finally, it may be that there is a difference between recognizing the identity and the emotion of a face. The inversion effect has typically been investigated in the contexts of recognition of individual faces (Tanaka & Farah, 1993; Yarmey, 1971). Recognizing an emotional expression merely requires matching it to the correct prototypical representation of the expression (e.g., Ekman & Friesen, 1975), whereas recognizing the individual needs a more detailed and exhaustive analysis of a larger number of features. Needless to say, the former process is likely to be quicker and more robust than the latter. Therefore, it may also be more resilient to disturbances such as inverting the stimulus display. Indeed, if what is special about face perception is conceptualized in terms of its holistic nature (Farah et al., 1998), this may be even more valid for the recognition of emotion, because it requires a simpler stimulus analysis than that required for recognizing the individual exhibiting the display. With schematic stimuli, perceiving an emotional facial configuration may even block perceiving its parts as physical features (Suzuki & Cavanagh, 1995). Thus, emotional schematic faces may immediately become robustly represented without the extensive experience needed to achieve this type of representation for the identity of a real face (Tong & Nakayama, 1999). Accordingly, a schematic emotional face may also be more robust to disturbances such as inversion. Be this as it may, the important conclusion from this experiment is that the preferential selection of the threatening face appears to be based on an intact facial configuration irrespective of whether it is presented upright or inverted. Experiment 5 Experiments 1-4 consistently resulted in faster and more accurate detection of threatening than of friendly schematic faces across a variety of experimental conditions. But do these results justify the conclusion that the effect is attributable to the threat value of the threatening faces? Or may they be accounted for by some other characteristic of the threatening target faces we used? There are at least two such alternative interpretations. The first one is that threatening and friendly faces are at opposite ends with regard to emotional valence, the former being

11 THE FACE IN THE CROWD REVISITED 391 negatively and the latter positively valenced (see, e.g., Lundqvist et al., 1999). Thus, the apparent threat advantage observed in Experiments 1-4 could reflect a more general processing bias for negative social information (e.g., Cacioppo & Berntson, 1994; Pratto, 1994). For example, negatively valenced words (denoting personality traits) grabbed attention more effectively in Stroop tasks than did equally extreme positively valenced words (Pratto & John, 1991). Furthermore, negatively valenced pictures elicited larger late positive event-related potentials (ERPs) than did equally extremely evaluated positive pictures when presented as deviant stimuli in a sequence of pictures of contrasting (Cacioppo, Crites, Berndtson, & Coles, 1993) or neutral valence (Ito, Larsen, Smith, & Cacioppo, 1998). Even though this negative bias in evaluative categorization of stimuli may be derived from the need to attend to threat (Ito et al., 1998), it nevertheless seems important to delineate further whether our threat advantage specifically concerns threat rather than, more generally, negative valence. A second alternative interpretation of our findings could be framed in terms of the differential frequency with which different facial expressions are encountered in typical human environments. Using different indices of reported frequency of facial expressions in a college environment, Bond and Siddle (1996; see also Whalen, 1998) consistently found that happy expressions were the most common ones, followed by anger, surprise, sadness, disgust, and fear. It is a well-established principle that novel stimuli among familiar ones are effective attention catchers (e.g., Ohman, 1979; Ohman, Hamm, & Hugdahl, 2000; Sokolov, 1963). Thus, because angry expressions are more seldom seen than are happy expressions in the ecology of college students, our threatening schematic face may have been a more novel target than the friendly face was, and, consequently, it may have captured attention for this reason rather than because of its threat value. The purpose of Experiment 5 is to examine and hopefully to rule out these two competing interpretations. To this end, we compared target facial expressions that resulted from combining two types of eyebrows (angry and happy) with two types of mouth (angry and happy) with eyes held constant in a neutral mode. This resulted in four target expressions: anger (i.e., threat), happiness (i.e., friendliness), sadness, and an expression that