Feeling the Future: The Role of Current in

Simon M. Laham

University of New South Wales

A thesis submitted in fulfillment of the requirements for the degree of

Doctor of

at the University of New South Wales

September 2005 Originality Statement

I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgment is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.

Signed ……………………………………

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Acknowledgments

I would like to thank my supervisor, Prof. Joseph P. Forgas for his constant help and guidance during this project. I would also like to thank A/Prof Bill von Hippel and

Dr. Kevin Bird for their help and advice.

Thanks to my friends and family. Thanks Mum, Dad and Nick and thanks Brad,

Carrie, Norman, Rebekah, Amirali and anyone else who helped and supported me over the last few years.

Finally, thanks Kate. You got me there in the end.

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Abstract

The aim of this thesis was to examine the effects of people’s current moods and

emotions on affective forecasting. The primary hypothesis was congruence: people

currently happy were expected to generate relatively positive affective forecasts

and people currently feeling sad were expected to generate relatively negative forecasts

compared to people currently in a neutral state. In addition, a moderated mood

congruence hypothesis, predicted by the Infusion Model (AIM, Forgas, 1995),

stating that mood congruence effects are more pronounced under conditions of

constructive processing, was tested. Two secondary hypotheses concerning the

processing and motivational consequences of transient moods were also examined in

each of the studies in this thesis.

Studies 1 to 3 examined these hypotheses using a variety of manipulations of

constructive processing. In Study 1, happy, neutral and sad participants made affective

forecasts about a variety of everyday events, under overt instructions to process

constructively or not. In Study 2, the ambiguity of the forecast was the manipulation of

constructive processing. In Study 3, Need for Cognition, was used as a measure of

constructive processing. Results of Studies 1-3, however, did not support either the mood

congruence or the moderated mood congruence hypotheses. Further, neither of the

secondary hypotheses was supported.

Studies 4 and 5 examined the influence of transient high emotions on

affective forecasts. In Study 4, anxious or neutral people forecasted their about

an upcoming public speaking engagement and also rated their willingness to give a public speech. Study 5 replicated and extended Study 4 by examining how happy, sad and angry iii

participants made those same predictions. In both Study 4 and 5, anxious people made more pessimistic forecasts and were less likely to engage in a public speech than were neutrals. This effect did not generalize to . Results suggest that while does

have implications for affective forecasting and decision making, lower intensity moods and anger do not. Implications of these results are discussed with a focus on the benefits of an -specific approach to the study of affect and affective forecasting.

iv

Table of Contents

Acknowledgments…………………………………………………………………………i

Abstract……………………………………………………………………………………ii

Table of Contents…………………………………………………………………………iv

List of Tables……………………………………………………………………………...x

List of Figures…………………………………………………………………………….xi

List of Appendices……………………………………………………………………….xii

Chapter1: Introduction…………………………………………………………………….1

Biases in Affective Forecasting: Can We Predict the Future?……………………4

Biases of Intensity and Durability in Affective Forecasting………………5

Forecasting by Proxy: A Model of Affective Forecasting………………...7

Misconstrual: Imagining the Wrong Event………………………………..8

Inaccurate Theories: Imagining the Wrong Consequences…………….…9

Focalism: Ignoring the Periphery………………………………………...11

Immune : Ignoring Nature………………………………12

Motivated Distortions: Imagining What We Want..……………………..13

Summary…………………………………………………………………13

Affect, Mood and Emotion: Some Definitions…………………………………..14

Affect and Social Judgment……………………………………………………...15

Content Effects: Mood and What We Think…………………………….16

The Affect Infusion Model (AIM)……………………………………….21

Summary…………………………………………………………………26 v

Empathy Gaps……………………………………………………………………26

Processing Effects: Mood and How We Think…………………………………..29

The Present Research…………………………………………..………………...31

Chapter 2: Study 1: The Effects of Mood and Elaboration Instructions on Affective

Forecasts…………………………………………………………………………………33

The Present Study………………………………………………………..33

Method…………………………………………………………………………...35

Participants and Design………………………………………………….35

Procedure………………………………………………………………...35

Manipulations and Measures…………………………………………….36

Results……………………………………………………………………………40

Mood Manipulation……………………………………………………...40

Elaboration Manipulation.……………………………………………….40

Intensity Forecasts……………………………………………………….42

Durability Forecasts……………………………………………………...44

Discussion………………………………………………………………………..46

Chapter 3: Study 2: The Effects of Mood and Target Ambiguity on Affective

Forecasts…………………………………………………………………………………51

Mood Congruence and Target Ambiguity……………………………….51

The Present Study………………………………………………………..53

Method…………………………………………………………………………...54 vi

Participants and Design………………………………………………….54

Procedure………………………………………………………………...54

Manipulations and Measures…………………………………………….55

Results……………………………………………………………………………56

Mood Manipulation……………………………………………………...56

Intensity Forecasts……………………………………………………….57

Duration Forecasts………………………………………………………59

Discussion……………………………………………………………………….59

Chapter 4: Study 3: The Effects of Mood and Need for Cognition on Affective

Forecasts…………………………………………………………………………………61

Motivated Forecasts, Personal Relevance and Mood Incongruence…….61

Personal Relevance and Mood Congruence……………………………..63

Mood Congruence and Need for Cognition……………………………...64

Mood and Processing: Implications for Forecasting Accuracy………….66

The Present Study………………………………………………………..67

Method…………………………………………………………………………...67

Overview…………………………………………………………………67

Participants and Design………………………………………………….68

Testing Session 1………………………………………………………...68

Testing Session 2………………………………………………………...69

Testing Session 3………………………………………………………...70 vii

Results……………………………………………………………………………70

Mood Manipulation……………………………………………………...70

Intensity Forecasts and ……………………………………..71

Durability Forecasts and Experiences……………………………………74

Discussion………………………………………………………………………..76

Chapter 5: Study 4: State Anxiety and Public Speaking: The Influence of Anxiety on

Affective Forecasts and Decisions……………………………………………………….80

Empathy Gaps in the Prediction of Visceral States……………………...80

Empathy Gaps in the Prediction of Affective States…………………….82

The Present Study………………………………………………………..84

Method…………………………………………………………………………...86

Participants and Design…………………………………………………..86

Procedure………………………………………………………………...86

Manipulations and Measures…………………………………………….87

Results……………………………………………………………………………89

Emotion Manipulation…………………………………………………...89

Intensity and Durability Forecasts……………………………………….90

Decisions…………………………………………………………………90

Mediational Analysis…………………………………………………….90

Discussion………………………………………………………………………..92

viii

Chapter 6: Study 5: High Arousal Emotions and Public Speaking: The Influence of

Anxiety, Anger, and on Affective Forecasts and Decisions………...95

Emotion-General Effects: The Role of Arousal in Social Judgments…...95

Emotion-Specific Effects………………………………………………...98

The Present Study………………………………………………………102

Method………………………………………………………………………….102

Participants and Design…………………………………………………102

Procedure……………………………………………………………….103

Manipulations and Measures…………………………………………...104

Results…………………………………………………………………………..105

Affect Manipulation…………………………………………………….105

Intensity and Duration Forecasts……………………………………….106

Decisions………………………………………………………………..108

Mediational Analysis…………………………………………………...108

Discussion………………………………………………………………………109

Chapter 7: General Discussion………………………………………………………….110

Overview of Findings…………………………………………………………..110

Happiness, Sadness and Affective Forecasts: Studies 1-3……………………..112

Is Affective Forecasting a Constructive Process?………………………113

Are Affective Feelings Relevant to Affective Forecasts?………………116

Affective Forecasts and Subjective Probability Judgments: A

Dissociation?……………………………………………………………117 ix

Personal Relevance and Mood-as-Motivation………………………….118

Anxiety and Affective Forecasting: Studies 4 and 5…………………………...119

Anxiety and Decisions: Evidence of Mediation………………………..120

Emotion-Congruent Effects on and ………………...122

Anxiety and Vividness………………………………………………….123

Anxiety and Appraisals…………………………………………………124

The Relevance of Anxiety to Affective Forecasts……………………...126

Limitations and Future Directions……………………………………………...129

Practical Implications…………………………………………………………..133

Conclusions…………………………………………………………………….134

References………………………………………………………………………………136

Appendix A……………………………………………………………………………..169

Appendix B……………………………………………………………………………..170

x

List of Tables

Table 1. Mean intensity forecasts as a function of mood and elaboration

instructions……………………………………………………………………….44

Table 2. Mean durability forecasts as a function of mood and elaboration

instructions……………………………………………………………………….46

Table 3. Mean intensity forecasts as a function of mood and feedback

ambiguity………………………………………………………………………...58

Table 4. Mean duration forecasts as a function of mood and feedback

ambiguity………………………………………………………………………...59

Table 5. Mean intensity predictions and experiences as a function of induced

mood and event valence………………………………………………………….73

Table 6. Mean durability predictions and experiences as a function of induced

mood and event valence………………………………………………………….76

Table 7. Emotion ratings as a function of emotion condition and emotion

descriptor…………………………………………………………………………90

Table 8. Emotion ratings as a function of emotion condition and emotion

descriptor………………………………………………………………………..106

Table 9. Intensity and durability forecasts as a function of emotion

condition……….……………………………………………………………….107

xi

List of Figures

Figure 1. Wilson and Gilbert’s model of affective forecasting (adapted from Wilson and Gilbert (2003))………………………………………………………………8

Figure 2. Affective forecasts as a mediator of the effect of anxiety on decisions to engage in a public speaking task. Path coefficients are unstandardized regression coefficients (Bs). The italicized coefficient is from a linear regression model; other coefficients are from logistic regression models. Coefficients in parentheses represent parameter estimates from a logistic regression model containing both predictors. Asterisks indicate significance at p < 0.05…………………………………92

Figure 3. Affective forecasts as a mediator of the effect of anxiety on decisions to engage in a public speaking task. Path coefficients are unstandardized regression coefficients (Bs). Italicized coefficients are from a linear regression model, other coefficients are from logistic regression models. Coefficients in parentheses are from a logistic regression model containing both predictors. Asterisks indicate significance at p < 0.05…………………………………………………………………109

xii

List of Appendices

Appendix A……………………………………………………………………………..169

Appendix B……………………………………………………………………………..170

Chapter1

Introduction

“Prediction is very difficult, especially about the future.”

-Niels Bohr

There is no that predicting the future is a difficult task. It is, however, an important and necessary part of everyday social judgment and . People often base important decisions on their predicted affective responses to different decision- outcomes (Kahneman, 1994; Kahneman & Snell, 1992). We choose to marry someone because we think that person will make us happy; we don’t take a trip because we predict that it won’t make us feel good for very long. Generally, people assess which alternative will make them feel better and then they choose it. Recent research however, suggests that people are not very good at judging how future events will make them feel (e.g.,

Buehler & McFarland, 2001; Gilbert, Driver-Linn, & Wilson, 2002; Ross & Buehler,

2001; Wilson, Gilbert, & Centerbar, 2003, and see Loewenstein & Schkade, 1999;Wilson

& Gilbert, 2003 for reviews). People often get the valence of the emotion right, but are not as good at judging emotional intensity or duration. Typically people overestimate the strength and longevity of their future emotional states. These biases can have serious consequences for the judgments people make. We might go to great lengths to marry someone who does not make as happy as we had predicted, or pass on a holiday that we would have come to enjoy very much.

Recent work suggests that these biases in forecasting arise for a number of reasons (see Wilson & Gilbert, 2003, for a review). People may place too much emphasis on focal aspects of their predictions (Wilson, Wheatley, Meyers, Gilbert, & Axsom,

2000), for example, or ignore the resilience of their psychological immune systems

(Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998). Another possibility is that forecasts may be unduly influenced by circumstances at the time of prediction (Gilbert,

Gill, & Wilson, 2002). The particular situational circumstance of in this thesis is affect: how a person feels at the time of prediction. Imagine predicting how you would

feel about seeing a movie next week, if you had just been fired from your job. Surely,

your feelings about your recent misfortune would impact upon your prediction. Indeed, much research on affect and cognition suggests that people’s current affective states often and significantly influence their social judgments (see Forgas, 1995, 2002; Loewenstein

& Lerner, 2003; Schwarz & Clore, 1996, for reviews). Although some authors have speculated about the role of current feelings in affective forecasts (e.g., Gilbert & Wilson,

2000), little empirical work has explicitly examined the link between transient affective states (moods and emotions) and predictions about future affective states (cf.

Loewenstein and colleagues’ work on visceral states and decision making, e.g.,

Loewenstein, 1996). The current thesis will address just this issue.

There are a number of foreseeable ways that affect might influence affective forecasts. One possibility is an influence on the content of affective predictions: an affect- congruence effect. In short, people might make more positive predictions when they are happy and more negative predictions when sad. Indeed, much research has found such affect congruence effects in numerous domains (e.g., Bower, 1983, 1991; Clark & Isen,

1982; Fiedler, 1991; Forgas, 1992, 1993; Isen, 1987; Schwarz & Bless, 1991; Schwarz &

Clore, 1983). A second possibility is that affect might influence the process of affective prediction. Happy people process information in a somewhat heuristic fashion, relying on pre-formed knowledge structures and cognitive shortcuts, whereas sad people use a more systematic, elaborate strategy (Bless, 2000; Fiedler & Bless, 2001; Forgas, 1995).

One consequence of such processing differences is judgmental accuracy. Research has shown, for example, that the detail-oriented processing associated with sad moods can attenuate various cognitive biases (e.g., Forgas, 1998). A simple consequence of this might be an increase in accuracy of the affective forecasts of sad people due to a reduction in some of the cognitive biases that cause forecasting errors.

A third conceivable affective influence on forecasting is one consistent with affect regulation. Under some conditions, people in negative moods seek to repair their moods by engaging in actions that alleviate negative affect (e.g. McFarland & Buehler, 1998).

Some researchers indeed suggest that affective forecasting may play such a mood- enhancing role in everyday life (Totterdell, Parkinson, Briner, & Reynolds, 1997). As such, sad people may in fact generate more positive forecasts than their happy or neutral counterparts in an attempt to repair their negative moods.

A final possibility is that affective states may exert similar effects on forecasts as

do other visceral states such as hunger and sexual arousal (see Loewenstein & Lerner,

2003; Loewenstein & Schkade, 1999, for reviews). It has been suggested that people in

‘cold’ or neutral states cannot fully appreciate how they will feel or behave in future ‘hot’ or aroused states and so make various forecasting errors. These so called ‘empathy gaps’ in affective forecasting may be reduced by having people forecast future affective states while in a current affective state that matches the valence or arousal state of the event to be forecasted. When such a match exists people can better simulate the emotional consequences of future events and so increase the accuracy of their forecasts.

The remainder of this chapter will outline the general issues of concern in this thesis. First, I will review evidence of the intensity and durability biases in affective prediction with a focus on some proposed underlying mechanisms. Second, a general model of affective prediction will be presented. Next I will turn to affect and its role in social judgments, and provide a brief review of a number of cognitive and motivational theories of affective influence. Following this will come an integration of these two research areas, and the development of the central hypotheses of the thesis.

Biases in Affective Forecasting: Can We Predict the Future?

The process of predicting future feelings, or affective forecasting (Gilbert &

Wilson, 2000), has recently been the object of a flourish investigation in the social psychological and decision-making literatures (e.g., Dunn & Laham, in press; Gilbert et al., 2002; Gilbert & Wilson, 2000; Kahneman, 1994; Kahneman & Snell, 1990;

Loewenstein & Frederick, 1997; Loewenstein, Nagin, & Paternoster, 1997; Loewenstein

& Prelec, 1993; Loewenstein & Schkade, 1999; Mellers, 2000; Mellers & McGraw,

2001; Snell, Gibbs, & Varey, 1995; Wilson, Wheatley, Kurtz, Dunn, & Gilbert, 2002;

Zeelenberg, van Dijk, Manstead, & van der Pligt, 2000). The typical finding in this domain is that people are not very good at accurately predicting their future emotional states. To be accurate forecasters, people need to correctly predict the valence of the emotion they will (“Will I be happy or sad if I fail my university course?”), the intensity of that emotion (“How happy or sad?”), and its duration (“Happy for a couple of days or for a few months?”). People are generally good at predicting what they will feel about a future event (Robinson & Clore, 2001; Wilson & Gilbert, 2003), but

tend to be less accurate when it comes to predictions of intensity and durability; predictions of how much and for how long (e.g., Buehler & McFarland, 2001; Gilbert et

al., 1998; Wilson et al., 2002). Specifically, we tend to overestimate both the intensity and duration of our forecasted emotions. Such biases have been found in affective predictions about events as diverse as receiving university course grades (Buehler &

McFarland, 2001), university accommodation allocation (Dunn, Wilson & Gilbert, 2003), elections, receiving tenure, relationship break-ups (Gilbert et al., 1998), sporting events

(Wilson et al., 2000) and food (Gilbert, et al., 2002) among others (see

Wilson & Gilbert, 2003 for a review).

Biases of Intensity and Durability in Affective Forecasting

The search for and the avoidance of have been considered

fundamental motives of human behavior for centuries (Higgins, 1997). Indeed, these ideas have played a leading role in the history of psychology (Festinger, 1957; Heider,

1958; Thorndike, 1935). Quite simply, people seek out those things that will prolong their happiness and avoid things that will produce extended periods of sadness. This seems sensible enough. This seemingly simple strategy for happiness, however, breaks down if people cannot accurately predict the relationship between an event and the affective consequences of that event. People may find themselves pursuing ends that are less attractive than first thought or avoiding things that may very well not be so bad. In fact, recent research suggests that people are not very accurate judges of the durability or intensity of their emotional states (e.g., Gilbert, et al., 1998; Wilson et al., 2000). Even rare and seemingly powerful events produce unexpectedly short-lived alterations to our general feelings of well being. Brickman, Coates and Janoff-Bulman (1978), for example, found that something as terrible as losing a child in a car accident impacts less upon a parent’s long-term happiness than most people expect. Not surprisingly, less extreme events also produce less dramatic changes in general feelings than is predicted by the naïve judge (Suh, Diener, & Fujita, 1996). This is not to say that affective events do not impact upon people’s lives, just that their impact is less than expected. Quite simply, people expect their affective responses to last longer than they actually do. Gilbert and colleagues (Gilbert et al., 1998; Wilson et al., 2000) have termed this tendency to overestimate the duration of emotional responses the durability .

Not only do people overestimate the duration of their affective states, they also appear to overestimate the intensity of their immediate affective responses to future events (Buehler & McFarland, 2001; Mitchell & Thompson, 1994; Mitchell, Thompson,

Peterson, & Cronk, 1997). This intensity bias has received little empirical attention in comparison to biases of durability. This is surprising given the importance of predicted emotional responses in guiding decisions and behavior (Dunn & Laham, in press; Mellers

& McGraw, 2001; Mellers, Schwartz, & Ritov, 1999). If people expect a party to produce immediate and intense pleasure, then it’s likely that they will attend. They may their decision to go, however, if the party is not as pleasing as expected. Research does, indeed, provide some evidence for the existence of the intensity bias. Buehler and

McFarland (2001), for example, showed that people predict more powerful emotions for both positive and negative events than they actually experience when those events occur.

Although empirical evidence for the intensity and durability biases is plentiful, little serious attempt had been made at a theoretical consolidation of these findings until Wilson and Gilbert (2003) proposed an integrative model of the forecasting process. In

the next section I will present Wilson and Gilbert’s (2003) affective forecasting model

and review a number of mechanisms that underlie the intensity and durability bias in the

context of this model. In addition, I will briefly outline the potential influences of

transient affect at various stages of the forecasting process as a preview to the more

thorough treatment later in the chapter.

Forecasting by Proxy: A Model of Affective Forecasting

Gilbert and colleagues (Gilbert, Gill, & Wilson, 2002; Wilson & Gilbert, 2003)

suggest that when people predict their affective responses to an event, they (a) generate a

mental representation of that event (a mental proxy), (b) assess their initial affective responses to that representation, and (c) adjust their initial proxy reactions as a function

of the temporal location of the event. This process of affective forecasting is represented

in a model of affective forecasting adapted from Wilson and Gilbert (2003) and presented here in Figure 1. If someone wants to predict how she will feel about seeing a new movie in a week’s time, for example, she will imagine that event occurring now and use her affective reaction to this mental picture as a basis for prediction. Such a strategy is generally well-founded: real and imagined events evoke many of the same psychological

and neural processes (McGuire, Shah, & Murray, 1993), and responses to imagined

events are often a good indication of reactions to real events (Kahneman & Tversky,

1982; Sanna, 2000; Schwarz, 1990). An important point, however, is that the event to be

predicted is not happening now, but will happen sometime in the future. Therefore, some

temporal correction is needed. So we imagine the event happening now, gauge our

affective response to this imagined event, and then correct our proxy reactions for differences between the temporal locations of the predicted and actual events. The end

product of this process is an affective forecast.

Let us now consider a number of mechanisms that may lead to forecasting

inaccuracies and a number of possible influences of affective states in the context Wilson and Gilbert’s (2003) model of affective forecasting.

Affective Correction for Theories Unique Influences

Event Proxy Affective Affective Representation Reaction Forecast

Construal

Figure 1. Wilson and Gilbert’s model of affective forecasting (adapted from Wilson and Gilbert (2003)).

Misconstrual: Imagining the Wrong Event

The first stage in the affective forecasting process involves generating a mental representation of the event to be forecasted. Ample evidence suggests that people’s representations of the future often take the form of scenarios or narratives (Buehler,

Griffin & Ross, 1994; Dawes, 1988; Johnson & Sherman, 1990; Zukier, 1986). Further, when people make hedonic judgments about a future event, they typically base them on these imagined scenarios (Kahneman, 1994; Ross & Buehler, 2001). This construal process is the site of one of the mechanisms that cause forecasts to be inaccurate: the misconstrual problem (Gilbert et al., 1998; Loewenstein & Schkade, 1999, Wilson &

Gilbert, 2003). Quite simply, our forecasts may be inaccurate because we imagine the target event unfolding in a particular manner; a manner different to the event’s actual

unfolding (Gilbert et al., 1998; Wilson & Gilbert, 2003; Woodzicka & LaFrance, 2001).

While it seems obvious that the future may unfold in any number of ways, this fact seems

to escape the average forecaster (Ross & Buehler, 2001). Typically, people construct a

single representation (or a small number of representations) of a future event on which to

base their judgments (e.g., Griffin, Dunning, & Ross, 1990; Griffin & Ross, 1991).

