Effects of negative mood states on risk in everyday decision making

COGNITION AND EMOTION, 2000, 14 (6), 823–855

Effects of negative mood states on risk in everyday decision making G. Robert J. Hockey University of Leeds, UK

A. John Maule University of Leeds, UK

Peter J. Clough University of Hull, UK

Larissa Bdzola University of Leeds, UK How does negative mood affect risk taking? A brief questionnaire was used to measure state anxiety, depression, and fatigue, and a daily mood diary allowed state and trait (average level) mood to be separated. Studies 1 and 2 used natural moods and Study 3 a mood induction procedure. Risk was assessed using hypothetical everyday choice scenarios. Study 1 showed that riskiness was affected by state fatigue, but not by anxiety and depression. Study 2 showed that increased riskiness over a two-week period was predicted by fatigue changes, after controlling for riskiness and trait and state mood at time 1. Fatigue effects were stronger for more important scenarios, and when state anxiety was also high. In Study 3, covariance analyses showed that the observed increased in riskiness was related to induced fatigue, rather than to anxiety or depression. The effects are discussed in relation to the literature on fatigue effects, and models of mood and cognition.

Please send correspondenc e and requests for reprints to to Professor G. Robert J. Hockey, Department of Psychology, University of Leeds, LS2 9JT; Email: [email protected] k This research was supported by the UK Economic and Social Research Council’s Risk and Human Behaviour Programme, Grant No 414206. We thank Christine Watson and Abi Hebbard for their help in collecting data.

Ó 2000 Psychology Press Ltd http://www.tandf.co.uk/journals/pp/02699931.html

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INTRODUCTION The impact of strong negative emotions on decision making and risk taking is recognised both in anecdotal accounts (Janis & Mann, 1977; Kogan & Wallach, 1964; Mann, 1992), and in the analysis of major risk situations (Orasanu, 1997). However, there has been little research on either the effects of more subtle emotional changes, such as negative moods, or of decision making in more typical everyday contexts. Moods may have greater relevance for behaviour in everyday decision making, where extreme emotional states are relatively uncommon. In addition, temporary mood may be expected to play a smaller part in major life decisions, because these are normally taken over a protracted period (buying a house, choosing a partner, deciding on a career). The present study examines the effect of negative mood states on risk-taking behaviour in everyday situations. The most extensive programme of work on mood and risk has been carried out by Isen and her colleagues (e.g., Isen & Geva, 1987; Isen & Patrick, 1983). Their main findings are that positive moods (typically induced by small gifts) produces risk-averse behaviour in gambling and lottery tasks. However, in ‘‘low risk’’ tasks, where success is more likely, positive mood usually gives rise to increased riskiness. Such results have been interpreted in terms of a mood regulation model, which assumes a desire to maintain positive moods and to repair negative moods. Risky decisions are thought to be rejected under positive moods because the likely loss will upset the good mood state, whereas the likely gain from a low risk decision would serve to enhance or maintain it. The mood regulation interpretation has considerable appeal, though it is unable to account for all effects of mood on cognition. For example, the maintenance of negative states may be necessary in order to deal with the problems they signal (Martin, Ward, Achee, & Wyer, 1993). Different mood regulation strategies (such as rumination or distraction) may be used to either enhance or reduce negative moods, such as depression, anxiety, and anger (Rusting & Nolen-Hoeksema, 1998; Thayer, Newman, & McClain, 1994). Within the extensive social judgement literature, Forgas (1995) identifies four different processing strategies in which mood effects (‘‘affect infusion’’) might occur—direct access, motivated processing, heuristic processing, and substantive processing. On this model, mood regulation is but one example of motivated processing, in which decision outcomes are directed towards attaining pre-existing goals, such as feeling good, or staying angry (Martin et al., 1993), rather than on achieving optimal task solutions. Forgas argues that affect infusion occurs more readily in tasks that encourage heuristic and substantive processing. Mood congruent effects occur when current mood primes access to affect-specific memory-based information (e.g., Bower, 1981; Mayer, Gashke, Braverman, & Evans, 1992), biasing perceptions and values used in judgements. For example, positive events are judged more frequently and negative events

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less frequently in positive and negative moods, respectively (Johnson & Tversky, 1983). A second effect of mood concerns changes in processing strategies. Theorists, such as Frijda (1986), have argued that emotion has a primary motivational function, helping to regulate actions by signalling the presence or absence of threat (and other) states. Models based on this perspective (e.g., Schwarz & Bless, 1991) argue that negative moods (signalling the presence of a problem) are more likely to promote analytic processing (the use of rational, elaborative strategies) directed towards the source of the problem. By contrast, positive states (signalling that all is well) give rise to simpler heuristic strategies. For example, Forgas (1998) found that effects of induced mood on attribution errors were strongly related to changes in information-processing style. Subjects in sad moods performed better, making more effective use of memory for task information than controls, whereas those in happy moods made more errors and recalled less. However, such effects of negative moods are not always found. In one of their studies, Leith and Baumeister (1996) found that increased riskiness in lottery choices under induced anger was effectively counteracted by instructions to use rational decision strategies, implying that anger reduced analytic processing. Although most of this evidence comes from social judgements, it may also have relevance for decision making and risk, although specific predictions are difficult to make, particularly for the effects of negative moods. From a mood repair perspective, people in negative moods may be expected to choose risky options, in order to give themselves a chance of obtaining the positive outcome that might improve their state. If negative affect acts to increase analytic processing, the choice of the safe option may be more likely, but only where this results in resolution of the problem that initially gave rise to the state. Otherwise, it may be directed towards a detailed assessment of the benefits and costs of the risky outcomes. Conversely, if analytic processing is reduced under some negative moods, risk choices may be more likely, as information will be processed less completely. In fact, there is little systematic research on the effects of negative mood on risk, and the findings are inconclusive. Broadly in line with the mood regulation hypothesis, Leith and Baumeister (1996) found that a range of induced negative states (such as anxiety and anger) increased the choice of risky options, in terms of preferences for long-shot gambles over safe ones. However, another negative mood, sadness, did not affect riskiness, and Pietromonaco and Rook (1987) found that mild depression reduced the selection of risky options in everyday decision scenarios. Taken together, these last two findings may indicate that increased risk is found only with ‘‘high arousal’’ negative states (as suggested by Leith & Baumeister, 1996). This may not be entirely satisfactory as an explanation, as Mano (1992) found contradictory effects of aroused negative moods within the same study. Individuals required to present a course report

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(making them more tense and distressed) were more risky on one measure (being willing to pay more for a lottery), but less risky on another (preferring a ‘‘sure thing’’ over a gamble). Overall, it seems likely that different negative moods may have distinctive effects on risk behaviour, although the nature of these differences has not been systematically explored to date. A rather different source of evidence comes from research on stress and human performance. Studies of decisional conflict (Baradell & Klein, 1993; Janie & Mann,1977; Keinan, 1987) suggest a range of strategy changes under stress, all associated with reductions in the amount of information used in reaching decisions. In our own work (Hockey, 1997; Maule & Hockey, 1993), decision making under time pressure and heavy workload is characterised by the use of short-cuts in information processing, and reduced levels of mental effort. In stress contexts such effects occur most strongly when fatigue is a part of the strain response (Hockey, 1997). Shingledecker and Holding (1974) found that subjects fatigued by up to 32 hours of continuous work gambled on low probability/low effort solutions on a post-work circuit testing task, instead of working systematically through all possible options. Similarly, Webster, Richter and Kruglanski (1996) showed that fatigued subjects gave a more stereotyped response in a social judgement task, and made less use of available information in reaching their decision. Interestingly, there have been no direct studies of the effects of fatigue on risk in decision making. Clearly, changes such as the foregoing are inconsistent with the social judgement literature on negative moods (e.g., Forgas, 1998; Schwarz & Bless, 1991), which generally predict an increased use of analytic processing. One problem is the tendency to treat all negative states as the same, despite their different motivational dynamics. Whereas anxiety is normally considered a response to threat, and depression the result of loss (e.g., Frijda, 1986), fatigue is more likely to signal a threat from overcommitment of processing resources to a particular activity. On this view, although an analytic processing style may be considered appropriate for anxiety and depression, it may not be for fatigue. Feeling tired is a signal to disengage from current activity—to stop, or switch to something else (Hockey & Meijman, 1998). At the psychological level, this is most readily measured as a reduction in effort expenditure, persistence, and task involvement (Hockey, 1997). Whether fatigue will elicit safe or risky actions will then depend on the processing requirements of available alternatives. What is needed at this stage is a study that compares the effect of fatigue and other negative moods directly in the risky decision situation. Although previous research has provided a valuable empirical and theoretical base for the study of mood effects in risk behaviour, it has a number of limitations for research on everyday decision making. For example, the widespread use of gambling and lottery tasks provide an effective way of defining rational behaviour, but may have only limited relevance to everyday choices, which normally have to be made in the face of uncertainty and ambiguity. In the


