3. Study 2

3.1. The present experiment

The goal of this study was to provide a conclusive test of an informational mood impact on resource mobilization as conceptualized in the MBM (Gendolla, 2000). We did this by manipulating moods’

effective diagnostic value for demand appraisals while we simultaneously manipulated the difficulty of a cognitive task to provide another type of information for evaluations of task demand. After being induced into a positive or into a negative mood, participants performed an easy or a difficulty memory task. Directly prior performance, half the participants received a cue that their affective state might have been manipulated. Cardiovascular and electrodermal reactivity with reference to a habituation baseline were assessed during the mood inductions and task performance. Due to the mixed evidence for demand effects on EDA, this physiological parameter was assessed exploratively. By contrast, we tested theory-based predictions on cardiovascular reactivity: Based on the MBM and previous studies (see Gendolla & Brinkmann, 2005), we expected a crossover interaction pattern of cardiovascular reactivity (especially SBP) in the no-cue conditions. Here, reactivity was anticipated to be high in the negative-mood/easy and the positive-mood/difficult conditions and low in the positive-negative-mood/easy and the negative-mood/difficult conditions. In the cue-conditions the impact of mood was anticipated to be significantly reduced, leaving only an effect of task difficulty with lower reactivity in the easy than in the difficult conditions. Moreover, mood was not anticipated to have an effect on physiological arousal during the mood inductions, because moods themselves are not considered as motivational states.

3.2.Method Participants and design

Eighty-eight university students (71 women, 17 men, average age 22 years) were randomly assigned to a 2 (mood: negative vs. positive) x 2 (cue: no-cue vs. cue) x 2 (task difficulty: easy vs. difficult) x 2 (time: mood inductions vs. task performance) mixed model design with repeated measures on the last factor. Participation was anonymous and voluntary; respondents received partial course credit.

Apparatus and physiological measures

Blood pressure (in millimeter mercury [mmHg]) and HR (in beats per minute [bpm]) were recorded using a computer–aided Vasotrac APM205A monitor (MEDWAVE, St. Paul, MN). This system uses applanation tonometry: a pressure sensor is placed on the wrist on top of the radial artery applying a varying force on it. Internal algorithms yield systolic and diastolic pressures as well as heart rate approximately each 12-15 heart beats, i.e. 4-5 values per minute (see Belani et al., 1999, for a validation study).

Electrodermal activity was assessed with 8-mm Ag/AgCl electrodes (Hellige) placed with adhesive collars on the medial phalanx of the index and middle fingers of participants’ nondominant hand.

Participants washed their hands with water and nonabrasive soap and dried them before the attachment of the electrodes. A commercially available 0.05 molar NaCl electrolyte solution gel was used for the recordings and a constant voltage (0.5 V) coupler (Contact Precision Instruments, London, UK) was applied to the electrodes. After filtering (low pass filter at 10 Hz) and digitization (24 bit) at a sampling rate of 500 Hz, tonic SCL was calculated for each 1-min intervals during the habituation, mood induction and task performance periods and stored as number per minute scores. Electrodermal measures were sampled with a Psylab System (Contact Precision Instruments, London, UK).

Experimenter and participants were ignorant of all measured physiological values during the experimental session. The beginning and end of the measurement periods (habituation, mood induction, and task performance) were synchronized via Transistor–Transistor Logic signals sent by the experimental software (Inquisit 2.0, Millisecond Software, Seattle, WA) through a parallel port. During these periods, the blood pressure measures, the HR, and electrodermal activity were assessed continuously and stored on computer disk. Data reduction was performed with Bluebox—software developed in our lab (Richter, 2007).

Procedure

The study was introduced as an investigation in physiological activity during relaxation (watching films excerpts) and demand (performing a task). Participants were run in individual sessions (ca. 45-min) and took a seat in front of a computer screen. Once they had given informed consent, the hired experimenter (who was not aware of the hypotheses) attached the electrodes and blood pressure cuff for the physiological measures. Then the experimenter started the experimental software and left the room. The whole experiment was run on a personal computer using experiment generation software (Inquisit, Millisecond Software). In order to minimize experimenter-participant interactions, all instructions and questions were displayed on the computer screen and all responses were entered with a mouse click or with the computer keyboard.

