Emotion 2015, Vol. 15, No. 6, 846 – 853
© 2015 American Psychological Association 1528-3542/15/$12.00 http://dx.doi.org/10.1037/emo0000091
The Effect of Emotional State on Visual Detection: A Signal Detection Analysis Andrea M. Cataldo and Andrew L. Cohen
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University of Massachusetts, Amherst This research explores the effect of emotional states on visual detection. Previous research has shown that emotional states characterized by an intolerance of uncertainty, such as anxiety, can affect performance on visual detection tasks. It is unclear, however, to what extent these effects are a result of increased perceptual ability, a decisional bias, or both. The present study used signal detection theory to determine whether uncertain emotional states affect perceptual discriminability and/or decisional bias. In 2 experiments, an anxious, angry, or calm emotional state was induced, and participants were asked to identify which of a series of noisy images contained an embedded target image. The target images were either faces or houses. Emotional state had no effect on decisional bias for either target, but the ability to detect a face was higher for anxious participants. No effect on discriminability was found for houses. These results suggest that emotional state can change perceptual discriminability, but that this change may be limited to certain stimulus classes. Keywords: anxiety, signal detection theory, visual detection, perception
a decrease both in their need for structure and their likelihood of perceiving illusory correlations. These findings lend strong support to the idea that feelings of uncertainty can increase our need for stability and affect the way we perceive random information. That study suggests that a feeling of uncertainty not only increases the ability to detect patterns, but also the likelihood of seeing patterns where they do not exist. Simonov, Frolov, Evtushenko, and Sviridov (1977) examined the effect of stress on the ability to recognize visual symbols embedded in noise. By testing participants periodically during a flight that would culminate in a parachute jump, the researchers were able to test performance across increasing stress levels. Consistent with the idea that uncertainty increases the ability to detect patterns, participants became more skilled at identifying the symbols as their stress levels rose. Importantly, however, they also became more likely to make false identifications by pointing out nonexistent symbols in what was actually pure noise. Thus, it is unclear whether uncertain emotional states increase perceptual ability, bias people to identify target stimuli, or both. The main goal of the present research was to directly determine whether uncertain emotional states affect perceptual accuracy or decisional bias. In the present study, the effects of three discrete emotions on a visual detection task were investigated: anxious, angry, and calm. Based on research suggesting that anxiety is highly correlated with intolerance of uncertainty (Jovic, 2008; Koerner & Dugas, 2006), anxiety served as the primary experimental condition. Because it is an arousing, negative emotional state (Russell & Mehrabian, 1974), like anxiety, but lacks an uncertainty component (Lerner & Keltner, 2001), anger served as a comparison emotional state. Calmness served as the control emotional state condition. One of these emotional states was induced and participants were asked to determine whether a target stimulus was present in a visual noise display.
Emotional states can affect visual perception. In particular, emotions characterized by a lack of certainty about the details and outcome of a situation, such as fear and anxiety, can have marked effects on information processing. For example, participants who viewed fearful stimuli demonstrated increased contrast sensitivity for subsequently presented Gabor patches (Phelps, Ling, & Carrasco, 2006) and increased sensitivity to the orientation of low spatial frequency stimuli (Bocanegra & Zeelenberg, 2009). Several studies have found that anxious participants exhibit enhanced stimulus-driven attention, perhaps as a way to enhance threat detection (e.g., Bishop, Duncan, Brett, & Lawrence, 2004; Cornwell, Mueller, Kaplan, Grillon, & Ernst, 2012; Eysenck, Derakshan, Santos, & Calvo, 2007). The effect of such emotions on brain activity has also been noted. For example, exposure to fearinducing emotional stimuli increases activity in early visual processing regions (Morris et al., 1998). Whitson and Galinsky (2008) examined the relationship between feeling a lack of control and pattern perception. When they felt they lacked control, participants were more likely to develop superstitions, perceive conspiracies, see nonexistent images in noise displays, and claim to find patterns in stock market information. Furthermore, participants who performed self-affirmation tasks to bring down their stress levels showed
This article was published Online First July 6, 2015. Andrea M. Cataldo and Andrew L. Cohen, Department of Psychology, University of Massachusetts, Amherst. We thank Nilanjana Dasgupta for her guidance on the emotion induction process and scale selection, Caren Rotello for her guidance in the signal detection analyses, and Jason Gold for use of the face stimuli. Correspondence concerning this article should be addressed to Andrea M. Cataldo, 436 Tobin Hall, 135 Hicks Way, Department of Psychology, University of Massachusetts, Amherst, MA 01003. E-mail: amcataldo@ psych.umass.edu 846
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EFFECT OF EMOTION
A key contribution of this research is the use of signal detection theory to measure the independent effect of emotional states on both accuracy and bias. Signal detection theory (e.g., Macmillan & Creelman, 2005) is a set of powerful statistical techniques that can be used to uncover the component processes underlying object detection. Specifically, this approach allowed us to measure two distinct aspects of detection: the sensitivity to the presence or absence of a pattern, or discriminability, and the overall willingness of a participant to respond either affirmatively or negatively in regards to the presence of a stimulus, or bias. Signal detection theory has been widely used to precisely discriminate between performance effects resulting from perceptual discriminability and decisional bias in many areas of psychology, including perception (e.g., Chen, Lu, & Holyoak, 2014; Kim, Rothrock, & Laberge, 2014; Rogé & Gabaude, 2009), memory (e.g., Bowles, Harlow, Meeking, & Köhler, 2012; Starns, Hicks, Brown, & Martin, 2008; Wixted & Mickes, 2014), and judgment and decision making (e.g., Abbott & Sherratt, 2013; Lueddeke & Higham, 2011; Smillie, Quek, & Dalgleish, 2014). In the emotion literature, signal detection analysis has been used to clarify the effects of the emotional content of stimuli on detection tasks independent of emotional state (Ben-David, Chajut, & Algom, 2012; Pessoa, Japee, & Ungerleider, 2005; Pixton, 2011). Its use here allowed us to do the same for the effect of emotional state on the visual detection of neutral stimuli. In a typical signal detection paradigm, participants are asked to determine whether or not a target stimulus is present on a trial. Targets are usually present on half of the trials. Responses can fall into one of four event categories: hits, the participant correctly responds affirmatively when the target is present; false alarms, the participant incorrectly responds affirmatively when the target is not present; misses, the participant incorrectly responds negatively when the target is present; or correct rejections, the participant correctly responds negatively when the target is not present. Because the number of target-present and target-absent trials are known, misses and correct rejections are redundant with hits and false alarms, respectively. Thus, hits and false alarms provide a complete description of the data. Hits and false alarms can be transformed into measures of discriminability and bias (Macmillan & Creelman, 2005). Consider the following generic example. A participant who is perfect at discriminating target-present and -absent trials will have 100% hits (0% misses) and 0% false alarms (100% correct rejections). At the other end of the spectrum, a participant with no ability to discriminate target-present and -absent trials will have 50% hits (50% misses) and 50% false alarms (50% correct rejections). Bias acts differently. A participant who always claims the target is always present will have 100% hits (0% misses), but also 100% false alarms (0% correct rejections). A bias to always claim that the target is absent will result in 0% hits (100% misses) and 0% false alarms (100% correct rejections). The precise measures of discriminability and bias used are discussed in the Results section. In terms of predictions, if uncertainty affects the ability to detect the target, we would expect anxious participants (but not angry participants) to have a significantly higher discriminability than calm participants; for example, they would have greater perceptual ability in detecting the presence of a target image. If uncertainty affects the likelihood of claiming that a target is present, we would
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expect anxious participants (but not angry participants) to be more willing to state that a target image is present than calm participants. Because humans excel at detecting faces, Experiment 1 examined the ability of anxious, angry, and calm participants to distinguish between trials on which only visual noise was displayed (target-absent) and trials on which a face was displayed in visual noise (target-present). To preview, anxious participants, but not angry participants, were better able to discriminate target-present and -absent trials than calm participants. To determine whether the effect of uncertainty on perceptual discriminability was restricted to social stimuli (i.e., faces), Experiment 2 replicated Experiment 1, but with houses rather than faces. No effect of emotional state on discriminability was found. No effect of emotional state on bias was found in either experiment.
Experiment 1 The goal of Experiment 1 was to investigate the effect of an uncertain emotional state on visual detection and to use signal detection theory to determine whether any such effect was due to differences in discriminability or bias. Participants were asked whether or not a face was present in visual noise. Face detection is a natural task for most people (e.g., Golan, Bentin, DeGutis, Robertson, & Harel, 2014) and participants can be expected to perform well with minimal instruction or practice. A particular emotional state was induced by having participants write about a personal memory that focused on that state. Anxiety was used as a representative uncertain emotional state (Jovic, 2008; Koerner & Dugas, 2006). Anger shares many features with anxiety (Russell & Mehrabian, 1974), but is not uncertain (Lerner & Keltner, 2001). Calmness served as a control emotional state. An emotion manipulation check was used to confirm that the appropriate emotional state had been induced. The stimuli for each participant were individually calibrated. That is, the relative signal-to-noise contrast levels of the stimuli were set to equate task difficulty across participants.