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present study, we adopt an approach based on Kogan and Wallach’s (1964) use of real life scenarios, although emphasising everyday situations rather than major life events. Pietromonaco and Rook’s (1987) depression study is the only one of this kind that we have encountered in the current literature. A second issue relates to the usual practice of inducing mood states experimentally. Mood manipulation methods have the major advantage that they permit the inference of cause-effect relationships. Induction procedures are, however, rarely precise, typically giving rise to concomitant changes in several emotional states (Polivy, 1981). By definition, such changes cannot be adequately measured without the use of multidimensional mood analyses. In addition, because induction procedures are, in effect, stressors that disturb the state of equilibrium, they may actually create an additional regulatory burden for task management, especially when negative moods are induced. An alternative to mood induction is to sample naturally occurring mood states, which may be assumed to be already randomly assigned to participants (Mayer et al., 1992) This has the advantage of avoiding transient stress effects of the induction process. On the negative side, the lack of control with the use of natural moods reduces the internal validity of the design, and the possibility for making causal interpretations. We include both methods in the present approach. Studies 1 and 2 explore the broad relationships between mood and risk using naturally occurring states. Study 3 uses a mood manipulation procedure to test our inferred interpretation of the correlational findings through direct experimental manipulation. Finally, there is a possibility that mood effects are confounded with more stable emotion-related traits. Rusting (1998) suggests that effects of mood on cognitive processing are likely to be either moderated or mediated by personality factors, such as extraversion, neuroticism, or stable levels of positive and negative affectivity. This has rarely been tested formally, although it has major implications for theorising in this area. For example, models based on the hypothesised informational function of affect (e.g., Schwarz & Bless, 1991) assume that effects are attributable to current mood, rather than to stable traits. It is clear that such information can be useful only if it is carried by dynamic changes in affect, rather than by average levels.1 Experimentally induced moods may be less vulnerable to problems of this kind, although effects may still be moderated by trait differences, or constrained by predispositional tendencies. In the case of natural moods, a single mood rating is likely to be strongly related to stable mood traits, although the two influences may be teased apart by relating measured moods to established baseline patterns. In the present study, this is accomplished by assessing individual baseline mood over an extended period, and using this to standardise current mood measures.

1

We are grateful to an anonymou s referee for pointing out the relevance of this.

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Conceptual framework for mood analysis As we have observed, most research on mood effects in risk and decision making has adopted a unidimensional valence merit. Circumplex models indicate that measurement of differences in affect requires at least two dimensions. One type of model (Russell, 1980) describes affect in terms of valence (pleasant–unpleasant) and arousal (degree of intensity or activation). An alternative interpretation (Watson & Tellegen, 1985) is based on a rotation of the dimensions, to give positive affect (PA: enthusiasm and energy for life goals) and negative affect (NA: active distress, anxiety, anger). A modification of the rotated model (Thayer, 1989) expresses moods in terms of differentiated arousal: ‘‘energetic arousal’’ and ‘‘tense arousal’’, broadly equivalent to PA and NA. There is still considerable debate about the relative merits of different models (e.g., Feldman-Barrett & Russell, 1998), and detailed consideration of the issues would be out of place here. The rotated structure is preferred as the basis for our own research, as it provides a more suitable framework for measuring changes in well-being under stress or task demands (e.g., Hockey, Payne, & Rick, 1996; Hockey, Wastell, & Sauer, 1998; Warr, 1990). In particular, it allows us to make distinctions between different negatively valenced mood states associated with the response to stress. In the present analysis we include three measures of negative mood: anxiety, depression, and fatigue. Conceptually, anxiety is closely related to NA and also to Thayer’s (1989) ‘‘tense arousal’’. Depression and fatigue are similarly related to (low) PA and Thayer’s (low) ‘‘energetic arousal’’.

The present research The three studies presented here are concerned with the analysis of changes in everyday risk behaviour under negative mood states. In particular, we address the following questions. (1) Can we identify separate effects of the three negatively valanced mood states: anxiety, depression, and fatigue? (2) To what extent are effects of current mood attributable to stable or trait differences? We adopt a diary-based methodology to identity individual mood patterns over an extended period before administering the risk task. This provides a baseline for standardising raw mood ratings, and allows us to separate the effects of state and trait effects on risk behaviour. (3) Are there distinctive effects on risk of complex affective states, identified by conjunctions of fatigue or depression with anxiety? (4) How are the effects of mood on risk moderated by the personal context of decision making, as defined by within-person differences in the familiarity, importance, and emotional impact of decision making problems? Finally, we ask (5) whether the effects of natural moods can be replicated by inducing a negative mood state experimentally. This provides better control and allows causal interpretations may be made more confidently.


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STUDY 1 Study 1 was an exploratory analysis of the mood-risk relationship. It also allowed us to compare different assumptions about appropriate state and trait indices of mood differences. By measuring moods at three times a day over a 14day period, we can obtain reliable individual differences at trait level (in terms of the average levels of each mood dimension). Predictions from these stable (or trait) measures can then be compared with those from state measures (obtained at the time of testing). A further comparison may also be made between raw and standardised mood scores. As the time of testing on the decision task was fixed, we can also ask whether diary reports made at the same time of day as the task provided a better baseline for the standardising procedure than either those made at other times or the whole day aggregate.

METHOD Participants A total of 34 students (17 men and 17 women, mean age 20.2 years, range 18– 31) were recruited for the study through advertisements placed around the university campus. They were not paid for their participation.

Materials Risk behaviour (PRI). Risk behaviour was assessed through an instrument specially developed for this study, the Personal Risk Inventory (PRI).2 Participants were presented with a set of everyday scenarios (in the form of common choice dilemmas), chosen to be representative of a wide range of situations (e.g., legal, health, social, moral, financial). These were designed to be typical of choice situations frequently confronted by individuals in their normal lives, based on a pilot study, in which respondents were asked to keep diaries of frequently occurring decisions involving an element of risk. They were instructed to imagine how they would feel in each situation, and to choose which of two actions (A or B) they would take. For scoring purposes, one of these was identified as ‘‘risky’’ and one ‘‘safe’’ (randomly designated as A or B). In view of the demonstrated link between fatigue and the choice of low effort strategies (Hockey, 1997) it was initially proposed to include two kinds of item, differing in the implied effort required in order to carry out the safe option. However, this goal was not pursued systematically, as it proved difficult to match items satisfactorily for other features. The eventual set of 20 items used in the pilot study attempted to balance effort differences across risky and safe choice options. 2

Details of the full 13-item set of PRI scenarios may be obtained from the authors.