has been dubbed scheming (see Lundqvist et al., 1999; McKelvie, 1971); all of these expressions were presented among neutral distractors. These expressions are shown along the abscissas in Figure 7. Sad faces, like angry ones, are located toward the negative end of the pleasure-displeasure dimension that can be used to order emotional facial expressions (e.g., Russell, 1997; Schlosberg, 1952). Consequently, the negative valence account predicts faster detection of a sad than of a friendly face and little difference between sad and threatening faces. However, because sadness implies withdrawal and has been characterized as an action set of "nonbehavior" (Frijda, 1986), a sad face is, almost by definition, nonthreatening, and thus a threat account would definitely predict faster detection of a threatening than of a sad face and little difference between sad and friendly faces. Sad faces are less frequently encountered than are both happy and angry faces (Bond & Siddle, 1996), and, consequently, the novelty account would predict faster detection of sad than of both threatening and friendly faces. Furthermore, the scheming expression, both because it is rare and because of its incongruous combination of angry eyebrows and a happy mouth, would be likely to (a) «700 (b) 100 r O Oil 1 01 t 01 SO Figure 7. Reaction times (a) and accuracy (b) for detecting friendly, scheming, sad, and threatening targets among neutral distractors in Experiment 5. be even more effective than a sad face would be in commanding attention (e.g., Berlyne, 1960). Thus, the novelty account would predict the ordering of these expressions to be scheming, sadness, threat, and friendliness, whereas the threat account would predict the order to be threat, scheming (because of its position in the threat area of the emotional space; Lundqvist et al., 1999), and a tie between sadness and friendliness. Method Participants. We recruited 18 participants (9 women and 9 men) from the same source as in the previous experiments. They were paid for their participation. Design. Four different expressions threatening, friendly, sad, and scheming (see Figure 7) were used as targets against a background of neutral distractors. The faces and the 3 X 3 matrices had the same size on the screen as in the previous experiments. The experiment was divided into three blocks of 72 trials each, for a total of 216 trials. Within each block, each of the four targets was presented once in each of the nine positions in the matrix, and there was an equal number of matrices without targets in each block. The order of presentation within each block was randomized. Statistical analysis. The data reduction procedure was identical to that of the previous experiments. RTs and correct detections were analyzed using 2 (gender) X 4 (expressions) ANOVA.

12 392 OHMAN, LUNDQVIST, AND ESTEVES Results The RT data are shown in Figure 7a, and the accuracy data are shown in Figure 7b. According to this figure, it appears that the threatening face was faster and more accurately detected than were any of the other expressions. This impression was vindicated by the statistical analysis, which showed significant effects of expression both for RT, F(3, 48) = 11.44, p <.00001, and for correct responses, F(3, 48) = 5.67, p <.002. Tukey's HSD tests showed that the RT to threatening faces was shorter than were the RTs to any of the other expressions, p <.01, which did not differ from each other. Similarly, for correct responses, the accuracy was higher for the threatening face than for any of the other expressions, p <.04, which did not differ from each other. There was no effect of gender. Discussion Consistent with the findings in the previous four experiments, a threatening target among neutral distractors was more quickly and accurately detected than was a friendly target, even though in the expressions used here, the eyes were neutral and constant across expressions. Furthermore, consistent with the threat interpretation of this effect, the threatening target was also more quickly and accurately located than were the sad and scheming facial expressions. In contrast to the negative valence interpretation, therefore, a sad face was not more effectively found than was a friendly face, and in contrast to the novelty interpretation, sad and scheming targets did not differ from friendly ones. Thus, this set of findings provides strong support for the thesis that the consistent threat advantage that we have observed across all our experiments should be attributed to the threatening value of angry faces. This interpretation, in turn, is in accordance with the evolutionary perspective that we presented in the opening of this article. Having said this, however, we should perhaps also point out that such a conclusion does not exclude