Method Participants. Undergraduates (N ⫽ 122) from the University of Massachusetts participated in this experiment for course credit (44 anxious, 39 angry, 39 calm). Materials. The stimuli for the detection task were constructed in the following manner. First, visual noise was generated. The noise looked much like the “snow” on an untuned TV. The grayscale value for each pixel was generated from a normal distribution centered on neutral gray with a fixed standard deviation. The visual noise was presented and randomly sampled on every trial. Second, on target-present trials, the pixel-by-pixel values of a face were added to the visual noise. For both targetpresent and target-absent trials, the noise for any pixel that went beyond the maximum or minimum luminance of the screen was resampled. The initial face was cropped in an oval to remove the face outline and hair. To reduce the probability of relying on features specific to a single face, three faces from Gold, Bennett, and Sekuler (1999) were used. All faces were of neutral affect. Low-pass filtered noise was added to the cropped region (around the face), which made the relative contrast for each face identical. The contrast of the faces relative to the noise was adjusted during
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calibration, as described below. All stimuli were grayscale and were 256 ⫻ 256 pixels. See the left and middle panels of Figure 1 for sample stimuli. Procedure. Participants were recruited under the impression that they would participate in two unrelated studies: one on visual perception and another on autobiographical memory recall. The experiment was broken into five phases: calibration, emotion induction, testing, emotion manipulation check, and questionnaires. Each phase is discussed separately. Calibration. Calibration was used to ensure that all participants began the testing phase with a similar level of sensitivity to the stimuli. A staircase method was used to determine the signalto-noise contrast level at which each individual participant would be able to correctly identify the target approximately 72% of the time (Macmillan & Creelman, 2005). The participant was presented with 100 stimuli (half targetpresent, half target-absent in random order) and asked to judge whether or not a face was present. Higher contrast images are easier to discriminate. The contrast of the face was altered after each trial in the following way. After a hit, the contrast was reduced one step (i.e., the luminance values of the face were moved toward neutral gray by approximately 1% of the maximum luminance value of the screen). After a miss, the contrast was increased by three steps. After a false alarm, the contrast was increased by four steps. No change in contrast was made after a correct rejection. The final calibration contrast level was used during the testing trials. All three face images were calibrated together in this phase, with one of the three images randomly chosen for presentation on each trial. Emotion induction. During emotion induction, participants were placed into one of three emotion conditions: anxious, angry, and calm. Recall of emotional memories reliably induces relevant emotions in the present moment (Damasio et al., 2000). Participants were asked to focus on a personal memory in which they felt either very anxious, very angry, or very calm and then write about it for approximately 5 min (DeSteno, Dasgupta, Bartlett, & Cajdric, 2004; see the Appendix for full instructions). Testing. During testing, participants were presented with 400 stimuli with the stimulus contrast ratio determined from the calibration phase. Participants were asked to respond to what extent they were sure a face was or was not present in the stimulus on a 6-point scale (1 ⫽ definitely absent, 6 ⫽ definitely present). As described in the Results section, using a scale allowed for a more detailed view of discriminability and bias. Half of the trials were target-present trials. On target-present trials, one of the three face stimuli was selected randomly. Trials were presented in random order.
Figure 1. Sample stimuli. (Left) Target-absent (noise only) trial. (Center) Target-present trial, Experiment 1. (Right) Target-present trial, Experiment 2.
To ensure that the induced emotional state would not wear off before the testing phase was complete, after 200 trials, participants completed an emotion reinstatement task in which they continued writing about their chosen memory from the emotion induction phase for another 5 min (see the Appendix for details). Emotion manipulation check. An emotion manipulation check was administered immediately following the testing phase to verify that the proper emotional state was induced during emotion induction. Participants were asked to rate on a 5-point scale the extent to which they felt each of 18 different emotions while they were writing about their memories (happy, angry, anxious, calm, irritated, sad, content, worried, afraid, good, annoyed, nervous, fearful, relaxed, gloomy, miserable, neutral, scared; DeSteno et al., 2004). Questionnaires. Participants were asked for demographic information (not used here), as well as whether or not they were able to guess the true purpose of the study.
Results The results were analyzed as follows. First, outliers were removed. Second, the data from the manipulation check were used to ensure that the appropriate emotional state was induced in each emotion condition. Finally, a signal detection analysis was performed to determine the relative discriminability and bias in each emotion condition. Outlier removal. To reduce noise, we removed participants who were not engaged in the task from analysis. Because emotion may affect performance, such participants were identified based on their calibration performance before an emotional state was induced. Recall that a high signal contrast determined by the calibration phase was an indicator of poor performance. Indeed, inspection of the data indicated that there were high-, but not low-, contrast outliers. Because high-contrast images would result in a particularly easy, and therefore meaningless, testing phase, the goal was to remove these participants. Therefore, we started with the simplest assumption that the contrasts should be normally distributed. Our goal was to remove outliers under this assumption. We started with the complete data set and then incrementally removed points from the high end of the distribution. After removing each point, we calculated the skew of the remaining distribution. A plot of these skewness values showed a clear elbow at a contrast value of 10.5. After this point, the skewness value decreased relatively slowly. Fourteen participants with contrast values that exceeded 10.5 were removed (five anxious, four angry, five calm), leaving 108 participants to be used in the analyses (39 anxious, 35 angry, 34 calm). As discussed below, the qualitative results of the important planned contrasts were not affected by the specific participants removed. Manipulation check. The emotions listed on the emotion manipulation check were collapsed into three aggregate groups measuring the three target emotions. The aggregate groups were formed by performing a reliability analysis (DeSteno et al., 2004) to determine which emotional states were likely to be reported similarly. In particular, we calculated Cronbach’s alpha, which is a measure of the internal consistency of a psychometric test and, in this case, determined how well the rated emotions covaried or grouped together (Cronbach, 1951). Higher scores indicated better grouping. Cronbach’s alpha ranges from 0 to 1 and a cutoff of .70
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EFFECT OF EMOTION
is often applied (Nunnally, 1978). We adopted that practice here. The aggregate groups were created as follows: angry, irritated, and annoyed formed the angry group (␣ ⫽ .788); anxious, worried, and nervous formed the anxious group (␣ ⫽ .808); calm and relaxed formed the calm group (␣ ⫽ .721). Other aggregate groups for calm including content, good, or neutral were below the .70 cutoff. The results are shown in the left panel of Figure 2. Each data point is the mean participant emotion rating in the aggregate groups. The goal was to verify both that the target emotion was stronger than the other emotions (e.g., reported anger was the strongest emotion in the angry condition) and that the target emotion in a given condition was stronger than in the other two conditions (e.g., reported anger was stronger in the angry condition than in the anxious or calm conditions). An analysis of variance (ANOVA) was performed, with emotion condition as a between-subjects variable and the reported emotion group as a within-subject variable. There was a main effect of emotion condition, F(2, 119) ⫽ 6.87, p ⫽ .002, MSE ⫽ 0.63, p2 ⫽ .024, and a main effect of reported emotion group, F(2, 238) ⫽ 11.82, p ⬍ .001, MSE ⫽ 1.13, p2 ⫽ .072. Importantly, a strong interaction effect of reported emotion group and condition was also found, F(4, 238) ⫽ 34.78, p ⬍ .001, MSE ⫽ 1.13, p2 ⫽ .314. Planned contrasts indicated that emotions in the angry group were rated highest by participants in the angry condition, t(66.10) ⫽ 6.42, p ⬍ .001, d ⫽ 1.31; emotions in the anxious group were rated highest by participants in the anxious condition, t(65.05) ⫽ 6.54, p ⬍ .001, d ⫽ 1.37; and emotions in the calm group were rated highest by participants in the calm condition, t(86.70) ⫽ 7.35, p ⬍ .001, d ⫽ 1.35.1 Furthermore, emotions in the angry group were rated higher than emotions in the anxious or calm groups among participants in the angry condition, t(68.36) ⫽ 5.90, p ⬍ .001, d ⫽ 1.21; emotions in the anxious group were rated higher than emotions in the angry or calm groups among participants in the anxious condition, t(75.37) ⫽ 2.78, p ⫽ .006, d ⫽ 0.54; and emotions in the calm group were rated higher than emotions in the angry or anxious groups among participants in the calm condition, t(60.69) ⫽ 13.62, p ⬍ .001, d ⫽ 2.92. The emotions were appropriate to the conditions. Signal detection analysis. Recall that responses were made on a 6-point scale ranging from definitely absent to definitely present. Using a response scale rather than a categorical absent– present distinction allowed performance to be determined at a set of confidence levels. In particular, the confidence ratings were used to construct a receiver operating characteristic (ROC) curve for each participant in each emotion condition (Macmillan & Creelman, 2005). An ROC curve illustrates how performance varies with confidence. Consider the left panel of Figure 3. The proportion of hits on target-present trials and false alarms on target-absent trials at Confidence Level 6 (definitely present) averaged across participants was used to plot the leftmost point for each condition. Data from Confidence Levels 5 and 6 were combined to create the next point. This procedure continued until all confidence levels were included, which led to 100% hits and 100% false alarms, that is, the upper right corner point. A continuous curve can then be used to describe these data. Both the raw data and fit curves for each emotion condition are shown in the left panel of Figure 3. Note that, although Figure 3 shows data averaged within each emo-