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Because of the need for the PRI to measure changes in risk behaviour as well as consistency, items were eliminated from the original set of 20 on the basis of a preliminary study if their test-retest reliability was too high (>.8) as well as too low ( .05. Thus, risky and safe choice options were effectively balanced for effort demands. Two examples of scenarios are shown later. In the first, choosing the safe option has a greater effort or cost, in terms of both time and physical work. In the second example, perceived effort is rated as higher for the risky option. The risky choice is shown by an asterisk in both cases. Hospital parking You have to visit a close relation in hospital, and you manage to get away from work for an hour at a busy time. As usual, the small visitors’ car park opposite the hospital is full, and you know from experience that you will probably have to wait 15 minutes or so at this time for a space. You could drive into the staff car park, but this is occasionally patrolled by hospital security staff, and you know that cars have been clamped. You wonder where you should park: (A) Use staff car park* (B) Use visitors’ car park Pub visit You have been in a new job for a week and enjoy it. On the Friday, you overhear people talking about visiting a pub together at the end of work. You would like to get to know your colleagues better, but you have not received an invitation to go along with them. You are unsure whether this is just an oversight or a deliberate snub. On your way home you pass the pub where everyone is


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meeting, and consider whether you should go straight home or call in. They may be very pleased to see you, but it may be embarrassing, and make future work less enjoyable. You wonder what you should do: (A) Go straight home (B) Call in to the pub* In order to obtain a more sensitive measure of choice, respondent s were asked to indicate their strength of commitment to the selected option on a 10-point scale (from ‘‘definitely A’’ to ‘‘definitely B’’). This provided a graded measure of riskiness, rather than the dichotomous index of risk choices. Riskiness was scored by averaging across the items in the set (after reverse-scoring in cases where choice A was the risky one), so that higher values of riskiness refer to increased endorsement of the risky alternative. Respondents were also asked to rate how risky they perceived their decision to be in each case, using a 1–5 scale. The average of these ratings was used as a measure of perceived risk. Finally, they were asked to rate each scenario for three other attributes: (a) familiarity— how familiar they were with this kind of problem; (b) importance—how important it would be in real life for them to obtain a favourable outcome; and (c) emotional impact—how much they were affected emotionally by the problem. A post-hoc classification of scenarios (high or low on each of the three attributes) was carried out separately for individuals, to test for their possible moderating role in the mood/risk relationship in Study 2. Mood measurement. To establish a reliable basis for differentiating individual traits and states, moods were measured over a large number of occasions (Epstein, 1984). Participants completed a mood diary three times a day for the 14 days preceding the PRI, and during the PRI test session itself. They were asked: ‘‘How you have felt over the past few hours’’, with respect to a set of 12 mood adjectives, four items (two reverse-scored) representing each of the three negative mood measures included in the study: anxiety (Anx), depression (Dep), and fatigue (Fat). The relatively small number of items in the mood checklist was adopted in order to minimise the daily demands on respondents, and encourage completion of the 14-day diary phase of the study. Items were presented in alphabetical order in a single column (dimension in brackets; R = reverse-scored): alert (Fat, R) anxious (Anx), calm (Anx, R), cheerful (Dep, R), depressed (Dep), energetic (Fat, R), enthusiastic (Dep, R), fatigued (Fat), miserable (Dep), relaxed (Anx, R), tense (Anx), tired (Fat). Participants rated their feelings by putting a mark on a 100 mm line (labelled ‘‘not at all like this’’, at one end, to ‘‘very much like this’’ at the other). Scale reliabilities from accumulated use of these measures over a number of current studies are acceptable (Cronbach a for anxiety = .78, depression = .88, fatigue = .83). Scale scores were obtained for the three dimensions, for each diary day, by averaging over the four items, to give a value on a 0–100 scale.

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Procedure Participants were instructed to complete the mood diary over a 14-day period, three times on each day (around 08.00 h, 13.00 h and 18.00 h). This allowed us to obtain reliable estimates of trait differences in affect (based on averaged reports) and to compare different indices of state changes, using the diary sample to calibrate reports at the time of the PRI. Approximately one week following completion of the diary, participants were invited to the laboratory to complete the PRI. Testing took place in small groups of 2–4 persons during the early afternoon (between 14.00 h and 16.00 h). This involved an orientation briefing, in which they were introduced to the PRI and reminded that they should imagine themselves in the various situations represented in the 13 scenarios. They were taken through three practice items, emphasising the roleplaying aspect of the task, and any queries answered. Mood state was assessed by administration of the standard mood questionnaire at the end of the practice period, and before the test scenarios were encountered.

Data analysis Effects of mood on risk behaviour were assessed by carrying out three separate analyses, designed to separate the contributions of trait and state effects. First, we tested for possible effects of stable (trait) mood level by relating PRI risk scores to the average level on each of the three mood dimensions for each participant, over the whole 14-day period of the mood diary (mean Anx, Dep, and Fat). Second, as the primary focus of the study was on effects of state changes, we assessed the effects of between-person variations in mood at the time of PRI testing. A conventional analysis of raw scores related individual risk behaviour to the observed mood scores at the time of completion of the PRI (raw Anx, raw Dep, and raw Fat). Such scores are, however, likely to be influenced by individual differences in baseline mood and reporting biases. Consequently, a third analysis was carried out in which effects of stable between-person differences in mood level and variability were removed. For this, raw scores for each dimension were adjusted separately for each person by standardising with respect to their 14-day baseline of diary reports (i.e., subtracting the mean and dividing by the standard deviation). This procedure removes all between-person effects, leaving only within-person variability, in the form of standardised (z) state mood scores (zAnx, zDep, zFat). For both average mood and standardised scores, four different sets of diary data were available: the entire set of 42 observations (mean Anx-42, etc.) and the samples of 14 observations for each of the three times of day (mean Anx-14, etc.). There are various possible baselines for the standardising procedure. Means for the 42-occasion sample may be best, since they are more representative of overall trait differences, and more reliable. However, means for different times of day were assumed more sensitive to individual differences in


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diurnal mood patterns, based on personal lifestyle (times of meals, sleep habits, structure of work, and social activities). In particular, because the PRI was administered in the early afternoon, the sample of lunchtime reports was predicted to provide a more appropriate baseline. For the computation of average mood, both the full set (based on 42 reports) and the midday sample (based on 14 reports) were expected to be the most meaningful indices of stable (trait) differences in mood.

RESULTS AND DISCUSSION Comparison of mood baselines As expected, the average of the midday diary reports proved to be the best predictor of mood scores at the time of testing for all three mood dimensions: for Anx, r = .56; Dep, r = .60; Fat, r = .45 (all ps < .01), and it was used as the basis of the standardisation procedure. Despite having less face validity, the greater reliability of the whole day average meant that it was also a reasonable predictor of test mood, for all three dimensions (Anx, r = .37, p < .05; Dep, r = .65, p < .01; Fat, r = .42, p < .05). For the analysis of trait effects, both midday (Anx-14, etc.) and whole day (Anx-42 , etc.) averages were included.