Further, evidence suggests that people often fail to appreciate that their representations of

the future are merely construals and not veridical representations of an objective reality

(Griffin & Ross, 1991). Probabilistically, of course, their construals typically do not occur and affective predictions based on these misconstrued representations are sometimes inaccurate (Kahneman & Lovallo, 1993; Kahneman & Tversky, 1979).

The misconstrual problem can bias forecasts in a wide variety of ways depending

on the nature of the misconstrual. One possible influence on construal is a person’s current affective state (Gilbert & Wilson, 2000). Much research has shown that transient

affective states ‘color’ cognitive representations in mood congruent ways (see Forgas,

1995 for a review). This implies that when people in a particular mood construe a future

event, the resulting mental representation will be affectively congruent with that current

mood because of the selective retrieval of, attention to, and use of, affect congruent

information (Bower & Forgas, 2001). Consequently, affective forecasts based on such

affect-congruent construals will also be affect congruent: Happy people will generate

positive construals and forecasts, while sad people will imagine a more negative version of events and so generate more pessimistic affective forecasts. This mood congruence hypothesis is in fact a central concern of the current thesis and will be examined empirically in Studies 1, 2 and 3.

Inaccurate Theories: Imagining the Wrong Consequences The second step in the forecasting process is to gauge one’s affective response to the mental representation of the event to be forecasted (Robinson & Clore, 2002a; Wilson & Gilbert, 2003). Now, while misconstruing the target event is one cause of forecasting biases, not understanding the relationship between the target event and its affective consequences is another

(Loewenstein & Schkade, 1999; Wilson & Gilbert, 2003). People have implicit theories about what causes happiness and what leads to pain, but these theories are not always correct. McFarland, Ross, and DeCourville, (1989), for example, found that Americans have inaccurate views about the emotional consequences of menstruation for women: typically, they overestimate the intensity and frequency of emotional distress caused by menstruation (see also Ross, 1989). Even one’s knowledge of one’s own emotional life events is often subject to error (Fredrickson & Kahneman, 1993; Robinson & Clore,

2002a). As time passes, the episodic details of fade and people need to rely on theories about how events relate to affective consequences. So a person can remember a colonoscopy as a painful experience, for example, but the exact details of the pain itself are not stored in memory (Robinson & Clore, 2002a). In fact, in a relatively recent review of emotional memory phenomena, Christianson and Safer (1996) concluded that “there are apparently no published studies in which a group of subjects has accurately recalled the intensity and/or frequency of their previously recorded emotions” (p. 235). When people base their judgments about the future on inaccurate memories of and theories about the past, errors are sure to result. If we think that infinite wealth will make us extremely happy (an inaccurate theory; Huppert, in press; Myers, 2000) then our forecasts about winning the lottery, for example, will be less than perfect.

As affective theories of this kind are hypothesized to be stable, crystallized forms of knowledge (Robinson & Clore, 2002a), there is no strong reason to expect any influence of transient affective states on the use of affective theories in affective forecasting. In fact, if forecasts are based exclusively on these kinds of immutable semantic theories, affect may not exert a congruency effect on forecasts at all.

Focalism: Ignoring the Periphery

Wilson et al. (2000) suggest that one reason why people commit forecasting biases is because they focus primarily on the event to be predicted (the focal event) at the expense of other, non-focal occurrences. Our emotional experiences do not occur in a vacuum and there are many peripheral events that impact upon our emotional states. For instance, when thinking about how happy one will feel about losing weight (focal event), one tends to ignore the impact that a strict exercise and diet regime (non-focal events) will have on affective states. As a result, the affective response associated with the focal event receives undue weight in the prediction, producing biases of intensity and durability. This mechanism has been termed focalism (Wilson et al., 2000; or the focusing , Schkade & Kahneman, 1998).

Wilson and his colleagues (Wilson et al., 2000) have found evidence for focalism in affective predictions in a number of domains. For example, when college sports fans were asked to predict how they would feel if their football team lost, they overestimated the impact of the loss. However, when asked to consider non-focal post-game factors that they would typically ignore (such as going to class and socializing with friends), the durability bias was significantly reduced. Schkade and Kahneman (1998) also found evidence of focalism, or the focusing illusion in their terms, when they asked people to predict their subjective well being as a function geographic location. Participants seemed to focus on, and so amplify, the effect of climate on their predictions leading them to forecast greater happiness in California than in the Midwest of the United States, even though no differences in well being were reported by inhabitants of the two regions. This effect has been found in other domains, both positive and negative, including predictions about international peace, and about tragic accidents, for example (Wilson et al., 2000).

To the extent that forecasting biases are influenced by a narrowing of the range of non- focal events considered when making a forecast, negative mood may attenuate forecasting biases by widening the range of considered events, thereby reducing focalism.

Negative moods have indeed been associated with more elaborate and systematic processing styles and, consequently, reduced in a number of social judgment domains (e.g., Forgas, 1998). A similar processing effect of negative mood may indeed operate here to increase the accuracy of affective forecasts.

Immune Neglect: Ignoring

In an interesting series of studies, Gilbert et al. (1998) showed that people commit the durability bias when predicting their feelings about negative events because they neglect to consider the role of their psychological immune systems. That is, they

underestimate (or completely ignore) the cognitive processes that modify their

representations of negative events and attenuate their negative affective experiences. The

idea of such a palliative, regulatory system is not new in psychology and has its foundations in research on rationalization, dissonance reduction and self-deception (e.g.,

Aronson, 1968; Festinger, 1957, and see Wilson & Gilbert, 2003, for a review). Clearly, if people try to predict their affective responses to future negative events without considering the activity of these regulatory processes, they will overestimate the duration of their responsesand be subject to the durability bias.

Gilbert and colleagues (Gilbert et al., 1998) have taken the first steps in delineating the role of immune system neglect in affective forecasting. In a number of different judgmental domains these researchers have found that immune neglect produces biases in predictions of affect durability. Judgments of relationship break-ups, tenure denial, electoral failure, personality feedback and aesthetic preferences all show durability biases stemming from an ignorance of the workings of the psychological immune system. There is no strong prediction about the effects of transient affective states on psychological immune system neglect.

Motivated Distortions: Imagining What We Want

The mechanisms discussed thus far are based on cognitive distortions of one kind or another (misconstruing the target, failing to understand the relationship between the target and its consequences and so on). However, forecasting biases may also arise for motivational reasons. Research on motivated distortions shows that people may overestimate their reactions to an aversive event to ensure a pleasant when that event turns out to be less aversive than predicted (Norem & Cantor, 1986; Rachman,

1994), or they may use negative forecasts to motivate themselves to action (Mischel,

Cantor, & Feldman, 1996).

Clearly, affective forecasts do not merely guide our decisions and ; they also influence our current affective states (Elster & Loewenstein, 1992; Gilbert et al.,

1998). Just as people often use cognitive strategies such as favorable social comparison, rationalization and positive to regulate their affective states (e.g., McFarland &

Buehler, 1997; Taylor & Brown, 1988; Wilson et al., 2001), they may also use affective forecasts to maintain their happiness or alleviate distress (e.g., Totterdell et al., 1997). It has been shown that people in negative moods often engage in various strategies of mood repair (e.g., Forgas, 1989, 1991). So it is possible that people currently in a negative mood may generate mood-incongruent construals and forecasts in aid of affect regulation.

This hypothesis is central to Study 3 and so will be explored in further detail therein.

Summary

In sum, biases in affective forecasting are multiply determined. People may misconstrue the event to be predicted or misremember the relationship between an event- type and its affective consequences. Further, people may focus too narrowly on the target of the affective forecast or not focus enough on the role of the psychological immune system. In addition, there are cases when people may be motivated to generate particular forecasts. The current thesis will add another potential source of bias to the list: transient affect. I have already briefly previewed the ways in which a person’s transient affective states may influence the forecasting process. So far, I have considered how affective forecasting works, and have reviewed the cognitive and motivational biases that might influence forecasting accuracy, and the role that affective states might play in the forecasting process. Now let us more fully explore these proposed influences in light of theoretical and empirical work on affect and social cognition. But first, let us consider a point of definition. Affect, Mood and Emotion: Some Definitions

Until now I have been using the terms affect, mood and emotion interchangeably, but there are some important distinctions among the three. Affect is the superordinate term that comprises both moods and emotions and typically refers to a positively or negatively valenced subjective experience (Schwarz & Clore, 1996; Wyer, Clore, &

Isbell, 1999)

Emotions are one kind of affective state and generally have clear cognitive content and a definite cause (Forgas, 1992). Emotions typically have an object – one feels emotions about something (Averill, 1980; Schwarz & Clore, 1996). As such, emotions are likely to carry with them information about the source of the emotion and so are less likely to be mistaken as responses to other stimuli (Keltner, Locke, & Audrain, 1993;

Schwarz & Clore, 2003). Although emotions are usually considered to be more intense than moods, they can often lead to residual, lower arousal affective states that persist after the original emotional response has dissipated (Nowlis & Nowlis, 1956). Recent research and theorizing proposes a wide range of emotion-specific appraisal tendencies and cognitive and behavioral consequences of particular emotions that go beyond the general valence distinction (e.g., Ortony, Clore, & Collins, 1988). So, in short, emotions may influence social judgments in emotion-specific ways that qualify mere valence effects, but such influences are most likely when the emotion has dissipated so as to obscure its

cause.

Moods, on the other hand, are low-intensity affective states that tend to have no

obvious cause (Forgas, 1992). One would be experiencing a mood, for example, when

one is generally ‘feeling good’ or ‘feeling bad’. These states are diffuse, relatively enduring, contain little cognitive content and are typically characterized as generally positive or negative. Because of their non-specific nature, moods can have a pronounced impact on our thoughts, judgments and behaviors (Bless, 2000; Forgas, 2002). In sum, to quote Schwarz and Clore’s (1996) characterization of the cognitive model of affect,

“…emotion refers to the consequences of ongoing, implicit appraisals of situations with respect to positive or negative implications for goals and concerns (Arnold, 1960), while mood refers to feeling states themselves, when the object or cause is not a focus”(p. 434).

The primary focus of the first three studies in this thesis is the role of moods in the affective forecasting process. As such the review of the affect literature in this chapter is concerned largely with moods and social judgment. The role of specific emotions in affective forecasting will be more fully addressed in Chapters 5 and 6.

Affect and Social Judgment

The relationship between affect and cognition has been the subject of philosophical debate for centuries. Only relatively recently, however, has scientific turned its gaze on this important relationship and with interesting results (see

Forgas, 2002; Schwarz & Clore, 1996; Wyer, Clore, & Isbell, 1999, for example, for reviews). Research on affect and cognition suggests that moods and emotions can influence both what people think (the content of thoughts, judgments and behaviors) and how people think (the processes used to deal with social information).

The following sections will review a number of cognitive theories of affect congruence, processing and motivational effects. First I will address mood congruence, paying particular attention to the affect-priming (Bower, 1981) and affect-as-information accounts (Schwarz, 1990; Schwarz & Clore, 1983) and ending with the integrative Affect Infusion Model (AIM; Forgas, 1995). Mood congruence is the primary focus of this

thesis and so is covered in substantial depth in the next sections. Next, I briefly review research on empathy gaps as a preview to a more thorough review in later chapters. The information processing and motivational consequences of transient affective states are secondary concerns and so receive a more cursory treatment in this chapter and more thorough discussion in the later chapters where relevant.

Content Effects: Mood and What We Think

The content effects of mood have received a lot of attention in affect and cognition research (see Forgas, 2002; Schwarz & Clore, 1996; 2003 for reviews) and are the central concern of this thesis. The primary finding in this research domain is mood congruence: if you are happy your judgments tend towards the positive, and if you are sad, towards the negative. Such mood congruence effects have been documented in various domains including impression formation (Forgas & Bower, 1987; Forgas, Bower,

& Krantz, 1984), interpersonal preferences (e.g., Locke & Horowitz, 1990; Schachter,

1959), consumer judgments (Isen, Shalker, Clark, & Karp, 1978), issues (Salovey

& Birnbaum, 1989), risk judgments (Johnson & Tversky, 1983; Mayer, Gaschke,

Braverman, & Evans, 1992), helping (e.g., Salovey & Rosenhan, 1989), self- and self-evaluation (Levine, Wyer, & Schwarz, 1994; Schwarz & Clore, 1983; Sedikides,

1995) and often have important and interesting practical implications (see Forgas, Chan

& Laham, 2001, for a review). Early explanations of such effects were based on conditioning (Clore & Byrne, 1974; Griffit, 1970) and psychodynamic (Feshbach &

Singer, 1957; Murray, 1933) approaches, although more recent cognitive explanations will form the basis of the current thesis. Affect priming. This account of mood content effects is based on an associative

network model of mental representation (Bower, 1981; Isen et al., 1978). Fundamental to

such a model is the assumption that affective and cognitive representations are linked in an associative network. As we saw earlier, affective forecasting relies heavily on mental representations and memories about events, so affective influences on memory and scenario construction are likely to have important consequences for the forecasting process.

Within the associative network framework, retrieving a concept and using it requires that it receive spreading activation through associated nodes. Thus, activation of affective nodes can spread to non-affective cognitive nodes through associative connections (Forgas, 2002). Hence, affect can infuse judgments by facilitating or priming access to related cognitive categories (Bower, 1981; Isen, 1987). Evidence for affect infusion via affect priming exists in a number of domains including mood-state dependent retrieval (Bower, 1981; Fiedler, 1990; 1991; Forgas & Bower, 1987), mood- congruent retrieval (Lloyd & Lishman, 1975; Teasdale & Fogarty, 1979), attention

(Niedenthal & Setterlund, 1994) and learning (Forgas & Bower, 1987). The selective

accessibility of mood congruent information (or the mood congruency effect) has

important implications for affect infusion in number of judgment domains. Numerous

studies show, for instance, that happy people are more optimistic about the likelihood of

positive events occurring than are sad people. One well-replicated finding that is of

particular interest to this research project is that of affect congruence in predictions about

the likelihood of future events (e.g., Johnson & Tversky, 1983; Mayer et al., 1992; Mayer

& Hanson, 1995; Wegener, Petty, & Klein, 1994; Wright & Bower, 1992). This research shows that happy people tend to overestimate the likelihood of positive events, whereas sad people overestimate the likelihood of negative events. Johnson and Tversky (1983), for example, found that participants who read short newspaper reports describing positive or negative events overestimated the likelihood of mood congruent future events compared to controls. They further showed that this effect was not merely a semantic priming difference dependent on the content of the induction, but rather a valence effect that generalized across types of judgment.

Another example of this generalized mood effect on probability estimations comes from Mayer et al (1992). These researchers found that inducing participants into positive and negative moods increased probability estimates of mood congruent events.

They further found evidence supporting the generality of these effects across judgment types.

Given the consistency of these results for probability judgments, it is reasonable to posit similar congruence effects for the affective consequences of (as opposed to the likelihood of) future events. If people overestimate the likelihood of mood congruent events, they may also overestimate the intensity and durability of the consequences of those events. It is possible that the general that happy people exhibit for probability judgments also promotes optimism about the affective consequences or value of future events. Based on the assumption that incidental moods call to mind affect- congruent information (e.g., Blaney, 1986; Bower, 1981), people’s prediction- contemporary moods may play a biasing role in the construal stage of Wilson and

Gilbert’s forecasting model. Happy people, for example, with facilitated access to mood congruent, positive memories, may produce more optimistic proxy representations of future events and so make more positive forecasts. A corresponding series of events should occur for sad people to produce relatively negative forecasts.

Affect-as-information. There is another, more direct route via which mood can influence the content of social judgments. In brief, mood may have direct informational effects serving as a heuristic cue from which to infer judgments. This direct effect of mood on judgment is the basis of the affect-as-information approach (Schwarz & Clore,

1983). When presented with a judgmental target, instead of deriving a response from a constructive, elaborative information search, people may simply ask: “How do I feel about it [the target]?” and base their judgments on this affective response (Schwarz,

1990; Schwarz & Clore, 1996; 2003). Here, mood serves as a shortcut, an informative cue about the target. Evidence for these effects comes form a variety of sources including research on consumer judgments (Isen, 1984), past life events (Bower, 1981), global social issues (Forgas & Moylan, 1987), and global well-being and

(Schwarz & Clore, 1983; Schwarz, Strack, Kommer, & Wagner, 1987).

In a large field study conducted by Forgas and Moylan (1987), for example, even the mild, naturally occurring moods invoked by movies had important consequences for probability estimates as well as other social judgments. In this study, participants were approached in a cinema foyer upon leaving either a happy or sad movie and asked to make a series of brief global judgments about (1) political issues, (2) the likelihood of future events, (3) responsibility and , and (4) of life. Results showed a significant mood congruence effect: happy people (those who had just seen a positive film) made more lenient, optimistic and positive judgments than sad people. Participants here may simply have asked themselves “How do I feel about these judgments?” (Schwarz, 1990) and so misattributed their incidental moods to the judgmental target.

Importantly, according to the affect-as-information approach, feelings can influence judgments only if they are deemed relevant to the object of judgment (see

Schwarz & Clore, 1996; 2003 for reviews). If, for instance, a transient mood has been attributed to an irrelevant cause it is no longer relevant to or informative about the target of the judgment and so is not used in the judgment (e.g., Schwarz & Clore, 1983). In a now classic study, Schwarz and Clore (1983) asked participants to rate their general happiness, the extent to which they wanted to change their lives, and their life satisfaction on rainy or sunny days. They found that when participants could not readily attribute their transient moods to the weather, happy people (those questioned on a sunny day) made more positive judgments than sad people (those questioned on a rainy day). When participants’ attention was drawn to the weather, however, the impact of negative mood on judgments disappeared. Participants’ current sad moods could now be attributed to the weather and so were rendered irrelevant to the well-being judgments (see Schwarz &

Clore, 2003 for a review of the asymmetry of this mood misattribution effect).

Further, mood is especially likely to directly influence judgments when a person is asked how she feels about a judgment object (see Schwarz & Clore, 1996 for a review).

Judgments of and liking, for instance, are particularly susceptible to affect infusion (Clore & Byrne, 1974; Zajonc, 1980). One would thus expect affect infusion into affective forecasts, which essentially involve asking oneself “How do I feel about this or that future event?” If a person is in a particular mood at the time of prediction, the answer to “How do I feel about it?” may be influenced by this current, incidental mood, and so bias predictions in a mood congruent direction. We thus have two mechanisms that may lead to mood congruence in affective forecasts: affect priming and affect as

information. Let us now consider an integrative theory of affect infusion that places some constraints on mood congruence effects and also outlines when these two mechanisms of affect infusion might operate: the Affect Infusion Model (AIM; Forgas, 1995).

The Affect Infusion Model (AIM)

The AIM is a multi-process model of mood congruence (or affect infusion) effects

(see Forgas, 1995, 2002, for a comprehensive account of the AIM). The primary assumption of this model is that the nature and extent of mood congruence effects depends on the information processing strategy used for a particular task. Generally, the more elaborate and constructive the processing strategy, the greater is the affect infusion.

The second assumption of the AIM is effort minimization: people should adopt the least effortful processing strategy capable of producing a response, all other things being equal. The AIM proposes four different processing strategies that vary along two dimensions: the effort involved in deriving a response and the degree of openness and constructiveness of the information search. Combining values on these dimensions produces the following four strategies: direct access processing (low effort/closed

search), motivated processing (high effort/closed search), heuristic processing (low

effort/open search), and substantive processing (high effort/open search). Mood

congruence effects are predicted to be most likely when some degree of open,

constructive processing is used (heuristic and substantive strategies), but less likely when

more closed search strategies are adopted (direct access and motivated processing).

Low affect infusion processing strategies. Direct access processing requires the

direct retrieval of a pre-existing, stored response. This strategy is used when the judgmental target is familiar, there is little personal relevance or involvement and there are no motivational, cognitive or situational factors requiring more elaborate processing

(Forgas, 1995; 2002; e.g., making judgments about familiar consumer products, Srull,

1983, 1984). Direct access is generally immune to affect infusion because little or no constructive processing is involved in the direct retrieval of crystallized, pre-determined evaluations and responses (Fiedler, 1988; Levine et al., 1994; Snyder, 1984; Srull, 1983,

1984; Swann, 1992).

Judgments made under motivated processing strategies are also relatively resistant

to mood congruence effects. This processing strategy will likely be used when a specific outcome is desired in a judgmental task (e.g. maintaining a positive self-concept; Tesser,

1988). Here, the judge uses a selective search strategy to reach a preferred outcome. If people have their self-concepts threatened, for example, they may show a positivity bias in their judgments, using optimistic future prospects to bolster their current feelings.

Because this type of information search is highly directed, little constructive processing occurs and mood congruence effects are unlikely to result (see Forgas, 1995; 2002 for

reviews).

Motivated processing strategies may actually promote mood incongruence effects in the service of mood management (Clark & Isen, 1982; Erber & Erber, 1994; Forgas,

1995). If a person is currently in a negative mood she may be motivated to repair that

mood and so engage in more targeted judgments and behaviors that will aid in mood

repair (e.g., Erber & Erber, 1994; Forgas, 1989, 1991). The adoption of such a mood

repairing strategy is most likely when the situation is highly personally relevant. In a

study by Forgas (1989), for example, participants in a good or bad mood were asked to select an interaction partner for himself or herself or for someone else. Sad participants who had to choose partners for themselves (high personal relevance) chose rewarding partners while other participants tended to select task-competent interaction partners. A similar pattern of results was found in a subsequent set of studies (Forgas, 1991).

It is possible that a similar motivated processing approach will be taken to personally relevant affective forecasts. In such cases a sad person may generate mood incongruent, positive forecasts as a means of mood repair. While some research suggests that affective forecasts might serve as a mood regulation tool (e.g., Totterdell et al.,

1997), little research has explicitly examined the influence of transient negative moods on affective forecasts. Based on the AIM one might expect that, under conditions of high personal relevance, sad people will generate more positive forecasts than neutral people in an effort to repair their negative moods. This mood management hypothesis, however, is a secondary concern of this thesis and will be addressed in more detail in Chapter 4. Of primary concern are the mood congruence effects typically observed under conditions of constructive processing.