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tion condition, all analyses below were performed at the individual subject level. At a coarse level, both discriminability and bias can be visually determined from Figure 3. The farther the curve is from the diagonal, the higher discriminability. The ROC curve for an individual who is guessing will approximate a straight line from the lower left to upper right corner. For an individual with perfect performance, the ROC curve will pass through the upper left corner. Bias manifests as a shift in points along the ROC curve. A tendency to say that a target is not present will cause a leftward shift in the points. A tendency to say that a target is present will cause a rightward shift. First, consider bias. More formally, bias for each emotion condition for each confidence level i was measured by calculating the criterion value, ci, for each subject in each emotion condition, as described in Macmillan and Creelman (2005).2 The criterion specifies how much evidence is needed to respond that a target is present at a particular confidence level. That is, a higher criterion results in an increase in not-present responses, which, as described previously, would manifest as points moving to the left in an ROC curve. To assess whether there were significant differences in bias across emotion conditions, for each ci, we performed an ANOVA with emotion condition as a between-subjects factor. The effect of emotion condition on c was not significant for any of these tests: for c1, F(2, 105) ⫽ 0.33, p ⫽ .72, MSE ⫽ 0.10, 2 ⫽ .010; for c2, F(2, 105) ⫽ 0.24, p ⫽ .78, MSE ⫽ 0.05, 2 ⫽ .009; for c3, F(2, 105) ⫽ 0.16, p ⫽ .85, MSE ⫽ 0.03, 2 ⫽ .007; for c4, F(2, 105) ⫽ 0.74, p ⫽ .48, MSE ⫽ 0.19, 2 ⫽ .011; for c5, F(2, 105) ⫽ 1.59, p ⫽ .21, MSE ⫽ 0.53, 2 ⫽ .021. Thus, there is no indication that bias varied across these emotional states. This result is manifested in Figure 3 by the relatively tight grouping of data points across emotion conditions. Discriminability was measured by calculating Az, which is a measure of the area beneath the ROC curve (Macmillan & Creelman, 2005).3 Recall that the more the ROC curve moves into the upper left corner, the better performance. Thus, the area under the curve, and therefore Az, increases with performance. Because it makes no assumptions concerning the standard deviation of the decision variable (Macmillan & Creelman, 2005), Az has an advantage over the traditional measure of discriminability, d=, for data in which the underlying distribution is unknown, such as these data. An ANOVA was used to assess whether or not there were any significant differences in discriminability across emotion conditions. There was a significant effect of emotional state, F(2, 105) ⫽ 3.59, p ⫽ .03, MSE ⫽ 0.01, 2 ⫽ .064. Planned contrasts revealed that anxious, t(58.78) ⫽ 2.85, p ⫽ .006, d ⫽ 0.68, but not angry, t(66.89) ⫽ 1.19, p ⫽ .24, d ⫽ 0.29, participants were better able to discriminate target-present and target-absent trials than calm 1
Welch’s t tests were used here and throughout. The criterion for each confidence level i is computed as the average of the z scores for the hit, Hi, and false alarm, Fi, rates at that level: ci ⫽ ⫺0.5 ⫻ [z(Hi) ⫹ z(Fi)]. 3 Az was calculated by the following formula: Az ⫽ (da/公2), where is the cumulative normal, da ⫽ 公[2/(1 ⫹ s2)] ⫻ [z(H) ⫺ sz(F)], H is the hit rate, F is the false alarm rate, and s is the ratio of the standard deviations of the noise and signal distributions. 2
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Figure 2. Emotion manipulation check. (Left) Experiment 1. (Right) Experiment 2. Error bars are betweensubjects standard errors.
participants.4 This is the key result5: Only anxious participants showed an increase in discriminability.
Discussion Anxious, angry, and calm participants were asked to determine whether a target was present in noise. Signal detection theory was used to establish whether any effects of emotional state were due to the ability to better discriminate target-present and target-absent trials or due to an overall decisional bias to respond target-present or target-absent. Relative to calm participants, discriminability was enhanced for anxious, but not angry, participants. Emotional state had no significant effect on bias. These results suggest that uncertainty increases the ability to discriminate stimuli. The effect of emotional state on discriminability in this task may have partially been due to the stimulus class. In particular, because the target stimuli were faces, it is unclear whether this effect extends beyond social stimuli. Socially anxious people are more likely to interpret nonthreatening faces as threatening (Heuer, Lange, Isaac, Rinck, & Becker, 2010). Furthermore, high trait anxiety individuals require shorter stimulus exposure times to become aware of threatening face targets in their stimuli (Ruderman & Lamy, 2012). It is possible, then, that anxious participants were uniquely skilled at detecting face targets. To extend this idea to nonface stimuli, Experiment 2 replicated Experiment 1, but using houses, rather than faces, as targets.
69 calm). To increase the interpretability of a potential null result, we selected the sample size via a power analysis. Using the effect size on Az in Experiment 1 and Cohen’s f of 0.22, this sample size resulted in a power of 0.75. Materials. The materials were prepared as in Experiment 1. The only difference was that three front, close-up views of houses (taken off the Internet) were used as stimuli. Houses have been used as stimuli in other detection experiments (e.g., Burianová, Lee, Grady, & Moscovitch, 2013). The same oval mask was used as in Experiment 1. A sample stimulus is shown in the right panel of Figure 1. Procedure. The procedure was identical to the procedure for Experiment 1.
Results The analysis proceeded as in Experiment 1. Outlier removal. Outliers were removed using the same procedure as that in Experiment 1. In Experiment 2, participants with a contrast level above 15 were removed. Twenty-two participants were removed (13 anxious, five angry, four calm), leaving 161 participants to be used in the analyses (45 anxious, 51 angry, 65 calm). The qualitative results, however, were not affected by the specific participants removed. Manipulation check. The same three aggregate groups from Experiment 1 were used. Angry, irritated, and annoyed formed the
Experiment 2 The goal of Experiment 2 was to replicate Experiment 1 using nonsocial target stimuli to assess the extent to which the effects in Experiment 1 may be better characterized in a social cognition context. Given past research indicating differential face perception performance in anxious participants (Heuer et al., 2010; Ruderman & Lamy, 2012), it is possible that participants in an uncertain emotional state are uniquely skilled at face perception. If this is the case, the results of Experiment 1 would not be expected to extend to the house stimuli used in Experiment 2.
Method Participants. Undergraduate college students (N ⫽ 183) participated in this experiment for course credit (58 anxious, 56 angry,
4 To test the effect of outliers on the qualitative results, these planned contrasts were also run removing none and nine (the next natural cutoff by inspection) of the participants with the highest contrast. In both cases, there was a significant (p ⬍ .05) effect of anxiety, but not anger. 5 To assess the effect of a general tolerance of uncertainty on detection, we measured a series of personality measures (locus of control, Rotter, 1966; need for structure, Neuberg & Newsom, 1993; and need for closure, Kruglanski et al., 1997) after the emotion manipulation check. To test for a relationship between these scales and the signal detection measures, we regressed Az and each of the five ci separately on these personality scales, for a total of six regressions. To correct for multiple comparisons, we set a Bonferroni-corrected alpha level at .008. Need for structure had a significant effect on bias at c1 ( ⫽ .03, p ⫽ .007). There was a marginal effect of locus of control on bias at c1 ( ⫽ ⫺.03, p ⫽ .008). No other effects were significant. Note, however, that the staircase procedure may reduce effects due to personality. Thus, future research is needed to more directly measure the effect of personality on detection performance.
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EFFECT OF EMOTION
Figure 3.