Descriptive and correlational analysis As described earlier, correlational analyses were carried out between risk measures and three different measures of mood: stable (trait) differences, and two measures of state changes—raw scores and standardised scores. These are reported separately later. Effects of trait mood on risk. The trait mood analysis was based on the average levels of reported Anx, Dep, and Fat over the 14-day period of the mood diary. An estimate of the reliability of these measures for each of the mood dimensions may be obtained by considering each rating as an item in a 14- or 42-item scale. This procedure gives Cronbach a values of between .71 and .94 (slightly higher for the 42 occasion samples), indicating an acceptable degree of stability in the day-to-day differences between respondents. Table 1 shows the outcome of the correlation analysis for the two average mood measures from the diary phase of the study and risk behaviour during the laboratory test. Table 1 indicates that risk behaviour on the PRI does not depend to any great extent on stable or trait differences in mood, at least as measured by average midday mood over the 14-day diary period. Possibly because of their greater reliability (Epstein, 1984), there is a stronger relationship with the trait measures based on 42 reports, particularly for Dep and Fat, which correlate positively with riskiness. The correlation for mean Fat-42 is significant (r = .36, p < .05). Thus, participants who are generally more fatigued (less energetic) over the two-week

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HOCKEY ET AL. TABLE 1 Summary statistics for stable mood measures and correlations between mood and risk variables (Study 1) Correlation with: Stable mood measure

M

SD

Risk

P-Risk

Mean Anx-14 Mean Dep-14 Mean Fat-14

34.2 48.6 41.1

11.4 12.5 10.0

.10 .18 .03

.11 7.04 7.07

Mean Anx-42 Mean Dep-42 Mean Fat-42

35.3 54.2 46.6

10.5 9.2 0.3

.17 .24 .36*

.13 .17 .21

Note: The table shows the product-momen t correlation of mood variables with Risk (riskiness rating) and with P-Risk (perceived riskiness rating). *p < .05.

period show a slightly stronger preference for risky options in the PRI. There were no correlations of stable mood with perceived risk. There is a problem in interpretation, however, because of intercorrelations between the three mood averages: anxiety and depression, r = .50; anxiety and fatigue, r = .70, fatigue and depression, r = .65, (all ps < .01). Controlling for the effects of anxiety and depression has the effect of slightly reducing the correlation between Fat-42 and riskiness, to a value which is no longer significant (partial r = .33, p > .05). Effects of state mood. The main focus of the study is the influence of state mood differences (i.e., how people felt at the time of PRI testing). As described earlier, separate analyses were carried out using both raw scores and standardised z-scores (based on the midday baseline data). The overall pattern for state mood is similar to that for trait measures (Table 2). Again, state depression and fatigue show positive correlations with riskiness, significant for both raw Fat (r = .56, p < .001) and zFat (r = .50, p < .01). There is no effect of state anxiety, and no correlations of any mood measure with perceived risk. Thus, riskiness is associated with both a high stable level of fatigue, and a relatively high state of fatigue at the time of testing. Again, it is necessary to control for the small correlations between the three state measures (r-values between 0 and .4), for both raw and standardised scores. Partial correlation analysis confirms that the precedence of fatigue as a predictor of riskiness is not dependent on these relationships. Correlations of mood with riskiness are little changed from those reported in Table 2 when effects of other measures are partialled out (partial r-values for zAnx, r = 2.10; zDep, r = .16; zFat, r = .45).


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TABLE 2 Summary statistics for state mood measures and correlations between mood and risk variables (Study 1) Correlation with: Stable mood measure

M

SD

Risk

P-Risk

raw Anx raw Dep raw Fat

51.0 45.8 46.1

27.3 21.1 26.7

7.07 .29 .56***

2.04 .09 .20

zAnx Dep Fat

0.42 0.33 0.17

1.14 0.95 1.24

7.01 .28 .50**

2.15z 2.03z .20

Note: The table shows product-momen t correlations of mood variables with Risk (riskiness rating) and with P-risk (perceived riskiness rating). **p < .01; ***p < .001.

The pattern of results in Tables 1 and 2 suggests a stronger effect of state than of trait mood, and of state fatigue in particular. The standardised scores offer strong evidence of the influence of a separate state effect, because they are statistically independent of individual differences in overall level and variability of reported mood. However, there may still be a functional link between trait and state measures. Specifically, individuals who generally feel tired may be more strongly predisposed to take a risky option when they are tired on a given occasion. A simple test of this, based on a median split of mean Fat-42 ratings (n = 17 per group), showed that the zFat/risk correlation was higher for participants who were habitually energetic (r = .68), and for those who were habitually tired (r = .44), although the two correlations did not differ significantly (z = 0.95, p > .05). It is also possible that individuals who experience large mood changes may be more susceptible to effects of state mood on risk.3 A similar analysis to the foregoing based on standard deviation measures over the 42 occasions, again showed no differences in the zFat/risk correlation for subjects with highly variable (r = .41) and less variable (r = .47) fatigue ratings (z = 0.18, p > .05). Although Study 1 was largely exploratory, it suggests that risk taking in everyday decision making may be affected by naturally occurring mood changes. In particular, fatigue at the time of testing is associated with higher levels of riskiness in terms of choice of action, but not with perceptions of risk associated with that choice. These effects were independent of stable differences in mood 3

We are grateful to an anonymou s reviewer for suggesting this analysis.

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levels, which had little effect themselves on risk behaviour. In interpreting these results in terms of naturally occurring mood changes, however, we need to consider the possibility that testing under laboratory conditions may induce a distinctive state of mild stress or test anxiety (e.g., Wine, 1971). Examination of Table 2 shows that the standardised values observed in the laboratory testing phase of Study 1 differed from the expected value (z = 0) for all mood dimensions. Single sample t-tests showed that both zAnx: z = 0.42; t(33) = 2.16, p < .05; and zDep: z = 20.32; t(33) = 1.98, p = .06, were higher than expected, though the testing situation did not affect zFat (z = 0.17; t < 1). Thus, the effect of state fatigue on risk may be partly accounted for by the induction of generally higher levels of state anxiety and depression by the testing session.

STUDY 2 Study 2 was carried out to examine the robustness of these findings, and to assess the role of possible moderator variables in the fatigue-risk relationship. In addition, Study 2 includes a separate within-study replication, by administering the PRI on two separate occasions over the diary period. A further procedural difference is influenced by the observation of mildly increased stress in the testing situation of Study 1. Study 2 investigates whether the same mood/risk relationship effect is observed in a more naturalistic context. This was achieved by arranging for subjects to complete the diary and the PRI in their own homes. Finally, in addition to using larger samples of participants, the mood diary phase was extended to 28 days. This provided a more reliable baseline and allowed the PRI to be administered twice, two weeks apart. There were two further goals of Study 2. The first was to examine the possible moderating effects on the fatigue-risk relationship associated with other aspects of the decisional context. Mood effects may be expected to depend on the personal significance of the problem scenario. More important or more familiar problems may be less vulnerable to the unpredictable effects of mood than less important or familiar scenarios, for example because of a stronger tendency to use direct access strategies (Forgas, 1995). To test this, we obtained ratings of the familiarity and personal significance of scenarios, and used these as moderators of the overall mood-risk effects. We also measured the overall emotional impact of scenarios, in order to assess the possible interaction between background and problem-generated effects. A final goal of Study 2 was to examine the effects of more complex mood states. In particular, we were surprised that state anxiety did not affect risk behaviour in Study 1, because it is recognised as a major threat to decision making (Eysenck, 1982; Wine, 1971). One possibility is that anxiety may interact with changes in fatigue or depression to produce distinctive ‘‘high risk’’ mood states. We tested this by analysing risk measures in relation to combinations of high and low levels of state fatigue and anxiety.


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METHOD Participants A total of 67 students (29 men, 38 women) were recruited by advertisements placed around the campus. None had taken part in Study 1. They were not paid for their participation, but received feedback at the end of the study on their mood patterns. They were asked to complete a number of questionnaires (for the purposes of other concurrent studies), so were not aware of any explicit link between completion of the mood diary and responses to the PRI. Nine participants (4 men, 5 women) had to be dropped from the full analysis, because of failure to complete at least 21 days of diary data, leaving a sample of n = 58 for most analyses. Two of these did not complete the second PRI test.

Procedure The mood diary and PRI were the same as those used in Study 1. Participants completed mood diaries each day for 28 days, and the PRI at the end of weeks 1 and 3. They were instructed to complete diaries at the end of each afternoon (17.00 h–19.00 h), followed by the PRI on day 7 (PR11) and day 21 (PR12). PRIs were posted to participants on the Thursday of the preceding week (5 days and 19) by first class post. (This normally gives delivery the next day, and almost always within 2 days). A fixed day (Saturday) for completing the PRI was adopted in order to control for possible day of the week effects in moods. Saturday was selected because there is evidence of greater variability of moods at weekends than during weekdays (Stone, Hedges, Neale, & Satin, 1985), thus maximizing the range of possible mood changes that might be observed across the sample. It also allowed for the possibility of completion on Sunday if necessary. In fact, of the 114 PRIs returned (2 were missing from the second set), 4 were completed on Fridays, 88 on Saturdays, 10 on Sundays, 9 on Mondays (sometimes because of late postal deliveries), and 3 on other days. Since there was no evidence of any differences between the data for Saturday and other days, all data were used in the analyses.

