2009 the future is bright effects of mood on perception

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R E S E A R C H P A P E R

The Future is Bright? Effects of Mood on Perception

of the Future

Silvia R. Hepburn Thorsten Barnhofer J. Mark G. Williams

Published online: 4 June 2008 Springer Science+Business Media B.V. 2008

Abstract Most people believe that the future will bring them more good things than bad,

and therefore have high hopes for the future (MacLeod et al. Cogn Emot 10:6985, 1996).

However, many patients with mood disorders do not hold this positive belief about the

future. At the extreme, low expectations of positive outcomes in the future can lead to

feelings of hopelessness (OConnor et al. Psychol Health Med 5:155161, 2000). This

paper aims to extend the literature on subjective probability of future events, using a mood

induction paradigm to examine the effects of transient mood change on perceived likeli-hood of future events in a non-clinical community sample. Participants rated likelihood of

future events from a standardized list and from their own lives. Ratings were made in both

normal and experimentally-induced positive or negative mood. Results show that self-

generated future events were perceived to be more likely than those from a standardized

list, and that negative mood significantly biased perceived likelihood of other-generated

future events. Participants rating standardized list events saw positive outcomes as less

likely and negative outcomes as more likely in induced negative mood than they did in

normal mood. Mood had no effect on ratings of self-generated events. Possible directions

for future research are discussed.

Keywords Future thinking Mood induction Subjective probability

Hopelessness Cognitive bias

Our attitude towards the future is influenced by our beliefs about how likely it is that good

things will happen to us relative to bad things (MacLeod and Tarbuck1994). Pessimism

about the future is one component of Becks depressive thinking triad, along with

S. R. Hepburn T. Barnhofer J. M. G. Williams

University of Oxford, Oxford, UK

S. R. Hepburn (&)

Department of Psychology, Institute of Psychiatry, Kings College London, De Crespigny Park,

P.O. Box 78, London SE5 8AF, UK

e-mail: [email protected]

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J Happiness Stud (2009) 10:483496

DOI 10.1007/s10902-008-9102-9


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negativity about the self and the world (Beck et al. 1979). Reduced positive expectations

about the future have been shown to relate to feelings of hopelessness in suicidal groups

(Conaghan and Davidson 2002; OConnor et al. 2000). This study investigates the

potential role of mood in biasing future-oriented judgment. Johnson and Tversky (1983)

have shown that manipulating peoples mood affects their perception of the risk of neg-ative future events. This study uses a mood induction paradigm to examine the effect of

mood state on subjective probability ratings of positive and negative self-generated and

other-generated future events made by a non-depressed community sample.

The existing literature on future-oriented judgment consists of two main types of study.

The first examines the ability to generate examples of positive and negative future events

within a given time limit. Fluency for positive future events has been found to be reduced

in suicidal and depressed individuals (e.g. Conaghan and Davidson 2002; Hunter and

OConnor 2003; MacLeod et al. 1997). Future fluency studies are reviewed elsewhere

(Hepburn et al. 2006; MacLeod 1999). The second type of study examines subjective

probability by asking participants to rate how likely they believe particular hypothetical

events are to happen in their personal future. MacLeod et al. (1996) showed that people

without mental health problems believed good things were more likely to happen to them

than bad things, but the same is not true of dysphoric groups (e.g. MacLeod and Cropley

1995). Studies comparing estimates of future event likelihood made by clinical and non-

clinical groups have reported mixed findings. For negative future events, studies concur

that individuals with dysphoric mood give higher likelihood estimates than controls

(depressed patients: Butler and Mathews 1983; MacLeod et al. 1997; dysphoric students:

Andersen et al. 1992; Pietromonaco and Markus1985). For positive future events, some

studies report that individuals with mood disturbance give lower estimates of positivefuture events than controls (depressed patients: MacLeod and Cropley 1995; Pysczcynski

and Greenberg1987; dysphoric students: Andersen et al.1992) but others found no group

differences (dysphoric students: Pietromonaco and Markus 1985; depressed patients:

Butler and Mathews1983).

These mixed results are due in part to the fact that participants are rating different lists

of events (MacLeod 1999). Two factors are noteworthy. First, some studies used lists

which consisted of very few items. Second, the studies presumed that events were salient

for participants. This is important because people tend to rate things they feel do not apply

to them as less likely than personally relevant examples (MacLeod and Tarbuck 1994;

Tyler and Lomax Cook1984). MacLeod and colleagues suggested that event salience maybe more important than valence in informing probability judgments (MacLeod et al.1997).