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Raw data and receiver operating characteristic curves. (Left) Experiment 1. (Right) Experiment 2.
angry group (␣ ⫽ .827). Anxious, worried, and nervous formed the anxious group (␣ ⫽ .838). Calm and relaxed formed the calm group (␣ ⫽ .792). The results are presented in the right panel of Figure 2. An ANOVA was performed, with emotion condition as a between-subjects variable and the reported emotion groups as a within-subject variable. There was a main effect of reported emotion group, F(2, 360) ⫽ 30.77, p ⬍ .001, MSE ⫽ 1.10, p2 ⫽ .115. There was no main effect of emotion condition (particular emotional states were not more intense than others), F(2, 180) ⫽ 2.77, p ⫽ .065, MSE ⫽ 0.68, p2 ⫽ .007. There was a strong interaction of reported emotion group and emotion condition, F(4, 360) ⫽ 63.90, p ⬍ .001, MSE ⫽ 1.10, p2 ⫽ .351. Planned contrasts indicated that emotions in the angry group were rated highest by participants in the angry condition, t(96.84) ⫽ 9.68, p ⬍ .001, d ⫽ 1.61; emotions in the anxious group were rated highest by participants in the anxious condition, t(94.30) ⫽ 7.85, p ⬍ .001, d ⫽ 1.34; and emotions in the calm group were rated highest by participants in the calm condition, t(147.33) ⫽ 10.29, p ⬍ .001, d ⫽ 1.56. Furthermore, emotions in the angry group were rated higher than emotions in the anxious or calm groups among participants in the angry condition, t(113.54) ⫽ 7.87, p ⬍ .001, d ⫽ 1.27; emotions in the anxious group were rated higher than emotions in the angry or calm groups among participants in the anxious condition, t(103.28) ⫽ 4.18, p ⬍ .001, d ⫽ 0.698; and emotions in the calm group were rated higher than emotions in the angry or anxious groups among participants in the calm condition, t(113.51) ⫽ 16.99, p ⬍ .001, d ⫽ 2.69. Signal detection analysis. The ROC analyses are provided in the right panel of Figure 3. All five ci and Az were determined for each participant. An ANOVA was performed with emotion condition as the between-subjects factor and each ci as the dependent variable. As in Experiment 1, the effect of emotion condition on c was not significant for any of these tests: for c1, F(2, 158) ⫽ 0.71, p ⫽ .49, MSE ⫽ 0.27, 2 ⫽ .009; for c2, F(2, 158) ⫽ 0.61, p ⫽ .55, MSE ⫽ 0.14, 2 ⫽ .008; for c3, F(2, 158) ⫽ 1.49, p ⫽ .23, MSE ⫽ 0.29, 2 ⫽ .019; for c4, F(2, 158) ⫽ 1.26, p ⫽ .29, MSE ⫽ 0.38, 2 ⫽ .016; for c5, F(2, 158) ⫽ 0.91, p ⫽ .41, MSE ⫽ 0.43, 2 ⫽ .011. Replicating Experiment 1, there was no effect of emotional state on bias.6 An ANOVA was used to assess whether or not there were any significant differences in discriminability across emotion condi-
tions. The effect of emotional state was not significant, F(2, 158) ⫽ 0.045, p ⫽ .96, MSE ⫽ 0.009, 2 ⫽ .0006. Inspection of Figure 3 shows no hint of differences across emotional states. Planned contrasts using calm as a control confirmed this finding for anxious, t(97.75) ⫽ 0.14, p ⫽ .89, d ⫽ 0.027, and angry, t(112.77) ⫽ 0.30, p ⫽ .76, d ⫽ 0.056, participants. Emotional state did not affect discriminability. Given the difficulty of arguing from a null result in a standard ANOVA (Rouder, Speckman, Sun, Morey, & Iverson, 2009), we computed a Bayes factor to determine the strength of any positive evidence for the null hypothesis in the ANOVA using the BayesFactor package in R (see Rouder, Morey, Speckman, & Province, 2012, for methods of computation). A Bayes factor is a ratio of the probability of the data given the alternative model over the probability of the data given the null model. Bayes factors greater than 10.00 indicate strong evidence for the alternative model, and factors less than 0.10 indicate strong evidence for the null model (Jeffreys, 1961). The Bayes factor was B10 ⫽ 0.065, indicating strong evidence for a null effect of emotional state on discriminability.
Discussion Consistent with Experiment 1, no significant effects of emotional state on bias were found. In contrast to Experiment 1, however, no significant effects of emotional state on discriminability were found. That is, the effect of emotional state on face detection found in Experiment 1 did not extend to the nonsocial object detection task of Experiment 2. An additional Bayesian analysis supported the null effect. Together, these findings suggest that the effects of emotional state on perceptual ability in visual detection are limited to certain stimulus classes, perhaps potentially threatening stimuli or social stimuli such as faces.
General Discussion Past research has demonstrated that uncertain emotion states, such an anxiety, can have an effect on performance in visual 6 The same three personality measures were administered in Experiment 2 (see footnote 5). The only significant relationship between the personality scales and signal detection measures was between need for closure and c1 ( ⫽ .01, p ⫽ .002). No other effects were significant at the .008 level.
CATALDO AND COHEN
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detection tasks. Both increased detection of targets (Simonov et al., 1977) and increased false alarms (Whitson & Galinsky, 2008) have been reported. As these two measures tend to covary, it is unclear whether these results are due to changes in discriminability, the ability to detect a target, and/or bias, a tendency to respond in a particular way. By framing these effects in a signal detection perspective, the present study offers new insight into the underlying cause of these effects. Participants were shown visual noise with or without embedded targets and were asked to determine whether a target was present in each. Participants were either in an anxious, angry, or calm emotional state. Anxiety is an uncertain emotional state (Jovic, 2008; Koerner & Dugas, 2006) and anger is matched on many features of anxiety (e.g., high arousal and negative valence), but is not uncertain (Lerner & Keltner, 2001). In Experiment 1, participants in an anxious emotional state were significantly better at detecting the presence of faces. When houses were used as the target in Experiment 2, no differences in discriminability were found across emotional states. Neither experiment found an effect of emotional state on bias. These results suggest that anxiety acts, not by changing a tendency to declare that a target is present, but by increasing the ability to detect targets; however, the effect of anxiety on visual perception appears to be restricted to certain stimulus classes, such as threatening stimuli or social stimuli such as faces. Given that otherwise nonthreatening face stimuli tend to be perceived as threatening by anxious individuals (Heuer et al., 2010), it is plausible that participants in an anxious emotional state exhibited the same effect. This research is then in line with the suggestion that the effect of an uncertain emotional state on visual perception might be to enhance threat detection (e.g., Bishop et al., 2004; Cornwell et al., 2012; Eysenck et al., 2007). Such increased threat detection would lead to heightened discriminability when the targets were faces (Experiment 1), but not houses (Experiment 2). The present study represents a step toward uncovering the perceptual and motivational effects of emotional state on visual detection. By measuring effects of both discriminability and bias on the processes involved in visual detection, signal detection analysis can provide valuable insights into the relationship between emotional state and visual detection.
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Appendix Initial Emotion-Induction Instructions In the next task we are interested in studying people’s memories for certain types of events. You will be asked to recall a specific type of event and then to describe it as vividly and in as much detail as possible using the paper and pencil provided by the experimenter. You’ll have a few minutes to work on this task before you proceed to other tasks. We do not expect you to have finished writing before the time period elapses. Rather, you’ll be asked to continue describing this event at different times in the experiment. Please pick up roughly where you ended your description the last time. Please take a moment to remember a time that you felt [VERY ANXIOUS/VERY ANGRY/CALM and RELAXED]. When choosing this memory, make sure you think of a memory that represents [anxiety/anger/calmness] only. Do not pick a memory in which you felt [anxious and angry or anxious and sad/angry and anxious or angry and sad/calm and sad, or calm and joyful]. Also, try to pick an incident in which you were [anxious/angry/calm] because of something that happened to you directly; do not describe a time when you were [anxious/angry/calm] because of something that happened to another person. When you have recalled this memory, focus on it for a few minutes so that you have a vivid recollection of the events in-
volved. Take a minute to experience all the feelings that you experienced at that time. Once you have done this, please describe the memory in as much detail as you can. Remember, you probably will not have time to finish the description right now, but we will return to this task later in this session.