Data analysis Three separate analyses of the effects of mood on risk were again carried out, in order to assess the separate effects of trait and state components of moods. Trait mood was measured by taking the average levels of moods over the entire 28day diary period (mean Anx, etc.). For mean Anx, Dep and Fat, Cronbach a values (treating days as items) were .87, .91, and .83, respectively. As in Study 1, we also included both raw mood scores at the time of PRI completion and standardised mood, based on the 28 days of mood reports.

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RESULTS AND DISCUSSION Descriptive and correlational analysis Effects of stable mood. Table 3 shows the outcome of the correlation analysis for stable mood and risk behaviour for both PRI occasions. The pattern of results is very similar. They show a small negative correlation of riskiness with mean Anx, significant for PR1-2 (r = 7.34; p < .05) but not fir PRI-1 (r = 7.20, p > .05), and a positive correlation for mean Fat, significant on the first occasion (r = .33, p < .05) but not the second (r = .26, p > .05). There is no indication of an effect of mean Dep, and no correlations with perceived risk. Unlike Study 1, these results hint at an effect of trait anxiety on risk behaviour. Being generally anxious appears to make individuals act more cautiously in the risk scenarios. One problem of interpretation is the high correlation between mean Anx and mean Dep (r = .82 in Study 2, compared to r = .50 in Study 1). By contrast, in the present study, mean Fat is relatively independent of both mean Anx (r = .20, p > .05) and mean Dep (r = .10, p > .05). The reason for these differences is unclear, though the more frequent mood reporting of Study 1 may have made participants more aware of mood differences, and reduce their focus on valence judgements (Feldman-Barrett & Russell, 1998). A partial correlation analysis, controlling for the other moods, removes the apparent correlation between riskiness and both mean Anx and mean Dep, for both PRI-1 (r = .01 and 7.09) and PRI-2 (r = 7.15 and .02). However, trait fatigue maintains its small relationship with riskiness, significant at PRI-1 (r = .31, p < .05), although still not at PRI-2 (r = .25, p > .05). Overall, the findings show that trait mood has only small effects on risk behaviour. TABLE 3 Summary statistics for stable mood measures and correlations between mood and risk for each testing occasion (Study 2) PRI-1 (n = 58) Stable mood measure Mean Anx Mean Dep Mean Fat

PRI-2 (n = 56)

Correlation with:

Correlation with:

M

SD

Risk

P-Risk

M

SD

Risk

P-Risk

40.8 48.1 44.3

16.2 11.1 10.8

7.20 7.131 .33*

7.04 7.11 .06

40.4 48.3 54.1

15.7 11.6 10.7

7.34* 7.23 .26

7.15 .05 7.01

Note: The table shows the product-momen t correlations of mood variables with Risk (riskiness rating) and P-Risk (perceived riskiness rating). The slight differences in n, M, and SD values between PRI-1 and PRI-2 reflect the loss of data for two participants in the PRI-2 analysis. *p < .05.


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TABLE 4 Summary statistics for state mood measures and correlations between mood and risk variables for each testing occasion (Study 2) PRI-1 (n = 58) Stable mood measure raw Anx raw Dep raw Fat zAnx zDep zFat

PRI-2 (n = 56)

Correlation with:

Correlation with:

M

SD

Risk

P-Risk

M

SD

Risk

P-Risk

37.7 45.3 53.6

24.0 17.3 21.8

7.26 7.12 .32*

.16 7.05 .25

38.8 45.9 52.4

20.6 17.0 18.1

7.24 7.08 .35*

7.17 7.14 7.01

70.26 70.26 0.06

0.99 0.87 0.93

7.06 .06 .33*

.18 .03 .22

70.20 70.28 70.17

0.74 0.74 0.70

.12 .16 .44

7.07 .01 7.03

Note: The table shows product-momen t correlations of mood variables with Risk (riskiness ratings) and P-Risk (perceived riskiness ratings). The slight differences in n, M, and SD values between PRI-1 and PRI-2 reflect the loss of data for two participants in the PRI-2 analysis. *p < .05, **p < .01.

Effects of state mood. The analysis of state effects again considered both raw scores and standardised z-scores. The results are summarised in Table 4. The only significant correlations, on both PRI occasions, are between riskiness and fatigue. This applies to both raw Fat (r = .32 and .35 for PRI-1 and PRI-2, both p < .05), and zFat (r = .33, p < .05; r = .44, p < .01). As with Study 1, risky options are more strongly endorsed when participants are tired. State anxiety shows only a nonsignificant negative correlation with riskiness (r = 7.26 and 7.24), and there is no sign of an effect of state depression. Again, there were no effects on perceived risk. Correlations between the mood states again need to be considered. Unlike average mood levels, raw Anx and raw Dep were only moderately correlated (r = .58 for both PRI occasions), and raw Fat was relatively independent of both raw Anx (r = .06), and .11) and raw Dep (r = .28 and .33). Standardised scores showed an even smaller correlation between anxiety and depression (r = .46 and .38). Again, zFat was independent of zAnx (r = .25 and 7.20), although there was a higher correlation between zFat and zDep (r = .44 and .46). Partial correlation analyses confirmed that the effect of state fatigue on riskiness is not affected by its correlation with other mood measures (for raw Fat, r = .34 and .36 for the two testing occasions; for zFat, r = .34 and .43) They also confirmed the lack of association between riskiness and state anxiety/depression (r < |.15| in all cases). Both raw and standardised scores thus show that effects of state mood on riskiness are best captured by variations along the energy-fatigue dimension,

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rather than by changes in anxiety or depression. As noted earlier, raw scores inevitably include a contribution from stable mood differences. By controlling for stable individual differences in reported mood, the standardised score analysis shows that there is a genuine effect of intra-individual changes in state fatigue. The two separate testing occasions serve as a replication, showing the same overall pattern of effect. Furthermore, although raw mood scores show significant correlations between PRI-1 and PRI-2 (r = .4 to .5, all p < .05), correlations between standardised mood scores on the two occasions were very small (all r < .2). This strongly suggests that the effect of mood on risk behaviour is also independent of individual-specific responses to the self-testing requirements of the PRI. Different participants varied in tiredness or energy on the two occasions, and their level of riskiness changed accordingly. It may be argued that the use of a specific day (Saturday) for the PRI testing may compromise the external validity of the findings. Although there is little direct evidence for ‘‘day of week’’ effects in previous mood diary research (e.g., Stone et al., 1985), our ongoing diary studies have consistently observed lower levels of strain (decreased levels of all three negative moods) at weekends. For the present data, Table 4 indicates that the overall level of mood is not greatly changed on PRI days (all z-values close to the expected value of 0). Nevertheless, one-sample t-tests show that, with the exception of zAnx at time 1, all standardised values of strain are significantly lower (one-tailed tests, p < .05 or better). As a group, participants were slightly less anxious and tired, and clearly less depressed at the weekend, when they carried out their PRI tests. Taken in conjunction with Study 1, where the opposite effects on mood state were induced by the testing situation, we may reasonably conclude that the effects of state fatigue on risk behaviour, as observed on three separate testing occasions, are relatively robust. The cross-sectional analyses show that these effects are independent of both general mood levels and inter-correlations between mood variables. However, a more stringent test of the effect of state changes is afforded by the quasi-longitudinal design of the study. Hierarchical multiple regression may be used to test whether changes in mood from time 1 to time 2 are associated with corresponding changes in rise behaviour. In particular, we tested the hypothesis that an increase in zFat between the two sessions gives risk to an increase in riskiness. To do this, change scores were computed, for the 56 participants who completed both PRI sessions, for each mood variable (z 2–1Anx, z2–1Dep, z2–1Fat) and for the dependent variable, riskiness (risk2–1). This was done by subtracting the values for time 1 from those for time 2. Although there was only a weak correlation between riskiness on the two PRI tests (r = .31, p < .05), we controlled for individual preferences for risky options by entering riskiness at time 1 (risk1) as the first step. In the next, we controlled for the standardised mood scores at time 1 (z1Anx, z1Dep, and z1Fat). These may be correlated with