High affect infusion processing strategies. Heuristic and substantive processing both involve some degree of constructive processing and so are likely to produce mood congruence effects. When no prior evaluations or motivational factors exist, judges should adopt a heuristic processing strategy. This is particularly likely when the target is familiar, personal relevance is low and no motivational, cognitive or situational requirements call for substantive processing (Forgas, 1995). Several studies that meet these criteria for heuristic processing do indeed show mood congruence effects in judgments about past life events (Bower, 1981), well being (Schwarz & Clore, 1983; Schwarz et al., 1987) and other people (Clore & Byrne, 1974), for example. Forgas and

Moylan (1987), for instance, found mood congruence effects for a variety of global

judgments (including life satisfaction and political issues judgments) in a ‘street survey’

conducted outside a cinema (movies served as the mood induction here). Given the

situational constraints of this type of judgment (relative unfamiliarity of judgments and

time restriction) heuristic processing was proposed as the most likely mechanism underlying these effects. The affect-as-information account has been theorized to best

describe affect infusion for this processing strategy (Forgas, 1994; 1995). Mood can

infuse judgments under heuristic processing by serving as a heuristic cue from which to

infer judgments about the target (Schwarz & Clore, 1983).

If the judgmental situation is novel and complex and the judge is not restricted by

motivational, cognitive or other factors (besides a motivation to be accurate), substantive

processing is likely to be used (Forgas, 1995). Here judges will attend to and learn new

information about the target, relate this information to pre-existing knowledge structures

and then form a judgment based on this constructive process. This kind of processing is

most likely when judgments need to be made about unfamiliar, complex events. The AIM

suggests that judges will adopt this strategy when none of the simpler strategies will do.

Because substantive processing involves memory phenomena such as selective encoding,

learning, interpretation and assimilation, affect infusion is most likely in this route of

processing (Bower, 1981; Forgas, 1992; 1995). Via the priming of affect congruent

mental representations affect can infuse judgments and produce substantial mood congruence effects (Bower, 1991; Forgas, 1994).

The key prediction of the AIM is that mood congruence effects will increase in magnitude with increasing constructiveness and elaborateness of processing (Forgas,

1995). This counterintuitive prediction has received some empirical support in recent

years (e.g. Forgas, 1992, 1993, 1994; Sedikides, 1995). In a number of studies Forgas and

his colleagues varied the typicality and complexity of numerous judgmental targets as a

manipulation of constructive processing. In one series of experiments, for example, happy, neutral and sad participants formed impressions of typical or atypical students

(Forgas, 1992). Impressions for both types of target were affect congruent, but affect

infusion was more pronounced for atypical targets. This greater affect infusion into

judgments of atypical targets was accompanied by longer reactions times for encoding

and judgment, providing supportive evidence for the claim that the increased substantive

processing of atypical targets led to increased affect infusion.

A similar pattern of results was obtained in a study examining judgments about

typical and atypical couples (Forgas, 1995). In this experiment, the physical

attractiveness and the racial composition of romantic couples were manipulated to create

couples of varying typicality. Typical couples were those couples matched on both

features (i.e. same race and same attractiveness). Atypical couples were mismatched on

both features and couples of intermediate typicality were matched on one feature. As

expected mood congruence effects were proportional to the degree of couple atypicality:

happy and sad participants made mood congruent judgments for all levels of couple

typicality but the magnitude of the congruence effects was largest for atypical couples. In

addition, judgmental latency data showed that participants took longer to process atypical

targets, lending further support to prediction of increased affect infusion under

substantive processing conditions. Similar moderated mood congruence effects have been found in judgments about

the self (Sedikides, 1995). Sedikides (1995), for example, showed that mood effects are

more pronounced for judgments about peripheral as compared to central personality

traits. He argues that because central traits are highly elaborated, they are likely to be

relatively stable and robust against the effects of transient moods. Peripheral traits, on the

other hand, are less elaborate and more susceptible to on-line reconstruction, allowing for

the infusion of current affect in to peripheral trait judgments. Sedikides also showed that

these effects are influenced by the extent of elaborative or constructive processing used in

the judgment. When people were asked to think elaborately about their peripheral trait judgments they showed more mood congruence than people not asked to processes elaborately. This finding lends support to the moderated affect infusion hypothesis of the

AIM.

Summary

While much research in the affect and social cognition domain demonstrates mood congruence in social judgments, via either the indirect route of affect priming or the direct route of affect-as-information, the AIM has recently placed constraints on the ubiquity of such effects. So while we might predict mood congruence effects in affective forecasts, these effects should emerge only when forecasts are made under constructive processing conditions (heuristic or substantive processing), such as high ambiguity or atypicality, for example. Further, if the forecasts to be made are particularly personally relevant, forecasters may use their forecasts to repair or maintain their moods, leading to an overall increase in the optimism of personally relevant forecasts.

Empathy Gaps Another way that transient affect might influence the content of our affective

forecasts is by reducing empathy gaps in the forecasting process. As suggested by

Loewenstein (1996), people in a “cold” or neutral, unaroused state have trouble imagining how they would feel or how they would behave when in a future “hot” or aroused state. In study by Loewenstein et al (1997), for example, young males were

asked to predict their behavior in a future sexually aroused state. At the time of

prediction, participants viewed either sexually arousing or nonarousing photographs and

were then asked to predict levels of sexual aggression in response to being refused sex

midway through a sexual encounter. Participants who were sexually aroused reported

significantly higher likelihoods of sexual aggression than nonaroused participants.

Presumably, being in a matching, aroused state at prediction allowed participants to better

simulate their future affective states and so better predict their behavior. Similar empathy

gap effects have been observed for predictions about and behavior for a variety of

other visceral states including hunger (Read & van Leeuwen, 1998) and pain (Read &

Loewenstein, 1999).

These results may seem contradictory to much work on the intensity and

durability biases. Research by Gilbert and Wilson (e.g., Gilbert & Wilson, 2000; Wilson

& Gilbert, 2003) shows that people tend to overestimate the influence of future affective events on their feelings, but work by Loewenstein and colleagues (e.g., Loewenstein,

1996; Loewenstein et al., 1997) shows that people tend to underestimate the impact of future affective events on their . Two related explanations for this fact have recently been offered by Van Boven and Kane (in press). The first is that choices are intuitively more stable than feelings and so may seem less susceptible to the influence of transient affective events when forecasted. While people may expect their feelings to fluctuate over time (Igou, 2004; McFarland et al., 1989; Ross, 1989) and so be readily influenced by affective events, they may believe their choices immune to such transient variations, reflecting preferences that are stable over time and across contexts (Van

Boven & Kane, in press).

The second reason for the difference in the direction of inaccuracy for predictions about feelings versus choices is the possibility that choices are more closely tied to dispositions than are feelings (Van Boven & Kane, in press). Although there are indeed dispositional differences in the experience of feelings (e.g., proneness to experience negative feelings, Wason & Clark, 1984; proneness to experience extreme feelings,

Larsen, Diener, & Emmons, 1986), if people regard choices as more dispostionally determined than feelings, as Van Boven and Kane suggest, one would expect forecasts of future choices to be less tied to particular affective events than forecasts of feelings. One possibility is that people may be motivated to forecast choices consistent with past behaviors. Research shows that people are often motivated to maintain consistency in their dispositions or self-concepts (Aronson, 1969; Greenwald & Ronis, 1978; Steele,

1988; Thibaut & Aronson, 1992) and so may underestimate the influence of affective events on choices in fulfillment of this motivation. Differences in the predictions of feelings and choices will, however, receive a more discussion in Chapter 6.

Whatever the reason for the difference in the direction of inaccurate prediction for feelings versus choices, the question remains: do transient affective states reduce empathy gaps? While a growing body of research has examined empathy gaps in the prediction of visceral states, such as hunger and pain, comparatively few published studies have examined empathy gaps in forecasts of emotional states (cf. Van Boven,

Loewenstein & Dunning, 2005). Further, at the time of writing, no published work had examined the role of transient emotions in bridging empathy gaps. So the question arises, does being in a high arousal emotional state of anxiety or anger, for example, at the time of prediction help one better appreciate future instances of an emotion and so reduce empathy gaps? Affective empathy gaps can be conceived of as a type of affect content effect: a current emotion helps us better appreciate a future affective state and so influences the content of our forecasts. However, physiological arousal may be central to reduction, while not being necessary for other mechanisms of affect infusion (such as affect priming or affect-as-information). As noted above, most research on empathy gaps in affective forecasting has examined the impact of visceral states, such as pain and hunger, on empathy gap reduction. Such states, by definition, have substantial arousal components. Further, it may be this arousal component of a currently experienced state that allows people to better appreciate a future aroused state. It is much easier to appreciate what it will feel like to be scared next week if you are scared right now. In other words, arousal may be necessary for empathy gap reduction. As such, we may only find empathy gap reduction in affective forecasts when the forecaster is in a current, high arousal affective state, such as anxiety or anger, and not when in a low arousal state such as sadness. Empathy gaps are the primary concern of Studies 4 and 5 and so receive a more thorough discussion there. Let us now consider the final type of hypothesized influence of affect on forecasts: mood processing effects.

Processing Effects: Mood and How We Think

In addition to the informational or content effects of mood described above, affect also influences information processing style: how we think. Generally, positive moods

elicit a top-down, less systematic and more heuristic processing strategy, often leading to

greater flexibility and creativity (Bodenhausen, 1993; Fiedler, 1988; Hertel & Fiedler,

1994; Isen, 1987; Mackie & Worth, 1991; Sinclair & Mark, 1992). Negative moods, on

the other hand, facilitate careful, detail-oriented and elaborative information processing

styles (Schwarz, 1990; Schwarz & Bless, 1991). These two general processing styles

have more recently been termed assimilative and accommodative, respectively (Fiedler &

Bless, 2001). Assimilation, associated here with positive affect, is a processing style

whereby people tend to rely on internal knowledge structures and routine, schematic

knowledge to deal with the environment. Negative mood-induced accommodation, on the

other hand, is an exhaustive and careful processing strategy that is largely bounded by

external details.

Three general explanations have been forwarded for these mood-related

processing differences. First, functional explanations argue that affective states signal

particular environmental conditions and prompt appropriate cognitive strategies for

dealing with these conditions. Positive moods suggest a favorable, benign environment

where top-down and rather less systematic processing is required. Here we can function

on “auto-pilot” and rely confidently on existing knowledge and engage in a more creative, heuristic processing style (Fiedler, 1988; Schwarz & Bless, 1991). When environmental conditions are aversive, however, signaled by negative affect, more externally focused, vigilant and systematic processing is required (Forgas & Bower,

1987; Schwarz, 1990). The second explanation of mood processing effects is

motivational: happy people try to maintain their positive states by avoiding cognitive effort (mood maintenance), and sad people seek to improve their negative states via increased cognitive work (mood repair)(Clark & Isen, 1982; Sedikides, 1994). The third account of these effects is a processing capacity account. Some researchers suggest that positive moods may impair information processing by limiting cognitive capacity (Isen,

1987; Mackie & Worth, 1991). Other studies, however, suggest similar cognitive impairments resultant of negative moods (Ellis & Ashbrook, 1988), and still others propose performance enhancing, rather than hindering, effects of positive moods (Bless,

Clore, Schwarz, Golisano, Rabe & Wolk, 1996).

Regardless of their explanation, mood-induced processing differences have important implications for social judgments in general and for affective forecasting in particular. Some research has shown that the more externally oriented, systematic and careful processing induced by negative affective states can reduce cognitive biases such as the fundamental attribution error (FAE, Forgas, 1998). In an interesting series of studies, Forgas (1998) presented participants with essays advocating favorable or unfavorable opinions written by students who could freely choose their expressed opinion or who were assigned an opinion to write about. Results showed that people in a positive mood were more likely and people in a negative mood were less likely to make internal attributions about the opinions expressed in the essays. The differential effects of positive and negative moods on the information processing strategies used in making attributions can explain these differences in the tendency to commit the FAE. According to some researchers, the FAE can be explained by people’s tendency to selectively attend to social actors and neglect situational information (Gilbert & Malone, 1995). In Forgas’ study, people in a negative mood may have been more likely to attend to situational factors and thus show reduced FAE because of the externally-oriented, detail-focused nature of their

information processing. This finding is consistent with other research that shows that sad people are more accurate than happy people in other judgment domains (see Sinclair &

Mark, 1992 for a review).

If the intensity and durability biases are due to cognitive errors of a similar kind

(e.g. focalism, a tendency to neglect the impact of events other than the target event on affective states, as is indeed suggested by recent theorizing; Gilbert et al., 1998), then it is possible that these errors may also be reduced by the more externally focused, careful, vigilant information processing associated with negative moods. Quite simply, then, sad people may be more accurate forecasters than happy people. These mood processing effects will be considered as secondary hypotheses in Studies 1 and 3.

The Present Research

The primary aim of the present research was to examine mood congruence effects on affective forecasts in the context of the AIM (Forgas, 1995). The central prediction was that mood congruence effects would be more pronounced under conditions of constructive processing. Studies 1 to 3 examined this hypothesis using a variety of manipulations of constructive processing in accordance with the AIM. In Study 1 happy, neutral and sad participants made affective forecasts about a variety of everyday events, such as losing a small amount of money, under instructions to process constructively or not. In Study 2, the ambiguity of the to-be-forecasted event was varied as a manipulation of constructive processing. In Study 3, an individual difference measure, Need for

Cognition (Cacioppo & Petty, 1982), served as the measure of constructive processing. In each of these studies, mood congruence effects were expected to be more pronounced under conditions of constructive processing.

Studies 4 and 5 examined the role of transient affective states in reducing

empathy gaps in affective forecasts. In Study 4, anxious or neutral people forecasted their

feelings about an upcoming public speaking engagement and also rated their willingness

to give a public speech. Study 5 replicated and extended Study 4 by examining how

happy, sad and angry participants made those same predictions.

Although affect content effects (mood congruence and empathy gaps) were the

central focus of this thesis, each study also examined the secondary predictions concerning the motivational and information processing effects of affect. Chapter 2

Study 1: The Effects of Mood and Elaboration Instructions on Affective Forecasts

The primary aims of Study 1 were to test the basic mood congruence hypothesis

in everyday affective forecasts and to test the more specific prediction of moderated

mood congruence as described by the AIM (Forgas, 1995, 2002). Recall that the central

prediction of the AIM is that mood congruence effects will be more pronounced under conditions of constructive information processing. The model also stipulates the conditions under which such constructive processing strategies will be used. Forgas

(1995) suggests that the processing strategy adopted for a particular judgment depends on aspects of the judge, the target, and the judgmental situation. Factors such as personal relevance, motivational goals and cognitive capacity are features of the judge that can influence processing style (e.g., Clark & Isen, 1982; Erber & Erber, 1994; Forgas, 1989,

1991). Target features include the familiarity, typicality and complexity of the

judgmental object (e.g., Forgas, 1992, 1994, 1995; Petty, Gleicher, & Baker, 1991;

Salovey & Birnbaum, 1989; Srull, 1984). Situational features include social desirability

expectations and the availability of objective criteria or task instructions (Forgas, 1995).

The Present Study

Study 1 was designed to provide an initial test of two primary hypotheses: 1)

mood congruence, as predicted by the affect priming and affect-as-information accounts,

and 2) moderated mood congruence, as predicted by the AIM. This second hypothesis

was tested using a situational manipulation of constructive processing. The situational

factor that was manipulated in this study was instructions to elaborate. This manipulation was based on Sedikides’ (1995) manipulation of ‘on-line’ elaboration in a study

examining the effects of mood on judgments about personality traits. In Sedikides’

(1995) study, sad, neutral and happy participants, under low or high instructions to

elaborate, made judgments about whether they possessed various positive and negative

traits. In the low elaboration condition, participants were asked to “decide whether you

have each of these traits”(p. 771, Sedikides, 1995). Participants in the high elaboration condition made exactly the same trait judgments but were instructed to:

Try to decide whether you have or do not have each of the following 6 traits; ask

yourself whether friends and relatives could or could not describe you accurately

using each of these traits. Why would and why would not friends and relatives

describe you accurately using these traits? Please think of two behaviors that

would justify describing you with each of these traits; then think of two behaviors

that would not justify describing you with these traits. What are these behaviors?

How likely or unlikely are you to perform each behavior? (p. 771)

Consistent with the central prediction of the AIM, results showed that mood congruence in this self-judgment task was more pronounced in the high than in the low elaboration condition.

Study 1 employed a similar elaboration instruction manipulation in an affective forecasting task. In short, happy, sad and neutral participants were instructed to make affective forecasts of intensity and durability for a number of everyday events under either high or low elaboration conditions. In accordance with much previous research on affect and social judgment, it was expected that happy people would make more positive

forecasts, and sad people, more negative forecasts, than their neutral counterparts. In

addition, and based on the AIM, it was predicted that mood congruence into affective forecasts would be more pronounced in the high compared to the low elaboration condition.

Two secondary hypotheses were also considered. One alternative hypothesis, based on the motivational account of mood effects, was that sad people, in an attempt to repair their moods, would make more positive affective forecasts than their neutral counterparts. A second alternative hypothesis was based on the information processing consequences of mood. Mood processing effects on forecast accuracy were indirectly assessed by comparing the extremity of sad people’s forecasts to those in the neutral condition. Even though affective experiences were not measured in the current study, thus preventing the calculation of intensity and durability biases, forecasting accuracy could still be indirectly assessed. Given that neutral participants were assumed to be overestimating the intensity and durability of their future affective states (as indicated by much previous research), less extreme forecasts by sad people would indicate increased accuracy.

Method

Participants and Design

185 first year psychology students (128 female, 51 male, 6 gender not reported) from the University of New South Wales participated in this study for course credit. This study employed a 3(mood: positive vs. neutral vs. negative) x 2(processing instruction: high elaboration vs. low elaboration) between participants design. The dependent variables were intensity and durability forecasts, as well as reading and judgment

response latencies.

Procedure

Upon arrival at the laboratory, participants were seated and informed of the nature

of the tasks they would be performing during the experimental session. They were told

that they would be completing a number of unrelated activities including (a) watching a

short video, (b) completing a computer-based questionnaire about everyday social

judgments, and (c) answering some general questions about the video they watched

earlier in the session. After completing a consent form, participants began by watching a

short film designed to induce a positive, neutral or negative mood. The purpose of the

film as a mood induction was disguised so as to reduce demand effects. Participants were told that they had to evaluate the content of the film for a future study and were instructed to watch the film as if watching television at home and involve themselves in the themes and emotive qualities of the story.

Next, participants were taken to a cubicle to complete a computer-based questionnaire about everyday social judgments. This computer task was in fact the affective forecasting task. After reading the instructions and completing some practice examples to familiarize themselves with the response format, participants read about a number of everyday events (e.g., losing a wallet at a crowded party, receiving a small chocolate from a friend) and were then asked to rate the intensity and durability of their predicted affective reactions to these events. With the exception of the instructions

(which were different for the high and low elaboration conditions), this task was identical for the high and low elaboration groups. Finally, participants completed a questionnaire about the film they watched. This

questionnaire included items that measured current mood along with numerous distracter items to reduce demand characteristics. Participants were then thanked for their time and debriefed.

Manipulations and Measures

Mood induction. Participants watched a short film, about 10 minutes in length,

designed to induce a positive (excerpts from “Fawlty Towers”), neutral (a nature

documentary) or negative mood (excerpts from “Who Will My Children”).

Participants were advised to watch the film as if watching television at home and to

become involved with the themes and emotive qualities of the story. Video mood

inductions such as these have been used in previous research and consistently induce appropriate mood states (see Gross & Levenson, 1995; Philippot, 1993; Westerman,

Spies, Stahl, & Hesse, 1996 for reviews). Further, the particular induction videos used in

this study have been shown to induce appropriate mood states in previous research

(Forgas, 1990; Forgas, 1991; Forgas, 1994; Forgas, Bower, & Moylan, 1990; Forgas &

Fiedler, 1996).

Affective forecasting measure. This computer-based task involved making

affective forecasts of the intensity and durability of 12 everyday events and was

implemented via MediaLab software (Jarvis, 2000). Participants were first informed via

computer-presented instructions of the nature of the task:

In this task you will be asked to read a number of scenarios describing events that

may happen to you in the future. You will be asked to respond to these scenarios by answering a number of simple questions.

Two questions will be asked about each scenario. Responses are to be made on 7-

point Likert scales. For example:

Hard 1 2 3 4 5 6 7 Soft

To answer a question simply press a number at the top of the keyboard

corresponding to your response (DO NOT USE THE NUMBER PAD ON THE

RIGHT HAND SIDE OF THE KEYBOARD).

Following these instructions, participants completed the task proper. The forecasts to be

made were identical for high and low elaboration conditions, however, instructions about

how to forecast were slightly different. Those in the high elaboration condition were

presented with the following instruction screen preceding each target event:

Please form an elaborate mental picture of the following event. Consider the event

unfolding in your mind. Imagine the event in detail for about 10 seconds before

advancing to the next screen.

Press SPACE BAR to continue.

On the following screen participants saw the target event (e.g., “You get caught in the

rain without an umbrella”). They were instructed to press the space bar to advance to the

next screen where they made an intensity prediction: “How would you feel about being

caught in the rain without an umbrella?” They responded on a 7-point Likert scale (1 = happy; 7 = sad). On the next screen participants made a durability prediction: “How would you feel in general a week after being caught in the rain?” They responded to this on a similar 7-point Likert scale anchored with “happy” and “sad.”

Participants in the low elaboration condition made the same forecasts about the same events but the target event was preceded by the following instruction: “Please briefly consider the event that follows. Please spend only a couple of seconds imagining the event before answering the questions on the next screen.”

The events to be forecasted were everyday occurrences similar to those constituting the Life Expectations Scale (Lerner & Gonzalez, 2005) or Weinstein’s

(1980) Optimism Scale. Events to be forecasted included “You lose (find) $2 in the street”, “You receive a high distinction (fail) on your next psychology assignment”, “You

meet a very attractive person at a party who seems interested in you”, “You break-up

with a romantic partner you have been seeing for 3 months”, “You are fired from your

job”, “You receive an unexpected gift from a friend”, “You receive a $200 work bonus”,

“You lose your wallet at a crowded party”, “You receive a small chocolate from an

acquaintance.”

In addition to the forecasts obtained about each of the 12 items, response latencies

were measured for the elaboration of the event (time taken to read and elaborate the event

description) and for each of the intensity and durability forecasts. These latencies were

used as a manipulation check of elaboration instructions. Although longer response

latencies do not necessarily imply more constructive, elaborative processing, response

latencies as a measure of processing elaboration correlate with other indices of message

elaboration (e.g., memory, Forgas, 1992; Forgas & Bower, 1987, and see Forgas, 1995

for a review). Mood validation. Despite much evidence of the effectiveness of the induction

videos used in this study, a mood manipulation check was nevertheless included,

primarily to assess the longevity of the induced mood states. Upon the completion of the

computer task, participants rated their current moods on three 7-point Likert scales. Two

scales assessed affective valence: 1 = happy; 7 = sad and 1 = good; 7 = bad. The third

scale measured arousal (1 = aroused; 7 = unaroused)(e.g., Forgas, 1998). These scales were embedded in a “post-experimental questionnaire” amongst numerous distracter items to disguise the experimenter’s primary concern with mood and to minimize demand

characteristics.