Emotion-Induction Reinstatement Instructions We would now like you to continue describing the memory of a time you were [VERY ANXIOUS/VERY ANGRY/CALM and RELAXED]. So please, once again, take a moment to remember the time that you were [VERY ANXIOUS/VERY ANGRY/ CALM and RELAXED]. When you have recalled this memory again, focus on it so that you have a vivid impression of the events involved. Take a minute to experience the feelings you felt at that time. Once you have done this, please continue to describe the memory in as much detail as you can, picking up roughly where you left off. Received August 4, 2014 Revision received January 15, 2015 Accepted May 2, 2015 䡲
© 2015 American Psychological Association 1528-3542/15/$12.00 http://dx.doi.org/10.1037/emo0000091
The Effect of Emotional State on Visual Detection: A Signal Detection Analysis Andrea M. Cataldo and Andrew L. Cohen
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University of Massachusetts, Amherst This research explores the effect of emotional states on visual detection. Previous research has shown that emotional states characterized by an intolerance of uncertainty, such as anxiety, can affect performance on visual detection tasks. It is unclear, however, to what extent these effects are a result of increased perceptual ability, a decisional bias, or both. The present study used signal detection theory to determine whether uncertain emotional states affect perceptual discriminability and/or decisional bias. In 2 experiments, an anxious, angry, or calm emotional state was induced, and participants were asked to identify which of a series of noisy images contained an embedded target image. The target images were either faces or houses. Emotional state had no effect on decisional bias for either target, but the ability to detect a face was higher for anxious participants. No effect on discriminability was found for houses. These results suggest that emotional state can change perceptual discriminability, but that this change may be limited to certain stimulus classes. Keywords: anxiety, signal detection theory, visual detection, perception
a decrease both in their need for structure and their likelihood of perceiving illusory correlations. These findings lend strong support to the idea that feelings of uncertainty can increase our need for stability and affect the way we perceive random information. That study suggests that a feeling of uncertainty not only increases the ability to detect patterns, but also the likelihood of seeing patterns where they do not exist. Simonov, Frolov, Evtushenko, and Sviridov (1977) examined the effect of stress on the ability to recognize visual symbols embedded in noise. By testing participants periodically during a flight that would culminate in a parachute jump, the researchers were able to test performance across increasing stress levels. Consistent with the idea that uncertainty increases the ability to detect patterns, participants became more skilled at identifying the symbols as their stress levels rose. Importantly, however, they also became more likely to make false identifications by pointing out nonexistent symbols in what was actually pure noise. Thus, it is unclear whether uncertain emotional states increase perceptual ability, bias people to identify target stimuli, or both. The main goal of the present research was to directly determine whether uncertain emotional states affect perceptual accuracy or decisional bias. In the present study, the effects of three discrete emotions on a visual detection task were investigated: anxious, angry, and calm. Based on research suggesting that anxiety is highly correlated with intolerance of uncertainty (Jovic, 2008; Koerner & Dugas, 2006), anxiety served as the primary experimental condition. Because it is an arousing, negative emotional state (Russell & Mehrabian, 1974), like anxiety, but lacks an uncertainty component (Lerner & Keltner, 2001), anger served as a comparison emotional state. Calmness served as the control emotional state condition. One of these emotional states was induced and participants were asked to determine whether a target stimulus was present in a visual noise display.
Emotional states can affect visual perception. In particular, emotions characterized by a lack of certainty about the details and outcome of a situation, such as fear and anxiety, can have marked effects on information processing. For example, participants who viewed fearful stimuli demonstrated increased contrast sensitivity for subsequently presented Gabor patches (Phelps, Ling, & Carrasco, 2006) and increased sensitivity to the orientation of low spatial frequency stimuli (Bocanegra & Zeelenberg, 2009). Several studies have found that anxious participants exhibit enhanced stimulus-driven attention, perhaps as a way to enhance threat detection (e.g., Bishop, Duncan, Brett, & Lawrence, 2004; Cornwell, Mueller, Kaplan, Grillon, & Ernst, 2012; Eysenck, Derakshan, Santos, & Calvo, 2007). The effect of such emotions on brain activity has also been noted. For example, exposure to fearinducing emotional stimuli increases activity in early visual processing regions (Morris et al., 1998). Whitson and Galinsky (2008) examined the relationship between feeling a lack of control and pattern perception. When they felt they lacked control, participants were more likely to develop superstitions, perceive conspiracies, see nonexistent images in noise displays, and claim to find patterns in stock market information. Furthermore, participants who performed self-affirmation tasks to bring down their stress levels showed
This article was published Online First July 6, 2015. Andrea M. Cataldo and Andrew L. Cohen, Department of Psychology, University of Massachusetts, Amherst. We thank Nilanjana Dasgupta for her guidance on the emotion induction process and scale selection, Caren Rotello for her guidance in the signal detection analyses, and Jason Gold for use of the face stimuli. Correspondence concerning this article should be addressed to Andrea M. Cataldo, 436 Tobin Hall, 135 Hicks Way, Department of Psychology, University of Massachusetts, Amherst, MA 01003. E-mail: amcataldo@ psych.umass.edu 846
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EFFECT OF EMOTION
A key contribution of this research is the use of signal detection theory to measure the independent effect of emotional states on both accuracy and bias. Signal detection theory (e.g., Macmillan & Creelman, 2005) is a set of powerful statistical techniques that can be used to uncover the component processes underlying object detection. Specifically, this approach allowed us to measure two distinct aspects of detection: the sensitivity to the presence or absence of a pattern, or discriminability, and the overall willingness of a participant to respond either affirmatively or negatively in regards to the presence of a stimulus, or bias. Signal detection theory has been widely used to precisely discriminate between performance effects resulting from perceptual discriminability and decisional bias in many areas of psychology, including perception (e.g., Chen, Lu, & Holyoak, 2014; Kim, Rothrock, & Laberge, 2014; Rogé & Gabaude, 2009), memory (e.g., Bowles, Harlow, Meeking, & Köhler, 2012; Starns, Hicks, Brown, & Martin, 2008; Wixted & Mickes, 2014), and judgment and decision making (e.g., Abbott & Sherratt, 2013; Lueddeke & Higham, 2011; Smillie, Quek, & Dalgleish, 2014). In the emotion literature, signal detection analysis has been used to clarify the effects of the emotional content of stimuli on detection tasks independent of emotional state (Ben-David, Chajut, & Algom, 2012; Pessoa, Japee, & Ungerleider, 2005; Pixton, 2011). Its use here allowed us to do the same for the effect of emotional state on the visual detection of neutral stimuli. In a typical signal detection paradigm, participants are asked to determine whether or not a target stimulus is present on a trial. Targets are usually present on half of the trials. Responses can fall into one of four event categories: hits, the participant correctly responds affirmatively when the target is present; false alarms, the participant incorrectly responds affirmatively when the target is not present; misses, the participant incorrectly responds negatively when the target is present; or correct rejections, the participant correctly responds negatively when the target is not present. Because the number of target-present and target-absent trials are known, misses and correct rejections are redundant with hits and false alarms, respectively. Thus, hits and false alarms provide a complete description of the data. Hits and false alarms can be transformed into measures of discriminability and bias (Macmillan & Creelman, 2005). Consider the following generic example. A participant who is perfect at discriminating target-present and -absent trials will have 100% hits (0% misses) and 0% false alarms (100% correct rejections). At the other end of the spectrum, a participant with no ability to discriminate target-present and -absent trials will have 50% hits (50% misses) and 50% false alarms (50% correct rejections). Bias acts differently. A participant who always claims the target is always present will have 100% hits (0% misses), but also 100% false alarms (0% correct rejections). A bias to always claim that the target is absent will result in 0% hits (100% misses) and 0% false alarms (100% correct rejections). The precise measures of discriminability and bias used are discussed in the Results section. In terms of predictions, if uncertainty affects the ability to detect the target, we would expect anxious participants (but not angry participants) to have a significantly higher discriminability than calm participants; for example, they would have greater perceptual ability in detecting the presence of a target image. If uncertainty affects the likelihood of claiming that a target is present, we would
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expect anxious participants (but not angry participants) to be more willing to state that a target image is present than calm participants. Because humans excel at detecting faces, Experiment 1 examined the ability of anxious, angry, and calm participants to distinguish between trials on which only visual noise was displayed (target-absent) and trials on which a face was displayed in visual noise (target-present). To preview, anxious participants, but not angry participants, were better able to discriminate target-present and -absent trials than calm participants. To determine whether the effect of uncertainty on perceptual discriminability was restricted to social stimuli (i.e., faces), Experiment 2 replicated Experiment 1, but with houses rather than faces. No effect of emotional state on discriminability was found. No effect of emotional state on bias was found in either experiment.
Experiment 1 The goal of Experiment 1 was to investigate the effect of an uncertain emotional state on visual detection and to use signal detection theory to determine whether any such effect was due to differences in discriminability or bias. Participants were asked whether or not a face was present in visual noise. Face detection is a natural task for most people (e.g., Golan, Bentin, DeGutis, Robertson, & Harel, 2014) and participants can be expected to perform well with minimal instruction or practice. A particular emotional state was induced by having participants write about a personal memory that focused on that state. Anxiety was used as a representative uncertain emotional state (Jovic, 2008; Koerner & Dugas, 2006). Anger shares many features with anxiety (Russell & Mehrabian, 1974), but is not uncertain (Lerner & Keltner, 2001). Calmness served as a control emotional state. An emotion manipulation check was used to confirm that the appropriate emotional state had been induced. The stimuli for each participant were individually calibrated. That is, the relative signal-to-noise contrast levels of the stimuli were set to equate task difficulty across participants.