One way to ensure that events are personally relevant is to ask each person to generate their

own list of future events.

Another question is left unanswered by the existing literature. The studies mentioned

above found differences in perceived likelihood of future events between depressed and non-

depressed individuals. However, such samples also differ dramatically in mood, which was

not controlled by these studies. Hence the possibility remains that differences in the existing

literature are driven simply by differences in prevailing mood (i.e. dysphoric in the depressed

group, euthymic in controls). Mood affects attention, memory and many other cognitiveprocesses (Williams et al.1997), and biases decision-making (Schwarz and Clore2003).

To investigate whether mood has a biasing effect on judgments of future event likeli-

hood, we conducted an analogue study of the effects of experimentally-induced mood on

perceived likelihood in a non-clinical community sample. Manipulation of mood in the

laboratory is well-established in experimental psychology research, using film clips, music,

memories or self-referent statements to alter mood state briefly (Westermann et al. 1998).

484 S. R. Hepburn et al.

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MacLeod and Campbell (1992) looked at differences in likelihood judgments between

groups receiving positive versus negative mood induction, and found differences for both

positive and negative outcomes. However, their study had no baseline comparison. The

current study looks at within-subject changes in likelihood judgments across mood states.

This is analogous to normal transient mood shifts, and therefore has some ecologicalvalidity. Ethical considerations preclude the possibility of exposing clinical patients to

mood induction procedures, so we use an analogue sample. This limits the generalizability

of findings to clinical populations, because laboratory-induced mood is transient and the

relationship between this and depressed mood in clinical patients is uncertain (Clark1983).

However, the current study takes the preliminary step of establishing whether or not

judgments of future event likelihood in non-depressed individuals are subject to mood

biasing effects.

The current study extends the literature on subjective probability in three ways. First, the

standardized list of events used is longer than in previous studies, and some studies suggest

increased reliability with additional items (Cortina 1993). Second, current participants rate

events from their own personal future as well as events from a standardized list, bringing

increased salience (MacLeod and Tarbuck1994). Third, instead of comparing a depressed

group to non-clinical controls, a mood induction paradigm is used to elicit within-subject

comparisons of a non-depressed community sample in different mood states. This allows

examination of the extent to which likelihood ratings are susceptible to transient mood change.

Non-clinical volunteers rated self- and other-generated future events following a labo-

ratory-based musical induction of either positive or negative mood (Velten1968), and these

ratings were compared to ratings of the same events made in normal mood during a follow-up

telephone call. We expected to replicate previous findings (e.g. MacLeod et al. 1996) ofhigher likelihood ratings for positive events than for negative ones in this non-clinical sample,

higher likelihood ratings were expected for self-generated events than for the less salient

standardized list events (MacLeod and Tarbuck 1994). Depressed patients show higher

probability ratings for negative events and lower ratings for positive events than controls with

no mood disturbance (MacLeod et al. 1997). Hence we expected our analogue sample to

show higher probability ratings for negative events and lower probability ratings for positive

events in induced negative mood than in normal mood. These hypotheses were informed by

comparisons between control groups and patients with depressed (rather than elevated) mood,

so no hypotheses were made for the positive mood induction. Recent research (Huppert and

Whittington2003; MacLeod and Moore2000) supports earlier suggestions (Watson et al.1988) that positive and negative affect1 are best seen as orthogonal dimensions, rather than

opposite ends of a single dimension. Hence it seems inappropriate to hypothesise that the

effect of positive mood would simply be opposite to that of negative mood.

1 Method

1.1 Participants

Fifty-two non-depressed volunteers responded to advertisements on community notice

boards, websites and university mailing lists. Participants were aged between 18 and 65,

1 The terms affect and mood are used interchangeably by Watson and colleagues, though some

psychologists prefer to distinguish them, with affect referring to the behavioural expression of a mood,

rather than the mood itself.

The Future is Bright? 485

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and were in the non-clinical range (M= 5.13, SD = 3.88) on the Beck Depression

Inventory (BDI-II; Beck et al.1996). They were randomly allocated to receive the positive

mood induction (Positive MI group, n = 26) or the negative mood induction (Negative MI

group, n = 26). Within-subject conditions were used to control for time and repeated

ratings, rather than including a third group undergoing neutral mood induction. Thismethod is more analogous to real-life mood shifts and may have more ecological validity

than an induced neutral mood state.

