MOOD AND ATTENTIONAL CONTROL Mood effects

MOOD AND ATTENTIONAL CONTROL 1 RUNNING HEAD: MOOD AND ATTENTIONAL CONTROL

Mood effects on attentional control: A preregistered replication study and critical analysis

Helen Tibboel

Institute of Psychology Erasmus University Rotterdam

mailing address:

Helen Tibboel Institute of Psychology Erasmus University Rotterdam Burgemeester Oudlaan 50 3062 PA Rotterdam The Netherlands Email: [email protected]

MOOD AND ATTENTIONAL CONTROL 2 Abstract In a widely cited paper, Jefferies, Smilek, Eich, and Enns (2008) report a study in which they manipulated participants’ mood and examined the effects of this manipulation on their performance on the Attentional Blink (AB; Raymond, Shapiro, & Arnell, 1992) task. Their results revealed an interaction between emotional valence and arousal: attentional control of participants who experienced a negative mood with low arousal (i.e., sadness) was best, whereas it was worst for participants who experienced a negative mood with high arousal (i.e., anxiety). Performance for participants who were in a positive mood, either with low arousal (i.e., calmness) or high arousal (i.e., happiness) had intermediate scores. In this study, I examined the replicability of this effect and performed additional analyses to investigate the extent to which this effect is due to perceptual or attentional processes and to examine the role of distraction on AB performance. Importantly, the results showed that the crucial interaction between emotional valence and arousal did not reach significance. This could be due a diversity of factors that are addressed in the discussion.

MOOD AND ATTENTIONAL CONTROL 3 1. Introduction Mood states are widely assumed to affect a range of cognitive processes (e.g., Ashby, Ihssen, & Turken, 1999; Chajut & Algom, 2003; Compton, Wirtz, Pajoumand, Claus, & Heller, 2004; Dreisbach & Goschke, 2004; Gasper, 2004; Gasper & Clore, 2002; Johnson, Waugh, & Fredrickson, 2010; Martin & kerns, 2011; Rowe, Hirsh, & Anderson, 2007; Van Wouwe, Band & Ridderinkhof, 2011). More specifically, several studies have focused on the influence of mood on attention. For instance, happy moods are generally assumed to broaden attentional processing (e.g., Fredrickson, 2001; Fredrickson & Branigan, 2005), whereas sad moods have the opposite effect (e.g., Fredrickson, 2001). However, other researchers have failed to find mood effects on attentional processing (e.g., Bruyneel et al., 2013; Finucane, Whiteman, & Power, 2010). These discrepancies suggest that an effort should be made to critically examine and replicate studies that examine mood effects on attention. The current study focusses on selective temporal visual attention, examined with the attentional blink (AB) task (Raymond, Shapiro, & Arnell, 1992). In this task, participants are presented with a rapid serial visual presentation (RSVP) stream consisting of a range of stimuli. Within the stream, two masked targets are embedded (T1 and T2) that need to be reported at the end of each trial. Crucially, the temporal interval or “lag” between T1 and T2 is manipulated. Commonly, participants perform well on the T1 identification task, but performance on the T2 task depends on the lag between the targets. When this lag is short (i.e., less than approximately 500 ms), T2 performance is hampered, but it improves as the lag between the two targets increases. The performance deficit on short lags is referred to as the Attentional Blink effect. There are different theories regarding the cause of this effect, in which the common idea is that our attentional resources are not sufficient to deal with all the stimuli that are presented within the AB sequence. This results in competition in early, encoding stages of attention (e.g. Chun & Potter, 1995; Jolicoeur & Dell’Acqua, 1998; Raymond et al., 1992; Shapiro, Raymond, & Arnell, 1994). Therefore, little attention is left to process T2 when the lag between two targets is short, causing detriments in T2 performance. As the lag increases, attentional resources recover, and T2 performance is improved.

MOOD AND ATTENTIONAL CONTROL 4 Olivers and Nieuwenhuis (2005; 2006) suggested that this limitation is not structural, but is instead due to poor allocation of attentional resources. According to their overinvestment hypothesis, the AB effect is due to an overinvestment of attentional resources into processing of T1. This leaves insufficient resources to also process T2 when it is presented quickly after T1. In order to examine this, they altered participants’ mental state by providing them with distraction and examined effects of this manipulation on AB performance. Olivers and Nieuwenhuis (2005) found that participants who listened to an irrelevant beat and participants who thought about their holiday or the groceries needed for a dinner with friends showed a smaller AB effect (i.e., improved T2 accuracy on shorter lags). Similarly, the same authors (2006) found that the AB effect was diminished for participants who were presented with affectively positive pictures during the AB task. Olivers and Nieuwenhuis reasoned that the divided attentional state they induced in their experiments lead to less over focusing on T1, leaving more attentional resources to process T2 (see also Arend, Johnston, & Shapiro, 2006). Interestingly, Olivers and Nieuwenhuis linked their findings to the literature on mood effects on attention: their manipulations were likely to induce positive moods, which are known to broaden the attentional focus (e.g., Fredrickson, 2001). Previous studies had already suggested that mood influences the AB effect. For instance, Arend and Botella (2002) found a smaller AB effect in anxious participants compared to controls, when an emotional T1 stimulus was presented, whereas Rokke, Arnell, Koch, and Andrews (2002) found a longer-lasting AB effect in moderately to severely dysphoric participants compared to mildly dysphoric and non-dysphoric participants. However, the impact of arousal and valence (and possible interactions between these two dimensions) was never examined directly, until the seminal study of Jefferies, Smilek, Eich, and Enns (2008). In their study, they manipulated participants’ mood along two dimensions: affective valence and arousal. Participants could receive one of four mood induction procedures (MIPs), aimed to instate a sad (low arousal, negative affect), anxious (high arousal, negative affect), calm (low arousal, positive affect), or happy (high arousal, positive affect) mood. Participants then performed an attentional blink (AB) task

MOOD AND ATTENTIONAL CONTROL 5 based on the original task developed by Raymond et al. (1992). Results indicated that participants who were in a sad mood were best at identifying T2 (i.e., the difference between T1 and T2 performance on short lags was smaller), whereas participants who were in an anxious mood performed worst, and performance of participants who were in a happy or calm mood was intermediate. The current study has four aims. First, I examined the replicability of the study of Jefferies et al. Clearly, their study provided new and valuable insights in the interaction between different emotional dimensions and cognitive processes, which is likely the reason why this study has a large impact on the literature: it is widely cited (Web of Science reports 83 citations on 19 December 2016). However, as mentioned above, manipulations of mood or mental states do not always result in clear attentional effects (e.g., Bruyneel et al., 2013). Importantly, other studies suggest that attentional processing is improved in anxious participants (depending on the type of T1 target; Arend & Botella, 2002) and impaired (i.e., a larger and longer lasting AB effect) in moderately to severely dysphoric participants (Rokke et al., 2002). Even though these studies examined personality traits and not mood states, it is interesting to note that these effects are opposed to the effects reported by Jefferies et al. It is therefore important to verify whether this effect is replicable. In the current study, I therefore performed an exact replication of the study of Jefferies et al., but with a larger sample. The study was originally part of the Reproducibility Project of the Open Science Collaboration (2015), but was not included in the final manuscript.1 Second, Jefferies et al. tested five groups of participants: four groups who received MIPs to induce an anxious, calm, happy, or sad mood; and one control group who did not receive an MIP. For their AB analyses, however, Jefferies et al. re-assigned participants to groups on the basis of their ratings. This means that each of the conditions could consist of participants who were assigned to very different manipulations (i.e., one of the four MIPs or no MIP at all), eliminating random

1

Because of several practical problems (e.g., many participants were not eligible for the experiment because of high BDI scores) data collection lasted longer than anticipated, making it impossible to meet the deadlines for the OSF paper.