Debriefing. A thorough debriefing session followed the last questionnaire. In this

session, participants were first asked of their suspicions regarding the aims of the study and whether they perceived any direct link between the video-watching task and the computer-task. Next, they were told of the aims and expected results of the study. Finally, it was ensured that any residual negative affect due to the negative mood induction was relieved. No one reported any suspicions about the hypotheses of the study or reported noticing any links between the video and forecasting tasks.

Results

Mood Manipulation

Based on previous research (e.g., Forgas, 1998) and the observed pattern of correlation among the three mood validation variables, a valence index was created by averaging the happy-sad and good-bad scales (Cronbach’s α = 0.88). As expected, a

3(mood: positive vs. neutral vs. negative) x 2(processing instruction: high elaboration vs. low elaboration) ANOVA revealed a significant Mood main effect, F(2, 182) = 28.24, p < 0.001. Participants in the positive mood condition (M = 2.84, SD = 0.90) felt better than neutral participants (M = 3.42, SD = 1.09), t(182) = 2.91, p = 0.004, who in turn, felt better than participants in the negative mood condition (M = 4.32, SD = 1.26), t(182) =

4.56, p < 0.001. As expected, the Processing Instruction main and interaction effects were not significant, Fs<1, ns.

A similar analysis of arousal ratings revealed no significant main or interaction effects, Fs < 1.5, ns. In short, the mood induction induced appropriately valenced and lasting affective states with arousal levels not significantly different from a neutral state.

Elaboration Manipulation

Reading time. To establish supportive evidence that the elaboration instructions induced processing strategies of different constructiveness, reading times were analyzed.

These reading times were submitted to a 3(mood: positive vs. neutral vs. negative) x

2(processing instruction: high elaboration vs. low elaboration) x (12)(item) MANOVA.

Data from 16 outliers was excluded from the analysis (scores on one or more of the 12

items outside 3SD) (Stevens, 1996). These outliers were approximately equally

distributed across conditions and likely represented random errors. As expected, results

revealed a significant multivariate Processing Instruction effect, Wilk’s Λ = 0.83, F(12,

153) = 2.61, p = 0.003, η² = 0.17, indicating that processing instructions significantly

influenced reading time. The multivariate Mood, Wilk’s Λ = 0.85, F(24, 306) = 1.11, p =

0.34, and the multivariate Mood x Processing Instruction interaction effect, Wilk’s Λ =

0.85, F(24, 306) = 1.07, p = 0.38, were not significant.

To more specifically examine the nature of the Processing Instruction effect, and

to determine for which stimuli the elaboration manipulation was effective, 99% standardized intervals (SCIs) were constructed around pairwise comparisons

of high and low elaboration condition for each of the twelve items (see Appendix A).

Examination of these confidence intervals revealed 3 items (“You meet a very attractive

person at a party who seems interested in you,” “You break-up with a romantic partner

you have been seeing for 3 months,” and “You receive a small chocolate from an

acquaintance”) for which the 99% SCIs included zero (corresponding to non-significant

α= 0.01 t-tests). These items were excluded from the subsequent analyses of mood congruence given that the elaboration instructions did not significantly influence

processing for these items. The other nine items had lower SCI limits that ranged from

0.10 to 0.32 suggesting that the Processing Instruction manipulation had, at the very least,

moderately small to small effects on these items (Cohen, 1969). These 9 stimuli were

selected for the primary analyses.

Intensity and durability response latencies. Response latencies for the intensity

and durability predictions were also submitted to 3(mood: positive vs. neutral vs.

negative) x 2(processing instruction: high elaboration vs. low elaboration) x (12)(item)

MANOVAs. Excluding data from 12 outliers (scores outside 3SD), results for the

intensity forecast response latencies revealed no significant multivariate effects, Fs < 1.4,

ns. Further, a similar analysis of durability forecast response latencies, excluding data

from 14 outliers, revealed no significant multivariate effects, Fs < 1, ns. Although

processing instructions did not influence response latencies for intensity and durability

judgments, they did influence reading times. This suggests that participants in the high

elaboration condition took longer than low elaboration participants to read and elaborate

the target events, but not to make judgments about them. Intensity Forecasts

Of the nine items selected for analysis based on reading time differences, four described positive events and five described negative events. Composite scales formed of items of the same valence, however, had low reliability (Cronbach’s αs of 0.51 and 0.53

for the negative and positive composites respectively). As such, intensity predictions for

the nine selected items were submitted to a 3(mood: positive vs. neutral vs. negative) x

2(processing instruction: high elaboration vs. low elaboration) x (9)(item) MANOVA.

Summary statistics are presented in Table 1. Contrary to predictions, results revealed no

significant multivariate effects for Mood, Wilk’s Λ = 0.93, F(18, 342) = 0.73, p = 0.78,

Processing Instruction, Wilk’s Λ = 0.97, F(9, 171) = 0.61, p = 0.79, or the predicted

Mood x Processing Instruction interaction, Wilk’s Λ = 0.89, F(18, 342) = 1.09, p = 0.36.

Although it is not typical practice to conduct multiple univariate ANOVAs following

non-significant multivariate tests of homogeneity, this strategy was, nevertheless,

employed here in an exploratory fashion. It should be noted, however, that this more

liberal approach of conducting multiple univariate ANOVAs on intensity forecasts for

each of the nine items, controlling the per analysis error rate at α = 0.15, did not reveal any significant main or interaction effects. These results suggest that induced mood did not influence intensity forecasts either in interaction with elaboration instructions (as predicted by the AIM) or independently of these instructions.

Table 1 Mean intensity forecasts as a function of mood and elaboration instructions

Positive Mood Neutral Mood Negative Mood Low High Low High Low High Elaboration ElaborationElaborationElaborationElaboration Elaboration Item

M 1.20 1.55 1.97 1.24 1.68 1.71 SD 0.61 1.48 2.23 0.58 1.56 1.55 n 30 31 33 29 31 31 M 1.56 1.74 1.67 1.79 1.52 1.87 SD 1.38 1.57 1.45 1.76 0.81 1.09 n 30 31 33 29 31 31 M 8.40 8.48 8.03 8.41 8.26 7.94 SD 0.77 0.63 1.53 0.82 0.89 0.85 n 30 31 33 29 31 31 M 1.73 1.71 1.58 1.69 1.45 1.87 SD 1.55 0.78 0.83 1.31 0.62 0.92 n 30 31 33 29 31 31 M 8.53 8.35 8.48 8.55 8.35 8.26 SD 0.73 0.75 0.80 0.69 0.75 0.77 n 30 31 33 29 31 31 M 8.10 8.39 8.33 8.21 8.23 8.32 SD 1.75 0.92 0.74 1.15 0.76 0.83 n 30 31 33 29 31 31 M 2.90 3.19 3.27 2.79 3.10 3.29 SD 1.37 1.25 1.23 1.40 1.54 1.27 n 30 31 33 29 31 31 M 6.13 5.87 6.09 6.34 6.19 5.77 SD 1.20 0.92 1.21 1.20 1.28 0.76 n 30 31 33 29 31 31 M 5.47 5.65 6.06 5.93 5.65 5.48 SD 1.83 1.56 1.75 1.58 1.66 1.36 n 30 31 33 29 31 31

Durability Forecasts

Durability forecasts for the selected nine items were submitted to a similar

3(mood: positive vs. neutral vs. negative) x 2(processing instruction: high elaboration vs.

low elaboration) x (9)(item) MANOVA. Summary statistics are presented in Table 2.

Again no significant multivariate effects emerged for Mood, Wilk’s Λ = 0.94, F(18, 342) = 0.60, p = 0.88, Processing Instruction Wilk’s Λ = 0.98, F(9, 171) = 0.47, p = 0.89, or the Mood x Processing Instruction interaction, Wilk’s Λ = 0.88, F(18, 342) = 1.23, p =

0.23. The more liberal, exploratory approach of conducting multiple univariate ANOVAs

on each of the nine durability forecasts revealed no significant main or interaction effects.

Table 2 Mean durability forecasts as a function of mood and elaboration instructions

Positive Mood Neutral Mood Negative Mood Low High Low High Low High Elaboration ElaborationElaborationElaborationElaboration Elaboration Item

M 2.27 2.68 2.58 1.83 2.16 2.65 SD 1.17 1.62 1.62 1.04 1.29 1.54 n 30 31 33 29 31 31 M 2.30 2.87 2.94 2.14 2.52 2.61 SD 1.32 1.41 1.54 1.13 1.23 1.09 n 30 31 33 29 31 31 M 7.40 6.68 6.36 7.38 7.16 6.74 SD 1.35 0.98 2.16 1.40 1.49 1.37 n 30 31 33 29 31 31 M 2.87 3.00 2.88 2.76 2.61 2.74 SD 1.14 1.15 1.54 1.15 0.95 1.12 n 30 31 33 29 31 31 M 7.43 6.90 6.82 7.31 7.00 7.00 SD 1.36 1.11 1.86 1.42 1.69 1.26 n 30 31 33 29 31 31 M 7.13 7.03 6.97 6.97 7.03 7.16 SD 1.55 1.25 1.65 1.57 1.64 1.24 n 30 31 33 29 31 31 M 4.10 4.35 3.97 4.41 3.94 4.29 SD 1.30 1.20 1.59 1.48 1.15 1.10 n 30 31 33 29 31 31 M 5.00 4.77 4.24 4.55 4.61 4.68 SD 1.20 0.72 1.41 1.02 1.31 1.08 n 30 31 33 29 31 31 M 4.03 3.97 3.64 4.00 4.23 4.06 SD 2.08 1.76 2.01 2.41 1.91 2.06 n 30 31 33 29 31 31 Discussion

Contrary to the primary predictions, mood did not influence intensity or durability

forecasts either in interaction with processing instructions (the moderated mood

congruence effect predicted by the AIM) or independently of them. Despite evidence of

the effectiveness of both the mood and processing instruction manipulations, induced mood had no impact on affective forecasts. As such, the pattern of results does not support either the mood-as-motivation or mood processing accounts described in

Chapter 1. Motivational effects would have been evidenced in sad participants’ making more positive forecasts than neutral participants. There was, however, no evidence of this. This absence of mood-as-motivation effects, however, is not surprising given that the judgmental task in this study was of rather low personal relevance. Recall that moods typically have motivational consequences when participants are engaged in highly personally relevant tasks (see Forgas, 1995 for a review).

In addition, the results of this study did not support the mood processing hypothesis. Despite no direct assessment of the magnitude of the intensity and durability biases, some indication of forecast accuracy would have been suggested by sad people making less extreme forecasts than neutrals. There was, however, no evidence that sad people differed from neutral participants in their forecasts.

More surprising, than the absence of motivational or processing effects, however, is the absence of the proposed interaction between induced mood and processing instructions. Instructions to elaborate have indeed been found to interact with mood in the manner predicted by the AIM in previous research (e.g., Sedikides, 1995). Perhaps even more surprising still, is the absence of any mood congruence effects at all (moderated by processing or otherwise). One possibility for the absence of mood congruence here,

however, is that the experimental stimuli used may have been too unambiguously-

valenced to have been influenced by mild mood states. Receiving a high distinction in an

exam, for instance, is an obviously positive event, the affective consequences of which

are clear and potentially not labile under the influence of mild moods. In the language of

the AIM, the affective consequences of such simple and clearly-valenced everyday events

may be crystallized and available to direct access and so may be immune to affect

infusion.

An examination of Sedikides’ (1995) work on mood and self-conceptions lends

credibility to this explanation of the absence of mood congruence. In Experiment 4 of

Sedikides’ (1995) series of studies, elaboration instructions moderated mood congruence

effects for judgments about peripheral personality traits. Experiments 1 to 3 of

Sedikides’ research program, however, showed that mood does not exert a congruence

effect in judgments of central personality traits. Sedikides’ argument for personality

traits’ differential susceptibility to affect infusion was that central traits are more

consolidated and possess relative invariance in comparison to peripheral traits. Sedikides

argues that central self-conceptions are immune to affect infusion because they “…are

more familiar, typical, and unambiguous…are held with more certainty; and are more

cumulatively elaborated and consolidated – attributes that are likely to instigate the direct

access strategy…” of the AIM (p. 762). So while on-line elaboration instructions may

moderate affect infusion into malleable peripheral self-conceptions, the question of whether such overt instruction to elaborate actually produces elaboration for otherwise

direct access judgments (e.g., central self-conceptions) was not addressed. In the context of the present study, if direct access to the affective consequences of the forecasted

events were available, affect infusion would have been precluded.

So do forecasters have direct access to such theories linking affective events to

their consequences? Although the first, construal stage of Wilson and Gilbert’s (2003) forecasting model suggests that forecasters need to construct a representation of the to- be-forecasted event, if people have affective theories of how the event influences their feelings, no ‘construction’ is necessary. Forecasters may simply bring to mind semantic knowledge bearing on the forecast and so override any need for generative representation construction. Robinson and Clore (2002a) refer to this kind of crystallized knowledge about the affective consequences of events as situation-specific beliefs. They argue that these kinds of beliefs are part of the semantic memory network and so are rarely updated and “are relatively static and stereotyped” (p. 936). According to Robinson and Clore

(2002a), even episodic memories of specific emotional events eventually shift to semantic memory and take the form of generalized affective theories relating a type of event to its affective consequences. These theories may not be accurate (see Wilson &

Gilbert, 2003; Loewenstein & Schkade, 1996 for reviews), but they may, nevertheless, be immune to affect infusion.

So, it is possible that such crystallized situation-specific beliefs were driving forecasts in the current study. When asked how you would feel about receiving an unexpected gift or being fired from your job you may readily access knowledge that links gifts to happiness and a redundancy package to despair, and these normative beliefs may be immune to affect infusion. In the language of Wilson and Gilbert’s (2003) forecasting model, the stimuli in the current study may have placed too stringent a constraint on the construal stage of the forecasting process. Regardless of processing instructions,

participants may not have actually generated detailed, scenario-based representations of

the events to be forecasted, but based their forecasts on normative theories about the

influence of different situations on feelings. Research does, indeed, show that if people

can recall information that has direct implications for their judgments, their current

affective experiences have little influence on those judgments. Schwarz, Strack, Kommer

and Wagner (1987), for instance, showed that participants’ affective reactions to the

cleanliness of an experimental laboratory influenced predictions of general life satisfaction, but not specific evaluations of their personal living quarters. Presumably, when making the latter judgment, participants based their judgments on descriptive criteria, using the experimental room as a comparative standard in the assessment of their personal living quarters. In this case, the affect elicited by the experimental room did not

inform judgments as participants had other information directly relevant to the judgment

at hand. In addition, work by Srull (1983, 1984), for example, shows that moods exert

congruence effects on consumer judgments about unfamiliar, but not familiar products.

When consumers have factual, judgment relevant knowledge about familiar products, their incidental affect is rendered irrelevant and so receives no weight in the judgment.

In the current study, then, forecasts may not have been influenced by transient moods because participants may have had access to affective theories that were directly relevant to the forecasts to be made. One potential remedy to this problem is to decrease the affective clarity of the valence of the forecasted events. By increasing the valence ambiguity of the event to be forecasted, one might decrease the likelihood of participants possessing crystallized, normative theories about the event. People may possess situation- specific beliefs about birthdays and redundancy, for example, but not about affectively ambiguous scenarios. This issue was addressed in Study 2. Chapter 3

Study 2: The Effects of Mood and Target Valence Ambiguity on Affective Forecasts

When people predict their affective responses to future positive or negative events, they may directly access their beliefs about the influence of positive and negative events on their feelings (Robinson & Clore, 2002a). In Study 1, participants made affective forecasts about relatively simple, familiar and clearly valenced everyday events and so may have had ready access to theories or beliefs about the effects of such events on their feelings. If these beliefs are crystallized and part of semantic memory, they may be directly accessible and so immune to affect infusion (Forgas, 1995). To address this possible shortcoming, in Study 2, participants made forecasts about events of varying valence ambiguity. It was assumed that people would not have affective theories about how an ambiguous event would impact on their emotions and so their affective forecasts for such ambiguous events would be more susceptible to affect infusion.

Mood Congruence and Target Ambiguity

Some research does indeed suggest that mood congruence effects are more likely to occur when making judgments about ambiguous information (e.g., Baron, 1993;

Bower, 1981; Butler & Mathews, 1983; Erber, 1991; Forgas, Bower, & Krantz, 1984;

Isen & Shalker, 1982; Krantz & Hammen, 1979; Manstead & van der Pligt, 2002;

Schiffenbauer, 1974). When the target of a judgment can be interpreted in a variety of ways, no directly accessible response is available and so transient affect can infuse the judgment. Bower (1981), for example, found that happy and sad participants made mood congruent interpretations of the situations portrayed in the Thematic Apperception Test (TAT). These TAT stimuli are designed to be ambiguous and allow for a variety of interpretations. When presented with such ambiguously valenced stimuli, participants

have no direct access to a stored response and so their current moods can inform their

judgments. In the same study, Bower also found that participants made mood congruent

free associations to ambiguous phrases like my career or ambiguous words such as life or

future.

Forgas, Bower and Krantz (1984) also showed that ambiguous social interactions

are interpreted in a mood congruent fashion. In this study, happy and sad participants

watched a videotape of a previously recorded social interaction episode between

themselves and another person. Happy participants ‘saw’ more positive, skilled behaviors

and fewer negative, unskilled behaviors in the interaction than did sad participants.

Research by Erber (1991) on mood effects on trait judgments provides further

support for mood congruence into judgments of ambiguous targets. In this study, people

in a positive, neutral or sad mood were presented with a personality description of a

target that was of ambiguous valence. Targets were described as ‘moody and warm’

(p.485) for instance. Participants were asked to use this trait information to rate the

likelihood that targets would engage in positive or negative trait relevant or positive or

negative trait irrelevant future behaviors. Results showed that the behaviors rated as

likely in the future were not only trait congruent but were also mood congruent. Although

the trait information provided in the target descriptions provided some basis for

likelihood judgments, the ambiguity of the descriptions may have prevented a strict

application of trait-behavior theories or beliefs. Although participants may have held

theories about how traits should predict behaviors, introducing ambiguity into the judgments made these theories less applicable and allowed affect infusion.

Baron (1993) found further evidence of mood effects under conditions of ambiguity in an applied, organizational context. In this study, participants were induced into positive or negative moods or underwent no mood induction. They then participated in simulated job interviews in which they interviewed candidates who were clearly qualified or unqualified for the job or had ambiguous qualifications. When the qualifications were ambiguous, happy participants rated the applicant higher on several

dimensions than did sad participants. When the applicant appeared to be highly qualified

for the job, interviewers' moods had no significant influence on ratings.

An unpublished experiment on mood and judgments of ambiguous stimuli by

Kelly and Wyer (cited in Wyer & Srull, 1989) is of particular relevance to the present

study. This experiment required happy and sad people to make judgments about how a

target person would feel in response to an ambiguously valenced event. Participants were

asked, for example, to interpret how a boy would feel if his grandmother died and left

him enough money to attend the college of his choice. Results showed a mood

congruence effect: participants tended to interpret the protagonists’ feelings as being of a

similar valence to their own induced moods. Participants in this experiment were

essentially making affective forecasts for another person in response to an ambiguously

valenced event. Although people may have affective theories about deaths in the family

or inheritance, judges were unlikely to have direct access to a theory of how one is likely

to feel if his grandmother dies but also leaves him a considerable amount of money. In

the absence of a directly accessible affective theory, affect infused these judgments.

The Present Study A similar methodology to the Kelly and Wyer study was used here in Study 2.

Happy and sad participants made intensity and durability forecasts for ambiguously valenced as well as clearly positive and clearly negative events. The primary hypothesis was that affective forecasts would be mood congruent for ambiguous target events but not for clearly valenced events.

Method

Participants and Design

Participants were 129 undergraduate psychology students (96 female, 33 male) at the University of New South Wales who completed this experiment for course credit. The design of this study was a 2(mood: positive vs. negative) x 3(feedback ambiguity: positive vs. ambiguously valenced vs. negative) between participants design. Dependent variables included intensity and durability predictions.

Procedure

Upon arrival at the experimental session participants were informed that the study would involve watching a short film and completing a questionnaire about judgments of assignment feedback. These tasks were presented as unrelated. Participants first watched a short film designed to induce a positive or negative mood. The purpose of the film was disguised so as to minimize demand characteristics, and was thus presented as a task on plot comprehension. Students were instructed to watch the film as if watching television at home and involve themselves in the themes and emotive qualities of the story.

After the mood induction, participants completed an ostensibly unrelated questionnaire about assignment feedback (actually the affective forecasting task). They were told that the university was currently reviewing its marking procedures and was looking for students’ opinions on various methods of presenting assignment feedback.

This questionnaire presented them with an example feedback passage for a psychology assignment. After reading this passage students predicted how they would feel if they were to receive this feedback and how long this feeling would last.

Finally, participants completed a questionnaire about the video they watched. This questionnaire contained the mood validation scales along with numerous distracter items to disguise the purpose of the study. Students were then informed of the nature of the tasks they had completed and were fully debriefed.

Manipulations and Measures

Mood induction. The mood induction was identical to that used in Study 1. Film segments were used to induce positive (excerpts from “Fawlty Towers”) or negative moods (excerpts from “Who Will Love My Children”).

Affective forecasting measure. This measure was introduced as a survey, being conducted by the university, of opinions about the format and structure of psychology assignment feedback. This questionnaire presented a paragraph of assignment feedback ostensibly taken from an actual tutor’s grading records for a social psychology assignment. There were three versions of the questionnaire each with a paragraph of feedback of different valence ambiguity. The clearly valenced positive feedback paragraph was as follows:

You provide good coverage of the general subject area and make reasonable use

of recent research and literature in supporting your ideas. You also cover most of

the issues that were of relevance to the question. Your emphasis on theoretical points is sensible, and your discussion of the practical side of this issue is

acceptable. Your argument is generally logical and sound although your

expression is sometimes convoluted and imprecise. And while you do make some

interesting and novel suggestions, your attention to detail is sometimes lax.

The other feedback passages were clearly negative and ambiguously valenced (see

Appendix B for full transcripts of these passages). These passages were pre-tested and

selected based on 7-point Likert scale (1 = positive; 7 = negative) responses to the question “How positive-negative is this passage?” The ambiguously valenced passage (M

= 4.00, SD = 1.08) was judged to be of significantly different valence to both the positive

(M = 2.55, SD = 1.05), t(19) = 4.22, p<0.001, and negative passages (M = 5.85, SD =

0.89), t(19) = 6.53, p<0.001.