1.2 Materials

1.2.1 Depression

The BDI-II (Beck et al. 1996) was used to ascertain that participants were not currently

depressed. This self-report questionnaire has high internal consistency, validity and reli-

ability in clinical and non-clinical samples (Beck et al. 1988).

1.2.2 Mood Ratings

Participants rated mood on visual analogue scales, marking a 100 mm line to indicate their

sadness and happiness at that moment (not at all to extremely). In the follow-up call,

participants were reminded of the line and asked to give a number between 0 and 100

corresponding to where they would put their mark. Mood ratings were made throughout the

study, including immediately before and after mood induction.

1.2.3 Self-generated Events

Personally relevant future events were generated using the Personal Future Task (PFT;

MacLeod et al.1993). In an adapted version of the original task, participants were asked to

think of events they were looking forward to or not looking forward to over four time

periods in the future (next week, month, year, 510 years). For each trial, participants

generated as many examples as possible in 30 s. The eight trials were divided into blocks

of four (Block A: positive week, negative month, positive year, negative 510 years; Block

B: negative week, positive month, negative year, positive 510 years). Order of trials was

counterbalanced within-group by block, so that half the participants had Block A as their

first set of events (pre-induction) and Block B as their second (post-induction), while the

others had the reverse. Fluency for this task (number of events generated per trial) is

reported elsewhere, along with the effects of mood on fluency (Hepburn et al. 2006).

Participants gave ratings of the valence of their self-generated events, in order to check

they were positive or negative as required. Overall, negative events had a larger spread of

valence ratings than positive ones, perhaps reflecting a tendency to categorize more neutral

items as negative rather than positive. The self-generated items were similar to the events

on the standardized list, with some overlap in content. In order to make other-generated

events applicable to most participants, their tone was more general than the self-generatedevents (e.g. self: going to see my friend John in London at the weekend versus other:

meeting up with friends). As in previous studies, events expected in the distant future

were less specific than imminent events, but a preliminary analysis found no effects of time

period.


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1.2.4 Other-generated Events

A standardized list of 48 hypothetical events (6 positive and 6 negative events for 4 time

periods) was developed as follows. A written version of the PFT was administered to a

pilot sample of 15 non-depressed volunteers (graduate students aged 2435, 8 females, 13white). Responses were adapted to be less idiosyncratic, and then combined with those

used in MacLeods work (MacLeod et al.1991,1996,1997; MacLeod and Cropley1995).

A shortlist of 64 items was then rated for likelihood and valence on 7-point scales, by a

convenience sample of eight non-depressed volunteers (university employees aged 1955,

all white females). The final list of 24 positive and 24 negative items had clear positive or

negative valence (events were cut if mean positivity and negativity ratings differed by less

than 1 point), and were perceived as possible but not inevitable (events were cut if mean

likelihood lay below 2.5 or above 5.5). The final list was divided into two sub-lists. Post-

induction, participants rated the likelihood of events from one of the two sub-lists. At

follow-up all participants rated both sub-lists of events in recovered mood.

1.2.5 Likelihood Ratings

Participants rated how likely each event was to happen, using a scale of 1 (not at all) to 7

(extremely). The experimenter read items aloud and wrote down participant responses.

Mean ratings were calculated for each category of events (positive self-generated, negative

self-generated, positive other-generated, negative other-generated) for each phase of the

experiment.

1.3 Procedure

1.3.1 Pre-induction Phase (Laboratory, Before Mood Induction)

Volunteers were sent an information sheet before their assessment, and the experimenter

went through this with them in person before consent was taken. The sheet explained that

they were equally likely to be allocated to receive a positive versus a negative mood

induction. Participants answered socio-demographic questions and completed the BDI-II.

In the first block of the PFT, they generated their first set of self-generated future events,which they rated for likelihood. Then the mood induction was administered for 8 min.

1.3.2 Mood Induction

The mood induction procedures involved participants reading 30 uplifting or depressogenic

self-statements (Velten 1968) while listening to appropriate music (Positive: Gigue from

Corellis Violin Sonata, opus 5.9; Negative: Russia Under the Mongolian Yoke by Pro-

kofiev, at half speed). Participants reflected on the statements in their own time for eight

minutes while music played. Music was played at low volume throughout the post-induction phase. A booster was administered for two minutes (after the second block of

PFT), during which the volume was increased and participants were asked to focus again

on the statements. The purpose of the mood induction was made explicit; participants were

asked to go with the feelings it evoked. Although this raises possible demand effects,

Research Ethics Committees in the United Kingdom prefer instructions to be explicit, and


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meta-analysis suggests that demand effects cannot fully account for observed mood shifts

(Westermann et al. 1998).