MOOD AND ATTENTIONAL CONTROL 6 assignment and possibly confounding effects of personality characteristics with effects of moodstates. For instance, participants with high trait anxiety are likely to end up in the “anxious” group regardless of the MIP they were exposed to. If the “anxious” group in Jefferies’ study consisted mainly of participants with high trait anxiety, differences in attentional processing is not likely to be due to temporary mood states, but to personality traits instead. I therefore re-analyzed the data on the basis of the condition participants were originally assigned to, excluding those participants for whom the MIP was unsuccessful (i.e., participants for whom mood-ratings were not in line with the MIP they received). Third, it is important to note that Jefferies et al. report only a significant interaction effect of valence and arousal on the difference between T1 and T2 performance on Lag 2 and Lag 4, but not a three-way interaction between arousal, valence, and the temporal lag between T1 and T2. In other words, they do not report evidence that the AB effect (i.e., the effect of the temporal lag between T1 and T2 on T2 performance) was diminished for participants who were in a particular mood. This suggests that the conclusion that MIPs affect attentional processing might be premature. For instance, it is possible that T2 performance was consistently poor, even when attentional resources were no longer depleted (i.e., at longer lags). In this case, it seems unlikely that the poor performance is due to attentional limitations. Alternatively, the effect could be the result of impairments in perception or working memory. The analyses that were performed by Jefferies et al. thus only suggest that there are differences in T2 identification, but it is unclear what cognitive processes underlie this effect. In order to check whether Jefferies et al.’s conclusion regarding the effect of mood on attentional processes was valid, I performed additional analyses. Besides replicating the analyses of Jefferies et al. that focused on the effect of the mood manipulations on the difference in T1 and T2 performance on short lags, I also explored the interaction between mood and lag, including all the lags that were used in the design. Fourth, I examined the effect of distraction. Even though Olivers and Nieuwenhuis (2006) hypothesize that there is a link between participants’ mood and attentional overinvestment, there is

MOOD AND ATTENTIONAL CONTROL 7 no clear-cut evidence for this. The previously reported diminished AB effect for participants who listened to music, who ruminated, or who were presented with positive pictures (Olivers & Nieuwenhuis, 2005; 2006) could have been due to different factors. First, the diminished AB in “distracted” participants compared to control participants could be due merely to changes in participants’ mood and not to distraction per se. Second, it could be due merely to the distraction that was present in the experimental conditions, regardless of the extent to which the distraction could be considered as “fun”. Third, it could be due to an interaction between mood and distraction: possibly, diversion has positive effects on attentional control when this distraction induces specific emotions (e.g., happiness), but not when it induces other emotions (e.g., anxiety). Whereas the ideal experiment to examine separate effects of mood and distraction would be to manipulate mood while controlling for distraction in one condition, and to manipulate distraction while controlling for mood in another condition, the design of Jefferies et al. does allow for exploring the hypothesis that distraction in itself (regardless of mood) diminishes the AB effect. I examine this by comparing the control condition (in which participants did not receive an MIP and did not listen to music during the experiment) with the experimental conditions (in which participants did receive an MIP and listened to music during the experiment), a comparison that Jefferies et al. did not make. 2. Method I used the same method as Jefferies et al., with the exception that the instructions were provided in Dutch, as I tested native Dutch speakers at Ghent University. Second, participants were seated in a regular comfortable chair instead of a reclining lounge chair. The protocol can be found on the OSF website, https://osf.io/g67tv/ 2.1. Participants I aimed to obtain a sample of at least 180 participants to obtain 80% power to detect the crucial interaction between valence and arousal on the difference between T1 and T2 performance on Lag 2 and Lag 4 of Jefferies et al., F(1, 92) = 4.05, MSE = 144.58, η2p = .04. In line with their study, I planned to exclude: anyone with a score of 15 or higher on the Beck Depression Inventory (BDI), or a

MOOD AND ATTENTIONAL CONTROL 8 score above 0 on the question regarding suicidal thoughts; anyone who did not have normal or corrected-to-normal vision; anyone with an accuracy rate of less than 75% on T1 on average or with an accuracy rate of less than 75% on T2 at the longest lag in the AB task. Because Jefferies et al. did not report how many participants were lost due to high BDI scores, I aimed to recruit 200 participants in order to achieve a final sample of 180 participants. I recruited participants via the faculty of psychology and educational sciences experimentrecruitment website of Ghent University, and offered a payment of 7,50 euro. 196 participants signed up for the experiment. Of these participants, 24 (12.24%) scored too high on the BDI to be eligible to participate. They were thanked, debriefed, and received their payment. Thus, a sample of 172 participants performed the experiment. For three participants, mood ratings were missing and for three more participants, data were missing due to experimenter error. The 166 participants that were left were randomly assigned to one of five groups: 34 were in the “anxious” group, 31 were in the “calm” group, 35 were in the “happy” group, 33 were in the “neutral” group, and 33 were in the “sad” group. In the analysis stage, I excluded the data of another additional nine participants (one in the anxious group, three in the calm group, and five in the happy group). They either identified T1 correctly on less than 75% of trials and/or identified T2 presented at Lag 8 correctly on less than 75% of trials. Thus, the final sample consisted of 157 participants. The anxious, sad, and neutral group each consisted of 33 participants, the calm group consisted of 28 participants and the happy group consisted of 30 participants (see also Table 2). 2.2. Stimuli and materials The materials can be found on the Open Science Framework (OSF) website https://osf.io/g67tv/ and consist of an exact translation of the materials used by Jefferies et al. The music selection and the instructions for the mood induction can also be found there. I implemented the AB task using E-prime version 2.0 (Schneider, Eschman, & Zuccolotto, 2002a, 2002b). The task was presented at a viewing distance of approximately 57 cm. Each trial started with the presentation of a white fixation cross, subtending 0.25ᵒ by 0.25ᵒ. Distractor items were digits (0-9) and target