After reading one of these passages, participants were then asked “How would you feel if you received this feedback on your next assignment?” and responded on a 9- point Likert scale (1 = happy; 9 = sad). Next participants estimated the number of days they predicted this feeling would last.

Mood validation. The same post experimental questionnaire as used in Study 1 was used here to validate the mood manipulation. Participants rated their current moods on 7-point Likert scales assessing happiness (1 = happy; 7 = sad), how “good” they felt

(1 = good; 7 = bad), and arousal (1 = aroused; 7 = unaroused)(Forgas, 1998).

Debriefing. The same debriefing protocol as used in Study 1 was used here.

Again, no one reported any suspicions about the hypotheses of this study. Results

Mood Manipulation

Based on previous research and the pattern of observed correlation among the

three mood validation variables, a valence index was created by averaging the happy-sad

and good-bad scales (Cronbach’s α = 0.89). This valence index was submitted to a

2(mood: positive vs. negative) x 3(feedback ambiguity: positive vs. ambiguous vs.

negative) ANOVA. As expected, and consistent with Study 1, results reveled a

significant Mood main effect: participants who received a positive mood induction (M =

3.65, SD = 1.45) reported feeling better than those who received a negative mood

induction (M = 5.07, SD = 1.42), t(127) = 5.54, p < 0.001. In addition, however, a

significant Feedback Ambiguity main effect emerged, F(2, 125) = 3.25, p < 0.05. Follow- up Scheffé contrast tests, controlling the familywise error rate at α = 0.05, revealed a non- significant pattern in the data suggesting that participants who rated the positive feedback passage (M = 4.21, SD = 1.66) reported feeling better at the study’s end than those who rated the ambiguous passage (M = 4.90, SD = 1.67), F(2, 125) = 4.24, ns. Participants who rated the negative feedback passage (M = 4.12, SD = 1.41) also felt better than judges of the ambiguous passage, F(2, 125) = 5.57, ns. The Mood x Feedback Ambiguity interaction was not significant.

Arousal ratings were submitted to a similar analysis. Again, consistent with Study

1, the Mood main, t(127) = 0.00, p = 0.998, Feedback Ambiguity main, F(2, 125) = 0.70, p = 0.50, and Mood x Feedback Ambiguity interaction effects, F(2, 125) = 0.31, p = 0.73, were not significant. Intensity Forecasts

Participants’ affective intensity predictions were submitted to a 2(mood: positive

vs. negative) x 3(feedback ambiguity: positive vs. ambiguous vs. negative) ANOVA.

Data are presented in Table 3. As expected, planned contrast tests for the Feedback

Ambiguity main effect revealed that participants predicted feeling significantly better

about receiving the positive (M = 5.04, SD = 2.02) than the ambiguous feedback (M =

6.00, SD = 1.40), t(123) = 2.71, p < 0.01 (95% SCI: -1.15, -0.02). Participants also

predicted feeling marginally worse about receiving the negative (M = 6.83, SD = 1.23) as

opposed to ambiguous assignment feedback, t(123) = 2.33, p = 0.02 (95% SCI: -1.10,

0.06). These results are largely consistent with the pre-test for feedback valence and

support the validity of the feedback valence manipulation.

Table 3 Mean intensity forecasts as a function of mood and feedback ambiguity Feedback Valence Mood Positive Ambiguous Negative Condition

Positive M 5.00 5.94 6.90 SD 1.93 1.63 1.25 n 23 18 20 Negative M 5.08 6.05 6.77 SD 2.15 1.21 1.27 n 24 22 22

Contrary to predictions, however, mood did not influence intensity forecasts. It was predicted that mood congruence effects would be larger for the ambiguous than for either of the clearly valenced feedback passages. Contrast tests of these planned interactions did not obtain significance, Fs < 1, ns. Mood did not differentially influence intensity estimates for the positive versus the ambiguous feedback condition (95% SCI: -

1.12, 1.14); nor did mood differentially influence predictions for the ambiguous versus

the negative feedback condition (95% SCI: -1.30, 1.02). In addition, the Mood main

effect was not significant, t(123) = 0.00, p > 0.05 (95% SCI: -0.47, 0.45). Quite simply, mood did not influence intensity predictions.

Duration Forecasts

Data from two outliers (data points outside 3SD) were excluded from the analysis of duration forecasts (Stevens, 1996). Participants’ predictions of the duration of their affective reactions were submitted to a 2(mood: positive vs. negative) x 3(feedback ambiguity: positive vs. ambiguous vs. negative) ANOVA. This analysis revealed no significant effects (see Table 4). The Mood main, t(120) = 0.00, p > 0.05 (95% SCI: -

0.46, 0.47) and planned Feedback main effect contrasts, Fs < 1, ns, were not significant

(95% SCIs: -0.45, 0.69 for positive vs. ambiguous; -0.74, 0.44 for ambiguous vs.

negative). Planned interaction contrasts examining the same questions as asked in the

intensity analysis were not significant, Fs < 1, ns.

Table 4 Mean duration forecasts as a function of mood and feedback ambiguity Feedback Valence Mood Positive Ambiguous Negative Condition

Positive M 2.23 1.56 2.21 SD 2.40 0.87 2.11 n 22 18 19 Negative M 1.85 2.14 1.98 SD 1.04 1.30 1.68 n 24 22 21

Discussion

The hypothesis of an interaction between mood and event valence ambiguity was not supported. It was expected that mood congruence would be more pronounced for

ambiguously valenced feedback than for clearly valenced feedback, but this was not

observed. In fact, an examination of Tables 3 and 4 shows no evidence of mood congruence effects for any level of valence ambiguity. Despite successful mood and ambiguity manipulations, as in Study 1, mood simply had no impact on affective forecasts of intensity and durability.

In both Study 1 and in this study the judgmental stimuli used were of low personal relevance to participants. In Study 1, participants made forecasts about everyday events such as being caught in the rain without an umbrella or finding $2 in the street. In

Study 2, participants made forecasts about receiving hypothetical feedback about an assignment. Even though the kinds of forecasts made in these studies may have involved imagining the self experiencing the events to be forecasted (and so may have been technically self-relevant), these judgments were unlikely to have had any real consequences for forecasters. This lack of meaningful personal relevance may explain the absence of mood effects. The AIM predicts that affective infusion will occur for highly personally relevant judgments because such judgments are likely to recruit substantive processing (Forgas, 1995). Further, mood-as-motivation effects are most likely when judges are faced with a personally relevant task for which there are real consequences

(Forgas, 1989, 1991). Increasing the personal relevance of the event to be forecasted, then, may increase the likelihood of mood congruence effects or may promote mood-as- motivation effects. This possibility was examined in Study 3. Chapter 4

Study 3: The Effects of Mood and Need for Cognition on Affective Forecasts

Studies 1 and 2 provide no evidence of mood congruence effects into affective

forecasts under conditions of relatively low personal relevance or involvement.

According to the AIM, increasing the personal relevance of the judgmental task may do

one of two things. One possibility is that it may increase the likelihood of forecasters

using constructive processing and so lead to mood congruence effects (Forgas, 1995).

Alternatively, and derived from the mood-as-motivation account, forecasts of high personal relevance that have real consequences for forecasters may recruit a motivation to repair negative moods and maintain positive moods (e.g., Forgas, 1989, 1991). Thus,

Study 3 had university students predict their feelings about receiving actual, real psychology assignment marks, a real event with personally relevant and important affective consequences.

Motivated Forecasts, Personal Relevance and Mood Incongruence

As mentioned in Chapter 1, affective forecasts may be used as a means of affect regulation. Totterdell et al. (1997), in a study examining the prediction of everyday moods, noted that people might indeed use their affective forecasts as a mood regulation strategy. More specifically, Totterdell et al. (1997) showed that people often make optimistic affective forecasts that can have real consequences for improving mood. In this study, participants made mood ratings three times a day as well as at the beginning of each week, over a two-week testing period. They also made affective forecasts at the beginning of each week for each upcoming week, and each morning for the upcoming day. Results largely supported an affect regulation account of affective forecasting. In

general, participants tended to make optimistic forecasts (i.e., forecasting moods more

positive than their current moods) suggesting that people may in fact use forecasts to

regulate their current affective states

A study by Wilson et al. (2001), not only further attests to the mood regulatory

implications of affective forecasts, but also lends some support to the prediction that sad

people will recruit optimistic forecasting as a mood-repairing strategy. Participants in this study received positive (an ‘A’), negative (a ‘D’) or no false feedback immediately after completing an ostensible test of social aptitude. Five minutes later, participants rated their on-line feelings. Next, participants made predictions about how they would feel about receiving an A or a D on a variety of similar and different tests. Of particular importance to the present research was the finding that participants in the negative feedback condition predicted that they would feel better about future successes and future failures than those in the neutral, no feedback condition. This pattern of findings is consistent with a motivational account of mood effects. Sad participants (those who received negative false feedback) may have generated more optimistic future forecasts to make themselves feel better.

It is important to note that the Wilson et al. (2001) study demonstrated motivational consequences of mood for forecasts of personally relevant events. Receiving feedback on ostensibly meaningful tests of personality and ability has meaningful consequences for forecasters and may have triggered the use of a mood-repair strategy.

Research does indeed show that when sad participants are involved in personally relevant tasks they may respond with mood-incongruent judgments and behaviors in an attempt to improve their moods (Berkowitz, 1993; Berkowitz & Troccoli, 1990; Cervone, Kopp,

Schaumann, & Scott, 1994; Clark & Isen, 1982; Erber & Erber, 1994; Forgas, 1989,

1991; Parrott & Sabini, 1990). As was discussed in Chapter 1, a series of studies by

Forgas (1989, 1991), found that sad people who were required to select interaction partners for themselves (high personal relevance) chose rewarding partners, presumably in an effort to repair their negative moods. In addition, research by Berkowitz and colleagues (Berkowitz, 1993; Berkowitz, Jaffee, Jo, & Troccoli, 2000; Berkowitz &

Troccoli, 1990), shows that when judgments are personally relevant and participants direct attention towards themselves, typical mood congruence effects give way to mood incongruent judgments.

Personal Relevance and Mood Congruence

Although some research does indeed suggest that increasing the personal relevance of a judgment promotes motivated processing and thus mood repair, other work suggests that personally relevant judgments may promote substantive, constructive processing and so enhance mood congruence effects (Forgas, 1995). Fiedler (1991) argues that the constructive (or productive, in Fiedler’s words), “…nature of any cognitive task increases with self-reference, because self-reference means enrichment with evaluative reactions and a complex network of self-related knowledge…”(p. 86).

Consistent with this reasoning are results from studies that show greater mood congruence effects for self- versus other-judgments (e.g., Forgas, Bower, & Krantz,

1984; Forgas, Bower & Moylan, 1990). Forgas et al., (1984), for example, found that people in a sad mood made significantly more negative judgments and fewer positive judgments about their own behaviors, but not about the behaviors of others. Forgas et al., (1990) found a similar pattern of results. In this study, participants in positive and

negative moods made attributions about their own and others’ behaviors. Again, sad

people showed a mood congruence effect for personally relevant judgments: sad

participants were critical of their own behaviors but not of the behavior of others. So

while some research does suggest that personally relevant tasks may lead to mood

incongruence, other research suggests that personal relevance may increase the likelihood

of constructive processing and thus promote mood congruence. Both possibilities were

considered for affective forecasts in the current study.

Mood Congruence and Need for Cognition

To allow for a more precise examination of the moderating role of constructive processing in affect infusion into affective forecasts, a personality variable was also included in the design of Study 3. Recall that the AIM locates influences on constructive processing in the situation (e.g., elaboration instructions; Study 1), the target (e.g., target ambiguity; Study 2) and the judge. Study 3 considered a judge characteristic as a determinant of constructive processing by examining the moderating role of Need for

Cognition (NC)(Cacioppo, Petty, & Morris, 1983).

NC is an individual difference variable that measures a person’s tendency to engage in, and enjoy, effortful, elaborative information processing (Cacioppo & Petty,

1982; Cacioppo, Petty, Feinstein, & Jarvis, 1996; Cacioppo, Petty, Kao, & Rodriguez,

1986). Individuals high in NC seek, acquire and process information in order to make

of their worlds. Individuals low in NC, on the other hand, rely more on cognitive

heuristics and peripheral cues (e.g. the opinions of friends or experts) to reach the same

ends. Evidence for high NC individuals’ tendencies toward elaborative processing

comes from a variety of sources. For example, high NC individuals typically remember

more information than low NC people (see Cacioppo, et al., 1996 for a review). This

effect is found in a variety of judgmental domains including advertising (Meyers-Levy &

Peracchio, 1992; Mueller & Johnson, 1990), face recognition (Mueller, Keller, &

Dandoy, 1989), (Boehm, 1994) and forensic psychology (Kassin, Reddy,

& Tulloch, 1990). Individuals high in need for cognition are also more influenced by the quality of persuasive arguments than low NCs (Cacioppo, et al., 1983; Petty, Wells, &

Brock, 1976), generate more task-relevant thoughts (Axsom, Yates, & Chaiken, 1987;

Lassiter, Briggs, & Slaw, 1991), and make judgments that are more highly correlated

with such task-relevant thoughts (Verplanken, 1989).

Combining the implications of this research with the predictions of the AIM, one would expect greater affect infusion for high NC individuals than for low NC individuals.

Quite simply, mood congruence effects on cognitive activity, and subsequent judgments, should be more pronounced for people who engage in such activity. Wegener, Petty and

Klein (1994) found exactly this. Applying this reasoning to mood effects on likelihood judgments and persuasion, these researchers found that positive and negative moods produced mood congruent likelihood judgments, and subsequent persuasion for high NC individuals, but not low NC individuals. Here, happy and sad participants were presented with persuasive messages emphasizing either the benefits of following a recommendation or the costs of not following a recommendation. These people then rated the likelihood of the consequences presented in the message and their attitudes towards the message. As predicted, happy high NC people were more persuaded by the benefits and sad high NC people were more persuaded by the costs. In addition, these effects were mediated by

likelihood estimates. Low NC participants, on the other hand, showed no mood effects on

either likelihood judgments or attitudes. It seems that because high NC individuals process information more elaborately, transient moods were more likely to infuse their likelihood judgments and bias their subsequent attitudes.

Other research, however, suggests that low NC individuals may also be subject to

mood congruence effects, although via a different mechanism (Batra & Stayman, 1990;

Petty, Schumann, Richman, & Stratham, 1983). These studies found mood effects on

attitudes for both high and low NC individuals, but the mediation of this effect by mood

congruent thoughts was present only for high NCs. Petty et al. (1983), for example,

exposed happy, neutral and sad people to a persuasive argument advocating change in the

foster care system. Results showed that happy people were more persuaded than neutral

or sad people regardless of NC. However, this mood effect on attitudes was mediated by

thoughts only for high NC people. This pattern of results suggests that affect priming

may be operating for high NC individuals, but affect-as-information may be the

mechanism for the effect in low NC people. Although the empirical evidence is mixed, it

is clear that NC can influence the nature and possibly the extent of affect infusion into

various social judgments. Such effects on affective forecasting were considered in the current study.

Mood and Processing: Implications for Forecasting Accuracy

Finally, it should be noted that Study 3 used a longitudinal design, measuring participants’ forecasts and experiences. As such, the processing consequences of mood

for forecast accuracy could be directly assessed in this study by comparing forecasts with experiences for each mood condition. Recall that some research reviewed in Chapter 1 suggests that sad people may be more accurate social judges than their neutral and happy counterparts (Forgas, 1998; Sinclair & Mark, 1992). Forgas (1998), for example, found that sad people were less susceptible to the fundamental attribution error than were happy people. It was suggested that people in a negative mood may have been more likely to weigh situational factors in their judgments and so be relatively immune to the FAE because of their externally-oriented, detail-focused information processing. This finding is consistent with other research that shows that sad people are more accurate than happy people in other judgment domains (e.g. Sinclair & Mark, 1992). If forecasting biases are indeed due to cognitive biases of a similar kind (e.g. focalism, the tendency to neglect the impact of non-focal events on affective states, Gilbert et al., 1998), then it is possible that these biases may also be reduced by the more externally focused information processing style associated with negative moods. This account would be supported by sad participants exhibiting less pronounced intensity and durability biases than neutral or happy people.

The Present Study

In brief, Study 3 examined the impact of positive and negative moods on forecasts about a personally relevant event: receiving real university assignment feedback. Four hypotheses were considered. The first was that of mood congruence. The second was moderated mood congruence predicted by the AIM: do high NC participants make more mood congruent forecasts that low NC participants? The thrid was the mood-as- motivation hypothesis. It was possible that the high personal relevance of the forecast could prompt motivated processing resulting in mood-repair strategies, and thus mood incongruent forecasting, for sad participants. This would be supported by sad people

making more positive forecasts than neutral participants. Finally, mood processing effects were considered, with the tentative expectation that sad people would be more accurate forecasters than happy and neutral individuals and so exhibit less pronounced intensity and durability biases.

Method

Overview

Upon arrival at the testing session, participants completed the short from of the

NC scale (Cacioppo, Petty, & Kao, 1984). Participants were then induced into a positive, neutral or negative mood and asked to predict their affective responses to receiving certain grades on an assignment. Two weeks later, the assignments were returned and students rated their affective responses to their actual grades. One week later still, participants reported on the durability of their grade-related affective responses.

Participants and Design

Participants were 158 (107 female, 40 male, 11 not reported) psychology students at the University of New South Wales who completed this study as part of an undergraduate course in social psychology. Due to the longitudinal design of the study, participant attrition occurred. The number of participants involved in each analysis is conveyed via the reported error degrees of freedom. This study employed a 3(mood: positive vs. neutral vs. negative) x 3(event valence: positive vs. neutral vs. negative) x

NC x (2) (testing session: prediction vs. experience) mixed design. NC was included as a continuous independent variable. Event valence was the discrepancy between predicted and actual assignment grades: receiving a grade higher than expected was treated as a positive event; lower than expected, a negative event; and as expected, a neutral event

(Buehler & McFarland, 2001). Dependent variables included intensity and durability

predictions and experiences.

Testing Session 1

Upon arrival, students were informed that they would be completing a number of activities as part of the day’s session. They were told that they would complete a short personality inventory, watch a brief film and complete a number of questionnaires about psychology assignments. These tasks were presented as unrelated so as to reduce the likelihood of demand characteristics.

Need for cognition. First, participants completed the short from of the NC scale

(Cacioppo, Petty, & Kao, 1984). This measure allows for the efficient assessment of NC and has sound psychometric qualities (Cacioppo et al., 1996).

Mood induction. After completing the NC scale, participants watched a short film, about 10 minutes in length, designed to induce a positive (excerpts from “Fawlty

Towers”), neutral (a nature documentary) or negative mood (excerpts from “Who Will

Love My Children”). As in Studies 1 and 2, participants were advised to watch the film as if watching television at home and to become involved with the themes and emotive qualities of the story.

Affective forecasts. After the mood induction, participants completed a forecasting questionnaire about a developmental psychology assignment that they had just recently completed but were yet to know the results of. This questionnaire asked them to predict their marks (“What is the actual mark you expect to get for your developmental psychology assignment?”) as well as how they would feel about receiving every possible grade (i.e. HD, D, C, P, F). Answers to the latter questions were made on 7-point Likert

scales assessing happiness (1 = happy; 7 = sad) and (1 = proud; 7 = embarrassed).

This questionnaire also asked participants to predict (on the same 2 scales) how they would feel in general a week after receiving each possible grade (estimates for the durability bias). This forecasting task was adapted from Buehler and McFarland (2001).

Mood validation. Upon the completion of the forecasting questionnaire, participants rated their current moods on three 7-point Likert scales. Two scales assessed affective valence: 1 = happy; 7 = sad and 1 = good; 7 = bad. The third scale measured arousal (1 = aroused; 7 = unaroused)(e.g., Forgas, 1998). These scales were embedded in a “post-experimental questionnaire” amongst numerous distracter items to disguise the

experimenter’s primary concern with mood and to minimize demand characteristics.

Testing Session 2

Affective experiences. Approximately 2 weeks after testing session 1, assignments

were returned to students along with a questionnaire asking about actual assignment

marks and affective reactions to these marks. Participants were asked “What grade did you receive for your developmental psychology assignment?” and “How do you feel about receiving this grade?” As in Studies 1 and 2, participants responded to the latter question using 7-point Likert scales that again measured happiness, pride and arousal.

Testing Session 3

Affective duration. One week after receiving their grades, participants were asked questions about their general feelings of happiness (“How do you feel at this moment?”), to assess if receiving good or bad grades a week earlier had any discernible long-term influence on their affective state. Ratings were made on the same 3 Likert scales as used for the forecasting and experience questionnaires, assessing happiness, pride and arousal.

Debriefing. The same debriefing procedure as used in the previous studies was

used here. No participants reported any suspicions about the hypotheses of the study or of

any links between the mood induction procedure and the forecasting tasks.

Results

Mood Manipulation

Based on previous research and the observed pattern of correlation among the mood validation variables, a valence index was created by averaging the happy-sad and

good-bad scales (Cronbach’s α = 0.89). This index was submitted to a 3(mood: positive vs. neutral vs. negative) x NC hierarchical regression analysis. NC was entered as a subject variable at the first step. Next, two dummy-coded variables with neutral mood as the comparison condition were entered at step two. Two product variables describing the interaction of NC with each of the dummy-coded mood vectors were entered at step 3. As expected this analysis revealed a significant main effect for mood, F(2, 154) = 50.10, p <

0.001. People in the positive mood group (M = 2.70, SD = 0.81) reported feeling more

positive on the valence index than neutral participants (M = 3.21, SD = 1.10), t(154) =

2.47, p < 0.01, who, in turn, were more positive than participants in the negative mood

group (M = 4.43, SD = 0.86), t(154) = 6.89, p < 0.001. The NC main and the Mood x NC

interaction effects were not significant, Fs ≤ 1.5, ns. These results, in combination with

previous validations of these mood induction materials demonstrate that the mood

manipulation successfully induced moods that lasted until the end of the testing session.

A similar analysis on the arousal measure yielded no significant Mood main or

Mood x NC interaction effects, Fs < 1, ns. A significant NC main effect, β = -0.18, t(156) = -2.32, p < 0.05, however, suggested that higher NC individuals tended to feel more

aroused than low NC individuals. Importantly though, arousal levels for the mood

conditions did not differ significantly from the neutral condition, attesting to the

relatively low arousal intensity of the induced mood states.