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time 2 moods, because of characteristic lifestyle influences on mood patterns. Finally, in step 3, we included the change scores for the three standardised negative mood variables. The effect of interest concerns the last step, which predicts a significant increase in R 2 for the combined effects of mood change, and, in particular, a significant positive value for the effect of change in fatigue (z2–1Fat). The results are summarised in Table 5. The results of step 3 confirm that an increase in state fatigue between the two PRI sessions is associated with an increase in riskiness (b = .574, p < .01). It also confirms the absence of effects associated with other mood variables. An overall indication of the size of the effect associated with the change in mood state over the two-week period is given by the increase of 13% in variance accounted for by step 3 (p < .05). In the absence of an overall change in riskiness, or of ceiling/ floor effects, the highly significant negative coefficients for Risk1 may be assumed to reflect the influence of regression to the mean, commonly observed in longitudinal studies. Subjects who are highly risky at time 1 are generally less risky at time 2, and vice versa. In addition, there is a significant effect of entering time 1 state fatigue at step 2 (b = .336, p > .05). This is difficult to interpret, but it implies an increase in riskiness for participants who were more tired at time 1. Because the zero-order correlation between zFat1 and risk2–1 is negligible (r = .136, p > .05), this implies the operation of suppressor effects at steps 1 and 2 (Cohen & Cohen, 1983).

TABLE 5 Summary of hierarchical regression analysis of riskiness (Study 2) Predictors

b(Step 1)

b(Step 2)

b(Step 3)

Step 1: Riskiness at time 1 Risk1

7.554***

7.644***

7.557***

Step 2: Mood at time 1 z1Anx z1Dept z1Fat

.018 7.046 .336*

Step 3: Change in mood z2–1Anx z2–1Dept z2–1Fat R2 DR2

.307***

.402*** .095

.078 .046 .681* .135 7.023 .574** .534*** .132*

Note: The table shows standardised b-weights for each step of the analysis. * p < .05, ** p < .01, *** p < .001.

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Effects of scenario characteristics In addition to making decisions about whether to take risky or safe options, participants also rated each scenario on a 1–5 scale for degree of familiarity, personal significance, and emotional impact. Each of these may have an effect on the way in which risk is interpreted or acted on, and moderate the effect of background states, such as fatigue and anxiety. In terms of main effects of these factors, we may expect more familiar everyday problems to reduce uncertainty about outcomes, so be considered less risky in absolute terms. This would predict an increased preference for the choices designated ‘‘risky’’ in our scenarios, with a corresponding decrease in perceived risk. Scenarios rated as more important are hypothesised to give rise to lower riskiness, as the outcome is perceived as more critical, and losses more imaginable (Arkes, Herren & Isen, 1988). Finally, the rated emotional impact of the scenarios is taken to be an index of the emotion generated by the problem itself. As such, it may be interpreted as a signal of potential loss and give rise to more cautious decision making (Damasio, 1993). The effects of these three variables are examined in turn later, in relation to state fatigue. In order to increase the power for these analyses, data from the two studies were combined (considering only the first PRI session of Study 2), and treated as a single sample of n = 92. Mean ratings on all three characteristics were in the middle of the 1–5 range, with no marked asymmetry: emotional impact 2.93 (SD = 0.61), familiarity 2.73 (0.62), importance 3.14 (0.51). For each analysis scenarios were sorted on a within-person basis into ‘‘high’’ (ratings of 4 or 5) and ‘‘low’’ (1 and 2), with scenarios rated 3 omitted. Riskiness scores for high and low items were averaged for each person. For some participants very few items were rated in a particular category (notably low familiarity, high importance, high emotion), so that riskiness scores were sometimes based on very small numbers. To avoid using measures of very low reliability, only averages based on at least 4 items at both low and high levels were included, resulting in a loss of 3–6 subjects for different analyses. Each of the three dichotomous scenario variables was included in separate mixed model ANOVAs, with state fatigue (defined as a three-level between-subjects factor by recoding zFat scores as low, medium, or high). The pattern of results for riskiness is shown in Figure 1. In all three cases, the analyses confirm the main effect of zFat shown by the correlational data. (For different analyses, degrees of freedom for the error term varied between 80 and 83, and F between 7.02 and 12.10; all ps < .01.) The main interest in these analyses was in the main effect of the scenario characteristic and interactions with zFat. Familiarity of scenarios. Riskiness of decisions was greater for scenarios rated as more familiar than those rated unfamiliar (5.59 vs. 4.89). There was also a difference in perceived risk for familiar and unfamiliar items (2.22 vs. 2.41).

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Figure 1. Riskiness ratings as a function of level of state fatigue and scenario characteristics: left panel, familiarity (fam); centre, importance (imp); right, emotional impact (emot).

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Thus, participants made more risky choices for familiar scenarios, but perceived these actions to be less risky than those for unfamiliar events. Analysis of the riskiness data (Figure 1, left panel) confirms the highly significant main effect of familiarity, F(1, 84) = 20.77, p < .001. Although the effect of zFat appears greater at high familiarity the interaction term was not significant, F(2, 84) = 1.89, p > .05. Importance of scenarios. Riskiness was lower for scenarios rated as more important (4.80 vs. 5.77), though there was little difference in perceived risk (2.26 vs. 2.32). The centre panel of Figure 1 shows the highly significant main effect of importance on riskiness, F(1, 81) = 66.50; p < .001, but also the interaction between importance and fatigue, F(2, 81) = 5.59; p < .01. Contrary to expectations, the overall effect of increased riskiness under state fatigue is stronger for scenarios rated as more important to the individual. Fatigue may be interpreted as reducing the normally higher level of caution with which more important decisions are taken. Emotionality of scenarios. There were no large differences between scenarios which had a strong or a weak emotional impact, either for riskiness (5.22 vs. 5.35) or perceived risk (2.32 vs. 2.36). Figure 1 (right panel) suggests that riskiness is reduced under the highest level of fatigue for items which attract a stronger emotional response. However, the analysis shows that there were no overall effects of emotionality, and no interaction with zFat (both Fs < 1). Effects of combined mood states A final analysis was carried out to investigate the possible effects of different combinations of mood dimensions. Although high levels of state fatigue have shown the most consistent association with riskiness, there was evidence in both studies of covarying changes in anxiety. Some theorists (e.g., Thayer, 1989; Tomkins, 1963) have argued that emotional states are dynamically interdependent, so that the implications for a person’s behaviour of high levels of depression or fatigue may depend on whether anxiety is also present. In addition, high anxiety may provide a more distinctive and enduring emotional context, augmenting effects of other changes (Taylor, 1991). Matthews and Westerman (1994) found an interaction between energy and tension [equivalent to our (low) fatigue and anxiety] on visual and memory search tasks. Expressed in the terms used here, anxiety impaired search performance, but did this more when subjects were less fatigued. In the context of decision making, it is not possible to make strong predictions about such interactions. Anxiety may predispose participants to avoiding the negative consequences of risky choices, and so reduce the riskpromotion effects of fatigue. Alternatively, we may expect it to further increase distraction from task processing, and therefore add to effects already present.