Baseline measures. After having answered some biographical questions, participants rated four adjectives of the UWIST mood adjective checklist (Matthews et al., 1990) focusing on the experience of positive (joyful, cheerful) and negative (sad, depressed) affect. Participants read ‘‘Right now, I’m feeling... ’’ and rated the adjectives on 7-points scales ranging from not at all (1) to very much (7). This measure later constituted mood baselines, but to prevent suspicion in the later mood manipulation it was introduced as a standard measure to control participants’ states upon entering the laboratory.

Next, the so called “relaxation period” was introduced: Participants received instructions to sit still and

to relax during the presentation of the film excerpts, during which the physiological measures would be taken. During the first 10-minutes, which served as habituation period to assess physiological baselines, all participants watched a hedonically neutral documentary movie about Portugal to assess physiological baseline values. Then the so called “relaxation period” continued with the presentation of different video excerpts.

Mood manipulation. To maintain with the cover story and to prevent suspicion in the mood manipulation, participants were asked about their media use habits (how frequently they would go to the cinema and hours per week watching TV). Then participants in the positive mood condition watched an excerpt of the comedy “Naked Gun 2-1/2”, whereas those in the negative mood condition watched the depressing end of the movie “Love Story.” The choice of these excerpts was based on their previous efficient use in other mood studies from our laboratory (e.g., Gendolla & Krüsken, 2002a, 2002c). Each video excerpt was 8 min long. To maintain with the cover story participants answered some more distraction questions about the film content on 7-point rating scales (e.g., ‘‘Was the movie interesting?’’,

‘‘Was the movie pleasant?’’) after its presentation.

Task performance. After the “relaxation phase” with the video presentations followed the “demand phase” in which participants worked on a task. This period began with a new assessment of participants’

current mood state with the ratings of the four items of the UWIST mood adjective checklist as at the beginning of the experiment. The items were again presented as a standard measure assessed at each new phase of a study. After the mood ratings, which later constituted a post mood manipulation measure, participants received the instructions for a memory task. Participants were informed that they would be presented with a list of letter series, each consisting of 4 letters (e.g., WPQA, HLYC). The task was to correctly memorize the entire list within 5 minutes and to note all items afterwards. In the easy condition, the list consisted of 4 letter series, while it consisted of 9 series in the difficult condition. A similar memory task paradigm has already been successfully administered in previous studies (e.g., Gendolla & Krüsken, 2002c).

Before the computer program presented the corresponding list of letter series, participants rated subjective task difficulty (‘‘How difficult is the task?’’) and the importance of success (‘‘How important is for you to accomplish this task?’’) on 7-points scales ranging from very easy / not at all (1) to very difficult / very much (7), respectively. This was followed by the cue manipulation: In the cue-condition, the following message appeared on the screen: “Previous studies have revealed that the films you just watched can have a longer impact on affective states than the watchers might notice. During

performance of the following task you should take into account that your feeling state could still be influenced by the films.” (cf. Gendolla & Krüsken, 2002a). Then the letters series appeared. In the no-cue condition this statement was absent and the letters series were presented immediately. After the 5 min of task performance, the experimenter re-entered the room and gave participants a sheet of paper to let them note the series they could recall. This was followed by a third mood measure with the four adjectives of the UWIST mood adjective check list to control for participants’ mood after performance.

Debriefing. The last question focused on participants’ idea about the specific goal of the study – they were solicited to express their suspicions. The results indicate that our cover story was efficient: 55% of the participants did not express any suspicion, while the rest mentioned diverse aspects – such as stress, memory capacity, arousal during movies, self-esteem, and achievement – without mentioning effort or resource mobilization. Participants in the no-cue condition did not mention any relationship between feeling states and task performance. Finally, the experimenter removed the blood pressure cuff and the electrodes, interviewed participants with regard to suspicion (once again), debriefed, and thanked them.