Intensity Forecasts and Experiences

In order to make within-participants comparisons of predicted and experienced

affect intensity (and so compute a measure of intensity bias), I first established whether

each participant received a grade higher than (positive event valence), lower than

(negative event valence), or the same as (neutral event valence) expected (see Buehler &

McFarland, 2001). The affective predictions for the received grade were selected for

analysis. Composite indexes of predicted (Cronbach’s α = 0.90) and experienced

(Cronbach’s α = 0.92) affect intensity were computed for analysis by averaging the

happy-sad and proud-embarrassed scales.

To assess the impact of mood and NC on the intensity bias, difference scores

(prediction minus experience) were submitted to a 3(mood: positive vs. neutral vs.

negative) x 3(event valence: positive vs. neutral vs. negative) x NC hierarchical

regression analysis. Summary statistics are presented in Table 5. NC was entered first as a continuous subject variable. The two fixed factors (Mood and Event Valence) were

dummy coded with the respective neutral conditions as comparison groups. The two

Mood vectors were entered at step 2 and the Event Valence vectors were entered at step

3. Product variables were computed for all two-way and three-way interactions. Two way

interaction vectors were entered at step 4 and the three-way interaction vectors were

entered at the final step. No overall main, two-way or three way interaction effects were significant at the α = 0.05 familywise level, Fs < 2.7, ps > 0.07. There was a marginal

overall Mood main effect, F(2, 154) = 2.69, p = 0.07, but follow-up contrast tests

examining the difference between the neutral and each of the positive and negative

groups were not significant, ts< 1.10, ns. Indeed, even with a more liberal approach

allowing for tests of planned contrasts each at α = 0.05, no contrast tests were significant.

Tests of the predicted three-way interaction variables describing the interaction of mood,

valence and NC did not approach significance at the α = 0.05 per-contrast level, ts< 1.7,

ns.

Table 5 Mean intensity predictions and experiences as a function of induced mood and event valence

Positive event Neutral event Negative event Prediction Experience Prediction Experience Prediction Experience Mood

Positive M 1.75 2.10 3.20 3.89 5.16 5.22 SD 0.95 1.02 1.12 1.36 1.29 1.32 n 10 10 23 23 16 16 Neutral M 2.35 2.73 3.63 3.63 5.43 5.66 SD 0.92 1.18 1.25 1.43 1.04 0.96 n 13 13 16 16 22 22 Negative M 1.70 1.93 4.13 3.73 5.08 5.14 SD 0.72 0.91 1.42 1.05 1.55 1.23 n 20 20 20 20 18 18

Although the mood processing hypothesis pertains directly to the size of the

intensity bias, the mood congruence and mood-as-motivation accounts relate specifically

to affective predictions only. In order to examine these hypotheses more fully, intensity

predictions (not difference scores/biases) were submitted to a 3(mood: positive, neutral,

negative) x 3(event valence: positive, neutral, negative) x NC hierarchical regression analysis. Consistent with Buehler and McFarland (2001), this analysis revealed a

significant main effect for Event Valence, F(2, 152) = 94.91, p < 0.001. Participants who received a grade higher than expected (M = 1.92, SD = 0.87) predicted that they would feel better than those who received their expected grades (M = 3.63, SD = 1.30), t(152) =

-7.39, p < 0.001, who, in turn, predicted feeling better than participants who received a grade below what they expected (M = 5.24, SD = 1.28), t(152) = 6.98, p < 0.001. Again, however, consistent with the analysis of difference scores and contrary to hypotheses, the

Mood and NC main effects, and all interactions (two-way and three-way) were not significant, overall Fs < 1.5, ns.

A secondary, but still important question not addressed by the previous analyses is this: Did participants overestimate the intensity of their affective experiences? That is, did participants exhibit the intensity bias? Simple effects contrast tests assessing the magnitude of the intensity bias separately for positive, neutral and negative events averaged across mood conditions were examined in the context of a 3(mood: positive vs. neutral vs. negative) x 3(event valence: positive vs. neutral vs. negative) x (2)(intensity measure: prediction vs. experience) MANOVA. Results revealed a marginal intensity bias for positive events: predictions were more positive than experiences for positive events, t(149) = 1.93, p = 0.056. No bias emerged for neutral events, t(149) = 0.72, ns, or negative events, t(149) = 0.82, ns. So while there was some evidence ofa positive intensity bias, the magnitude of this bias was not influenced by mood.

Durability Forecasts and Experiences

Durability composites for predictions (Cronbach’s α = 0.87) and experiences

(Cronbach’s α = 0.70) were computed by averaging the measures of happiness and pride. These indexes were submitted to the same analyses as the intensity measures.

To assess the impact of mood and NC on the durability bias, difference scores

(prediction minus experience) were submitted to a 3(mood: positive vs. neutral vs.

negative) x 3(event valence: positive vs. neutral vs. negative) x NC hierarchical

regression analysis. Summary statistics are presented in Table 6. Results revealed a

marginally significant Event Valence main effect, F(2, 117) = 2.74, p < 0.07. Contrast

tests revealed that the durability bias differed in magnitude for positive and neutral events

averaged across mood conditions, t(117) = 2.10, p = 0.04: participants predicted that

receiving a grade higher than expected would make them feel no different than it actually did a week later, t(117) = 0.42, ns, whereas participants predicted that a neutral event would make them feel better than it actually did, t(117) = 2.64, p = 0.01. Interestingly, and contrary to previous work on the durability bias, participants also predicted that negative events would make them feel better than they actually did, t(117) = 2.35, p =

0.02. Importantly, however, no other overall tests for main, two-way or three-way interaction effects obtained significance, Fs < 1.3, ns.

Again, to obtain more specific tests of the congruence and motivation accounts, durability predictions (not biases) were submitted to a 3(mood: positive vs. neutral vs. negative) x 3(event valence: positive vs. neutral vs. negative) x NC hierarchical regression analysis. Consistent with the analysis of intensity predictions, the only

significant effect was the Event Valence main effect, F(2, 117) = 26.53, p < 0.001.

Participants who received a grade better than expected (M = 2.71, SD = 0.98) predicted

feeling better one week after having received their marks than participants who received

what they expected (M = 3.66, SD = 0.99), t(117) = -3.83, p < 0.001. Participants who received a grade lower than expected (M = 4.53, SD = 1.08) predicted feeling worse one

week after having received their marks than those who received the grades they expected,

t(117) = 3.99, p < 0.001. Again, contrary to hypotheses the mood and NC main effects,

and all interactions (two-way and three-way) were not significant, overall Fs < 2.1, ns.

Table 6 Mean durability predictions and experiences as a function of induced mood and event valence

Positive event Neutral event Negative event Prediction Experience Prediction Experience Prediction Experience Mood

Positive M 2.21 2.71 3.79 4.00 4.46 4.81 SD 1.08 0.91 0.78 0.91 1.20 1.52 n 7 7 19 19 13 13 Neutral M 3.44 2.67 3.60 4.07 4.73 4.90 SD 0.90 1.19 1.27 1.24 1.03 0.78 n 8 8 15 15 15 15 Negative M 2.56 2.56 3.57 4.07 4.40 5.00 SD 0.81 0.93 0.98 1.22 1.06 1.18 n 16 16 15 15 15 15

Discussion

As in Studies 1 and 2, induced mood did not influence affective forecasts in the

current study. There was no evidence of moderated mood congruence: NC did not

influence the magnitude of mood congruence. Indeed, no mood congruence effects,

moderated or otherwise, emerged in Study 3. Further, the mood-as-motivation account

received no support. Sad participants did not make more optimistic forecasts than

neutrals. Finally, mood exerted no effects on forecast accuracy. This study measured both

forecasts and experiences and thus allowed for a direct test of the influence of induced

mood on the magnitude of the intensity and durability biases. Contrary to predictions, however, mood did not influence the accuracy of participants’ forecasts about receiving

assignment grades. In addition, there was only partial support for the intensity and durability biases obtained in previous research: a marginal intensity bias was observed for positive events (receiving a grade higher than expected) but not for negative events.

Further, there was no evidence of the durability bias for either positive or negative events.

Although previous research has shown that transient moods influence various kinds of future judgments, including judgments of global life satisfaction (Forgas, Bower,

& Moylan, 1990;Schwarz & Clore, 1983) and probabilities (e.g., Johnson & Tversky,

1983; Mayer et al., 1992), Studies 1 –3 show no evidence that low intensity positive or negative moods influence affective forecasts – they do not change the affective value of future events. Other research does show, however, that various high arousal, visceral states can impact the evaluation of future events (see Loewenstein & Lerner, 2003, for a

review). As mentioned in Chapter 1, research shows that people often project their

current visceral states on to the future (e.g., Loewenstein et al., 2000; Loewenstein,

1996). If forecasters are currently in a “cold” or unaroused state, they fail to appreciate

how being in a future aroused state will make them feel or act. If, however, they are

currently experiencing a visceral state, such as hunger, they can project this state into the future and better appreciate how they will act and feel in a future situation in which they are hungry. Such empathy gaps have been found in forecasts of numerous visceral states, including hunger (Read & van Leeuwen, 1998), pain (Read & Loewenstein, 1999) and sexual arousal (Loewenstein et al., 1997).

Why do certain visceral states influence affective forecasts, while the low intensity moods induced in the present studies do not? Central to most explanations of empathy gaps in affective forecasting is the notion of the “heat” or arousal component of

the visceral state being forecasted. Numerous researchers argue that empathy gaps exist

because people cannot adequately appreciate how “hot” states will make them feel (e.g.,

Loewenstein & Lerner, 2003; Loewenstein & Schkade, 1999; Van Boven & Kane, in

press; Wilson & Gilbert, 2003). This consistent reference to the “heat” of visceral states

suggests that it may be arousal differences between current and predicted states that

account for the existence of empathy gaps. The same may be the case for the prediction

of affective states.

In their review of the role of affect in decision making, Loewenstein and Lerner

(2003) argue that low intensity affective states, such as the moods induced in Studies 1-3,

often play a largely advisory role in decision making. Moods can act as informational

input into decisions (as in the affect-as-information approach) or prime affect congruent

thoughts in the decision making process (as in the affect priming account). They note,

however, as was noted earlier here, that low intensity affective states can often be

rendered irrelevant to the decision making process, either by being attributed to an

irrelevant source (e.g., Schwarz & Clore, 1983) or by being made irrelevant due to the

presence of information that bears directly on the decision or judgment to be made (e.g.,

Srull, 1983, 1984). They argue, however, that higher intensity emotions often have the potential to override cognitive inputs into decisions (e.g., subjective probabilities) and exert significant influence over decisions and judgments. This notion is consistent with findings that people experiencing various intense emotions often act as if “out of control”

(Baumeister, Heatherton, & Tice, 1994; Bazerman, Tenbrunsel, & Wade-Benzoni, 1998;

Hoch & Loewenstein, 1991; Loewenstein, 1996; Loewenstein, Weber, Hsee, & Welch, 2001). It is also consistent with clinical findings suggesting that patients with agoraphobia are significantly influenced by their of various situations even though they can often appreciate that there is in fact little, objectively, to fear (Barlow, 1988).

In short then, although low intensity positive and negative moods did not influence affective forecasts in Studies 1-3, higher arousal affective states might exert some effect upon future forecasts. While low intensity moods might be prevented from influencing affective forecasts due to a reliance on semantic affective theories, higher intensity emotions, such as anxiety, might, due to their visceral, motivational qualities, override cognitive theories and exert emotion congruent influences on future forecasts and decisions. This possibility was considered in Study 4. Chapter 5

Study 4: State Anxiety and Public Speaking: The Influence of Anxiety on Affective Forecasts and Decisions

One reason why people may make inaccurate predictions about affective events is that they cannot readily simulate what it would be like to be in a future emotional state.

People in a neutral or ‘cold’ state have trouble appreciating how they will feel or behave when in a future ‘hot’ or aroused state; they cannot accurately ‘empathize’ across time

(Loewenstein, 1996; Loewenstein, O’Donoghue, & Rabin, 2003). As discussed in

Chapter 1, these empathy gaps are evident in forecasts of various kinds of ‘hot,’ visceral states including hunger (Gilbert et al., 2002; Read & van Leeuwen, 1998), sexual arousal

(Loewenstein et al., 1997), pain (Read & Loewenstein, 1999) and thirst (Van Boven &

Loewenstein, 2003).

In this chapter I will begin with a review of relevant research on empathy gaps in the prediction of visceral states. I will then consider the little work that has been done on empathy gaps in the prediction of emotions and finally outline a study examining the role of transient anxiety in the prediction of feelings about and the decision to engage in an anxiety-provoking future experience.

Empathy Gaps in the Prediction of Visceral States

Most interesting for the current thesis is the finding that empathy gaps can be reduced by matching the judge’s visceral state at the time of prediction with the state to be predicted. In a study by Van Boven and Loewenstein (2003), for example, people who had just exercised and were thus thirsty predicted that they would be more bothered by a future thirst-inducing episode than people who were not thirsty at the time of prediction. Similar effects emerge for hunger, with sated people predicting less interest than hungry

people in eating spaghetti for breakfast (Gilbert et al., 1992), and less interest in the

consumption of high-calorie snacks at a specified future time than people who have not

eaten (Read & van Leeuwen, 1998).

Matching forecasters’ current and to-be-predicted states can similarly bridge

empathy gaps in the prediction of sex-related behaviors. As mentioned in Chapter 1, men who are sexually aroused predict that they will engage in more sexually aggressive behavior in a future state of sexual arousal than men who are unaroused at the time of prediction (Loewenstein et al., 1997). Further, work by Gold (1993, 1994) shows that empathy gap reduction can have a positive and lasting impact on the risky sexual behavior of gay men. Gold (1993) hypothesized that much AIDS-related, risky sexual behavior, including unprotected sex, occurs in the ‘heat of the moment.’ Since people find it hard to appreciate what this ‘heat’ feels like or its impact on behavior, they are often not prepared when such moments arise and thus risky behaviors result. To test this prediction, Gold (1994) had some participants vividly recall a sexual encounter involving unprotected anal intercourse as part of an intervention program. In another intervention condition, participants were presented with informational posters about condom use.

Gold found that future acts of unprotected sex were significantly reduced for those who imagined themselves in the heat of the moment during the intervention compared to the poster intervention participants and controls. So not only does bridging empathy gaps improve the veracity of forecasts, it can also produce relatively lasting changes in behavior.

Empathy Gaps in the Prediction of Affective States

Although much research has examined empathy gaps in forecasts of visceral states such as sexual arousal, thirst and hunger, little work has investigated such gaps in forecasts about emotions. This is somewhat puzzling given the apparent similarity between visceral or drive states and various high arousal emotions. Loewenstien (1996)

defines visceral states as states that have “…first, a direct hedonic impact, and second, an influence of the relative desirability of different goods and actions” (p. 273). So a drive such as hunger, for example, meets the definition of a visceral state: hunger has a

negative hedonic quality and increases the value of food. Pain also is negative and

increases the attractiveness of painkillers, food and sex (Loewenstein, 1996). Some

emotions, Loewenstein argues, can also be considered visceral states. He asserts that

“[a]nger is also typically unpleasant and increases one’s for various types of aggressive actions”(p. 273).

This is a view that is largely consistent with appraisal-theory approaches to emotions that emphasize the emotion-specific cognitive and motivational consequences of various emotional states (Arnold, 1960; Frijda, 1986; Izard, 1971; Izard & Ackerman,

2000; Lazarus, 1991; Tomkins, 1962). In the words of Loewenstien and Lerner (2003), such theories generally hold that

Emotions, like other visceral influences, have evolved to motivate people to

perform certain kinds of typically adaptive behaviors (Nesse, 1990). Hunger

provides a motive for eating, sex for copulation; and specific emotions likewise

are programmed to produce specific actions (p. 635). According to this view, specific emotions give rise to specific “action tendencies” or “appraisal tendencies” (Frijda, 1986; Frijda, Kuipers, & Ter Schure, 1989; Frijda &

Mesquita, 1994; Lerner & Keltner, 2000; Tiedens & Linton, 2001) that contain cognitive and behavioral procedures for dealing with the environment as signaled by the emotion.

So just as anger can increase “one’s taste for various types of aggressive tendencies”

(Loewenstein, 1996, p. 273), anxiety can trigger flight in response to a perceived threat

(Loewenstein & Lerner, 2003).

Given this prima facie similarity between various emotions and visceral states, it is surprising that empathy gaps in the predictions of emotions have gone relatively unstudied while such gaps in other visceral states have received much attention. An exception to this general trend that is of particular relevance to the current project is a study about fear of by Van Boven, Loewenstein and Dunning (2005). In

Experiment 1 of this series of studies, a group of university students made forecasts about miming in front of a university class for $5. Students were asked to indicate (1) the smallest amount of money they would need to be paid to mime, and (2) whether they would in fact mime for the offered $5. Some participants made these judgments about a purely hypothetical miming performance, but others made these predictions about a real miming performance. Results support the hypothesis that empathy gaps exist in the prediction of fear of embarrassment. As predicted, participants who made hypothetical judgments underestimated performance prices and indicated greater willingness to perform compared to those who faced a real miming performance. Presumably, participants in a cold state (those making judgments about a hypothetical event) failed to appreciate the anxiety induced by a potentially embarrassing upcoming performance and so required less money to perform and were quite willing to mime for $5, compared to those in a hotter state (those faced with a real performance). In Experiment 2, Van Boven and colleagues asked participants to make the same predictions about dancing in front of a university class for $5. Students in the hypothetical condition again underestimated the minimum price required and indicated greater willingness to dance than their counterparts in the real performance condition. The question remains, however: can empathy gaps in the prediction of future emotional states be bridged?

The Present Study

Although the results of the Van Boven et al., (2005) studies are suggestive of the presence of empathy gaps in forecasts about anxiety-arousing public performances, two questions are unanswered by this research. The first is this: Can these empathy gaps be reduced if forecasters are in a matching affective state at the time of prediction? That is, will anxious people be better able to appreciate the consequences of future anxiety- arousing events than people in a neutral, “cold” affective state? As discussed earlier, research on other visceral states shows that empathy gaps can be bridged if the forecaster is predicting from a hot state that matches the state to be predicted (Loewenstein et al.,

1997; Loewenstein, et al., 1996; Read & Loewenstein, 1999; Van Boven & Loewenstein,

2003). Study 4 examined the possibility of bridging the empathy gap in predictions about an anxiety-provoking public speech. Anxiety/fear is an affective state characterized by high levels of autonomic nervous system (ANS) arousal (see Levenson, 1992 for a review). In line with the reviewed theorizing about empathy gaps in the prediction of visceral states, it is predicted that currently anxious people will be able to appreciate the visceral qualities of a future anxiety-provoking event and so be less willing to engage in a public speech than neutral people.

The second issue not directly addressed in the work of Van Boven and his

colleagues is the role of predictions about feelings in empathy gaps in decisions. While

Van Boven’s research suggests that empathy gaps exist when making choices to engage in an embarrassing mime or dance, his research is silent about the role of forecasted feelings in such choices. As was discussed in Chapter 1, most work on empathy gaps in affective forecasting examines the impact of these gaps on predictions about choices or behaviors, not feelings per se. People are asked which food they would prefer when hungry or sated (Gilbert et al., 1992; Read & van Leeuwen, 1998) or the likelihood of sexual aggression when aroused or unaroused (Loewenstein et al., 1997), but are rarely asked how they would actually feel in any of these situations. One interesting possibility suggested by Van Boven and Loewenstein (in press) is a dissociation between people’s predicted feelings and their choices. Research does in fact show that feelings can be both empirically and conceptually independent of preferences and behaviors (Robinson &

Clore, 2002a, 2002b). So people may in fact overestimate the intensity of their feelings in

a particular situation (the intensity bias) while at the same time underestimate the impact

of those feelings on their behaviors (an empathy gap) (Van Boven & Kane, in press). One

possibility in the current study is that anxiety will exert a direct effect on participants’

decisions to engage in a public speech, an effect unmediated by participants’ feelings

about the event.

The other possibility is that people’s predicted feelings do mediate their decisions

and choices. Recent research does provide evidence that predicted feelings often guide

decisions (see Dunn & Laham, in press, for a review). In forecasts of regret (e.g., Crawford, McConnell, Lewis, & Sherman, 2002; Mellers et al., 1999; Zeelenberg, 1999;

Zeelenberg, Beattie, van der Plight, & de Vries, 1996) and in economic decision making

(Mellers, Schwarz, Ho, & Ritov, 1997; Mellers, Schwarz, & Ritov, 1999), for example,

people’s forecasted feelings do influence their subsequent decisions. One possibility in

the current study, then, is that participants’ forecasts of their expected feelings about an

anxiety-provoking speech will mediate their choices to perform or not to perform.

Anxious people will expect to feel worse about the upcoming speech and so be less likely

to agree to participate.

To address these issues, Study 4 employed a design similar to that used by Van

Boven, et al., (2005). However, not only did participants make a choice about engaging

in a public speaking performance, they also predicted how they would feel about such a

performance. In addition, some participants made these forecasts while anxious, while

others made these judgments in a neutral state.

Method

Participants and Design

Participants were 58 first year psychology students (37 female, 21 male) at the

University of New South Wales who completed this study for extra course credit. The

design of this study was a 2(emotion state: anxiety/fear vs. neutral) group between

participants design. Dependent variables included intensity and duration predictions and a

decision to engage in a public speaking performance.

Procedure

Upon arrival at the laboratory, participants were seated and advised of the tasks

they would complete during the experimental session. They were told that they would complete a number of unrelated tasks including watching a short video. After completing a consent form, participants watched a short film designed to induce either anxiety or a neutral state. The true purpose of these films as emotion inductions was disguised to minimize demand effects. Participants were told that they had to evaluate the content of a film for a future study and were instructed to watch the film as if watching television at home and involve themselves in the themes and emotive qualities of the story.

After the film, the experimenter proceeded as if to give participants a personality questionnaire to complete, but then acted as if she had just remembered something she needed to ask the participant. She said the following:

Oh, just before we proceed with the other activities, I have something to ask you.

We are conducting another study next week and I was wondering if you might

read this information sheet about the study and consider participating. Thanks.

The experimenter then handed the participant a “Participant Information Questionnaire” which was actually the affective forecasting measure. This form outlined the study the following week as one requiring the participant to tell a funny story in front of a group of people. Participants then rated how they would feel about doing this, the duration of this feeling and finally decided whether they would participate or not.