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In view of these possibilities, we decided to test the effect on the fatigue-risk relationship of differences in the level of state anxiety, again using the combined sample of 92 participants from Studies 1 and 2. Each of the state mood variables (zFat and zAnx) was recoded to give subgroups differing in combinations of energy and anxiety at the time of testing. Again, zFat was defined as low, medium, or high, however, because of the low numbers in some cells, zAnx could only be recoded as low or high. The two classifications of state mood were crossed to give six subgroups, and the effects on risk behaviour assessed through a 362 analysis of variance. The results for riskiness are shown in Figure 2. Consistent with the correlational data, there was a strong overall effect of zFat, F(2, 84) = 10.41, p < .001, and no main effect of zAnx F(1, 84) = 2.00, p > .05. There was, however, a small but significant interaction, F(2, 84) = 3.30, p < .05. As can be seen from Figure 2, the increase in risk with fatigue has a different pattern for low and high anxiety groups. For less anxious individuals, riskiness is increased at moderate levels of fatigue. However, for individuals who are more anxious at the time of testing, most of the effect on riskiness occurs in the transition from medium to high levels of fatigue. Thus, although state anxiety has no overall effect on risk

Figure 2.

Riskiness ratings as a function of state fatigue and state anxiety.

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behaviour in these studies, it appears to moderate the effects of fatigue. The pattern of results is consistent with the view that state anxiety predisposes individuals towards risk avoidance. Only at the highest level of fatigue is this effect nullified.

STUDY 3 The results of the first two studies show a large measure of agreement. They reveal stable effects of variations in state fatigue on risk behaviour. However, because they are based on naturally occurring mood states, they leave open the possibility that effects are due to other factors that covary with the observed mood changes. In order to check our interpretation, it is necessary to carry out an experimental study in which mood is manipulated. As we have already indicated, mood induction procedures may give rise to separate methodological problems, notably that state changes are often more complex than intended (Polivy, 1981). Nevertheless, they clearly provide much stronger internal validity than is possible with natural moods. In Study 3 the effect of mood changes on riskiness is investigated by using a manipulation designed to increase fatigue. However, because the most effective fatigue manipulations inevitably involve sustained periods of demanding work, it is also likely that anxiety (and possibly depression) will be increased. In this case, such effects may be partialled out by treating them as covariates.

METHOD Participants and design A total of 55 management trainees (32 men, 23 women), aged between 23 and 30 (mean 25: 4) years, were recruited for the study, representing all the participants in two successive five-day professional development courses for a large chemical engineering company. Because it was not possible to assign subjects randomly to treatment groups, the study effectively adopts a nonequivalent control group design (Cooke & Campbell, 1979). The first course group (n = 33) was designated the experimental group, receiving a mood-induction programme designed to increase fatigue. The second group (n = 22) acted as a control group, receiving no mood manipulation. A pre–post design was incorporated, in order to control for baseline mood changes from day 1 to day 2. The analysis was therefore carried out on post–pre change scores in PRI and mood scores.

Procedure All participants, in both groups, completed the PRI at the end of day 1 and again at the end of day 2, following either the mood manipulation or a period of free activity. The mood induction procedure was given as part of the training course


847

for the experimental group. It was carried out on the afternoon of the second day, with the PRI test and mood questionnaire immediately afterwards (as part of a batch of questionnaires, unconnected with the study). Trainees were given a set of demanding managerial planning exercises, lasting for about one hour, with little feedback or opportunities to exercise control. The exercises required the group to act as a team in carrying out a set of practical tasks, which could not be completed successfully because of ‘‘unexpected’’ problems and constraints. In previous work with management courses, this procedure has been found to induce a high level of stress, particularly a large increase in fatigue. In contrast, the control group spent the time in private study and unstructured group discussions. In order to obtain individual baselines for mood measures, all participants were asked to complete mood diaries for three weeks, as part of a ‘‘follow-up’’ to the training course. This took place about a month later. They were not paid for their participation, but received feedback at the end of the study (following the diary collection) on their mood pattern and its relation to risk taking.

RESULTS AND DISCUSSION Manipulation check Table 6 summarises the pre- and post-manipulation data for mood and risk variables, as well as the change scores. There was a strong overall effect of the mood manipulation. Change scores showed a market effect of the manipulation for the experimental group (shifts of around half a standard deviation for zAnx and zFat, and slightly less for zDep). There were no strong effects for the control group. To test for the effect of the manipulation, change scores for the three mood variables were subjected to a multivariate analysis of variance

TABLE 6 Mean pre- and post-test levels of moods and risk measures for control (C) and experimental (E) groups, with change (post± pre) scores (Study 3) Control

Measure

zAnx zDep zFat Risk P-Risk

Experimental

Pre

Post

Change

Pre

Post

Change

E–C (change)

.13 7.15 7.02

.24 .02 7.24

+.11 +.17 7.22

.17 7.15 7.04

.74 .25 .46

+.57 +.40 +.50

+.46 +.23 +.72

5.46 2.32

5.50 2.26

+.04 7.06

5.32 2.38

5.83 2.51

+.51 +.13

+.47 +.19

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(MANOVA) with group as the between-subjects factor. The multivariate test was highly significant on all criteria, F(3, 51) = 8.71, p < .001. Univariate tests revealed significant differences between conditions for zFat: F(1, 53) = 18.97, p < .001, and zAnx: F(1, 53) = 12.75, p < .001, but not for zDep: F(1, 53) < 1. (As can be seen in Table 6, although there is an increase of .34 in zDep for the E group, there is also an increase of .17 for group C.) Thus, following the mood induction procedure, subjects in the experimental group were more tired and anxious, but not more depressed, than the control group.

Effects of induced fatigue on risk behaviour Table 6 also shows that the mood manipulation produced a marked increase in risk, as well as an apparent increase in perceived risk. A MANOVA carried out on the change scores for riskiness and perceived risk showed an overall multivariate effect of the manipulation: F(2, 52) = 8.45, p < .001, with significant univariate effects on both riskiness: F(1, 53) = 16.48, p < .001, and perceived risk: F(1, 53) = 4.62, p < .05. The main goal of the manipulation was to induce fatigue, but as reported earlier, there were inevitably concomitant changes in other mood variables, which may have contributed to the observed effects. In order to test for this, MANCOVA examined the effect of conditions on the risk changes, with zAnx and zDep change entered as covariates. Multivariate tests confirmed the overall effect of conditions: F(2, 50) = 5.36, p < .01, and univariate tests that the effect was significant for riskiness: F(1, 51) = 10.32, p < .01. Although perceived risk also increased as a result of the manipulation, the univariate effect was not significant: F(1, 51) = 3.14, p > .05. In addition, there were no effects of either covariate on multivariate or univariate tests (all ps > .05). As a final check, zFat change was entered as the covariate instead. In this case, the effect of conditions was no longer significant on the multivariate test: F(2, 51) = 3.05, p > .05, but there was now an effect of the covariate itself: F(2, 51) = 3.60, p < .05. Univariate tests again showed that this was confined to riskiness: F(1, 52) = 7.02, p < .05, with no effect on perceived risk (F < 1). In addition, as in Studies 1 and 2, there was also a correlation between risk and fatigue at time 1. For the combined group on the pre-test, riskiness correlated with zFat (r = .34, p < .05), but not with zAnx (r = .09) or zDep (r = 7.19). These results confirm the relatively strong effect of state fatigue on riskiness, in comparison with other negative mood states. However, it should be noted that the average post-induction level of anxiety in the experimental group was high (z = 0.74, compared to 0.4 and 70.2 in Studies 1 and 2). Taken in conjunction with the results illustrated in Figure 2, it remains an interesting possibility that a high level of background anxiety facilitates increased risk taking under fatigue.