a role for learning in making threatening facial gestures effective attention catchers. We may be biologically programmed to learn easily to attend to threatening faces rather than programmed to automatically attend to them (e.g., Seligman, 1970), The results of this experiment appear to definitely rule out the novelty account of the threat advantage effect, but they are perhaps less decisive with regard to the negative valence account. This is because it is very hard to envision a threatening face that is not also highly negatively evaluated. The threatening face used in this series of studies was the most consistently negatively evaluated face of all the faces studied in our rating study (Lundqvist et al., 1999). In fact, the ordering of the mean RTs for the different facial expressions (see Figure 7a) corresponds with their degree of negative evaluation (see Lundqvist et al., 1999, Figure 4; even though only the RT for threatening targets was statistically separated from the others). Similarly, the fact that snakes and spiders are more quickly detected than flowers and mushrooms when they serve as targets in visual search tasks, and particularly that this effect is specifically enhanced for participants with specific fear of snakes or spiders (Ohman et al., in press), provides support for the threat hypothesis. Nevertheless, snakes and spiders are likely to be more negatively evaluated than are flowers and mushrooms, and particularly so by fearful individuals. Thus, negative valence cannot be ruled out as a factor behind these results, either. However, rather than taking for granted that threat is secondary to negative valence, one could appeal to evolutionary origins and invert the argument, suggesting that the origin of the negative valence effect probably rests with the need to attend to potentially threatening information in the environment (Ito et al., 1998). General Discussion Using schematic stimuli, the present series of studies establishes that discrepant threatening faces are more quickly and accurately found among distractor faces than are discrepant friendly faces. The stability across conditions of the advantage for detecting threatening stimuli is quite remarkable. Faster and more accurate detection of threatening than of friendly target faces was apparent both for neutral and for emotional distractors (Experiments 1, 3, 4, and 5), for crowds of all the sizes we studied (Experiments 2 and 3), and for both upright and inverted stimulus displays (Experiment 4). It is important to note that threatening faces were more effectively located than were sad or scheming faces (Experiment 5), which suggests that the effect is specific to threat rather than dependent on general properties of stimuli such as negative valence or uniqueness. The only condition that did not produce a threat advantage was the short (1 s) exposure with emotional distractors in Experiment 1. A further illustration of the robustness of this effect is provided by data from 1,157 people who completed our standard visual search paradigm with schematic stimuli as visitors of a science exhibition in Stockholm during the spring of In spite of the very noisy conditions, sometimes with several conversing persons in the facial search booth, the effect of target emotion on RT (1,450 vs. 1,575 ms for threatening and friendly targets, respectively) was highly significant,?(h55) = 10.22, p < Thus, we feel quite confident that our research has reestablished the "anger superiority effect" that was first reported by Hansen and Hansen (1988) but that has been considered undermined by subsequent research (see, e.g., Purcell et al., 1996). Having said this, however, it is important that we point out its limitations: We used schematic rather than real faces, and, in contrast to the data on visual search for threatening animals (Ohman et al., in press), there is no basis in our data for claiming a unique pop-out effect for threatening faces by themselves. Rather than producing a pop-out effect, the threatening schematic face was more quickly found than was the friendly face, regardless of whether the conditions favored efficient parallel or inefficient serial search. In the technical vocabulary of visual search, it was an intercept rather than a slope effect (Wolfe, 1998). The highly efficient search for emotional targets against a background of neutral distractors (as well as for neutral targets against a background of emotional distractors; Experiment 1) suggested essentially parallel search when targets and distractors were separable in terms of unique simple features. This is expected both from the original feature integration theory (Treisman & Gelade, 1980) and from a more recent conceptualization focused on stimulus similarity (Duncan & Humphreys, 1989), because our distractors were maximally similar, and the targets were distinctly dissimilar to the neutral distractors. Nevertheless, even though both the threatening and the friendly targets were equally dissimilar