MOOD AND ATTENTIONAL CONTROL 9 items were letters of the alphabet (excluding I, O, Q, and Z), subtending 0.09ᵒ vertically. The luminance of all items was approximately 90 cd/m2, and the luminance of the background was 2.3 cd/m2. 2.3. Procedure At the start of the session, participants performed 20 practice trials of the AB task. Participants were required to press the spacebar to start each trial, and then the RSVP stream was presented. The sequence started with a minimum of 8 and a maximum of 14 distractor stimuli before the T1 stimulus was presented. The number of pre-T1 distractors was randomly determined. T2 was presented 2, 4, or 8 lags after T1. A single digit was presented at the end of the trial, to mask T2. After this, they were asked to rate their mood on a 9*9 grid (i.e., a baseline mood assessment). One axis represented the arousal dimension (high arousal at the top of the grid, low arousal at the bottom) whereas the other represented the valence dimension (extremely unpleasant on the right and extremely pleasant on the left). Participants were verbally explained what the dimensions meant, and they were told that they would be asked to rate their mood six times during the experiment. Subsequently, participants who were assigned to one of the mood induction groups were given headphones and were instructed to keep them on during the remainder of the experiment. They were asked to convoke a specific mood during the experiment, by listening to classical music and by recalling specific emotional memories or by imagining emotional events (e.g., an important exam; the death of a loved one; a personal triumph; a lazy summer’s day). Participants in the control condition were not given headphones and were instructed to rest during the interval in which participants in the experimental conditions performed the mood induction. After the mood induction (or the rest period), participants were asked to rate their mood for a second time. This was followed by the AB task, during which participants in the experimental conditions continued to listen to the music. After 80 AB trials, the experiment paused and participants were asked to rate their mood for a third time. This was followed by a 5 minute break in which participants were asked to again use the music and their memories or imagination to induce

MOOD AND ATTENTIONAL CONTROL 10 the mood they were assigned to capture. After this, they again rated their mood, followed by another 80 AB trials. Subsequently, participants rated their mood for a final time and provided ratings regarding the extent to which they genuinely had experienced their mood, on a 10-point scale. 2.4. Analytic strategy I performed different types of analyses for this study. First, I performed the preregistered confirmatory analyses, in which I used the same analytic strategy as Jefferies et al. After performing these analyses, I also examined the effect of lag and effects of distraction. Finally, I performed similar analyses in which participants’ group assignment was based on the manipulation they underwent, excluding participants for whom the manipulation was not successful. 2.5. Results The data can be found at the OSF website, https://osf.io/uxvqb/ 2.4.1. Confirmatory analyses In these preregistered analyses, I used the same analytic method as Jefferies et al. First, to examine the effectiveness of the manipulation, I compared participants’ baseline mood ratings with their mean mood ratings, using t-tests. In line with the procedure of Jefferies et al, before I started the analyses, I assigned all participants (including participants in the neutral group) to the four mood groups (anxious, calm, happy, or sad) on the basis of their mean ratings (i.e., not on the mood induction they received). It was possible to give neutral ratings (i.e., a 5 on the mood grid), and because Jefferies et al. did not report how they handled such ratings, I proceeded as follows: if participants gave a neutral score on one dimension but not on the other, and the score on the other dimension was in line with the mood induction they received, they were assigned to the group for which they originally received the mood induction (i.e., if participants scored high on arousal but neutral on valence, and they received an “anxiety” induction, they were assigned to the “anxious” group; if participants scored high on arousal but neutral on valence, and they had received a “happy” induction, they were assigned to the “happy” group). Data were not included if: participants scored

MOOD AND ATTENTIONAL CONTROL 11 neutral on one dimension and their score on the other dimension was not in line with the mood induction they received (i.e., they received an “anxiety” induction and scored low on arousal and neutral on valence or neutral on arousal but high in valence); participants scored neutral on both dimensions; participants did not receive a mood induction (i.e., they were in the “neutral” condition) and their ratings were neutral on one or both dimensions. I then performed the AB analyses. First, I performed a repeated measures ANOVA with group (happy, calm, sad, and anxious) as a between-subjects factor and target (T1 and T2) and lag (2, 4, and 8) as within-subjects factors. These analyses can be found in Appendix A. I also performed two separate ANOVAs on the T1 and T2 data. For the T1 data, I performed a repeated measures ANOVA with arousal (high and low) and valence (positive and negative) as between-subjects factors, and lag (2, 4, and 8) as a within-subjects factor. For the T2 analyses, I used the difference between T1 and T2 performance as a dependent variable. I performed a repeated measures ANOVA with arousal (high and low) and valence (positive and negative) as between-subjects factors. Lag was used as a withinsubjects factor, but in line with Jefferies et al., only lag 2 and 4 were used because these were “the lags at which performance differences between the targets were greatest” (Jefferies et al., 2008, p. 293). Furthermore, I performed separate ANOVAs for the negative and positive valence groups, to examine whether there was a significant arousal effect (i.e., an interaction between lag and arousal) for the negative valence groups (and not for the positive valence groups), as reported by Jefferies et al. 2.4.1.1. Mood assessment In line with Jefferies et al., I recoded scores to -4 to +4. Overall, participants’ baseline ratings showed they were in a somewhat positive mood, M = 1.43, SE = .11, with a neutral arousal level, M = -.48, SE = .15. Jefferies et al. report the mean group ratings based on the type of mood induction they underwent, which I will do here as well. After the mood induction, participants’ moods changed in the desired direction. In the anxious group, participants’ mood became more negative, t(32) = 6.25, p < .001, d = 1.09, and more aroused, t(32) = 2.33, p < .05, d = .41. In the calm group, participants’

MOOD AND ATTENTIONAL CONTROL 12 mood became significantly more positive, t(27) = 3.29, p < .005, d = .63, and they became less aroused, t(27) = 7.23, p < .001, d = 1.36. In the happy group, participants’ mood became more positive, t(29) = 5.12, p < .001, d = .99, and more aroused, t(29) = 2.69, p < .05 d = .33. In the sad group, participants’ mood became more negative, t(32) = 9.03, p < .001, d = 1.57, and less aroused, t(32) = 3.00, p < .01, d = .55. Means can be found in Table 1. Overall, participants rated their mood as “genuine”, M = 8.05, SE = .11, although 15 participants declined to answer this question (excluding these participants from the analyses did not affect the results). Mean scores did not differ significantly across conditions based on the mood induction participants received, F < 1. Table 2 shows the number of participants who gave mood ratings that were consistent with the mood induction they received. It also shows which groups participants who did not respond consistently were assigned to. The means for each group can be found in Table 3. On average, the anxious group had a mean valence score of M = -1.26, SE = .16, and had a mean arousal score of M = .83, SE = .16. The calm group had a mean valence score of M = 1.74, SE = .15, and a mean arousal score of, M = -1.90, SE = .15. The happy group had a mean valence score of M = 1.97, SE = .19, and a mean arousal score of, M = 1.31, SE = .14. The sad group had a mean valence score of M = -1.19, SE = .15, and a mean arousal score of, M = -1.61, SE = .21. All these ratings differed significantly from zero, ps < .001. After excluding the participants with neutral scores, the final sample used for the final AB analyses consisted of 148 participants: 23 in the anxious group, 58 in the calm group, 36 in the happy group, and 31 in the sad group. I performed additional analyses to examine group differences in mood-ratings, as it is insufficient to merely compare changes within groups (e.g., Shackman et al., 2006). I performed an ANOVA with rating time (1 to 6) and rating type (arousal and valence) as within-subjects factors, and group (anxious, calm, happy, sad) as between-subjects factors. Most importantly, this yielded an interaction between rating time, rating type, and group, F(1, 92) = 10.67, p < .001, η2p = .18. All other effects were highly significant as well, Fs > 11.01.