3.3.Results

The main dependent variable was cardiovascular reactivity, especially SBP, in the context of task performance and the mood inductions with reference to the cardiovascular baselines assessed in the habituation period. We directly tested our theory-based predictions with a priori contrasts, which are the most powerful and thus the most appropriate statistical tool to test for predicted patterns of cell means and complex interactions in experimental designs (Rosenthal & Rosnow, 1985; Wilkinson & The Task Force for Statistical Inference of APA, 1999). As outlined above, we predicted a cross-over interaction between mood and task difficulty in the no-cue conditions and only a difficulty main effect in the cue conditions. Contrast weights were assigned in correspondence with this hypothesis. More specifically, in the no-cue condition we anticipated stronger cardiovascular reactivity in the negative mood condition (contrast weight +1) than in the positive mood condition (contrast weights -1) for an easy task and stronger cardiovascular reactivity in the positive mood (contrast weight +1) than in the negative mood (contrast weights -1) for a difficult task. In the cue conditions, we expected for both mood conditions stronger reactivity in the difficult condition (contrast weights +1) than in the easy condition (contrast weights -1). Given that reactivity during the mood inductions was not predicted, because moods themselves are not considered as motivational states in the MBM (Gendolla, 2000; de Burgo & Gendolla, in press) we further anticipated that this a priori contrast would be significant for

cardiovascular reactivity during task performance, but not during the mood inductions, resulting in a Contrast x Time interaction. Preliminary analyses tested for associations between cardiovascular baseline and reactivity scores. In case of significant associations the reactivity scores were corrected with regard to the respective baselines to prevent carry-over or initial values effects (Llabre et al., 1991).

EDA, task performance, and verbal measures were analyzed with conventional 2 (mood) × 2 (task difficulty) × 2 (cue) between-persons ANOVAs, because we did not test theory-based, directed predictions for these variables.

Mood manipulation checks

As in our previous studies, we computed mood sum scores for the mood baseline measure, the post-mood manipulation measure, and the post task performance measures by recoding the negative hedonic items and adding them to the positive hedonic items (Cronbach’s αs > .73). Next we created mood changes scores by subtracting the mood baseline scores from the manipulation and the post-performance scores.

There were no mood baseline differences between the conditions (all ps > .08) and a preliminary ANCOVA did not find reliable associations between the mood baselines and reactivity scores (p > .36). A 2 (mood) x 2 (task difficulty) x 2 (cue) x 2 (time: post-manipulation vs. post-performance) between persons ANOVA found a time main effect, F(1, 80) = 8.58, p < .004, reflecting general elation after performance (post-mood manipulation: M = 0.19, SE = 0.38 ; post-performance: M = 1.23, SE = 0.32).

Most relevant, there was also a significant Mood x Time interaction, F(1, 80) = 33.23, p < .001. Focused comparisons found a significant mood effect on the post-mood manipulation scores, t(80) = 5.92, p <

.001, due to the expected mood elation in the positive mood condition (M = 2.51, SE = 0.51) and mood depression in the negative mood condition (M = -2.07, SE = 0.56), reflecting a successful manipulation.

Mood had no longer a significant effect on the post-performance mood scores, (positive mood: M = 1.49, SE = 0.50; negative mood: M = 1.02, SE = 0.37; p > .53), and no other effects were reliable (all ps >

.50) – which means that the cue manipulation had no effect on the mood scores.

Task ratings

A 2 (mood) × 2 (task difficult) x 2 (cue) between-persons ANOVA of the task difficulty ratings found as the only significant result a task difficulty main effect, F(1, 80) = 12.58, p < .001. In support of a successful manipulation, ratings in the easy condition (M = 4.26, SE = 0.17) were lower than in the

difficult condition (M = 5.13, SE = 0.17). Regarding the attributed importance of success, the 2 x 2 x 2 ANOVA did not revealed any significant effects (ps > .14, overall M = 4.34, SE = 0.47).