After completing this from, participants were given a “Film Clip Questionnaire” that served as a mood validation. Finally, participants were informed that there was in fact no study the following week. They were then fully debriefed and thanked for their time. Manipulations and Measures

Affect induction. Short film clips were used to induce anxiety/fear and a neutral

affective state in the current study. Participants in the anxiety/fear condition watched an

82 second film clip of Stanley Kubrick’s “The Shining.” This clip has been shown to

reliably induce fear and tension, but not sadness, embarrassment, anger or (Gross

& Levenson, 1995). Those in the neutral condition watched a 90 second clip from the

same nature documentary used in Studies 1, 2 and 3.

Affective forecasting and decision measure. After being advised of the ostensible

study the following week, participants were given a “Participant Information

Questionnaire” which began as follows:

You can earn $20 or 2 hours additional course credit if you agree to return some

time next week and participate in another experiment. During this experiment you

will be required to tell a funny story in front of a group of ten people.

Participants were then asked “How would you feel about doing this?” and responded by making a vertical mark on a continuous horizontal line scale of 10cm length anchored good-bad. The next question was “How long would you feel this way?” to which participants gave an open-ended response in days. Finally, participants were asked, “Would you be willing to do this?” and responded by circling either yes or no.

Affect validation. The manipulation validation used in this study was adapted from the original Gross and Levenson (1995) validation questionnaire. The instructions for this “Film Clip Questionnaire” read:

Think back to the clip you watched earlier. On each of the scales below, please

circle the number that describes the greatest amount of each emotion you felt at

any time during the film clip. On these scales, 0 means you did not feel even the

slightest bit of the emotion and 8 is the most you have ever felt in your life.

Participants then responded in the instructed manner to the following emotion words: anxiety, arousal, anger, happiness, and sadness.

Debriefing. The same debriefing procedure as used in previous studies was used here. No participants reported any suspicions about the hypotheses of the study or perceived any links between the mood induction and forecasting tasks.

Results

Emotion Manipulation

Participants’ responses to the emotion descriptors were submitted to a 2(affect condition: neutral vs. anxiety/fear) x (5)(emotion term: anxiety vs. arousal vs. anger vs. happiness vs. sadness) MANOVA. Results revealed a significant multivariate effect for

Affect Condition, Wilk’s Λ = 0.26, F(5, 52) = 29.85, p < 0.001. Follow-up independent means t-tests showed that participants in the anxiety/fear condition felt significantly more anxious (M = 3.97, SD = 2.55), t(56) = 6.13, p < 0.001, and aroused (M = 2.60, SD =

2.56), t(56) = 2.74, p < 0.01, than neutral participants (M = 0.86, SD = 0.85, and M =

1.14, SD = 1.21, for the anxiety and arousal descriptors respectively) (see Table 7).

Anxious people (M = 1.40, SD = 1.52) also felt marginally sadder than neutrals (M =

0.89, SD = 1.20), t(56) = 1.40, p = 0.16, but not significantly differently angry or happy, ts < 1.2, ns. As expected, the anxiety manipulation induced a relatively specific and arousing affective state.

Table 7 Emotion ratings as a function of emotion condition and emotion descriptor Emotion descriptor Anxiety Arousal Anger Happiness Sadness Emotion condition

Anxious M 3.97a 2.60a 1.23a 0.93a 1.40a SD 2.55 2.56 1.45 1.57 1.52 n 30 30 30 30 30 Neutral M 0.86b 1.14b 0.86a 1.39a 0.89a SD 0.85 1.21 1.08 1.34 1.20 n 28 28 28 28 28 Note. Within columns, means that do not share a subscript differ significantly (p<0.01).

Intensity and Durability Forecasts

An independent means t-test on intensity forecasts yielded the predicted effect:

anxious participants (M = 7.03, SD = 1.98) predicted feeling significantly worse about

giving a talk in front of a group of people than did participants in the neutral condition (M

= 4.60, SD = 2.50), t(56) = 4.13, p < 0.001 (95% SCI: 0.56, 1.61). Anxious people (M =

2.64, SD = 2.27) did not, however, expect their predicted feelings to last any longer than those in the neutral condition (M = 2.18, SD = 2.01), t(56) = 0.75, p = 0.46 (95% SCI: -

0.36, 0.79).

Decisions

Did anxiety influence participants’ willingness to engage in the public speaking performance? Yes. Logistic regression analysis showed that neutral participants (38% agreement) were significantly more likely to agree to speak than anxious participants

(14% agreement), odds ratio = 3.82, Wald χ²(1) = 4.12, p = 0.04, (95% CI for odds ratio:

1.05, 13.94). Mediational Analysis

Did participants’ forecasts about their affective states mediate their decisions to engage in the public speaking task? To address this question, a mediational analysis was conducted (Baron & Kenny, 1986). Results are presented in Figure 2. As previous analyses indicate, induced anxiety (coded anxiety=1, neutral=0) resulted in a decreased likelihood of agreement to engage in the public speaking task (logistic B = -1.34, SE =

0.66, Wald =4.12, p = 0.04). Anxious participants also reported more pessimistic forecasts about the public speaking engagement (linear B =2.44, SE = 0.59, β = 0.48, t(56) = 4.13, p < 0.001). Importantly, when both induced emotion and reported forecasts were included as predictors of decisions, the regression model revealed that forecasts significantly predicted decisions (logistic B = -0.53, SE = 0.19, Wald = 8.21, p < 0.05), but induced anxiety did not (logistic B = 0.096, SE = 0.81, Wald = 0.014, p = 0.91). That is, anxiety led to a decreased likelihood of predicted engagement in a public speaking task because of its effects on affective forecasts about that task. Lending further support to the mediational role of affective forecasts, a Sobel (1982) test was significant, z = -

2.38, p < 0.05.

Affective forecasts (-0.53*) 2.44*

Agreement to

Anxiety participate -1.34* (0.096)

Figure 2. Affective forecasts as a mediator of the effect of anxiety on decisions to engage in a public speaking task. Path coefficients are unstandardized regression coefficients (Bs). The italicized coefficient is from a linear regression model; other coefficients are from logistic regression models. Coefficients in parentheses represent parameter estimates from a logistic regression model containing both predictors. Asterisks indicate significance at p < 0.05.

Discussion

As predicted, and consistent with previous work on empathy gaps in the

prediction of visceral states, anxious participants were less likely to agree to engage in a

public speech than were controls. In addition, anxious people also forecasted that they would feel worse about giving a talk in front of the class than did controls. Interestingly,

these predicted feelings mediated the effect of anxiety on decisions. These results are

consistent with the hypothesis that predicted feelings can serve as a guide to choices (see

Dunn & Laham, in press, for a review). An interesting question that remains unanswered,

however, is this: exactly how does anxiety exert its effects on forecasts and decisions?

The rationale for moving from low intensity states of sadness and happiness in

Studies 1-3 to the higher arousal state of anxiety in Study 4, was that higher arousal states

might more closely resemble the visceral states that have been shown to influence forecasts and decisions in previous work (e.g., Loewenstein, 1996). It was argued that the arousal or ‘heat’ component of anxiety might play a part in the bridging of anxiety- related empathy gaps. But is it the arousal component of anxiety that accounts for the observed effects in this study? Although previous work has shown that the physiological

arousal that accompanies anxiety does account for various observed judgmental effects

(e.g., Leith & Baumeister, 1996; Mano, 1992, 1994), the mechanism of influence in the

current study is unclear. The results of Study 4 show that anxious participants not only

report higher levels of arousal than controls, but also, quite predictably, more anxiety. In other words, not only do arousal levels differ between experimental conditions, but so does the quality or specific nature of the affective experience accompanying that arousal.

So one of the questions that need to be explored is: will other high arousal emotions exert the same effect on forecasts and decisions? In other words, how specific is this effect to anxiety?

Although research does show that emotions can have specific effects on judgments, promoting the accessibility of emotion-specific information (e.g., Hansen & Shantz, 1995; Niedenthal, Halberstadt, & Setterlund, 1997; Niedenthal & Setterlund,

1994) and increasing the likelihood of emotion-congruent behaviors and appraisals

(Keltner, Ellsworth, & Edwards, 1993; Lerner & Keltner, 2001; Tiedens & Linton, 2001),

other work suggests that arousal may have a more general effect, independent of the

particular emotion accompanying it. Consistent with this second possibility is research

that shows that emotional arousal can increase the tendency to categorize information based on emotional attributes in general rather than emotion-congruent attributes in particular (Halberstadt & Niedenthal, 1997; Niedenthal, Halberstadt, & Innes-Ker, 1999).

According to this second account, participants in the current study may have focused on

anxiety related information (and so made more pessimistic forecasts) not because of any

anxiety-specific effect on forecasts, but as a consequence of a general arousal effect on

their information processing styles. This issue of the specificity of the effect of anxiety on

forecasts and choices was examined in Study 5. Chapter 6

Study 5: High Arousal Emotions and Public Speaking: The Influence of Anxiety, Anger,

Sadness and Happiness on Affective Forecasts and Decisions

The results of Study 4 show that state anxiety leads people to avoid engagement in a future anxiety-provoking event: public speaking. Further, this effect is mediated by people’s affective forecasts: participants in Study 4 avoided public speaking because they predicted that giving a speech would make them feel bad. The question remains, however: Is this effect specific to anxiety? This question is addressed in Study 5. First, I review research pertaining to a general arousal account of the findings of Study 4. I then consider emotion-specific accounts with a focus on affect priming and appraisal approaches. Finally, I describe a study to test between the two approaches.

Emotion-General Effects: The Role of Arousal in Social Judgments

Much research in affect and social cognition has focused exclusively on the arousal and valence (pleasantness) dimensions of affective states (see Niedenthal,

Halberstadt, & Innes-Ker, 1999; Russell, 1980; Smith & Ellsworth, 1985; Watson &

Tellegen, 1985, for reviews) with something of a disregard for the specific qualities of particular emotions. The circumplex model of emotions, for instance, characterizes particular emotions as combinations of pleasantness and arousal and asserts that the effects of different emotions on judgments are functions of these two dimensions

(Russell, 1980; Watson & Tellegen, 1985). In addition, early work by Schachter and colleagues (Schachter, 1964; Schachter & Singer, 1962) presented emotions as composed of generalized autonomic nervous system (ANS) arousal and a cognitive label, positing ANS as central to an understanding of emotions. As discussed in previous chapters, much of the work from this tradition demonstrates valence-based congruence effects, showing that positive and negative emotions bias social judgments in valence-congruent ways (see

Forgas, 1995, 2002 for example, for a review). Other work, however, notes the importance of understanding the role of the arousal dimension of affect in social judgment and decision making (e.g., Clark, 1982, Levenson, 1992; Mano, 1992, 1994).

According to this general, dimensional approach, emotional arousal can have similar effects on judgments and behaviors regardless of the particular emotion or visceral state that accompanies it. Thus, in Study 4, anxious individuals may have made more pessimistic forecasts and decisions because of a general emotional arousal effect.

Consistent with this notion is research on excitation transfer by Zillman and colleagues, among others (e.g., Barclay, 1970; Cantor, Zillman, & Jennings, 1975; Dutton & Aron,

1974; Zillman, 1971, 1978). In these studies, emotional arousal is shown to be non- specific and transferable: arousal from one source can be attributed to a different source that people recognize as arousal producing. Findings in this domain demonstrate arousal transfer from disgust and erotic arousal to humor and enjoyment (Cantor, Bryant, &

Zillman, 1974; Cantor & Zillman, 1973; Cantor, Zillman, & Jennings, 1975), from sexual arousal and exercise arousal to anger (Zillman, 1971; Zillman, Katcher, & Milavsky,

1972), from anger to sexual arousal (Barclay, 1970), and from fear to sexual arousal

(Dutton & Aron, 1974).

In addition, work by Niedenthal and colleagues demonstrates that emotional arousal in general, and independent of valence, can influence the way that people categorize information (Halberstadt & Niedenthal, 1997; Niedenthal, et al., 1999). Halberstadt and Niedenthal (1997), for example, showed that emotional arousal,

independent of the particular emotion accompanying it, can increase attention to emotional dimensions in a face categorization task. When emotionally aroused, participants in this study tended to categorize faces according to as opposed to non-affective dimensions such as gender. Importantly, this effect was not consistently emotion-congruent. So although emotionally aroused individuals attended to emotional information, they did not consistently attend to emotion-congruent information.

Similar results were found in a series of four studies by Niedenthal et al., (1999).

In these studies, participants were required to pair a target concept with one of two comparison concepts. One of the comparison items bore an emotional association with the target, while the other shared a semantic or associative relation. Participants experiencing emotional arousal were more likely to group the target with the emotionally related comparison concepts than were controls. Niedenthal and colleagues (1999) argue that “…emotional states increase the use of all emotional response categories, not just the one related to the emotion that the perceiver is currently experiencing”(p. 341).

While these studies show that arousal in general can have important effects on

information processing, other work suggests that the judgmental effects of anxiety or distress, in particular, might be explained by physiological arousal (Leith & Baumeister,

1996; Mano, 1992, 1994). Work by Mano (1992, 1994), for instance, demonstrates that it

is not feelings of anxiety per se that influence judgments about risk, but rather the ANS

arousal that accompanies anxiety. Further, Leith and Baumeister (1996) show, in a

demonstration reminiscent of Zillman and colleagues’ excitation transfer studies, that the physiological arousal experienced due to exercise, when combined with a negative label, can exert similar effects on risk perception as those exerted by anxiety.

In light of this research, the results of Study 4 may be explained by a general

arousal effect. In other words, anxiety may have influenced forecasts, and thus decisions,

not due to the quality of anxiety per se but due to the influence of the accompanying

generalized arousal on emotional information processing. The emotional arousal associated with anxiety may have increased the accessibility of or participants’ attention to emotional information and thus increased the weight of such information in the forecasting process. According to this line of reasoning, another affective state with a similar level of emotional arousal should produce the same effects on forecasts and decisions. This possibility was examined in Study 5 by including anger, as well as anxiety, as a predictor of forecasts of a public speaking performance. One would predict, based on the aforementioned reasoning, that if arousal were the key factor impacting on affective forecasts, then both anger and anxiety would promote pessimistic forecasts and thus decisions to disengage from public speaking.

Emotion-Specific Effects

Another possibility, however, is that anxiety has a specific, emotion-congruent effect on forecasts and decisions independent of its arousal qualities. A growing body of research suggests that the influence of specific emotions on judgments and behaviors extends beyond simple valence or arousal effects and depends more upon the qualitative nature of the specific emotion under question (e.g., Frijda, 1986; Levenson, 1994; Oatley

& Johnson-Laird, 1996). Consistent with this notion are two broad areas of research that show emotion-specific effects on social judgments and behavior. Emotion-specific priming. First, some work suggests that specific emotions can

exert emotion-specific effects on social judgments and behaviors via emotion specific

influences on memory, categorization and attention (Bower, 1981; Gernsbacher,

Goldsmith, & Robertson, 1992; Hansen & Shantz, 1995; Laird, Cuniff, Sheehan,

Shulman, & Strum, 1989; Laird, Wagener, Halal, & Szegda, 1982; Niedenthal,

Halberstadt & Setterlund, 1997; Niedenthal & Setterlund, 1994; Small, 1985). A study by

Niedenthal et al., (1997), for instance, demonstrated word recognition facilitation for emotion-congruent words in a lexical decision task. Happy, sad or neutral participants made word/non-word judgments for various letter strings that included target words related to happiness, sadness, anger and love. Results showed emotion-congruence: sad participants showed greater facilitation for sad words than did happy participants, and happy participants showed greater facilitation for happiness related words than did sad participants. Importantly, however, facilitation for anger and love words was not influenced by the emotional state of the participant. Similar results were obtained in other studies of emotion and word recognition (e.g., Niedenthal & Setterlund, 1994; Small,

1985)

Such emotion-specific effects have been evidenced in other domains as well.

Memory for sentences, for instance, has been shown to be susceptible to emotion-specific influences. Laird et al., (1982) found that when people adopt facial expressions congruent

with the emotion-specific content of sentences that they read, they show superior memory

for those sentences on a subsequent memory task. Importantly, this effect is not valence

based, but emotion specific. In other words, adopting a facial expression reflecting anger

will facilitate memory for anger-related sentences but not for sentences related to sadness or fear. According to this emotion-specific account of affect and social judgment, specific emotions can exert specific effects on social judgments and behaviors.

Work by Bower (1981) also shows emotion-specific effects in memory.

Information that is learned in a state of fear, for example, is better remembered when one is fearful, but not angry or sad. Similarly, if participants learn information when sad, memory for that information is facilitated if participants are in a sad mood at recall, but not if they are angry. In Study 4 then, induced anxiety may have facilitated the mental accessibility of specifically anxiety-related information (e.g., Bower, 1981; Hansen &

Shantz, 1995; Niedenthal et al., 1997) and thus prompted more pessimistic forecasts and decisions about a future anxiety-provoking event. According to this thinking, another negative emotion such as anger may not exert the same kinds of effects. Anger may indeed increase the accessibility of anger-related information in memory, but such information bears no direct relevance to forecasts about an anxiety-provoking event and so may be deemed irrelevant to the judgment at hand. This reasoning suggests that anger would not influence forecasts or decisions about public speaking.

Appraisal tendencies. A second body of research that lends credence to an emotion-specific approach to affect and cognition is work on appraisal tendencies (e.g.,

Keltner, Ellsworth, & Edwards, 1993; Lerner & Gonzalez, 2005; Lerner & Keltner, 2000;

2001). Appraisal theorists argue that different emotions are experienced as a function of the way an individual appraises her environment (e.g., Arnold, 1960; Lazarus, 1991;

Mandler, 1975; Scherer, 1982). According to these thinkers, different emotions have different and rather specific antecedent appraisal patterns (Lazarus, 1991; Smith &

Ellsworth, 1985). Smith and Ellsworth (1985), for example, identify six dimensions of cognitive appraisal underlying specific emotions: certainty, control, attention, anticipated

effort, pleasantness and responsibility. Different emotions are characterized by different

patterns of appraisal on these dimensions. Not only do different emotions have different

antecedent appraisal patterns, but, as was discussed in the previous chapter, specific

emotions carry with them specific action or appraisal tendencies that comprise a set of

responses that help the individual respond to problems or deal with opportunities in the

environment (Frijda, 1986; Levenson, 1994; Oatley & Johnson-Laird, 1996). According

to this approach to affect and social cognition, affective states of the same valence and

same levels of arousal can have markedly different influences on judgments and

behaviors. More specifically, Lerner and Keltner (2000) suggest that “…each emotion

activates a cognitive predisposition to appraise future events in line with the central-

appraisal dimensions that triggered the emotion – what we call an appraisal tendency” (p.

477).

On the one hand, anxiety/fear is associated with appraisals of uncertainty and

situational (as opposed to personal) control (Lerner & Keltner, 2000; 2001; Smith &

Ellsworth, 1985). As such, feelings of anxiety and fear have been shown to increase

perceived risk in various social judgments (e.g., Lerner & Keltner, 2000, 2001;

Raghunathan & Pham, 1999). Because risk assessments include components that are

related to the appraisal theme of anxiety/fear, such as “unknown risk” (related to

uncertainty) and “dread risk” (related to lack of control), transient feelings of anxiety can

exacerbate the perception of these qualities in risky decisions and so lead to pessimistic risk assessments.

Anger, on the other hand, is defined by the opposite appraisal tendency with regard to certainty and control. Anger is high on certainty and on individual control

(Lerner & Keltner, 2000, 2001; Smith & Ellsworth, 1985). When angry people make risk assessments, they perceive low levels of unknown and dread risk and so are more optimistic and risk seeking than anxious individuals (Lerner & Gonzalez, 2005; Lerner &

Keltner, 2000; 2001). According to this general approach to emotions, anxiety may have

produced pessimistic forecasts and decisions in Study 4 because of its associated

appraisal tendency. Anxious people may have perceived more uncertainty and less

control in a public speaking situation and so made more pessimistic forecasts and

decisions. If emotions do exert effects on forecasts and decisions due to associated

appraisal tendencies, then we might expect anger to lead to more optimistic forecasts and

decisions in the current study.

The Present Study

Study 5, then, was designed to test the emotion-specific nature of the effects of

anxiety on affective forecasts. As such, Study 5 used the same methodology as Study 4, but, in addition to replicating the effects of anxiety on forecasts, this study also examined the effects of another relatively high arousal affective state, anger, on forecasts and decisions about a public speech. Research does indeed show that anger is accompanied by increased ANS arousal (see Levenson, 1992 for a review). If arousal is driving the effects of Study 4 one might expect anxiety and anger to exert similar effects on forecasts and decisions here in Study 5. If the effects of anxiety are specific to that emotional state, one might expect anxiety and anger to differ in their effects. On the one hand, it is possible that anger might promote the accessibility of judgment-irrelevant, anger-related information and so exert no influence on affective forecasts or decisions. On the other hand, and consistent with appraisal tendency research, anger may promote appraisals of certainty and control and thus lead to optimistic forecasts and a willingness to engage in public speaking. In addition, Study 5 also included positive and negative moods (induced as in Studies 1-3) to assess whether these lower intensity affective states would exert any influence on forecasts about an anxiety-provoking event.

Method

Participants and Design

Participants were 137 (102 female, 35 male) second year psychology students from the University of New South Wales who completed this study for course credit.

This study used a 5(affect condition: neutral vs. happiness vs. sadness vs. anger vs. anxiety/fear) group between participants design. Dependent variables included intensity and duration forecasts about a public speaking event as well as a decision to engage in this event.

Procedure

Upon arrival, participants were informed that part of that day’s session would involve watching and evaluating a short film clip. Participants then viewed a video designed to induce a particular affective state. The purpose of the film as an induction was disguised and participants were told that they had to evaluate the film and to watch the film as if watching television at home and involve themselves in the themes and emotive qualities of the story.

After the film, the experimenter proceeded as if to give participants a questionnaire to complete, but then acted as if she had just remembered something she needed to tell students. She said:

Oh, just before we proceed with the other the activities, I have something to ask

you. We are conducting another study next week and I was wondering if you

might read this information sheet about the study and consider participating.

Thanks.

The experimenter then distributed a “Participant Information Questionnaire” which was identical to that used in Study 4. The form outlined the study proposed for the following week as one requiring people to tell a funny story in front of a group. Participants rated how they would feel about doing this, the duration of this feeling and then decided whether they would participate or not.

Next, participants were given a “Film Clip Questionnaire” that served as the affect manipulation validation. Finally, participants were informed that there was in fact no study the following week. They were then fully debriefed and thanked for their time.