MOOD AND ATTENTIONAL CONTROL 13 I performed additional tests to compare each of the groups on their mean valence and arousal ratings. Most importantly, sad participants scored lower on the valence ratings than calm participants, t(87) = 12.31, p < .001, and happy participants, t(65) = 12.84, p < .001. Furthermore, anxious participants scored lower on valence than calm participants, t(79) = 11.32, p < .001, and happy participants, t(57) = 12.07, p < .05. There was no difference in valence ratings between happy and calm participants, and no difference between anxious and sad participants, ts < 1. Thus, on the basis of these ratings, the valence manipulation can be considered successful. Analyses of the arousal ratings showed that anxious participants were more aroused than calm participants, t(79) = 10.20, p < .001, and more aroused than sad participants, t(52) = 8.81, p < .001. Happy participants were more aroused than calm participants, t(92) = 14.26, p < .001, and more aroused than sad participants, t(65) = 12.10, p < .001. There was no difference between sad and calm participants, t < 1.10. However, the happy group was significantly more aroused than the anxious group, t(57) = 2.23, p < .05. Thus, overall the arousal manipulation resulted in the expected effects, except for the higher arousal levels reported by happy compared to anxious participants. Importantly, based on their mood ratings, only 90 participants out of 124 participants who received a mood induction (72.58%) were assigned to the group for which they actually received a mood induction. In the study of Jefferies, this percentage was similar (58 out of 76 participants; 76.32%). However, where most of the participants in the neutral condition were assigned to the calm condition based on their ratings in my study, most of these participants ended up in the happy group in Jefferies et al.’s study. This sample left us with 72% power to detect an effect of equal size to that which the original authors reported. It must be noted that in the current study, there was large variation in the number of participants in each group: there were 94 participants in the positive group (36 happy + 58 calm) and 54 participants in the negative group (23 anxious + 31 sad). There were 59 participants in the high arousal group (23 anxious + 36 happy) and 89 participants in the low arousal group (58 calm + 31 sad). However, in the study of Jefferies et al., there also seemed to be a comparable level of variability. They do not report how many participants were in the final groups

MOOD AND ATTENTIONAL CONTROL 14 based on the ratings, but they do report there were at least 15 participants in the anxious group, 15 participants in the sad group, 16 participants in the calm group, and 30 participants in the happy group. Thus, whereas 36% of the current sample ended up in the negative group, 39% of the sample of Jefferies et al. ended up in the negative group. 2.4.1.2. Attentional Blink task The T1 analyses yielded a significant lag effect, F(1, 144) = .85.67, p < .001, η2p = .37. The interaction between lag and valence approached significance, F(1, 144) = 3.00, p = 05, η2p= .02. Paired-samples t-tests reveal that for both the positive and the negative group, there were significant differences between performance on Lag 2 and Lag 4, t(93) = 9.12, p < .001, d = .98, and t(53) = 4.81, p < .001, d = .57, Lag 4 and Lag 8, t(93) = 2.29, p < .05, d = .34, and t(53) = 2.91, p < .01, d = .52, and Lag 2 and Lag 8, t(93) = 10.36, p < .001, d = 1.19, and t(53) = 7.69, p < .001, d = 1.12. Other effects failed to reach significance, Fs < 1.06. For the T2 analyses, the difference between T1 and T2 performance was used as dependent variable. Means can be found in Table 4. As mentioned above I only examined effects on Lag 2 and Lag 4, in line with Jefferies et al. They argued for focusing on these lags because for these lags the performance differences between the targets were greatest. Indeed, in the current study the difference between T1 and T2 performance was also larger at Lag 2 and Lag 4 than at Lag 8, as can be seen in Table 4. The ANOVA yielded a significant lag effect, F(1, 144) = 24.57, p < .001, η2p = .15, showing better performance as lag increased. There was no effect of valence, F(1, 144) = .29, p = .59, η2p = .00, nor was there an effect of arousal, F(1, 144) = .72, p = .40, η2p = .01. In contrast to the findings of Jefferies et al., there was no significant interaction between valence and arousal, F(2, 143) = .38, p = .54, η2p = .00. Whereas Jefferies et al. do not report the three-way interaction between lag, valence, and arousal, this effect approached significance in the current study, F(1, 144) = 3.15, p = .08, η2p = .02.

MOOD AND ATTENTIONAL CONTROL 15 I performed separate ANOVAs for the negative and positive valence groups. For the negative group, there was only a significant lag effect, F(1, 52) = 13.74, p < .001, η2p = .21, reflecting a smaller difference between T1 and T2 performance on Lag 4 compared to Lag 2. There was no effect of arousal, F(1, 52) = 1.07, p = .31, η2p = .02. Furthermore, the interaction between lag and arousal did not reach significance, F(1, 52) = 1.13, p = .29, η2p = .02. Independent samples t-tests to examine differences in performance on Lag 2 and Lag 4 between the anxious and sad group did not reach significance either, ts < 1.46. For the positive group, there was only a significant lag effect, F(1, 92) = 12.23, p < .001, η2p = .12, reflecting a smaller difference between T1 and T2 performance on Lag 4 compared to Lag 2. There was no significant effect of arousal, F(1, 92) = .03, p = .86, η2p = .00. There was no interaction between arousal and lag either, F(1, 92) = 2.45, p = .12, η2p = .03. Additional independent samples ttests to examine differences in performance on Lag 2 and Lag 4 between the happy and calm group also failed to reach significance, ts < 1. 2.4.2. Lag analyses and overinvestment analyses I performed additional analyses in which I included Lag 8, to specifically examine whether MIPs have an impact on the AB effect (i.e., the interaction between arousal, valence, and lag) in order to exclude non-attentional (e.g., working memory; perceptual) explanations for mood effects on T2 performance. Finally, I examined the overinvestment hypothesis. I analyzed the data on the basis of the absence or presence of distraction: whereas participants in the mood induction groups were presented with music throughout the experiment, participants in the neutral condition were not. I performed a repeated measures ANOVA with lag as a within-subjects factor and group (mood manipulation versus control) as a between-subjects factor. For these analyses, I included participants with neutral or deviating ratings, as I only examined the effect of distraction, regardless of mood. On the basis of the overinvestment hypothesis, I would expect to find a smaller AB effect in the MIP conditions compared to the control condition.

MOOD AND ATTENTIONAL CONTROL 16 In both analyses, I used the difference between T1 and T2 performance as a dependent variable, in line with Jefferies et al. Means can also be found in Table 5. There was a main effect of lag, F(2, 143) = 163.98, p < .001, η2p = .53, but the interaction between lag, arousal, and valence failed to reach significance, F(2, 143) = 2.42, p = .09, η2p = .02. None of the other effects reached significance either, Fs < 1. Finally, I compared all MIP groups (N = 124) with the control group (N = 33) in an ANOVA with lag as a within-subjects factor and group (MIP versus control) as between-subjects factors, in order to examine effects of distraction. Means can be found in Table 5. This yielded a main effect of lag, F(2, 154) = 115.99, p < .001, η2p = .43. The main effect of group, F = 2.98, and the interaction between lag and group did not reach significance, F < 1.03. This contrasts with the predictions of the overinvestment hypothesis. 2.4.3. Manipulation-based analyses In the confirmatory analyses, participants were assigned to different mood groups on the basis of their mood ratings. As mentioned above, this strategy can be considered problematic. I reanalyzed the data based on the original MIPs, excluding participants for whom the manipulation did not work (i.e., participants for whom their mood ratings did not align with the manipulation). In line with the previous analyses, I considered the manipulation successful is participants’ ratings on both dimensions were in line with the manipulation (e.g., a participant who received the happy MIP who gave mood ratings that were positive on both arousal and valence), or if they gave a neutral score and a score that was in line with the manipulation on the other dimension (e.g., a participant who received the “happy” MIP who gave mood ratings that were neutral on arousal and positive on valence). Participants were excluded if their ratings on one or both dimensions were in the opposite direction of what we would expect on the basis of their MIP. A repeated measures ANOVA on the ratings, with rating time and rating type as withinsubjects factors and group as between-subjects factor again yielded a significant interaction between rating time, rating type, and group, F(15, 228) = 13.15, p < .001, η2p = .34. All other effects also