Physiological measures: Preliminary analysis

As a regular finding (see Wolf et al., 1997), women had lower baseline values of both SBP (M = 110.02, SE = 1.24 vs. M = 120.38, SE = 4.39) and DBP (M = 61.06, SE = 0.78 vs. M = 67.89, SE = 2.86) than men, as evident in significant gender main effects, Fs > 4.96, ps < .03. Some interactions were found including gender, although these results cannot be interpreted since there was an uneven distribution between women and men in each condition. Gender had no effects on the HR or SCL (ps > .07).9

Physiological baselines

Cell means and standard errors are depicted in Table 2. Due to their high internal consistency, the averages of the last two minutes of the habituation period constituted the baseline values for the physiological measures (Cronbach’s αs > .96). Two (mood) x 2 (task difficulty) x 2 (cue) between-persons ANOVAs found only a significant main effect of mood on the DBP baselines, F(1, 80) = 4.00, p < .05, reflecting higher values in a negative mood (M = 64.11, SE = 1.44) than in a positive mood (M = 60.73, SE

= 0.99). This was taken into account in the subsequent DBP analysis.

Physiological reactivity

We created change (delta) scores by averaging the physiological measures for the mood induction and task performance periods and subtracting the respective baseline values from them. Internal consistency was high (Cronbach’s αs > .98). To test possible associations between reactivity scores and baseline values, preliminary baseline-adjusted Contrast x Time ANOVAs were calculated. These analyses found Baseline x Contrast x Time interaction effects on the reactivity of SBP, F(1, 79) = 10.95, p < .001, and DBP, F(1, 79) = 8.29, p < .005. Consequently, SBP and DBP baseline values were considered as covariates in the subsequent analyses to prevent carry-over or initial values effects (see Llabre et al., 1991).

9 Due to measurement errors, there were missing data for some participants. Therefore, the sample sizes slightly varied across the analyses of the single dependent variables: N = 88 for SBP, DBP, and HR; N = 80 for SCL. The cell ns for the no-cue conditions were as follows: positive mood/easy condition = 12, negative mood/easy condition = 11 (for SBP, DBP, HR) and 10 (SCL), positive mood/difficult condition = 10 (for SBP, DBP, HR) and 9 (SCL), negative mood/difficult condition = 10 (for SBP, DBP, HR) and 8 (SCL). Cell ns for cue conditions were: positive mood/easy condition = 11 (for SBP, DBP, HR) and 10 (SCL), negative mood/easy condition = 11 (for SBP, DBP, HR) and 10 (SCL), positive mood/difficult condition = 12, negative mood/difficult condition = 11 (for SBP, DBP, HR) and 9 (SCL).

Table 2. Cell Means and Standard Errors (in Parentheses) of the Physiological Baseline Values.

No-Cue

Negative Mood Positive Mood

Easy Difficult Easy Difficult

SBP 116.89 120.59 112.07 109.58

(3.84) (4.02) (3.67) (4.02)

DBP 65.12 68.06 61.78 59.98

(2.46) (2.58) (2.36) (2.58)

HR 80.38 75.17 72.19 77.34

(3.64) (3.82) (3.49) (3.82)

SCL 3.42 2.88 1.74 1.77

(0.74) (0.83) (0.68) (0.78)

Cue

Negative Mood Positive Mood

Easy Difficult Easy Difficult

SBP 110.20 109.45 109.58 108.66

(3.84) (3.84) (3.84) (3.67)

DBP 62.27 61.35 61.19 59.90

(2.46) (2.46) (2.46) (2.36)

HR 78.43 78.93 78.41 74.36

(3.64) (3.64) (3.64) (3.49)

SCL 2.75 2.98 2.21 3.05

(0.74) (0.78) (0.74) (0.68)

Note: SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; SCL: skin conductance level. Units of measure are millimeters of mercury for SBP and DBP, beats per minute for HR, μSiemens for SCL.