13 THE FACE IN THE CROWD REVISITED 393 from the distractors and about equally extreme in emotional valence (albeit in different directions; Lundqvist et al., 1999), participants consistently found the threatening face faster than the friendly face. The fact that the threat advantage pertained to intercept rather than to slope of the regression of RT on the number of distractors (see Figures 4 and 5) suggests that the time needed to cognitively process each item was similar but that there was a constant affective activation effect exclusively pertaining to the threatening face. This is consistent with models of the role of affect in social perception (Niedenthal, 1992; Zajonc, 1980) and with notions of independent initiation of cognitive and emotional processes, with a temporal edge for the latter (e.g., LeDoux, 1996). For example, Ohman (1993) developed a model according to which the analysis of biological fear-relevant stimuli, after a first feature processing stage, bifurcated into a significance evaluation system and an arousal system. Fear-irrelevant stimuli, on the other hand, only passed through the feature analyses and significance evaluation systems. The arousal system was quickly activated by threatening stimuli after only a superficial analysis (LeDoux, 1996) and then provided input to the significance evaluator to speed further processing. Because of faster processing of information both in the significance evaluator and in subsequent processing stages, the threshold for a response was reached earlier, thus resulting in faster RTs. The fact that accuracy was better for threatening than for friendly targets in the present study implies that the RT advantage for threatening faces was not simply based on a lowered threshold to activate the response. Rather, information build up must have been more rapid for threatening targets, allowing quicker response activation with retained or even improved accuracy. This model may account for the data with emotional distractors as well, assuming that the feature analysis stage took longer to complete because of the lack of unique features to distinguish between targets and distractors. However, after the feature analysis stage, threatening targets would gain from parallel emotional activation, which would produce the threat advantage effect in the same way as with neutral distractors. The data from our experiments consistently indicate that the time needed to decide that there was no target in a display was independent of whether the display was composed of threatening or friendly distractors. Similarly, in the small animals study (Ohman et al., in press), if anything, the participants were faster in deciding about target absence for fear-relevant than for fearirrelevant displays. This is in contrast to Fox et al.'s (2000) study, which predicted and consistently observed longer time to decide that a target was not there if the display was threatening rather than friendly. Similar results were reported by Hampton et al. (1989) for real faces. Fox et al. (2000) interpreted this finding as reflecting enhanced dwell time of attention on threatening information. This is an interesting hypothesis, but further work is needed to delineate the boundary conditions of the effect. In contrast to the clear and consistent emotionality effect in our data, more variable results have been reported from other studies that have examined visual search for schematic faces. White (1995) reported pop outs for emotional faces (sad or happy) against both emotional and neutral backgrounds. With emotional distractors, there was no effect of target emotion, but with neutral distractors, White reported faster detection of the sad than of the happy target face. The effect, however, was not attributed to emotion, per se, but to perceptual masking of the happy mouth by the similarly curved chin line of the face (White, 1995). As in our Experiment 4, White (1995) found little disruption of the search by inverting the faces, even though the advantage of the sad face with neutral distractors disappeared in this condition. Similarly, using schematic faces, Nothdurft (1993) found little effect of face inversion in searches for emotionally discrepant targets or upright targets among inverted distractors. In contrast both to the results reported by White (1995) and to those reported in our Experiments 2 and 3, there was no evidence of pop out in Nothdurft's (1993) data for emotionally discrepant targets. In fact, the search rates he reported indicated very inefficient searches, possibly with the exception of conditions involving upright