Method Participants. Undergraduates (N ⫽ 122) from the University of Massachusetts participated in this experiment for course credit (44 anxious, 39 angry, 39 calm). Materials. The stimuli for the detection task were constructed in the following manner. First, visual noise was generated. The noise looked much like the “snow” on an untuned TV. The grayscale value for each pixel was generated from a normal distribution centered on neutral gray with a fixed standard deviation. The visual noise was presented and randomly sampled on every trial. Second, on target-present trials, the pixel-by-pixel values of a face were added to the visual noise. For both targetpresent and target-absent trials, the noise for any pixel that went beyond the maximum or minimum luminance of the screen was resampled. The initial face was cropped in an oval to remove the face outline and hair. To reduce the probability of relying on features specific to a single face, three faces from Gold, Bennett, and Sekuler (1999) were used. All faces were of neutral affect. Low-pass filtered noise was added to the cropped region (around the face), which made the relative contrast for each face identical. The contrast of the faces relative to the noise was adjusted during
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CATALDO AND COHEN
calibration, as described below. All stimuli were grayscale and were 256 ⫻ 256 pixels. See the left and middle panels of Figure 1 for sample stimuli. Procedure. Participants were recruited under the impression that they would participate in two unrelated studies: one on visual perception and another on autobiographical memory recall. The experiment was broken into five phases: calibration, emotion induction, testing, emotion manipulation check, and questionnaires. Each phase is discussed separately. Calibration. Calibration was used to ensure that all participants began the testing phase with a similar level of sensitivity to the stimuli. A staircase method was used to determine the signalto-noise contrast level at which each individual participant would be able to correctly identify the target approximately 72% of the time (Macmillan & Creelman, 2005). The participant was presented with 100 stimuli (half targetpresent, half target-absent in random order) and asked to judge whether or not a face was present. Higher contrast images are easier to discriminate. The contrast of the face was altered after each trial in the following way. After a hit, the contrast was reduced one step (i.e., the luminance values of the face were moved toward neutral gray by approximately 1% of the maximum luminance value of the screen). After a miss, the contrast was increased by three steps. After a false alarm, the contrast was increased by four steps. No change in contrast was made after a correct rejection. The final calibration contrast level was used during the testing trials. All three face images were calibrated together in this phase, with one of the three images randomly chosen for presentation on each trial. Emotion induction. During emotion induction, participants were placed into one of three emotion conditions: anxious, angry, and calm. Recall of emotional memories reliably induces relevant emotions in the present moment (Damasio et al., 2000). Participants were asked to focus on a personal memory in which they felt either very anxious, very angry, or very calm and then write about it for approximately 5 min (DeSteno, Dasgupta, Bartlett, & Cajdric, 2004; see the Appendix for full instructions). Testing. During testing, participants were presented with 400 stimuli with the stimulus contrast ratio determined from the calibration phase. Participants were asked to respond to what extent they were sure a face was or was not present in the stimulus on a 6-point scale (1 ⫽ definitely absent, 6 ⫽ definitely present). As described in the Results section, using a scale allowed for a more detailed view of discriminability and bias. Half of the trials were target-present trials. On target-present trials, one of the three face stimuli was selected randomly. Trials were presented in random order.
Figure 1. Sample stimuli. (Left) Target-absent (noise only) trial. (Center) Target-present trial, Experiment 1. (Right) Target-present trial, Experiment 2.
To ensure that the induced emotional state would not wear off before the testing phase was complete, after 200 trials, participants completed an emotion reinstatement task in which they continued writing about their chosen memory from the emotion induction phase for another 5 min (see the Appendix for details). Emotion manipulation check. An emotion manipulation check was administered immediately following the testing phase to verify that the proper emotional state was induced during emotion induction. Participants were asked to rate on a 5-point scale the extent to which they felt each of 18 different emotions while they were writing about their memories (happy, angry, anxious, calm, irritated, sad, content, worried, afraid, good, annoyed, nervous, fearful, relaxed, gloomy, miserable, neutral, scared; DeSteno et al., 2004). Questionnaires. Participants were asked for demographic information (not used here), as well as whether or not they were able to guess the true purpose of the study.
Results The results were analyzed as follows. First, outliers were removed. Second, the data from the manipulation check were used to ensure that the appropriate emotional state was induced in each emotion condition. Finally, a signal detection analysis was performed to determine the relative discriminability and bias in each emotion condition. Outlier removal. To reduce noise, we removed participants who were not engaged in the task from analysis. Because emotion may affect performance, such participants were identified based on their calibration performance before an emotional state was induced. Recall that a high signal contrast determined by the calibration phase was an indicator of poor performance. Indeed, inspection of the data indicated that there were high-, but not low-, contrast outliers. Because high-contrast images would result in a particularly easy, and therefore meaningless, testing phase, the goal was to remove these participants. Therefore, we started with the simplest assumption that the contrasts should be normally distributed. Our goal was to remove outliers under this assumption. We started with the complete data set and then incrementally removed points from the high end of the distribution. After removing each point, we calculated the skew of the remaining distribution. A plot of these skewness values showed a clear elbow at a contrast value of 10.5. After this point, the skewness value decreased relatively slowly. Fourteen participants with contrast values that exceeded 10.5 were removed (five anxious, four angry, five calm), leaving 108 participants to be used in the analyses (39 anxious, 35 angry, 34 calm). As discussed below, the qualitative results of the important planned contrasts were not affected by the specific participants removed. Manipulation check. The emotions listed on the emotion manipulation check were collapsed into three aggregate groups measuring the three target emotions. The aggregate groups were formed by performing a reliability analysis (DeSteno et al., 2004) to determine which emotional states were likely to be reported similarly. In particular, we calculated Cronbach’s alpha, which is a measure of the internal consistency of a psychometric test and, in this case, determined how well the rated emotions covaried or grouped together (Cronbach, 1951). Higher scores indicated better grouping. Cronbach’s alpha ranges from 0 to 1 and a cutoff of .70
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is often applied (Nunnally, 1978). We adopted that practice here. The aggregate groups were created as follows: angry, irritated, and annoyed formed the angry group (␣ ⫽ .788); anxious, worried, and nervous formed the anxious group (␣ ⫽ .808); calm and relaxed formed the calm group (␣ ⫽ .721). Other aggregate groups for calm including content, good, or neutral were below the .70 cutoff. The results are shown in the left panel of Figure 2. Each data point is the mean participant emotion rating in the aggregate groups. The goal was to verify both that the target emotion was stronger than the other emotions (e.g., reported anger was the strongest emotion in the angry condition) and that the target emotion in a given condition was stronger than in the other two conditions (e.g., reported anger was stronger in the angry condition than in the anxious or calm conditions). An analysis of variance (ANOVA) was performed, with emotion condition as a between-subjects variable and the reported emotion group as a within-subject variable. There was a main effect of emotion condition, F(2, 119) ⫽ 6.87, p ⫽ .002, MSE ⫽ 0.63, p2 ⫽ .024, and a main effect of reported emotion group, F(2, 238) ⫽ 11.82, p ⬍ .001, MSE ⫽ 1.13, p2 ⫽ .072. Importantly, a strong interaction effect of reported emotion group and condition was also found, F(4, 238) ⫽ 34.78, p ⬍ .001, MSE ⫽ 1.13, p2 ⫽ .314. Planned contrasts indicated that emotions in the angry group were rated highest by participants in the angry condition, t(66.10) ⫽ 6.42, p ⬍ .001, d ⫽ 1.31; emotions in the anxious group were rated highest by participants in the anxious condition, t(65.05) ⫽ 6.54, p ⬍ .001, d ⫽ 1.37; and emotions in the calm group were rated highest by participants in the calm condition, t(86.70) ⫽ 7.35, p ⬍ .001, d ⫽ 1.35.1 Furthermore, emotions in the angry group were rated higher than emotions in the anxious or calm groups among participants in the angry condition, t(68.36) ⫽ 5.90, p ⬍ .001, d ⫽ 1.21; emotions in the anxious group were rated higher than emotions in the angry or calm groups among participants in the anxious condition, t(75.37) ⫽ 2.78, p ⫽ .006, d ⫽ 0.54; and emotions in the calm group were rated higher than emotions in the angry or anxious groups among participants in the calm condition, t(60.69) ⫽ 13.62, p ⬍ .001, d ⫽ 2.92. The emotions were appropriate to the conditions. Signal detection analysis. Recall that responses were made on a 6-point scale ranging from definitely absent to definitely present. Using a response scale rather than a categorical absent– present distinction allowed performance to be determined at a set of confidence levels. In particular, the confidence ratings were used to construct a receiver operating characteristic (ROC) curve for each participant in each emotion condition (Macmillan & Creelman, 2005). An ROC curve illustrates how performance varies with confidence. Consider the left panel of Figure 3. The proportion of hits on target-present trials and false alarms on target-absent trials at Confidence Level 6 (definitely present) averaged across participants was used to plot the leftmost point for each condition. Data from Confidence Levels 5 and 6 were combined to create the next point. This procedure continued until all confidence levels were included, which led to 100% hits and 100% false alarms, that is, the upper right corner point. A continuous curve can then be used to describe these data. Both the raw data and fit curves for each emotion condition are shown in the left panel of Figure 3. Note that, although Figure 3 shows data averaged within each emo-