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GENERAL DISCUSSION The findings from the three studies show that the degree of risk taken in everyday decision making may be affected by variations in state mood. In addressing the five research questions raised earlier, they go some way towards understanding how mood affects risk, although they also raise a number of new questions. In all studies, and for all comparisons, the observed risk effects apply primarily to the degree of preference expressed for the risky or safe alternative. Little effect of mood was observed on judgements of how risky the decision was. Our first research question asked whether there were differences between the three negative moods. The findings are unequivocal in showing that the strongest effects on risk behaviour occurred with changes in fatigue, with little effect of concomitant changes in anxiety or depression. This was surprising, given the large literature on effects of anxiety and depression on social judgement and cognition, and also the less specific literature on effects of ‘‘negative mood’’ (which typically refers to anxiety or depression, rather than fatigue). The second question concerned differences between state- and trait-type mood effects. We were able to distinguish between current mood and more stable patterns of affect by obtaining two separate state measures. In addition to measuring raw scores, the use of a standard score transformation enabled us to correct for individual differences in average level and variability of reporting for each mood variable. Although raw scores produced the same pattern of effects as standardised scores, these are subject to bias from participant response style and stable differences in day-to-day levels of mood reporting. Our use of standardised scores allows us to conclude that the observed effects of fatigue on risk taking are associated specifically with state changes, rather than with stable mood differences. The third and fourth research questions related to possible moderating effects of state anxiety and the personal significance of scenarios on the fatigue-risk relationship in Study 2. Although there was no overall effect of state anxiety, fatigue effects were found to depend on whether individuals were anxious or not at the time of testing. At low or moderate levels of fatigue more anxious individuals showed a preference for the safe option, consistent with the view that anxiety promotes an increased concern for avoiding losses. However, when fatigue was very high the effect disappeared, and choices were more risky at both levels of anxiety. There was also a moderating effect of the personal importance of scenarios. The increase in riskiness under fatigue was greater for more important decisions, but there was no effect of the familiarity or emotional impact of scenarios. These findings are considered more fully later, in relation to our interpretation of the central effects of state fatigue.

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Finally, we asked whether the effects of naturally occurring negative moods could be replicated by inducing mood changes experimentally. It is possible that the unusual pattern of mood effects obtained in Studies 1 and 2 may partly reflect the difference between effects of natural moods and those of induced moods in other studies. However, the results of Study 3 showed the same overall pattern when fatigue was induced, rather than simply measured. In addition, the stronger control of such a study provides a stronger basis for assuming a causal interpretation of the effects of fatigue on risk. The present findings do not fit easily into the current mood/risk literature. Although they are superficially consistent with some findings of increased risk under negative mood (Leith & Baumeister, 1996; Mano, 1992), the specific link with fatigue, rather than with anxiety and depression, is at odds with the more usual operational definition of negative mood effects. There is also an apparent discrepancy between our results and those of Pietromonaco and Rook (1987). Using broadly similar hypothetical everyday scenarios, they found a reduction in riskiness in students with high levels of depression, whereas we found no such effects. An important difference may be that Pietromonaco and Rook defined depression in terms of BDI scores. These clearly represent a more stable and motivationally distinctive affective state than the day-to-day changes in background mood measured here. It is conceivable that the use of hypothetical, rather than real, decisions is a relevant factor in the observed pattern of effects, and it would be valuable to test effects of fatigue (and other negative moods) in actual decision situations. However, we believe that participants’ responses to our scenarios represent at least a predisposition to act in safe or risky ways in real situations. The summary data reinforce this view. The scenarios were clearly not considered trivial by participants, as mean ratings of both importance and emotional impact were around the midpoint on the 1–5 scale. The central interpretative problem is how to explain the effects of fatigue on risk behaviour. As we have previously argued, the human performance literature makes a strong link between risk and fatigue, but there is a conceptual discrepancy. What is meant by ‘‘risk’’ in this context is the adoption of a low effort information-processing style—cutting corners and preferring long shots in solving problems (Hockey, 1997; Holding, 1983), or making judgements (Webster et al., 1996). On the surface, this is more like the heuristic processing typically associated with positive mood states, rather than the analytic style expected of negative states (Schwarz & Bless, 1991). However, this may be misleading. As we said earlier, fatigue is not a typical negative emotion. Rather than giving rise to problem-focused processing, it appears to operate more as a signal for reducing engagement in active information processing. In what way is such a state likely to elicit risky choices in decision making? It would not be surprising for fatigued individuals to select risky options


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if a safe course of action involved a higher effort commitment, because effort conservation is a strong feature of the fatigued state. However, this is not the case here. As mentioned earlier, PRI scenarios were balanced in terms of the effort requirements of risky and safe options. Greater effort was implied in some items by taking the safe option, and in others by taking the risky option; still others showed no difference. A simple comparison between the safe/effort and risky/effort types (both based on mean responses to five items) shows that the correlation between riskiness and fatigue is slightly higher for safe/effort items, both for Study 1 (r = .454 vs .351) and Study 2 (.422 vs. .343). However, the effect is present (and significant) for both types of scenario, and a test for differences between non-independent correlations (Steiger, 1980) shows that the r-values do not differ significantly, t(34) = 0.63, and t(53) = 0.65; both ps > .05. Given the fatigue/performance literature (see Hockey, 1997), it is likely that effort considerations do have a role to play in determining the choice of actions under fatigue. However, the present series of studies shows that risky choices are made more often whether they involve more effort or less. A second possibility is that a natural bias exists in favour of risky options, because of the possible gains that such choices offer. Under normal conditions, such bias may be overridden by an inhibitory self-control process, allowing safe choices to be selected where appropriate. However, such control may be less effective under fatigue, because such a state is often the end result of extended regulatory control activity (Hockey, 1997). Muraven, Tice, and Baumeister (1998) have shown that this kind of manipulation strongly depletes the capacity for further self-control, as inferred from impaired performance on a range of tasks administered after the manipulation. This is an intriguing possibility, although at this stage, it must be considered largely speculative. One testable prediction is that fatigue will have a greater effect on increased riskiness whenever a choice situation is characterised by a preference for processing information about possible gains. Under these circumstances, less effective control under fatigue will increase the likelihood that the greater gains associated with the risky outcome will result in the choice of the risky alternative. There is some evidence that increased risk taking is associated with less effortful processing strategies. Mann and Ball (1994) showed that more risky choices followed briefer information searches, focusing more on gains than losses. Unfortunately, there is little direct research on the dynamic aspects of information use in risky choice behaviour—the order in which information is used in reaching decisions. This is a promising area for further work on mood effects in decision making. In addition to clarifying the basis of the main effect of fatigue in these studies, any new research must consider the unexpected pattern of interactions with importance and anxiety. The effect of importance is broadly consistent

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with the above regulatory hypothesis. Important items are those where the outcome matters to the individual, and show a much stronger endorsement of the safe option (through self-control?). Under fatigue, the loss of control capacity will then fail to provide protection from the attractiveness of the risky option. We are testing this interpretation in current work, by examining decision processes directly (in terms of the temporal pattern of information use), and by measuring the attractiveness and subjective probabilities of gains and losses. On the same reasoning, safe choices may be made more readily under anxiety because of increased inhibitory control, which protects against the possibility of loss. This is consistent with the view that analytic processing is increased under negative affect (Schwarz & Bless, 1991; Taylor, 1991). The hypothesised impaired control under high fatigue would then reduce the effectiveness of this protective function, leading to an increase in risky choices. If this interpretation is true, analytic processing under negative affect should occur only under low levels of fatigue (i.e., when subjects are alert and energetic). The regulation hypothesis needs to be tested formally, by manipulating regulatory capacity independently of fatigue and anxiety, for example using Muraven et al.’s (1998) regulatory loading tasks. In addition, we need to compare the effects of the background emotional state associated with current mood and the emotion generated by the decision problem itself. Natural state anxiety may not be strongly tied to problem information, and may have different motivational implications from anxiety generated by negatively valenced decision scenarios. For example, the increased use of analytic processing under negative affect may be more pronounced when the decision problem is, itself, the source of the state change. Such effects may also be less likely to occur when regulatory control capacity is depleted by tiredness or by previous activity. We are currently exploring these questions in experiments that manipulate the emotional context of decision problems independently of background mood. Manuscript received 4 November 1998 Revised manuscript received 5 January 2000

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