happy targets among inverted happy distractors. The most recent study (Fox et al., 2000), however, reported overall results for target selection that were more in line with ours. These authors reported faster and more accurate detection of threatening than of friendly targets in neutral crowds across four experiments. However, these results are less conclusive than those we report, primarily because of the stimulus displays that were used. Rather than the validated (Lundqvist et al., 1999), realistic (e.g., in the sense that the eye appeared focused), and physically well-controlled displays of our study (see Figure 1), Fox et al. used standard displays based on circles and semicircles. Most important, the threatening display was more different from the neutral display than was the happy one. The threatening display differed in having both V-shaped eyebrows and a mouth with an upward arc, whereas the friendly display, like the neutral one, had horizontal eyebrows but a mouth with a downward arc. Thus, the threatening face differed from the neutral face in two features, whereas the friendly display differed from the neutral one in only one feature. As a consequence, the faster and more accurate detection of threatening than friendly targets among neutral distractors could be due to better discriminability between targets and distractors rather than to the threat value of the angry face. To alleviate the potential eyebrow confound, Fox et al. (2000) omitted the eyebrows altogether in their last two experiments, but this change introduced new hazards, because 80% of their participants interpreted the resulting expression as one of sadness rather than of anger. Even granted that Fox et al., like White (1995), did find more efficient search with sad than with happy faces, which may support their conjectures that ambiguous display as interpreted in "the worst possible way" (i.e., ambiguous sadness = anger), it is preferable to use facial displays that are unambiguous with regard to the emotion they portray (see Figure 7). The inconsistent and weak findings reported by Nothdurft (1993) and White (1995) in their studies of visual search with schematic faces can probably be attributed to the same problem in their stimulus displays: the lack of eyebrows. In our rating study (Lundqvist et al., 1999), the shape of the eyebrows consistently accounted for the largest proportion of variance in the emotional ratings of schematic faces. Similarly, Aronoff et al. (1988) reported that isolated V-shapes reminding one of eyebrows were given consistent negative ratings by the study participants. Finally, from ethological considerations, it appears that eyebrows provide a conspicuous component of threat gestures in primates (e.g., Hinde, 1975; Ohman & Dimberg, 1984). Because our faces were more realistic than those previously used (Fox et al., 2000; Nothdurft, 1992; White, 1995), they are more likely to include critical threatening features, which may explain the stronger results of our

14 394 OHMAN, LUNDQVIST, AND ESTEVES study. For example, because the shapa of the eyes varies with the position of the eyebrows, it may be important to let the eye shape vary with emotional expression. Similarly, because the direction of the face is important for responding to it (e.g., Dimberg & Ohman, 1983), it may be important to design the eye to suggest a direction of gaze by including a pupil rather than representing the eye simply as an open circle (e.g., Fox et al., 2000). Even granted that our schematic faces were more realistic than those previously used, they still may appear artificial and lacking in ecological validity. However, there are arguments in favor of their use to study facial signaling. If we accept the evolutionary argument that faces are evolved modules for social interchange (e.g., Dimberg & Ohman, 1996; Ekman, 1973; Fridlund, 1994; Ohman & Dimberg, 1984), it follows that our brains must have genetically prepared "templates" for recognizing important facial gestures from stereotypical and exaggerated signal displays (Krebs & Davies, 1993). These templates should not be understood as genetic "givens" but as genetic programs or "blueprints" that help organize experience. They are organized by experience to assist adaptive responding to social gestures in the specific social context of individual development (Ohman & Dimberg, 1984). In this way, individuals end up with sets of templates that may serve as prototypes perhaps not for facial emotional expressions but at least for important categories of social interchange (Fridlund, 1994). The two targets that we have examined, threatening and friendly gestures, provide the endpoints of a functionally central behavioral dimension ranging from escape and avoidance to approach (e.g., Lang, Bradley, & Cuthbert, 