MOOD AND ATTENTIONAL CONTROL 17 reached significance, Fs > 6.35. Importantly, average valence ratings were higher for happy compared to sad, t(40) = 14.91, p < .001, and anxious participants, t(37) = 14.59, p < .001. Average valence ratings were also higher for calm compared to sad, t(41) = 10.12, p < .001, and anxious participants, t(38) = 9.69, p < .001. There was no difference in valence ratings between happy and calm participants, t < 1, nor was there a difference between sad and anxious participants, t < 1. Thus, the valence manipulation had the desired effects. Furthermore, average arousal ratings were higher for anxious compared to sad, t(37) = 8.31, p < .001, and calm participants, t(38) = 10.27, p < .001. Arousal ratings were also higher for happy compared to sad, t(40) = 9.99, p < .001, and calm participants, t(41) = 11.98, p < .001. There was no difference between calm and sad participants, t < 1. However, happy participants were still more aroused than anxious participants, t(37) = 2.08, p < .05. Thus, on the basis of these group assignments, the MIPs seem to be successful in the sense that they achieved the expected differences in arousal, but not completely successful in the sense that happy participants were more aroused than anxious participants. For the AB analyses, I focus on the repeated measures ANOVA with arousal (high and low) and valence (positive and negative) as between-subjects factors, lag as a within-subjects factor, and the difference between T1 and T2 performance as a dependent variable. I included all three lags in order to examine whether the AB effect differed across conditions. Additional analyses (the ANOVA with group as a between-subjects factor and target and lag as within-subjects factors, and the ANOVAs on the T1 data) can be found in Appendix B. When I assigned participants according to this strategy, the sample was divided as follows: 18 participants in the anxious group, 22 in the calm group, 21 in the happy group, and 21 in the sad group. Note that the exclusion of participants for whom ratings deviated from their MIP resulted in a considerable smaller sample. The repeated measures ANOVA yielded a significant effect of lag, F(2, 77) = 91.94, p < .001, η2p = .54. There was also a significant interaction between lag, arousal, and

MOOD AND ATTENTIONAL CONTROL 18 valence, F(2, 77) = 4.25, p < .05, η2p = .05. No other effects reached significance, Fs < 1.38. Means can be found in Table 7. In order to get a clearer view of the interaction, I performed two separate ANOVAs for the high and low arousal groups, with lag as a within subjects factor and valence as a between subjects factor. In other words, I compared the anxious (high arousal, negative valence) and happy (high arousal, positive valence) groups within one ANOVA, and the sad (low arousal, negative valence) and calm (low arousal, positive valence) in another ANOVA. Both ANOVAs yielded significant effects of lag, Fs < 38.30. Whereas the interaction between lag and valence did not reach significance for the high arousal groups, F = 1.95, the interaction did reach significance for the low arousal groups, F(2, 40) = 3.89, p < .05, η2p = .09. In other words, these results indicate that there was no significant difference in the AB effect for the anxious and happy group, whereas there was a difference in the AB effect for the sad and calm group. However, when I performed additional t-tests to examine differences between the low arousal negative (sad) and low arousal positive group (calm) none of these tests reached significance, ts < 1.52. I also performed two separate ANOVAs for the positive and negative valence groups, with lag as a within subjects factor and arousal as a between subjects factor. In other words, I compared the anxious (high arousal, negative valence) with the sad (low arousal, negative valence) group, and the happy (high arousal, positive valence) with the calm (low arousal, positive valence) group. For both groups, this yielded significant lag effects, Fs > 37.12. There were no other significant effects for the negative groups, Fs < 1.73, suggesting there was no difference in the AB effect for the sad and anxious groups. For the positive groups, the interaction between lag and arousal approached significance, F(2, 40) = 2.62, p = .08, η2p = .06. Additional t-tests comparing the high arousal (happy) and low arousal (calm) positive groups, revealed a marginally significant difference at Lag 4, t(41) = 1.88, p = .07, d = .40. The difference between T1 and T2 was smaller for the happy compared to the calm group. 3. Discussion

MOOD AND ATTENTIONAL CONTROL 19 3.1. Effects of mood and distraction attentional processing My confirmatory analyses did not reveal the same effects as those reported by Jefferies et al. (2008). Most importantly, I could not replicate the interaction effect of valence and arousal on the difference between T1 and T2 performance for Lag 2 and Lag 4. I have found no evidence for improved processing in participants who are in a sad mood, or impaired processing in participants who were in an anxious mood. It is important to note that I did not only fail to replicate the findings of Jefferies et al., which can be argued to be driven by perceptual instead of attentional processes. I also failed to find any interaction between valence and arousal on the AB effect. I also failed to find differences in the AB effect between the different MIP groups. Furthermore, in light of Olivers and Nieuwenhuis’ (2005; 2006) overinvestment hypothesis I expected that any type of distraction diminishes the AB effect, as it would diminish the over focusing of attentional resources on T1 processing. On the basis of the literature regarding effects of mood on attentional processing, I would also expect that this effect is more pronounced in participants who are in a happy mood, because they are assumed to have a broadened scope of attention (e.g., Fredrickson, 2001). However, I found no effects of distraction, nor did I find any effects of mood, nor an interaction between distraction and mood. When I made a comparison between AB effects of the participants who received a mood induction and participants who did not, no significant effects were obtained. Furthermore, when I compared only the control condition and the group who received a happy MIP, I found no difference in AB effect. Finally, I performed additional analyses on the basis of the manipulation (i.e., groups were based on the manipulation participants received, and participants for whom the manipulation was not effective were excluded). As in the study of Jefferies et al., the mood ratings of a relatively large percentage of participants were not in line with the MIP they underwent, which led to the exclusion of 66 out of 148 participants, resulting in a large decrease in power. Nevertheless, the manipulationbased analyses showed that differences in attentional processing are likely due to superior attentional processing in happy participants compared to anxious and calm participants, and not to