1.3.3 Post-induction Phase (Laboratory, After Mood Induction)

Music was played at reduced volume throughout the post-induction phase. Participants

generated their second set of self-generated future events on the second block of the PFT,

and rated these for likelihood. A mood booster was then administered for two minutes.

Participants rated the likelihood of other-generated events, from the standardized list.

Finally, arrangements were made for a follow-up telephone call no more than 24 h after the

end of the session (M= 15.3 h, SD = 8.16). The experimenter checked how participants

were feeling before they left the laboratory and discussed the experience of the mood

induction with them, but a full debrief was left until the end of the follow-up phone call. A

positive mood induction was made available for all members of the negative MI group, but

none took up the offer.

1.3.4 Follow-up (Telephone Call, Within 24 h of Laboratory Session)

In the follow-up call, participants rated their mood. They gave likelihood ratings for self-

generated events (first, then second, set), and other-generated events (list not yet rated, then

list rated during post-induction phase). They were thanked and debriefed.

1.3.5 Rationale for the Procedure

The follow-up phase was an integral part of the study design. The current procedure

was designed to accommodate two research questions on mood bias. As well as the

current question of mood effects on perceived likelihood of future events, we were also

interested in its effects on fluency for generating future events (i.e. number of events

generated in a given time period). Fluency for future events was measured using the

PFT pre- and post-mood induction (as described in Hepburn et al. 2006). The current

study used ratings of these self-generated events. It was important to compare ratings of

the same events made in different mood states: a difference in ratings of one set of

events rated pre-mood induction and ratings of a different set of events post-induction

might be attributable to different events being rated. It was important to present the

standardized list of events after the PFT was complete, as priming from the standardized

list might have contaminated subsequent events generated by participants. These com-

plications necessitated the use of a follow-up phase in normal mood, so that

standardized items could be rated in normal mood subsequent to the PFT. For con-

sistency and comparison, the same procedure was used for self-generated items, with

ratings in induced mood made post-induction, and re-ratings made at follow-up. This

also eliminated the risk of participants remembering their initial responses from pre-

induction when re-rating events a few minutes later post-induction. The telephone callwas arranged soon enough after the session that self-events events were still relevant,

but long enough afterwards for mood to return to normal. A statistical comparison

found that mood at follow-up was not significantly different from mood at baseline (all

p[ .90).


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2 Results

2.1 Sample Characteristics

Group means for demographic variables are given in Table 1, along with tests of signifi-cance for group differences. The groups were comparable on age, education and depression

scores, and there were no group differences in distribution of gender, students or rela-

tionship status.

2.2 Mood Manipulation Check

To check the effectiveness of the mood inductions, happiness and sadness ratings over time

were examined in two repeated-measures analyses of variance. These followed the form

Group (positive MI, negative MI) 9 Time (pre-induction, post-induction, follow-up).

Means are given in Fig. 1.

Significant Group 9 Time interactions were found for both happiness,

F(1,50) = 26.41, p\ .001, g2= .35, and sadness, F(1,50) = 13.77, p\ .001, g

2= .50.

The interactions were followed up with pair-wise post-hoc comparisons (Bonferroni-

corrected) which indicated three main things. First, both mood inductions were effective in

changing mood, though the negative one resulted in more mood change than the positive.

The positive MI significantly increased ratings of happiness compared to baseline,

Mi-j = 9.46, SE= 3.18, p\ .02, but did not affect sadness, Mi-j = 2.35, SE= 2.59,

p[ .90. The negative MI significantly decreased happiness, Mi-j = 22.27, SE= 3.18,

p\ .001, as well as increasing sadness, Mi-j = 20.19, SE= 2.59, p\ .001, compared tobaseline. Second, the interactions indicate group differences in mood over time. Post-

mood induction, the positive MI group was significantly happier than the negative MI

group, Mi-j = 33.19, SE= 5.49, p\ .001, and the negative MI group were significantly

sadder than the positive MI group,Mi-j = 30.04,SE= 4.64,p\ .001. The groups did not

differ in happiness either pre-induction or at follow-up (all p[ .10). Pre-induction, there

was a trend for higher baseline sadness in the negative MI group (perhaps a short-term

result of being informed of their group allocation), but this was not significant,