SBP reactivity. Cell means and standard errors are depicted in Figure 7. As outlined above, we anticipated for the task performance period – but not for the mood induction period – a Mood x Task Difficulty crossover-interaction in the no-cue condition and a task difficulty main effect in the cue condition. We directly tested our theory-based hypothesis with an a priori contrast (Rosenthal &

Rosnow, 1985) testing for the anticipated pattern for the task performance period and the interaction with the Time factor (mood inductions vs. task performance), including the SBP baseline values as covariate. This analysis revealed a significant time main effect, F(1, 79) = 22.72, p < .001, reflecting stronger SBP reactivity during task performance (M = 9.77, SE = 0.60) than during mood inductions (M = 1.14, SE = 0.28). As anticipated, this effect was moderated by a significant Contrast x Time interaction, F(1, 79) = 9.88, p < .002.10 To explore this effect, we tested the significance of the a priori contrast at both times of measure—the mood inductions and task performance. During the mood inductions the contrast was not significant (p > .21). Moreover, also the residual was not significant (F < 1), indicating that no significant variance remained unexplained. Most relevant, the contrast was significant for the task performance period, F(1, 79) = 14.78, p < .001, (residual F < 1). Additionally, focused cell comparisons revealed for the no-cue conditions, the expected cell differences: in the easy condition there were higher SBP values in a negative mood (M = 13.43, SE = 1.72) than in a positive mood (M = 8.01, SE = 1.63), t(79) = 2.28, p < .02. The opposite pattern appeared in the difficult condition. Here, reactivity was stronger in a positive mood (M = 14.87, SE = 1.79) than in a negative mood (M = 6.71, SE = 1.84), t(79) = 3.15, p < .002. In the cue condition, there was, as anticipated, stronger SBP reactivity in the difficult conditions (M = 10.06, SE = 1.19) than in the easy conditions (M = 7.52, SE = 1.21), although this effect was only marginally significant, t(79) = 1.50, p < .07.

DBP reactivity. As illustrated in Figure 8, the DBP reactivity pattern resembled that of the systolic responses. The Contrast x Time analysis on baseline-adjusted DBP revealed a time main effect, F(1, 79) = 19.44, p < .001, indicating stronger reactivity during task performance (M = 6.91, SE = 0.46) than during the mood inductions (M = 0.52, SE = 0.21). Also the Contrast x Time interaction was reliable, F(1, 79) = 7.49, p < .008.11 Moreover, the a priori contrast was not significant during the mood inductions (p > .30;

residual F < 1), but during task performance, F(1, 79) = 12.38, p < .001, (residual F < 1). Cell comparisons in the no-cue condition revealed stronger reactivity in a negative mood (M = 9.80, SE = 1.23) than in positive mood (M = 5.98, SE = 1.30) when the task was easy, t(79) = 2.13, p < .04. By contrast, when the task was difficult, reactivity was stronger in a positive mood (M = 10.29, SE = 1.36) than in a negative mood (M = 5.72, SE = 1.39), t(79) = 2.32, p < .02. In the cue condition, as for SBP reactivity, higher DBP values were observed in the difficult conditions (M = 7.51, SE = 0.65) than in the easy conditions (M = 6.32, SE = 0.64). However, the difference again only approached significance, t(79) = 1.78, p < .08.

10 It might be informative that a conventional 2 (Mood) × 2 (Cue) x 2 (Task difficulty) × 2 (Time) ANCOVA also found a four-way interaction effect: F(1, 79) = 6.65, p < .01.

11The 2 x 2 x 2 x 2 ANCOVA also found a reliable four-way interaction: F(1, 79) = 6.24, p < .02.

A: Mood induction phase

B: Task performance phase

Figure 7. Cell means and standard errors of systolic blood pressure (SBP) reactivity during the mood induction (top panel) and task performance (bottom panel) periods.

A: Mood induction phase

B: Task performance phase

Figure 8. Cell means and standard errors of diastolic blood pressure (DBP) reactivity during the mood induction (top panel) and task performance (bottom panel) periods.

HR reactivity. The Contrast x Time analysis revealed only a main effect of time: F(1, 80) = 113.58, p <

.001, indicating stronger reactivity during task performance (M = 7.22, SE = 0.65) than during the mood inductions (M = -0.81, SE = 0.40). Cell means and standard errors are portrayed in Table 3.