MOOD AND ATTENTIONAL CONTROL 20 inferior performance by anxious participants and superior performance by sad performance, as the results of Jefferies et al. showed. 3.2. Explanations for the failure to replicate One might argue that, even though I used the same instructions (albeit that they were translated in Dutch) and music as Jefferies et al., my manipulation was less successful because a considerate number of my participants’ final group assignment was not in line with the induction that they received. However, it is important to note that the success of my manipulation was similar to that of Jefferies et al.: The percentage of participants who reported a mood that was not in line with the MIP they received was comparable in the two studies. Furthermore, the analyses of mood ratings over time show that the manipulation did affect moods significantly, again with effects that are, on visual inspection, comparable to the ones reported by Jefferies et al.2 The manipulation was also successful on the basis of between-groups comparisons: happy and calm participants indeed scored higher on valence than anxious and sad participants, and that happy and anxious participants scored higher on arousal than sad and calm participants. The current study did show that it was specifically difficult to induce an anxious mood. Only 18 out of 33 participants who were assigned to this manipulation reported a negative and aroused mood state, and arousal levels were lower in the anxious group compared to the happy group. This can be considered a problem, because in the study of Jefferies et al. impaired AB performance in the anxious group was partly responsible for the interaction between valence and arousal. However, it is important to note that in their study, high arousal (in the happy and anxious group) was associated with intermediate (in the happy group) or poor (in the anxious group) AB performance, whereas in the current study, the group that scored highest on arousal (i.e., the happy group) performed better on the AB task compared to the other groups. Thus, even if the happy and anxious group had equal arousal scores, it is improbable that this would result in the detrimental effects of arousal described by Jefferies et al. Finally, it is difficult to

2

Because Jefferies et al. do not report effect sizes and correlations between the means are not available, I could not calculate effect sizes for their ratings.

MOOD AND ATTENTIONAL CONTROL 21 draw clear conclusions regarding any differences between the effectiveness of the manipulations in both studies, as Jefferies et al. did not make between-group comparisons in arousal and valence ratings. Overall, a discrepancy between the effects of the MIPs on participants’ mood ratings in the current study and the study of Jefferies et al. seems an unlikely reason for the failure to replicate the effects of MIPs on attentional control, suggesting we should examine other explanations. First, it is important to note that MIPs are sensitive to demand effects (Westermann et al., 1996). In studies in which participants are unaware of the goals of the mood manipulation, MIPs are generally less effective. In both the study of Jefferies et al. and in my replication, participants were explicitly told that it was very important to evoke a specific mood and to keep this mood during the experimental session. It is thus possible that participants felt pressured to respond in line with the mood induction they received. Ideally, additional (e.g., physiological) measures that do not rely on self-report should be used to examine the effectiveness of MIPs. If self-report is indeed unreliable, the possibility occurs that the MIPs had different effects on the two samples. It must be noted that neither Jefferies et al., nor I, controlled for variables that might affect mood or the sensitivity to MIPs. Even though MIPs like the one used in the study of Jefferies et al. that combine music and imagination can be considered as quite efficient (Gerrards-Hesse, Spies, & Hesse, 1994; Västfjäll, 2002; Westermann, Spies, Stahl & Hesse, 1996) it is important to note several issues. Importantly, the MIP used in these studies relied largely on the emotional memories that participants were required to invoke. Some participants might be more successful in achieving this than others, and it is difficult to control for these interindividual differences. Second, individuals differ in the extent to which they are sensitive to mood inductions. For instance, extraversion and neuroticism (Larsen & Ketelaar, 1989) and personality type and emotional intelligence (Gohm, 2003; Petrides & Furnham, 2003) are known to affect the extent to which participants are able to convoke specific moods. In sum, it is possible that my sample differed from the sample of Jefferies et al. in the extent to which participants were sensitive to demand effects or to other factors that might have influenced effects of the MIPs. Finally, it is interesting to note that

MOOD AND ATTENTIONAL CONTROL 22 some MIPs are more effective in producing some types of moods whereas other MIPs are more effective in producing other moods. For instance, Gerrards-Hesse et al. (1994) suggest that MIPs in which participants view emotional movie clips might be more useful to induce elated moods, whereas MIPs in which participants imagine specific scenarios might be recommendable to induce sad moods. The MPI used in the study of Jefferies et al. might have been more successful in inducing negative moods but not necessarily the positive moods, which could explain why their manipulation did not lead to clear effects in the positively valenced conditions. Furthermore, an important theoretical issue that likely contributes to problems in replicating effects of MIPs on attentional processing is that arousal and valence might not be independent dimensions that can be manipulated orthogonally. It has been shown that there are correlations between arousal and positive affect and between arousal and negative affect (i.e., the extent to which an event is considered positive or negative depends on the arousal that one experiences; e.g., Bradley & Lang, 2000). It is possible that participants in the low arousal groups (i.e., the sad and calm groups) did not experience intense sadness or happiness, thereby limiting the effects of these MIPs on attentional processing. Finally, effects of distraction on the AB effect failed to reach significance as well. Even though Jefferies et al., proposed that effects reported by Olivers and Nieuwenhuis (2005; 2006) might (partly) be due to changes in participants’ mood, the current study fails to find evidence for this explanation. Again, there are different explanations for this null-finding. The possibility that mood interacts with distraction in a way that there is no main effect of distraction (e.g., some moods are associated with a larger AB effect, whereas others are associated with smaller AB effects, resulting in an average AB effect when groups are collapsed) is unlikely, since I failed to replicate the mood effects reported by Jefferies et al. A more plausible explanation might be that effects of distraction are subtle, and that the MIPs (a combination of listening to complex classical music while ruminating about past or hypothetical events) were too taxing for the attentional system. Recent studies have shown that overinvestment in T1 processing can be successfully achieved through other means (e.g.,

MOOD AND ATTENTIONAL CONTROL 23 meditation; Van Vugt & Slagter, 2014). As I suggested in the introduction, future research should focus on disentangling effects of distraction and mood by manipulating one of these factors while controlling for the other, and vice versa. 4. Conclusion The current study did not replicate the effects reported by Jefferies et al. I found no effects of an interaction between arousal and valence on T2 identification overall, nor did I find effects of this interaction on the AB effect. Finally, I found little evidence for an effect of distraction. The only way to draw more clear-cut conclusions regarding effects of mood on the AB is to perform large scale studies in which demand effects and inter-individual differences in a range of personality traits are taken into account, and objective measures are used to examine the effectiveness of MIPs.

MOOD AND ATTENTIONAL CONTROL 24 References Arend, I., & Botella, J. (2002). Emotional stimuli reduce the attentional blink in sub-clinical anxious subjects. Psicothema, 14, 209-214. Arend, I., Johnston, S., & Shapiro, K. (2006). Task-irrelevant visual motion and flicker attenuate the attentional blink. Psychonomic Bulletin & Review, 13, 600 – 607. Ashby, F. G., Isen, A. M., & Turken, U. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106, 529–550. Bradley, M. M., & Lang, P. J. (2000). Measuring emotion: Behavior, feeling, and physiology. In Lane, R., & Nadel, L. (Eds.), Cognitive Neuroscience of Emotion. Oxford: University Press. Bruyneel, L., Van Steenbergen, H., Hommel, B., Band, G. P. H., De Raedt, R., & Koster, E. H. W. (2013). Happy but still focused: failures to find evidence for a mood-induced widening of visual attention. Buchner, A., Erdfelder, E., & Faul, F. (1997). How to Use G*Power [WWW document]. URL http://www.psycho.uni-duesseldorf.de/aap/projects/gpower/how_to_use_gpower.html Chajut, E., & Algom, D. (2003). Selective attention improves under stress: Implications for theories of social cognition. Journal of Personality and Social Psychology, 85, 231–248. Chun, M. M., & Potter, M. C. (1995). A 2-stage model for multiple-target detection in rapid serial visual presentation. Journal of Experimental Psychology-Human Perception and Performance, 21, 109-127. Compton, R. J., Wirtz, D., Pajoumand, G., Claus, E., & Heller, W. (2004). Association between positive affect and attentional shifting. Cognitive Therapy and Research, 28, 733–744. Dreisbach, G., & Goschke, T. (2004). How positive affect modulates cognitive control: reduced perseveration at the cost of increased distractibility. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 343 – 353. Finucane, A. M., Whiteman, M. C., & Power, M. J. (2010). The effect of happiness and sadness on alerting, orienting, and executive attention. Journal of Attention Disorders, 13, 629–639.