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tion condition, all analyses below were performed at the individual subject level. At a coarse level, both discriminability and bias can be visually determined from Figure 3. The farther the curve is from the diagonal, the higher discriminability. The ROC curve for an individual who is guessing will approximate a straight line from the lower left to upper right corner. For an individual with perfect performance, the ROC curve will pass through the upper left corner. Bias manifests as a shift in points along the ROC curve. A tendency to say that a target is not present will cause a leftward shift in the points. A tendency to say that a target is present will cause a rightward shift. First, consider bias. More formally, bias for each emotion condition for each confidence level i was measured by calculating the criterion value, ci, for each subject in each emotion condition, as described in Macmillan and Creelman (2005).2 The criterion specifies how much evidence is needed to respond that a target is present at a particular confidence level. That is, a higher criterion results in an increase in not-present responses, which, as described previously, would manifest as points moving to the left in an ROC curve. To assess whether there were significant differences in bias across emotion conditions, for each ci, we performed an ANOVA with emotion condition as a between-subjects factor. The effect of emotion condition on c was not significant for any of these tests: for c1, F(2, 105) ⫽ 0.33, p ⫽ .72, MSE ⫽ 0.10, 2 ⫽ .010; for c2, F(2, 105) ⫽ 0.24, p ⫽ .78, MSE ⫽ 0.05, 2 ⫽ .009; for c3, F(2, 105) ⫽ 0.16, p ⫽ .85, MSE ⫽ 0.03, 2 ⫽ .007; for c4, F(2, 105) ⫽ 0.74, p ⫽ .48, MSE ⫽ 0.19, 2 ⫽ .011; for c5, F(2, 105) ⫽ 1.59, p ⫽ .21, MSE ⫽ 0.53, 2 ⫽ .021. Thus, there is no indication that bias varied across these emotional states. This result is manifested in Figure 3 by the relatively tight grouping of data points across emotion conditions. Discriminability was measured by calculating Az, which is a measure of the area beneath the ROC curve (Macmillan & Creelman, 2005).3 Recall that the more the ROC curve moves into the upper left corner, the better performance. Thus, the area under the curve, and therefore Az, increases with performance. Because it makes no assumptions concerning the standard deviation of the decision variable (Macmillan & Creelman, 2005), Az has an advantage over the traditional measure of discriminability, d=, for data in which the underlying distribution is unknown, such as these data. An ANOVA was used to assess whether or not there were any significant differences in discriminability across emotion conditions. There was a significant effect of emotional state, F(2, 105) ⫽ 3.59, p ⫽ .03, MSE ⫽ 0.01, 2 ⫽ .064. Planned contrasts revealed that anxious, t(58.78) ⫽ 2.85, p ⫽ .006, d ⫽ 0.68, but not angry, t(66.89) ⫽ 1.19, p ⫽ .24, d ⫽ 0.29, participants were better able to discriminate target-present and target-absent trials than calm 1
Welch’s t tests were used here and throughout. The criterion for each confidence level i is computed as the average of the z scores for the hit, Hi, and false alarm, Fi, rates at that level: ci ⫽ ⫺0.5 ⫻ [z(Hi) ⫹ z(Fi)]. 3 Az was calculated by the following formula: Az ⫽ (da/公2), where is the cumulative normal, da ⫽ 公[2/(1 ⫹ s2)] ⫻ [z(H) ⫺ sz(F)], H is the hit rate, F is the false alarm rate, and s is the ratio of the standard deviations of the noise and signal distributions. 2
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Figure 2. Emotion manipulation check. (Left) Experiment 1. (Right) Experiment 2. Error bars are betweensubjects standard errors.
participants.4 This is the key result5: Only anxious participants showed an increase in discriminability.
Discussion Anxious, angry, and calm participants were asked to determine whether a target was present in noise. Signal detection theory was used to establish whether any effects of emotional state were due to the ability to better discriminate target-present and target-absent trials or due to an overall decisional bias to respond target-present or target-absent. Relative to calm participants, discriminability was enhanced for anxious, but not angry, participants. Emotional state had no significant effect on bias. These results suggest that uncertainty increases the ability to discriminate stimuli. The effect of emotional state on discriminability in this task may have partially been due to the stimulus class. In particular, because the target stimuli were faces, it is unclear whether this effect extends beyond social stimuli. Socially anxious people are more likely to interpret nonthreatening faces as threatening (Heuer, Lange, Isaac, Rinck, & Becker, 2010). Furthermore, high trait anxiety individuals require shorter stimulus exposure times to become aware of threatening face targets in their stimuli (Ruderman & Lamy, 2012). It is possible, then, that anxious participants were uniquely skilled at detecting face targets. To extend this idea to nonface stimuli, Experiment 2 replicated Experiment 1, but using houses, rather than faces, as targets.
69 calm). To increase the interpretability of a potential null result, we selected the sample size via a power analysis. Using the effect size on Az in Experiment 1 and Cohen’s f of 0.22, this sample size resulted in a power of 0.75. Materials. The materials were prepared as in Experiment 1. The only difference was that three front, close-up views of houses (taken off the Internet) were used as stimuli. Houses have been used as stimuli in other detection experiments (e.g., Burianová, Lee, Grady, & Moscovitch, 2013). The same oval mask was used as in Experiment 1. A sample stimulus is shown in the right panel of Figure 1. Procedure. The procedure was identical to the procedure for Experiment 1.
Results The analysis proceeded as in Experiment 1. Outlier removal. Outliers were removed using the same procedure as that in Experiment 1. In Experiment 2, participants with a contrast level above 15 were removed. Twenty-two participants were removed (13 anxious, five angry, four calm), leaving 161 participants to be used in the analyses (45 anxious, 51 angry, 65 calm). The qualitative results, however, were not affected by the specific participants removed. Manipulation check. The same three aggregate groups from Experiment 1 were used. Angry, irritated, and annoyed formed the
Experiment 2 The goal of Experiment 2 was to replicate Experiment 1 using nonsocial target stimuli to assess the extent to which the effects in Experiment 1 may be better characterized in a social cognition context. Given past research indicating differential face perception performance in anxious participants (Heuer et al., 2010; Ruderman & Lamy, 2012), it is possible that participants in an uncertain emotional state are uniquely skilled at face perception. If this is the case, the results of Experiment 1 would not be expected to extend to the house stimuli used in Experiment 2.
Method Participants. Undergraduate college students (N ⫽ 183) participated in this experiment for course credit (58 anxious, 56 angry,
4 To test the effect of outliers on the qualitative results, these planned contrasts were also run removing none and nine (the next natural cutoff by inspection) of the participants with the highest contrast. In both cases, there was a significant (p ⬍ .05) effect of anxiety, but not anger. 5 To assess the effect of a general tolerance of uncertainty on detection, we measured a series of personality measures (locus of control, Rotter, 1966; need for structure, Neuberg & Newsom, 1993; and need for closure, Kruglanski et al., 1997) after the emotion manipulation check. To test for a relationship between these scales and the signal detection measures, we regressed Az and each of the five ci separately on these personality scales, for a total of six regressions. To correct for multiple comparisons, we set a Bonferroni-corrected alpha level at .008. Need for structure had a significant effect on bias at c1 ( ⫽ .03, p ⫽ .007). There was a marginal effect of locus of control on bias at c1 ( ⫽ ⫺.03, p ⫽ .008). No other effects were significant. Note, however, that the staircase procedure may reduce effects due to personality. Thus, future research is needed to more directly measure the effect of personality on detection performance.
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EFFECT OF EMOTION
Figure 3.