SCL reactivity. Cell means and standard errors appear in Table 3. The 2 (mood) x 2 (task difficulty) x 2 (cue) x 2 (time) mixed model ANOVA found a strong main effect of time: F(1, 72) = 94.06, p < .001. Again lower reactivity was observed during the mood inductions (M = 0.42, SE = 0.06) than during task performance (M = 1.27, SE = 0.10). In addition, the analysis revealed interactions between Time x Mood, F(1, 72) = 4.07, p < .05, and Time x Task Difficulty, F(1, 72) = 4.77, p < .03. Follow-up 2 x 2 x 2 between persons ANOVAs for both times of measure found a task difficulty effect during task performance, F(1, 78) = 7.06, p < .01, reflecting stronger reactivity in the difficulty condition (M = 1.52, SE = 0.16) than in the easy condition (M = 1.01, SE = 0.11). By contrast, the later easy and difficult conditions did not differ during the mood inductions (p > .24). Mood had neither a significant effect during task performance, nor during the mood inductions (ps >.14).

Task performance

Cell means and standard errors appear in Table 4. The 2 (mood) x 2 (cue) x 2 (task difficulty) ANOVA of the percentage of correctly recalled letter series found only a significant task difficulty main effect, F(1, 80) = 29.82, p < .001, revealing that a higher share of the letter series was correctly memorized in the easy condition (M = 96.69%, SE = 2.80) than in the difficult condition (M = 74.86%, SE = 2.86). No other main effect or interaction was significant (ps > .18).

Table 3. Cell Means and Standard Errors (in Parentheses) of HR and SCL Reactivity.

No-Cue

Negative Mood Positive Mood

Easy Difficult Easy Difficult

Mood induction

HR 0.58 -0.84 -0.09 -1.64

(1.16) (1.22) (1.11) (1.22)

SCL 0.20 0.61 0.32 0.32

(0.17) (0.19) (0.16) (0.18)

Task performance

HR 4.81 8.43 9.03 10.18

(1.84) (1.92) (1.98) (1.92)

SCL 0.88 1.64 0.96 1.11

(0.27) (0.30) (0.25) (0.28)

Cue

Negative Mood Positive Mood

Easy Difficult Easy Difficult

Mood induction

HR -1.48 -1.11 -0.84 -1.13

(1.16) (1.16) (1.16) (1.11)

SCL 0.56 0.27 0.35 0.74

(0.17) (0.18) (0.17) (0.16)

Task performance

HR 4.93 7.10 8.27 5.36

(1.84) (1.84) (1.84) (1.76)

SCL 1.43 1.80 0.79 1.56

(0.27) (0.28) (0.27) (0.25)

Note: HR: heart rate; SCL: skin conductance level. Units of measure are beats per minute for HR, μSiemens for SCL.

Table 4. Cell Means and Standard Errors (in Parentheses) of the Percentage of Correctly Memorized Items During Task Performance.

No-Cue

Negative Mood Positive Mood

Easy Difficult Easy Difficult

97.73 74.44 95.83 74.44

(2.27) (10.08) (4.17) (7.60)

Cue

Negative Mood Positive Mood

Easy Difficult Easy Difficult

100.00 81.82 93.18 68.75

(.00) (5.03) (4.87) (6.39)

3.4.Discussion

The goal of this study was to provide a conclusive test of an informational mood impact on resource mobilization as conceptualized in the MBM (Gendolla, 2000). We did so by manipulating both moods’ effective diagnostic value for demand appraisals and task difficulty as another type of information for evaluations of task demand. Based on our theorizing and previous research on the MBM

The goal of this study was to provide a conclusive test of an informational mood impact on resource mobilization as conceptualized in the MBM (Gendolla, 2000). We did so by manipulating both moods’ effective diagnostic value for demand appraisals and task difficulty as another type of information for evaluations of task demand. Based on our theorizing and previous research on the MBM

Dans le document Mood and mental effort : informational mood impact on cardiovascular reactivity and the context-dependency of moods (Page 92-109)