MOOD AND ATTENTIONAL CONTROL 25 Fredrickson, B. L. (2001). The role of positive emotions in positive psychology—The broaden-andbuild theory of positive emotions. American Psychologist, 56, 218–226. Fredrickson, B. L., & Braningan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition and Emotion, 19, 313–332. Gasper, K. (2004). Do you see what I see? Affect and visual information processing. Cognition and Emotion, 18, 405–421. Gasper, K., & Clore, G. L. (2002). Attending to the big picture: Mood and global versus local processing of visual information. Psychological Science, 13, 34–40. Gerrards-Hesse, Spies, & Hesse. (1994). Experimental inductions of emotional states and their effectiveness: a review. British journal of psychology, 1, 55 -78. Gohm, C. L. (2003). Mood regulation and emotional intelligence: individual differences. Journal of personality and social psychology, 84, 594 – 607. Jefferies, L. N., Smilek, D., Eich, E., & Enns, J. T. (2008). Emotional valence and arousal interact in attentional control. Psychological Science, 19, 290 – 295. Johnson, K. J., Waugh, C. E., & Fredrickson, B. L. (2010). Smile to see the forest: Facially expressed positive emotions broaden cognition. Cognition and Emotion, 24, 299–321. Jolicoeur, P., & Dell' Acqua, R. (1998). The demonstration of short-term consolidation. Cognitive Psychology, 36, 138-202. Kuhbandner, C., Hanslmayr, S., Maier, M. A., Pekrun, R., Spitzer, B., Pastötter, B., & Bäuml, K-H. (2009). Effects of mood on the speed of conscious perception: behavioural and electrophysiological evidence. SCAN, 4, 286 – 293. Larsen, R. J., & Ketelaar, T. (1989). Extraversion, neuroticism and susceptibility to positive and negative mood induction procedures. Personality and individual differences, 10, 1221 – 1228. Martin, E. A., & Kerns, J. G. (2011). The influence of positive mood on different aspects of cognitive control. Cognition and Emotion, 25, 265–279.

MOOD AND ATTENTIONAL CONTROL 26 Olivers, C. N. L., & Nieuwenhuis, S. (2005). The beneficial effect of concurrent task-irrelevant mental activity on temporal attention. Psycological Science, 16, 265 – 269. Olivers, C. N. L., & Nieuwenhuis, S. (2006). The beneficial effects of additional task load, positive affect, and instruction of the attentional blink. Journal of Experimental Psychology: Human Perception and Performance, 32, 364 – 379. Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349, aac4716, Petrides, K. V., & Furnham, A. (2003). Trait emotional intelligence: behavioural validation in two studies of emotion recognition and reactivity to mood induction. European journal of personality, 17, 39 – 57. Raymond, J. E., Shapiro, K., & Arnell, K. M. (1992). Temporary suppression of visual processing in an RSVP task: An attentional blink? Journal of Experimental Psychology: Human Perception and Performance, 18, 849-860. Rokke, P. D., Arnell, K. M., Koch, M. D., & Andrews, J. T. (2002). Dual-task attention deficits in dysphoric mood. Journal of Abnormal Psychology, 111, 370 – 379. Rowe, G., Hirsh, J. B., & Anderson, A. J. (2007). Positive affect increases the breadth of attentional selection. Proceedings of the National Academy of Sciences of the United States of America, 104, 383–388. Schneider, W., Eschman, A., & Zuccolotto, A. (2002a). E-Prime User’s Guide. Pittsburgh: Psychology Software Tools Inc. Schneider, W., Eschman, A., & Zuccolotto, A. (2002b). E-Prime Reference Guide. Pittsburgh: Psychology Software Tools Inc. Shackman, A. J., Sarinopoulos, I., Maxwell, J. S., Pizzagalli, D. A., Lavric, A., & Davidson, R. J. (2006). Anxiety selectively disrupts visuospatial working memory. Emotion, 1, 40 – 61.

MOOD AND ATTENTIONAL CONTROL 27 Shapiro, K. L., Raymond, J. E., & Arnell, K. M. (1994). Attention to Visual-Pattern Information Produces the Attentional Blink in Rapid Serial Visual Presentation. Journal of Experimental Psychology-Human Perception and Performance, 20, 357-371. Van Vugt, M. K., & Slagter, H. A. (2014). Control over experience? Magnitude of the attentional blink depends on meditative state. Consciousness and Cognition, 23, 32- 29. Van Wouwe, N. C., Band, G. P. H., & Ridderinkhof, K. R. (2011). Positive affect modulates flexibility ä and evaluative control. Journal of Cognitive Neuroscience, 23, 524–539. Västfjäll, D. (2002). Emotion induction through music: a review of the musical mood induction procedure. Musicae Scientiae, 1, 173 – 211. Westermann, R., Spies, K., Stahl, G., & Hesse, F. W. (1996). Relative effectiveness and validity of mood induction procedures: A meta-analysis. European Journal of Social Psychology, 26, 557–580.

MOOD AND ATTENTIONAL CONTROL 28 Author note Correspondence regarding this article should be addressed to Helen Tibboel, Department of Experimental-Clinical and Health Psychology, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium. Email: [email protected]. This research was supported by grant BOF09/01M00209 of Ghent University to Jan De Houwer, whom the author thanks for his feedback and support. Furthermore, the author thanks Dr. Lisa Jefferies for her helpful instructions regarding procedural details.

MOOD AND ATTENTIONAL CONTROL 29 APPENDIX A – Confirmatory analyses with group as a between-subjects factor A repeated measures ANOVA with group (happy, calm, sad, and anxious) as a betweensubjects factor and target (T1 and T2) and lag (2, 4, and 8) as within-subjects factors yielded a significant main effect for target, F(1, 144) = 374.67, p < .001, η2p = .72, showing better performance for T1 compared to T2. There was also a main effect for lag, F(2, 143) = 283.88, p < .001, η2p = .66, showing that performance increased with lag, and an interaction between target and lag, F(2, 143) = 163.98, p < .001, η2p = .53, showing a stronger lag effect for T2 compared to T1. Whereas Jefferies et al. reported a significant interaction between target and group, I did not find such an effect, F(3, 144) = .30, p = .82, η2p = .01. None of the other effects reached significance, Fs < 1.11. Means can be found in Table 7.