1990). Because the behavioral implications are so different, the corresponding templates should be maximally discriminable. Indeed, good discriminability is a common characteristic of evolved communicative signals with opposite functions. In extreme cases, as Darwin (1872/1998) noticed, they are more or less opposite in form. Darwin named this the principle of antithesis, and, as is obvious in Figure 1, this principle is applicable to our threatening and friendly face stimuli. In this perspective, to the extent that it captured the critical features, a schematic face could even be more effective than a real face in matching the prototypical template. A real face is an individual instantiation of the template, and it may show numerous deviations from the prototype. Indeed, recognizing a face as belonging to a unique individual may require evaluating its pattern of differences from a template, prototype, or average face (Bruce, 1988, pp ), whereas recognizing an emotion or a social gesture may simply require a good enough match to the template. Thus, if it is responses to the template that are at the center of interest, a good schematic face may simply bypass the interindividual noise that is inevitable when real faces are used. If it can be accepted that schematic faces capture important aspects of real emotional faces, a number of important advantages follow. First, the between-individual variability in expressing facial gestures that led Hansen and Hansen (1988) to use the same picture as distractors is no longer relevant, because the schematic faces may represent the invariant prototype. In fact, because the schematic faces are abstract representations, the fact that distractors are identical may be less odd than when participants are asked to distinguish an angry version from identical happy ones of the same person (Gilboa-Schechtman, Foa, & Amir, 1999; Hansen & Hansen, 1988). Second, the physical features of a schematic face are much easier to control. With real faces, the confusability of angry and neutral targets may be problematic and result in elevated error rates (Hansen & Hansen, 1988). Furthermore, because angry and neutral real faces are more similar to each other than are happy and neutral real faces, happy faces may be more quickly found than angry faces in a neutral crowd (e.g., Byrne & Eysenck, 1995). With schematic faces, however, there are identical physical differences between angry and neutral, and happy and neutral, faces (see Figure 1). Furthermore, angry and happy schematic faces are built by identical physical features. 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16 396 OHMAN, LUNDQVIST, AND ESTEVES preattentive processing of separable features. Journal of Experimental Psychology: General, 114, Wagner, H. L., MacDonald, C. J., & Manstead, A. S. R. (1986). Communication of individual emotions by spontaneous facial expressions. Journal of Personality and Social Psychology, 50, Whalen, P. J. (1998). Fear, vigilance, and ambiguity: Initial neuroimaging studies of the human amygdala. Current Directions in Psychological Science, 7, White, M. (1995). Preattentive analysis of facial expressions of emotion. Cognition and Emotion, 9, Wolfe, J. M. (1998). Visual search. In H. Pashler (Ed.), Attention (pp ). Hove, England: Psychology Press. Yarmey, A. D. (1971). Recognition memory for familiar "public" faces: Effects of orientation and delay. Psychonomic Science, 24, Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35, Received October 25, 1999 Revision received May 26, 2000 Accepted June 20, 2000 AMERICAN PSYCHOLOGICAL ASSOCIATION SUBSCRIPTION CLAIMS INFORMATION Today's Date:_ We provide this form to assist members, institutions, and nonmember individuals with any subscription problems. With the appropriate information we can begin a resolution. If you use the services of an agent, please do NOT duplicate claims through them and directly to us. PLEASE PRINT CLEARLY AND IN INK IT POSSIBLE. PRINT FULL NAME OR KEY NAME OF INSTITUTION MEMBEROR CUSTOMER NUMBER (MAYBEFOUND ON ANYPAST ISSUE LABEL) DATE YOUR ORDER WAS MAILED (OR PHONED) CITY STATE/COUNTRY ZIP YOUR NAME AND PHONE NUMBER.PREPAID CHECK CHARGE CHECK/CARD CLEARED DATE:_ (If possible, send a copy, front and back, of your cancelled check to help us in our research of your claim.) ISSUES: MISSING DAMAGED TITLE VOLUME OR YEAR NUMBER OR MONTH Thank you. Once a claim is received and resolved, delivery of replacement issues routinely takes 4-6 weeks. ^ ^ (TO BE FILLED OUT BY APA STAFF) ^ i ^ DATE RECEIVED:. ACTION TAKEN: _ STAFF NAME: DATE OF ACTION: _ INV. NO. & DATE: LABEL NO. & DATE: Send this form to APA Subscription Claims, 750 First Street, NE, Washington, DC PLEASE DO NOT REMOVE. A PHOTOCOPY MAY BE USED.

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