Mi-j = 7.50, SE= 4.07, p = .07. The mean group difference in sadness was identical

between pre-induction and follow-up, Mi-j = 7.50, SE= 5.31, p = .16, so pre-induction

and follow-up mood can be seen as equivalent. Third, the analysis showed that happiness

Table 1 Group means and comparisons on demographic variables

Positive MI

group n = 26

Negative MI

group n = 26

Test of difference

Age (yrs) 25.46 (6.04) 25.92 (8.41) F(1,51) = .05, p = .82, r= .03

Age completed

education (yrs)

23.85 (3.39) 23.00 (3.46) F(1,51) = .80, p = .38, r= .13

Depression 4.65 (3.46) 5.62 (4.26) F(1,51) = .80, p = .38, r= .13

Gender 10 Male 9 Male v2(1,n = 52) = .08,p = .77, CramersV= .04

Student or not 18 Students 17 Students v2(1,n = 52) = .09,p = .77, CramersV= .04

Relationship

status

13 In relationship 15 In relationship v2(1,n = 52) = .08,p = .78, CramersV= .07


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and sadness at the time of the follow-up phone call did not significantly differ from

happiness and sadness in the pre-induction phase (all p[ .90).

2.3 Likelihood Ratings of Future Events

The main analysis compared likelihood ratings made by the two groups over time. Ratings

of individual items were averaged to give mean ratings for self- and other-generated events

of positive and negative valence. Means are shown in Fig. 2. Analysis compared ratings

made post-induction with re-ratings made at follow-up. It was predicted that self-generated

0

10

20

30

40

50

6070

80

90

100

Pre- mood induction

MoodRatin

g

Happiness (Pos MI) Happiness (Neg MI)

Sadness (Pos MI) Sadness (Neg MI)

Post- mood induction Follow-up

Fig. 1 Visual analogue scale ratings of happy and sad mood over time

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

Post-inductionTime

Likelihoodrating(1=notatall,

7=extremely)

Positive MI self-gen pos event Positive MI self-gen neg event

Negative MI self-gen pos event Negative MI self-gen neg event

Positive MI other-gen pos event Positive MI other-gen neg event

Negative MI other-gen pos event Negative MI other-gen neg event

Follow-up

Fig. 2 Mean perceived likelihood of self-generated and other-generated positive and negative future events

rated post-mood induction and at follow-up


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events would be rated more likely than other-generated events. Compared to normal mood,

we predicted that negative mood would cause participants to rate positive events as less

likely and negative events as more likely.

Preliminary analysis of likelihood ratings used a 4-way repeated-measures ANOVA:

Group (positive MI, negative MI) 9 Time (post-induction, follow-up) 9 Event (self-generated, other-generated) 9 Valence (positive, negative). This yielded significant main

effects of event, F(1,50) = 149.26, p\ .001, g2= .75, and valence, F(1,50) = 48.40,

p\ .001, g2= .49, and an Event 9 Valence interaction, F(1,50) = 5.31, p\ .05,

g2= .10. This interaction indicated that both groups deemed positive self-generated events

most likely, followed by negative self-generated events, positive other-generated events,

and negative other-generated events. To investigate the other significant findings from the

4-way analysis further (Group 9 Valence interaction, F(1,50), p\ .05, g2= .08,

Time 9 Valence interaction, F(1,50) = 23.25, p\ .001, g2= .32, Group 9

Time 9 Valence interaction, F(1,50) = 7.35, p\ .01, g2= .13), we examined ratings of

self-generated and other-generated events separately. Two analyses of variance were

performed, in the form Group (positive MI, negative MI) 9 Time (post-induction, follow-

up) 9 Valence (positive, negative).

2.3.1 Other-generated Events

For other-generated events (on Fig. 2, the lower four lines), analysis found a main effect of

valence, F(1,49) = 49.59, p\ .001, g2= .50, and interactions between group and

valence, F(1,49) = 4.95, p\ .05, g2= .09, and time and valence, F(1,49) = 13.98,

p\ .001, g2

= .22. These were qualified by a Group 9 Time 9 Valence interaction,F(1,49) = 7.75, p\ .01, g

2= .14, which was followed up with post-hoc pair-wise com-

parisons (Bonferroni). These showed that induced positive mood had no significant effect

for either positive or negative events (p[ .10). However, induced negative mood

decreased perceived likelihood of positive events, Mi-j = .27, SE= .08, p\ .01, and

increased perceived likelihood of negative events, Mi-j = .33, SE= .12, p\ .01. The

analysis was re-run with sub-list as a factor to control for the different sub-lists used, but

results remained virtually unchanged.