MOOD AND ATTENTIONAL CONTROL 30 APPENDIX B – Manipulation-based analyses with group as a between-subjects factor I performed a repeated measures ANOVA with group (happy, calm, sad, and anxious) as a between-subjects factor and target (T1 and T2) and lag (2, 4, and 8). Means can be found in Table 8. There was a significant effect of target, F(1, 78) = 230.87, p < .001, η2p = .75, showing better performance for T1 compared to T2. There was also a significant lag effect, F(2, 77) = 164.19, p < .001, η2p = .68, showing better performance as the temporal lag between the targets increased. There was a significant interaction between target and lag, F(2, 77) = 91.94, p < .001, η2p = .54, suggesting a smaller AB effect for T1 compared to T2. Whereas there was no interaction between target and group, F (3, 78) = .81, p = .49, η2p = .03, the three-way interaction between target, lag, and condition approached significance, F(6, 156) = 1.91, p = .08, η2p = .07. For the T1 data, I performed a repeated measures ANOVA with arousal (high and low) and valence (positive and negative) as between-subjects factors, and lag (2, 4, and 8) as a within-subjects factor. This analyses yielded only a significant effect of lag, F(2, 77) = 52.92, p < .001, η2p = .40, showing that performance increased with lag. No other effects reached significance, Fs < 1.56.

MOOD AND ATTENTIONAL CONTROL 31

Baseline Group

M

1 SE

M

2 SE

M

3 SE

M

4 SE

M

5 SE

M

SE

Arousal Anxious

-.61

.30

.12

.26

.18

.30

.61

.33

.39

.33

.09

.37

Calm

-.04

.35

-1.96

.33

-2.21

.33

-1.25

.30

-2.04

.37

-1.50

.33

Happy

-.133

.26

.67

.32

1.10

.32

.87

.30

.93

.34

.93

.32

Sad

-.58

.36

-1.52

.24

-2.03

.27

-1.24

.35

-1.94

.30

-1.58

.37

Neutral

-.94

.30

-1.55

.33

-1.64

.33

-.52

.33

-1.33

.27

-.82

.32

Valence Anxious

1.21

.24

-.97

.29

-1.52

.21

-.70

.20

-1.82

.21

-.85

.30

Calm

1.25

.26

2.00

.29

2.18

.32

1.89

.25

2.21

.30

1.96

.28

Happy

1.53

.22

2.50

.15

2.53

.21

2.17

.24

2.53

.20

2.33

.21

Sad

1.70

.22

-.91

.23

-1.79

.24

-.79

.25

-1.55

.24

-.91

.25

Neutral

1.36

.24

1.45

.25

1.30

.28

1.00

.24

1.06

.31

.94

.29

Table 1. Mood ratings per mood induction group.

MOOD AND ATTENTIONAL CONTROL 32

Final group Original group

Anxious

Calm

Happy

Sad

Excluded

Total

Anxious

18

3

4

7

1

33

Calm

1

22

4

1

0

28

Happy

0

9

21

0

0

30

Sad

3

7

2

21

0

33

Neutral

1

17

5

2

8

33

Total

23

58

36

31

9

Table 2. The number of participants originally (rows) and finally (columns) assigned to each condition.

MOOD AND ATTENTIONAL CONTROL 33

Baseline Group

M

1 SE

M

2 SE

M

3 SE

M

4 SE

M

5 SE

M

SE

Arousal Anxious Calm Happy Sad

.00

.31

.48

.26

.65

.31

1.83

.21

1.17

.27

1.30

.25

-1.00

.24

-2.22

.20

-2.43

.22

-1.71

.20

-2.22

.22

-1.72

.22

.67

.24

1.22

.20

1.31

.21

1.31

.19

1.33

.20

1.28

.25

-1.19

.31

-1.65

.22

-1.81

.25

-1.13

.28

-1.97

.24

-1.81

.32

Valence Anxious

.78

.27

-1.61

.23

-2.13

.26

-.78

.21

-2.13

.26

-1.09

.28

Calm

1.71

.15

1.81

.18

1.88

.21

1.59

.18

1.74

.22

1.71

.20

Happy

1.39

.25

2.19

.21

2.03

.26

1.75

.24

1.94

.26

1.94

.25

Sad

1.42

.24

-1.16

.25

-2.06

.17

-1.23

.24

-2.06

.17

-1.48

.21

Table 3. Mood ratings per group (assignment based on ratings, not the manipulation).

MOOD AND ATTENTIONAL CONTROL 34

MOOD AND ATTENTIONAL CONTROL 35 Lag 2 M

Lag 4 SE

M

Lag 8 SE

M

SE

Anxious

.26 .03

.21 .03

.03 .01

Calm

.22 .02

.19 .02

.03 .01

Happy

.25 .04

.17 .03

.04 .01

Sad

.25 .16

.16 .02

.05 .01

Table 4. Confirmatory analyses: the mean difference in the proportion of accurate responses between T1 and T2 for each lag

MOOD AND ATTENTIONAL CONTROL 36 Lag 2 M

Lag 4 SE

M

Lag 8 SE

M

SE

MIPs

.25

.01

.19

.01

.04

.00

Control

.20

.02

.17

.02

.02

.01

Table 5. Overinvestment analyses: The mean difference in the proportion of accurate responses between T1 and T2 for each lag

MOOD AND ATTENTIONAL CONTROL 37

Lag 2 M

Lag 4 SE

M

Lag 8 SE

M

SE

Anxious

.27 .03

.24 .03

.04 .01

Calm

.22 .03

.23 .03

.04 .03

Happy

.22 .05

.15 .03

.04 .01

Sad

.27 .03

.17 .03

.05 .01

Table 6. Manipulation-based analyses: the mean difference in the proportion of accurate responses between T1 and T2 for each lag

MOOD AND ATTENTIONAL CONTROL 38

Lag 2

Lag 4

Lag 8

Target M

SE

M

SE

M

SE

Type Anxious (N = 23) T1

.87

.02

.91

.01

.93

.01

T2

.61

.03

.7

.03

.90

.02

Calm (N = 58) T1

.85

.01

.93

.01

.94

.01

T2

.63

.02

.73

.02

.91

.01

Happy (N = 36) T1

.85

.02

.92

.01

.93

.01

T2

.6

.04

.75

.03

.89

.01

.01

.94

.01

Sad (N = 31) T1

.86

.01

.92

MOOD AND ATTENTIONAL CONTROL 39 T2

.61

.03

.75

Table 7. Confirmatory analyses: the mean proportion of accurate responses for T1 and T2 for each lag.

.03

.90

.01

MOOD AND ATTENTIONAL CONTROL 40

Lag 2

Lag 4

Lag 8

Target M

SE

M

SE

M

SE

Type Anxious (N = 18) T1

.87

.02

.91

.02

.93

.01

T2

.61

.03

.67

.04

.89

.02

Calm (N = 22) T1

.83

.02

.92

.01

.94

.01

T2

.61

.03

.69

.03

.90

.02

Happy (N = 21) T1

.85

.02

.91

.02

.93

.01

T2

.63

.05

.77

.04

.89

.02

Sad (N = 21) T1

.85

.02

.91

.01

.94

.01

T2

.59

.04

.74

.04

.90

.01

MOOD AND ATTENTIONAL CONTROL 41 Table 8. Manipulation-based analyses: the mean proportion of accurate responses for T1 and T2 per group and lag

MOOD AND ATTENTIONAL CONTROL 42