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Raw data and receiver operating characteristic curves. (Left) Experiment 1. (Right) Experiment 2.
angry group (␣ ⫽ .827). Anxious, worried, and nervous formed the anxious group (␣ ⫽ .838). Calm and relaxed formed the calm group (␣ ⫽ .792). The results are presented in the right panel of Figure 2. An ANOVA was performed, with emotion condition as a between-subjects variable and the reported emotion groups as a within-subject variable. There was a main effect of reported emotion group, F(2, 360) ⫽ 30.77, p ⬍ .001, MSE ⫽ 1.10, p2 ⫽ .115. There was no main effect of emotion condition (particular emotional states were not more intense than others), F(2, 180) ⫽ 2.77, p ⫽ .065, MSE ⫽ 0.68, p2 ⫽ .007. There was a strong interaction of reported emotion group and emotion condition, F(4, 360) ⫽ 63.90, p ⬍ .001, MSE ⫽ 1.10, p2 ⫽ .351. Planned contrasts indicated that emotions in the angry group were rated highest by participants in the angry condition, t(96.84) ⫽ 9.68, p ⬍ .001, d ⫽ 1.61; emotions in the anxious group were rated highest by participants in the anxious condition, t(94.30) ⫽ 7.85, p ⬍ .001, d ⫽ 1.34; and emotions in the calm group were rated highest by participants in the calm condition, t(147.33) ⫽ 10.29, p ⬍ .001, d ⫽ 1.56. Furthermore, emotions in the angry group were rated higher than emotions in the anxious or calm groups among participants in the angry condition, t(113.54) ⫽ 7.87, p ⬍ .001, d ⫽ 1.27; emotions in the anxious group were rated higher than emotions in the angry or calm groups among participants in the anxious condition, t(103.28) ⫽ 4.18, p ⬍ .001, d ⫽ 0.698; and emotions in the calm group were rated higher than emotions in the angry or anxious groups among participants in the calm condition, t(113.51) ⫽ 16.99, p ⬍ .001, d ⫽ 2.69. Signal detection analysis. The ROC analyses are provided in the right panel of Figure 3. All five ci and Az were determined for each participant. An ANOVA was performed with emotion condition as the between-subjects factor and each ci as the dependent variable. As in Experiment 1, the effect of emotion condition on c was not significant for any of these tests: for c1, F(2, 158) ⫽ 0.71, p ⫽ .49, MSE ⫽ 0.27, 2 ⫽ .009; for c2, F(2, 158) ⫽ 0.61, p ⫽ .55, MSE ⫽ 0.14, 2 ⫽ .008; for c3, F(2, 158) ⫽ 1.49, p ⫽ .23, MSE ⫽ 0.29, 2 ⫽ .019; for c4, F(2, 158) ⫽ 1.26, p ⫽ .29, MSE ⫽ 0.38, 2 ⫽ .016; for c5, F(2, 158) ⫽ 0.91, p ⫽ .41, MSE ⫽ 0.43, 2 ⫽ .011. Replicating Experiment 1, there was no effect of emotional state on bias.6 An ANOVA was used to assess whether or not there were any significant differences in discriminability across emotion condi-
tions. The effect of emotional state was not significant, F(2, 158) ⫽ 0.045, p ⫽ .96, MSE ⫽ 0.009, 2 ⫽ .0006. Inspection of Figure 3 shows no hint of differences across emotional states. Planned contrasts using calm as a control confirmed this finding for anxious, t(97.75) ⫽ 0.14, p ⫽ .89, d ⫽ 0.027, and angry, t(112.77) ⫽ 0.30, p ⫽ .76, d ⫽ 0.056, participants. Emotional state did not affect discriminability. Given the difficulty of arguing from a null result in a standard ANOVA (Rouder, Speckman, Sun, Morey, & Iverson, 2009), we computed a Bayes factor to determine the strength of any positive evidence for the null hypothesis in the ANOVA using the BayesFactor package in R (see Rouder, Morey, Speckman, & Province, 2012, for methods of computation). A Bayes factor is a ratio of the probability of the data given the alternative model over the probability of the data given the null model. Bayes factors greater than 10.00 indicate strong evidence for the alternative model, and factors less than 0.10 indicate strong evidence for the null model (Jeffreys, 1961). The Bayes factor was B10 ⫽ 0.065, indicating strong evidence for a null effect of emotional state on discriminability.
Discussion Consistent with Experiment 1, no significant effects of emotional state on bias were found. In contrast to Experiment 1, however, no significant effects of emotional state on discriminability were found. That is, the effect of emotional state on face detection found in Experiment 1 did not extend to the nonsocial object detection task of Experiment 2. An additional Bayesian analysis supported the null effect. Together, these findings suggest that the effects of emotional state on perceptual ability in visual detection are limited to certain stimulus classes, perhaps potentially threatening stimuli or social stimuli such as faces.
General Discussion Past research has demonstrated that uncertain emotion states, such an anxiety, can have an effect on performance in visual 6 The same three personality measures were administered in Experiment 2 (see footnote 5). The only significant relationship between the personality scales and signal detection measures was between need for closure and c1 ( ⫽ .01, p ⫽ .002). No other effects were significant at the .008 level.
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detection tasks. Both increased detection of targets (Simonov et al., 1977) and increased false alarms (Whitson & Galinsky, 2008) have been reported. As these two measures tend to covary, it is unclear whether these results are due to changes in discriminability, the ability to detect a target, and/or bias, a tendency to respond in a particular way. By framing these effects in a signal detection perspective, the present study offers new insight into the underlying cause of these effects. Participants were shown visual noise with or without embedded targets and were asked to determine whether a target was present in each. Participants were either in an anxious, angry, or calm emotional state. Anxiety is an uncertain emotional state (Jovic, 2008; Koerner & Dugas, 2006) and anger is matched on many features of anxiety (e.g., high arousal and negative valence), but is not uncertain (Lerner & Keltner, 2001). In Experiment 1, participants in an anxious emotional state were significantly better at detecting the presence of faces. When houses were used as the target in Experiment 2, no differences in discriminability were found across emotional states. Neither experiment found an effect of emotional state on bias. These results suggest that anxiety acts, not by changing a tendency to declare that a target is present, but by increasing the ability to detect targets; however, the effect of anxiety on visual perception appears to be restricted to certain stimulus classes, such as threatening stimuli or social stimuli such as faces. Given that otherwise nonthreatening face stimuli tend to be perceived as threatening by anxious individuals (Heuer et al., 2010), it is plausible that participants in an anxious emotional state exhibited the same effect. This research is then in line with the suggestion that the effect of an uncertain emotional state on visual perception might be to enhance threat detection (e.g., Bishop et al., 2004; Cornwell et al., 2012; Eysenck et al., 2007). Such increased threat detection would lead to heightened discriminability when the targets were faces (Experiment 1), but not houses (Experiment 2). The present study represents a step toward uncovering the perceptual and motivational effects of emotional state on visual detection. By measuring effects of both discriminability and bias on the processes involved in visual detection, signal detection analysis can provide valuable insights into the relationship between emotional state and visual detection.
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Appendix Initial Emotion-Induction Instructions In the next task we are interested in studying people’s memories for certain types of events. You will be asked to recall a specific type of event and then to describe it as vividly and in as much detail as possible using the paper and pencil provided by the experimenter. You’ll have a few minutes to work on this task before you proceed to other tasks. We do not expect you to have finished writing before the time period elapses. Rather, you’ll be asked to continue describing this event at different times in the experiment. Please pick up roughly where you ended your description the last time. Please take a moment to remember a time that you felt [VERY ANXIOUS/VERY ANGRY/CALM and RELAXED]. When choosing this memory, make sure you think of a memory that represents [anxiety/anger/calmness] only. Do not pick a memory in which you felt [anxious and angry or anxious and sad/angry and anxious or angry and sad/calm and sad, or calm and joyful]. Also, try to pick an incident in which you were [anxious/angry/calm] because of something that happened to you directly; do not describe a time when you were [anxious/angry/calm] because of something that happened to another person. When you have recalled this memory, focus on it for a few minutes so that you have a vivid recollection of the events in-
volved. Take a minute to experience all the feelings that you experienced at that time. Once you have done this, please describe the memory in as much detail as you can. Remember, you probably will not have time to finish the description right now, but we will return to this task later in this session.
Emotion-Induction Reinstatement Instructions We would now like you to continue describing the memory of a time you were [VERY ANXIOUS/VERY ANGRY/CALM and RELAXED]. So please, once again, take a moment to remember the time that you were [VERY ANXIOUS/VERY ANGRY/ CALM and RELAXED]. When you have recalled this memory again, focus on it so that you have a vivid impression of the events involved. Take a minute to experience the feelings you felt at that time. Once you have done this, please continue to describe the memory in as much detail as you can, picking up roughly where you left off. Received August 4, 2014 Revision received January 15, 2015 Accepted May 2, 2015 䡲