2.3.2 Self-generated Events

For self-generated events (on Fig. 2, the higher four lines), results were less clear-cut.

There was a significant Time 9 Valence interaction, F(1,49) = 5.82, p\ .05, g2= .11,

and main effect of valence, F(1,50) = 5.42, p\ .05, g2 = .10, but no effect of time

(p = .43). Pair-wise post-hoc comparisons (Bonferroni) of the Time 9 Valence interaction

showed that positive events were viewed as more likely than negative events, both at post-

induction, Mi-j = .14, SE= .05, p\ .01, and at follow-up, Mi-j = .61, SE= .18,

p\ .01. This finding was predicted, as the sample was non-depressed (MacLeod et al.

1996). Positive events were rated less likely post-induction than at follow-up, Mi-j = .14,

SE= .05, p\ .01, but mood induction did not significantly affect ratings of negativeevents (p = .36). This may reflect the fact that these non-clinical participants generated

relatively few negative events in the fluency task (indeed, some participants generated

none, reducing the power of this particular test). Perhaps such events were relatively

inaccessible for them during the generation task (Hepburn et al. 2006; Newby-Clark and

Ross 2003). To control for re-rating the events, a change score was calculated for repeat


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ratings of self-generated events from pre-induction to follow-up (i.e. irrespective of mood).

Re-running the analysis with this change score as a covariate did not affect results.

3 Discussion

This study aimed to extend the literature on subjectivity probability of future events.

Previous research shows that dysphoric individuals typically perceive future bad outcomes

as more likely than controls (e.g. Butler and Mathews 1983; MacLeod et al.1997). Some,

but not all, studies also report that mood-disturbed groups perceive future good events as

less likely (e.g. MacLeod and Cropley 1995, but c.f. Pietromonaco and Markus 1985).

Inconsistencies may result from studies: (a) using lists of events of differing length and

sometimes low reliability, (b) using events which may not have been salient for partici-

pants, and (c) not controlling for mood effects. The current study addressed these issues by

asking non-dysphoric individuals to rate idiosyncratic, personally-relevant future events

they had generated themselves, as well as an extended standardized list of events, under

different mood conditions. Within-subject comparisons showed significant effects of

experimentally-induced negative mood on estimates of future event likelihood. We first

discuss findings from the comparison between self- and other-generated events, before

considering the effects of mood, the limitations of the study, and directions for future

research.

The analysis showed that participants perceived self-generated future events to be more

likely than events from the standardized list, just as MacLeod et al. (1997) predicted. This

may be because other-generated events are harder to imagine, being less personally rele-vant. In this study, mood had differential effects on ratings of other-generated and self-

generated events. Compared to their ratings under normal mood, negative mood made

participants perceive negative other-generated events as more likely, but no such effect

was found for negative self-generated events. This discrepancy should be interpreted with

caution, given the studys limitations, outlined below. However, there are some interesting

potential explanations for this finding.

One would be that self-generated events are buffered from mood effects by the con-

textual details surrounding them, compared to events from the standardized list. Buehler

and McFarland (2001) found that participants tend to focus attention narrowly on events

when provided with hypothetical situations, neglecting to consider contextual information.With their participants, a narrow focus resulted in stereotypical, script-based responses

which were relatively automatic. When depressed people make judgments about the future,

they engage in low-effort, semi-automatic thinking (Andersen et al. 1992; Andersen and

Limpert2001), so their probability judgments may begin as a direct read-out of their mood.

In judging the likelihood of other-generated events in negative mood, our participants may

also have been responding semi-automatically rather than considering the event in a real-

life context. In contrast, if their self-generated events were more grounded in contextual

detail, making likelihood judgments more certain, they would be less susceptible to mood

bias. Future studies which test this theory would be welcomed, and if replicated, theobserved difference would be extremely interesting. However, it is possible that the dif-

ference is due to a ceiling effect: as perceived likelihood of self-generated events was

higher than other-generated events, there may have been less scope for changes in mood to

bring about changes in perceived likelihood of self-generated events. Higher likelihood of

self-generated events may have been due to the instruction to think of things they were

looking forward to or not looking forward to, or may reflect higher accessibility of more


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probable events during the generation process. Some participants generated very few

negative future events (none in some cases), so this category was relatively small (Hepburn

et al. 2006). In a related study, Newby-Clark and Ross (2003) found that non-clinical

participants typically recalled a mixture of positive and negative past events, but antici-

pated a homogeneously ideal future (i.e. exclusively non-negative events). In this respect,future studies with depressed samples (who may be able to generate a larger number of

potentially negative future events) may be informative. Further work is needed to fully

establish the differences between ratings of self- and other-generated events, but the

inclusion of self-generated events in the current study nevertheless represents a step for-

ward towards more ecologically valid findings.

The data shows significant effects of negative mood on likelihood judgments of other-

generated events. Both mood inductions were effective in changing mood, but the positive

induction was relatively weak, and it is not clear whether the absence of an effect of

positive mood on likelihood ratings reflects the weakness of the procedure, or whether a

strong increase in positive mood would still have no effect on likelihood ratings. Many

previous studies have found positive mood inductions to be less effective than negative

mood inductions (Westermann et al. 1998). Negative mood induction influenced ratings

both of mood and of perceived likelihood of events from the standardized list. Participants

in induced negative mood rated negative outcomes as more likely, and positive outcomes

as less likely, than they rated the same events in normal mood at follow-up. Ratings were

generally lower the first time than the second, irrespective of mood, but when this increase

was controlled, the effects remained significant.

4 Limitations

The study has limitations which must be taken into account when findings are interpreted.

The sample size was small, resulting in modest effect sizes, so the findings should be

viewed as preliminary, and requiring replication in a larger sample. In addition, the results

cannot be extrapolated to apply to clinical groups, as study participants were members of

the community who reported no difficulties with mood. The study did not measure

extraversion or introversion, which have been found to predict likelihood judgments

(Zelenski and Larsen 2002). There is also some controversy surrounding experimental

mood induction. Apparent mood effects may result from demand (i.e. participants tryingless hard to generate mood-incongruent events). However, we consider this unlikely. The

procedure is believed to successfully mimic the effects of organic negative mood on many

variables, and has been shown to influence variables which cannot be voluntarily con-

trolled (Clark 1983; Westermann et al. 1998). As this study employs new dependent

variables, we cannot be sure the results are not due to demand, but if they were, we would

expect to see more consistent increases in perceived likelihood of negative future events

under induced sad mood. There was a trend for the negative MI group to have higher

sadness scores at baseline than the positive MI group, probably a response to the bad luck

of being allocated to this group. This leaves open the possibility that the reported differ-ences following mood induction were due to pre-induction mood. However, this seems

unlikely given that the groups did not differ in pre-induction depression, happiness scores,

or future fluency (the number of positive or negative events they were able to generate for

themselves pre-induction, Hepburn et al. 2006). Finally, because the follow-up data was

collected over the phone rather than in the laboratory, it is possible that the within-subject

differences in likelihood estimates were due to differences in setting rather than differences


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in mood. However, the fact that there were no significant differences between ratings of

mood at baseline and at follow-up suggests that despite the change of setting, the follow-up

data reflects a realistic measure of the participants normal non-induced mood state.

5 Future Directions

Even taking into account the studys limitations, our finding that experimentally induced

mood biases future thinking in individuals without mental health issues is an important

insight which points to some questions for future work, particularly for clinical researchers.

The current data follow our previous report (Hepburn et al. 2006) in showing that non-

depressed participants become more pessimistic about the future after a minor and short-

term shift towards negative mood. People with a depressive illness experience intense and

prolonged low mood. Is intensity and duration of negative mood all that drives the dif-

ference in future-oriented judgment observed between depressed and non-depressed

individuals (e.g. Butler and Mathews1983)? To answer this question, future studies would

need to examine the disparities that remain between clinical and non-clinical samples once

mood is adequately controlled. Finally, research on memory retrieval in analogue samples

has found that non-depressed participants spontaneously engage in mood repair after

recalling a sad memory (Joormann and Siemer 2004; Rusting and DeHart2000). Similar

studies could help to establish whether similar repair processes occur for negative thinking

about the future in non-depressed individuals.

Acknowledgements This research was supported by a Wellcome Trust Prize Studentship to Silvia

Hepburn. Thanks to Catherine Crane, Danielle Duggan, Melanie Fennell and Wendy Swift.

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