Manipulations and Measures

Affect induction. Short film clips validated by Gross and Levenson (1995) were used to induce affective states. The anxiety (“The Shining”) and neutral (nature documentary clip) film clips were identical to those used in Study 4. A 171 second excerpt from “The Champ” showing a boy grieving over his dying father was used to induce sadness. An excerpt from “When Harry Met Sally” (155 seconds) in which the orgasm is discussed in a café was used to induce /happiness. And a 156 second scene from “Cry Freedom” depicting police abusing protesters was used as the anger manipulation. These films have been validated in a comprehensive review of

emotion elicitation procedures and have been shown to induce appropriate discrete

emotions (Gross & Levenson, 1995).

Affective forecasting and decision measure. This questionnaire was the same as

that used in Study 4. The questionnaire began:

You can earn $20 or 2 hours additional course credit if you agree to return some

time next week and participate in another experiment. During this experiment you

will be required to tell a funny story in front of a group of ten people.

Participants then answered the question: “How would you feel about doing this?” by making a vertical mark on a continuous linear scale of 10cm length anchored good-bad.

Next they reported the number of days they would feel this way and finally circled yes or no in response to the question: “Are you willing to do this?”

Affect validation. The affect validation was identical to that used in Study 4.

Participants recorded the extent to which they felt the following emotions: anxiety, arousal, anger, happiness, and sadness.

Debriefing. The same debriefing procedure as used in the previous studies was used in Study 5. No one reported suspicions about the hypotheses of the study or perceived any links between the mood induction and forecasting tasks.

Results

Affect Manipulation

Participants’ responses to the emotion descriptors were submitted to a 5(affect condition: neutral vs. happiness vs. sadness vs. anger vs. anxiety/fear) x (5)(emotion term: anxiety vs. arousal vs. anger vs. happiness vs. sadness) MANOVA. Results revealed a significant multivariate effect for Affect Condition, Wilk’s Λ = 0.05, F(20,

425.48) = 31.38, p < 0.001. To more specifically examine the effectiveness of these inductions, Dunnett tests (Dunnett, 1955) were conducted on each of the five emotion descriptors. Results are presented in Table 8. Dunnett tests of pairwise comparisons with the neutral condition, controlling per analysis error rate at α = 0.05, showed that films induced target emotions significantly more so than the neutral condition. Further, and largely consistent with Gross and Levenson’s (1995) original validation study, the films were, on the whole, specific in the emotions that they induced. The exceptions here were the sadness induction (which also induced significantly more anxiety and anger than the neutral condition) and the anxiety induction (which also induced significantly more sadness and anger than the neutral condition). These differences were, however, smaller than differences for the respective target emotions. Finally, as expected, the anxiety and anger inductions were both accompanied by significantly more arousal than the neutral condition. Happiness and sadness, as in Studies 1-3, did not differ from the neutral condition in terms of arousal.

Table 8 Emotion ratings as a function of emotion condition and emotion descriptor Emotion descriptor Anxiety Arousal Anger Happiness Sadness Emotion condition

Neutral M 0.86 1.14 0.39 1.39 0.25 SD 0.85 1.21 0.69 1.34 0.52 n 28 28 28 28 28

Anxious M 3.88a 3.63a 1.31a 1.06 1.81a SD 2.28 2.34 1.28 1.11 1.38 n 32 32 32 32 32

Sad M 1.89a 1.97 1.43a 0.80 4.17a SD 1.05 1.38 1.04 1.08 2.09 n 35 35 35 35 35

Happy M 0.79 1.42 0.47 2.68a 1.05 SD 1.23 1.61 1.22 1.53 1.68 n 19 19 19 19 19

Angry M 1.52 4.65a 6.00a 1.49 0.74 SD 1.27 1.47 1.54 1.24 1.18 n 23 23 23 23 23 Note. Within columns, means that have a subscript differ significantly from the neutral condition in the Dunnett test (p < 0.05).

Intensity and Duration Forecasts

Data from 17 participants were excluded from these analyses due to unforeseen

circumstances. Results of the analyses for intensity and duration forecasts are presented

in Table 9. Intensity predictions were submitted to a 5(affect condition: neutral vs.

happiness vs. sadness vs. anger vs. anxiety/fear) group one-way ANOVA which revealed

a significant overall effect for Affect Condition, F(4, 115) = 3.36, p = 0.01. Dunnett tests

of pairwise comparisons with the neutral condition revealed that, consistent with Study 4, anxious participants expected to feel worse about the public speech than did neutral

participants t(115) = 2.90, p =0.02, (95% Dunnett CI: 0.46, 5.42). No other affect

condition differed significantly from the neutral group, ts < 1.6, ns.

Table 9 Intensity and durability forecasts as a function of emotion condition Forecast Intensity Duration Emotion condition

Neutral M 5.34 1.04 SD 2.24 1.46 n 25 25 Anxious M 7.17a 1.92 SD 1.71 2.20 n 29 28

Sad M 5.38 1.84 SD 2.60 2.04 n 34 30

Happy M 6.62 2.27 SD 2.30 2.41 n 12 12

Angry M 5.70 1.76 SD 2.43 1.67 n 20 19 Note. Within columns, means that have a subscript differ significantly from the neutral condition in the Dunnett test (p < 0.05).

An additional 6 participants were excluded from the duration analysis because

they did not respond to the question. A similar analysis of duration predictions revealed

no significant effect of Affect Condition, F(4, 109) = 1.1, p = 0.36.

Decisions

In a replication of Study 4, logistic regression analysis revealed that participants

in the neutral condition (60% agreement) were significantly more likely to agree to

engage in the public speaking task the following week than were anxious participants

(22% agreement), odds ratio = 5.2, χ²(1) = 8.07, p < 0.005 (95% CI for odds ratio: 1.67,

16.19). Participants in the other affect conditions agreed to participate in the public speaking task at frequencies not significantly different to the control condition, χ²s < 2, ns. Mediational Analysis

Did anxious participants’ forecasts once again mediate their decisions to

participate in the public speaking task? Results of a mediational analysis akin to that conducted in the previous study are presented in Figure 3. Anxiety (coded anxiety=1, neutral=0) decreased the likelihood of agreement to engage in the public speaking task

(logistic B = -1.65, SE = 0.58, Wald = 8.07, p = 0.004). Anxious participants again reported more pessimistic forecasts than neutrals (linear B = 2.13, SE = 0.52, β = 0.49,

t(52) = 4.09, p < 0.001). When both induced emotion and affective forecasts were

included as predictors, the resultant logistic regression model revealed that forecasts

predicted decisions (logistic B = -0.59, SE = 0.224, Wald = 6.89, p < 0.05), but anxiety did not (logistic B = 1.10, SE = 0.70, Wald = 2.48, p = 0.12). In addition, a Sobel (1982) test was significant, z = -2.21, p < 0.05.

Affective forecasts (-0.59*) 2.13*

Agreement to

Anxiety participate -1.65* (1.10)

Figure 3. Affective forecasts as a mediator of the effect of anxiety on decisions to engage in a public speaking task. Path coefficients are unstandardized regression coefficients (Bs). Italicized coefficients are from a linear regression model, other coefficients are from logistic regression models. Coefficients in parentheses are from a logistic regression model containing both predictors. Asterisks indicate significance at p < 0.05.

Discussion

Consistent with the results of Study 4, participants who were anxious made more

pessimistic forecasts about engaging in a public speaking performance than did neutral

participants. In addition, anxious participants were less likely than neutrals to choose to

engage in the public speech and this influence of state anxiety on decisions was mediated

by forecasts. Consistent with an emotion specific account of the influence of anxiety on forecasts and decisions, induced anger, sadness and happiness did not influence forecasts or decisions. The implications of this study in conjunction with Studies 1-4 are discussed in the following chapter. Chapter 7

General Discussion

In sum, the relatively low intensity positive and negative moods induced in

Studies 1 –3 did not influence affective forecasts for a variety of events, but state anxiety did influence forecasts and decisions about a public speaking engagement in Studies 4 and 5. In this chapter I will review the implications of these findings for research into affect and affective forecasting. First, I will present a summary of the results of this thesis. I will then discuss the results of Studies 1-3 with a focus on why low intensity positive and negative moods did not influence affective forecasts. Next, I will review the results of Studies 4 and 5 with a view to delineating reasons why anxiety exerts an emotion specific effect on forecasts. Finally, I will present some limitations of the present studies, propose a number of future directions and consider some practical implications.

Overview of Findings

The central concern of this thesis was the investigation of the effects of current affective states on predictions about future affective states. The primary hypothesis was affect congruence. More specifically I sought to demonstrate, as predicted by the AIM, that mood congruence effects are more pronounced under conditions of constructive processing. In Study 1, this hypothesis was tested in the context of forecasts about a variety of everyday events. Happy, sad and neutral participants were asked to predict their immediate and enduring feelings about events such as losing a wallet in the street.

Some participants were instructed to think elaborately about their forecasts and others were asked to respond as quickly as they could. The results showed no evidence of mood congruence, moderated or otherwise. In addition, the secondary hypotheses derived from

the mood-as-motivation and mood processing accounts were not supported. One possibility for the absence of congruence effects in Study 1 was that participants might have directly accessed beliefs about the events to be forecasted thus precluding affect infusion. This issue was addressed in Study 2.

In Study 2, happy and sad participants made affective forecasts about clearly valenced and ambiguously valenced assignment feedback. It was hypothesized that the ambiguously valenced feedback would recruit more constructive processing and thus be subject to more pronounced mood congruence effects. Results showed, however, that mood had no effect on forecasts about either clearly or ambiguously valenced assignment feedback.

Both Study 1 and Study 2 required participants to make forecasts about hypothetical events of low personal relevance. Research suggests, however, that both mood congruence and incongruence effects are more likely when the judgmental task is personally relevant (see Forgas, 1995 for a review). Hence, in Study 3, happy, neutral and sad individuals made affective forecasts about receiving particular marks on an a real psychology assignment. A Need for Cognition measure was included to examine the moderating role of increased elaboration on affect infusion. Once again, however, the low intensity moods induced in this study did not influence affective forecasts. There was no evidence of mood congruence, mood incongruence (predicted by the mood-as-motivation account) or mood processing effects.

Although the low intensity moods induced in Studies 1-3 did not influence affective forecasts, it was predicted that affective states of higher arousal might yet exert an influence on forecasts and decisions due to their resemblance to visceral states. Study

4 thus examined the effects of state anxiety on predictions about and the decision to

engage in a public speaking performance. As predicted, anxious individuals predicted that they would feel worse about speaking in public than their neutral counterparts and were also less likely to agree to the performance. In addition, affective forecasts mediated

the effect of anxiety on decisions.

Was this effect specific to anxiety? To examine whether the results of Study 4

were due to the effects of anxiety per se or were the result of a more generalized arousal effect, Study 5 examined the influence of another high arousal emotion, anger, on

affective forecasts. Consistent with an emotion specific account, anxious people once

again made more pessimistic forecasts and decisions about a public performance, but

anger, happiness and sadness had no impact on either affective forecasts or decisions. So

in sum, happiness, sadness and anger did not influence affective forecasts or decisions

but anxiety did. Let us now consider some implications of these findings.

Happiness, Sadness and Affective Forecasts: Studies 1-3

While affect congruence is a robust effect, there are numerous documented

instances of failures to replicate mood congruence (see Fiedler, 1991; Forgas, 1995, for

reviews). Although recent efforts have been made to determine those variables that

moderate affect congruence (e.g., Forgas, 1995; 2002), one must still largely agree with

Fiedler’s (1991) contention that “…our a priori knowledge of the exact conditions under which cognitive processes will reflect emotional processes is still remarkably meager” (p.

84). The absence of mood effects in any given situation is typically explained with

recourse to one of several possibilities (Fiedler, 1991; Mayer & Hanson, 1995). Let us now consider Studies 1-3 in light of some of these explanations.

Is Affective Forecasting a Constructive Process?

One possible reason for the absence of mood congruence effects in Studies 1, 2 and 3 is that the judgmental strategies used by participants precluded affect infusion.

Research shows that mood effects are more pronounced under conditions that allow for the active transformation and integration of information. Fiedler (1991) argues, as does

Forgas (1995) as a central tenet of the AIM, that mood should influence productive tasks

(tasks that involve the active transformation of information), but have less of an impact on reproductive tasks (which involve the conservation of information). Consistent with this distinction, research has shown, for example, that mood congruency is more pronounced for free recall than for recognition measures of memory (Bower & Cohen,

1982; Gerrig & Bower, 1982). Transient mood states also exert a more pronounced influence on unstructured than on structured stimuli (Ellis, 1985; Fiedler & Stroehm,

1986). Both free recall tasks and judgments of unstructured stimuli are productive tasks, requiring the active transformation of information (Fiedler, 1991). This research, taken with evidence reviewed in Chapter 1, suggests that some degree of constructive, generative processing (heuristic or substantive processing, in the language of the AIM) is required for affect infusion. If the judgmental task precludes such processing, no affect infusion will result.

So does affective forecasting allow for constructive, generative processing? Is it a productive task? Wilson and Gilbert’s (2003) forecasting model characterizes the forecasting process as at least a partially constructive task. They propose that in the initial, construal stage of the model, forecasters “…need to construct a representation of what the event is likely to entail” (p. 354). While some research suggests that people often generate scenario or episode-based representations when making social judgments

(see Wyer, Adaval, & Colcombe, 2002, for a review), there is no direct evidence that implicates such episode construction in the affective forecasting process. Some researchers, in fact, note that predictions about the future do not necessarily require the kind of simulation process that Wilson and Gilbert suggest (e.g., Malle & Tate, in press).

The absence of significant mood effects on affective forecasts found here is certainly consistent with the suggestion that affective forecasts may involve little on-line, constructive processing of the kind that was found to be associated with mood-congruent judgmental effects in other tasks (Fiedler, 2001; Forgas, 1995, 2002; Sedikides, 1995).

As discussed in Chapter 2, it has been suggested that people often use semantic theories as the basis of their forecasts (McFarland, Ross, & DeCourville, 1989; Robinson

& Clore, 2002; Wilson & Gilbert, 2003; Wilson, Laser, & Stone, 1982). If this is indeed the case, transient moods may not exert the kinds of mood congruence effects that were expected. It has been argued, for instance, that mood congruence is much more robust for episodic than for semantic memory (see Weaver & McNeill, 1992 for a review). Weaver and McNeill (1992) note that although affect infusion into episodic memory (e.g.,

Teasdale & Fogarty, 1979; Teasdale, Taylor, & Fogarty, 1980) and into episode-based judgments (e.g., Forgas & Bower, 1987) are relatively robust effects, both semantic memory tasks (e.g., Clark, Teasdale, Broadbent, & Martin, 1983; Gerrig & Bower, 1982) and semantic memory-based judgments (Weaver & McNeill, 1992) are robust against affect infusion. If forecasts are largely the result of a consultation of semantic affective theories, then no affect infusion would be predicted.

This explanation was considered for the null results of Study 1, and so an attempt was made to address this issue in Study 2. It was argued that by increasing the valence ambiguity of the target event, reliance on affective theories would diminish and transient mood may be more likely to infuse forecasts. It was assumed that although people may indeed have theories about how clearly defined and valenced events (such as vacations,

Mitchell et al., 1997, or relationship failures, Gilbert et al., 1998) influence their emotions, they are less likely to be able to directly access theories about more ambiguous events. Why then did participants in Study 2 not exhibit mood congruence effects? One possibility is that people still accessed situational beliefs about the affective consequences of the target event and that such direct access precluded affect infusion.

The manipulation of ambiguity in Study 2 involved creating an assignment feedback passage that contained some positive and some negative comments. Further, when asked,

“How positive-negative is this passage?” in a pilot test people rated the ambiguous passage as exactly neutral (M= 4, SD = 1.08 on a 7-point Likert scale). So perhaps when asked to rate how they would feel about receiving this kind of feedback in an assignment, participants simply accessed knowledge that events that contain positive and negative elements typically produce neutral feelings.

The possibility that participants were directly accessing semantic affective theories may also explain why no mood processing effects were obtained in the first three studies. It was hypothesized that people in negative moods would be more accurate forecasters because they would be less susceptible to the cognitive causes of the intensity and durability biases. It was argued, for instance, that sad participants would less likely

succumb to focalism because the vigilant processing style associated with negative

moods might lead them to consider peripheral influences on feelings. If, however,

affective theories were the basis of forecasts, this mechanism of focalism reduction

would not come into play. Again, participants would simply access beliefs about how

particular events would make them feel rendering the influences of mood on processing,

and thus forecasting accuracy, null.

Are Affective Feelings Relevant to Affective Forecasts?

If participants in Studies 1-3 did in fact rely on semantic theories in making their

forecasts, their transient affective feelings may have been rendered irrelevant to the

forecasting process. Affective feelings are indeed only deemed relevant to a judgmental

task if there is little other judgment relevant information available (Schwarz, 1990) and mood effects on judgments are often eliminated when the relevance of transient feelings

states to the judgment is called into question (Keltner, Locke, & Audrain, 1993; Schwarz

et al., 1987). Research does show that if people have information that bears directly on a

judgmental task, transient affective feelings can be deemed irrelevant and not influence

judgment outcomes (Schwarz et al., 1987; Srull, 1983, 1984; Strack, Schwarz &

Gschneidinger, 1985). Srull (1983, 1984) showed, for example, that when people make

consumer judgments of familiar products about which they have ample judgment relevant

information mood has no impact. Mood does, however, influence consumer judgments about unfamiliar products. Presumably, when participants have no other available information their transient moods become informative for consumer judgments. So if people do have access to affective theories about the way in which various events influence their emotions (e.g., McFarland, Ross, & DeCourville, 1989; Robinson &

Clore, 2002; Wilson, Laser, & Stone, 1982) transient affect may be made irrelevant to the forecasting process.

This notion of relevance may also explain why moods have been found to influence judgments of global life satisfaction (e.g., Schwarz & Clore, 1983; Schwarz et al., 1987) but not affective forecasts about specific events. While people may indeed possess theories about the way specific events make them feel, they may not have ready access to global judgments of life satisfaction. According to Schwarz (1990), “…the evaluation of one’s life as a whole requires a multitude of comparisons along many dimensions with ill-defined criteria”(p. 535). Thus, the more complex and ill defined the judgmental domain, the more likely affect will infuse judgments. A study by Schwarz et al. (1987) supports this theorizing. In this study, participants were asked to make various judgments about global (life satisfaction) and specific (e.g., income) life domains after their national soccer team had either won or lost (the mood induction). Consistent with the reasoning outlined above, mood had pronounced congruence effects on judgments of general life satisfaction, but not on assessments of more specific life domains.

Presumably, specific life domains, much like affective forecasts, are well defined and contain ample judgment-relevant information, whereas global assessments do not

(Schwarz, 1990).

Affective Forecasts and Subjective Probability Judgments: A Dissociation?

An interesting question that emerges from the results of Studies 1-3 is this: Why do positive and negative moods influence probability judgments (e.g., Wright & Bower,

1992), but not affective forecasts? Research does suggest that positive and negative moods such as those induced in Studies 1-3 do bias subjective probability (SP) judgments in mood congruent ways (e.g., Johnson & Tversky, 1983; Mayer et al., 1992; Wright &

Bower, 1992). Wright and Bower (1992) argue that “To generate an SP for the likelihood of a future event, one must search long-term memory, retrieve salient episodic (and conceptual) knowledge, and combine retrieved knowledge into an SP inference”(p. 278).

One possibility is that the use of episodic knowledge in SP inference allows affect infusion, while the use of semantic theories in affective forecasting does not. Consider making an SP inference and an affective forecast about losing a wallet. An SP inference would involve answering a question such as: how likely is it that I will lose my wallet within the next year (Wright & Bower, 1992). To answer this, people may try to recall how often they have lost a wallet in the past or how absent minded they are, and current moods may indeed color the memories that come to mind in affect congruent ways, thus

leading happy individuals to make optimistic and sad individuals pessimistic SP

judgments. If asked, however, “How would you feel if you lost your wallet within the

next year?” people can simply access beliefs about the affective consequences of the

event (Robinson & Clore, 2002a). One general possibility that might be explored in

future research is the extent to which affect infuses affective forecasts that require the use

of episodic memory. This issue will be discussed in more detail later in this chapter.

Personal Relevance and Mood-as-Motivation

Not only was there no evidence of mood congruence effects in Studies 1-3, there

was no evidence of mood-as-motivation effects. Although this may have been expected in Studies 1 and 2 due to the low personal relevance of the hypothetical affective

forecasts made, Study 3 required participants to make forecasts about real assignment

grades that were assumed to be of high personal relevance to participants. The absence of

mood-as-motivation effects in Study 3 is thus more surprising. There are, however, two

possible explanations for the absence of mood-as-motivation effects in Study 3. One

possibility is that forecasts about assignment grades were not as personally relevant to

participants as was assumed. Recall that the participants of Study 3 were students in a second year psychology course at UNSW. Although some students in this course would have valued their assignment marks highly, others may not have. Some students, for instance, may have been taking this course as part of a degree other than psychology and as such the assignment marks forecasted in Study 3 may not have had meaningful consequences for them. Future work should address this shortcoming by including a measure that assesses the importance of the forecast for participants. One might expect greater mood-as-motivation effects for forecasts that are more important to forecasters.

A second possibility is that simply experiencing a negative mood when performing a task of high personal relevance is not sufficient to trigger mood-as-

motivation effects. Central to the mood-as-motivation account is the assumption that sad

individuals will be motivated to repair their negative moods and thus make mood incongruent, positive affective forecasts (Clark & Isen, 1982; Erber & Erber, 1994).

Recent work suggests, however, that such motivation to repair negative moods arises not from the experience of negative affect per se, but from the cognitive orientation adopted towards that experience (e.g., McFarland & Buehler, 1997, 1998). McFarland and

Buehler (1998), for example, showed that the effects of negative mood on memory depend upon whether participants adopt a ruminative or reflective towards their experience. If people adopt a ruminative focus, characterized by a sense that one’s feelings are inescapable and threatening (McFarland & Buehler, 1998), mood congruence results. If, on the other hand, people believe that their feelings are controllable and clear

(a reflective focus), mood incongruence is observed. These results are consistent with other research that shows that people’s beliefs about their affective states can affect the way those states influence judgments (e.g., McFarland & Buehler, 1997; Tice,

Bratslavasky, & Baumeister, 2001) and the way people regulate their moods (e.g.,

Berkowitz & Troccoli, 1990; Salovey & Mayer, 1990). Future research into the way affective forecasts may be used as an affect regulation strategy should thus take into account the moderating role of variables such as rumination, reflection and in promoting mood-congruent versus mood incongruent judgments.

Anxiety and Affective Forecasting: Studies 4 and 5

Studies 1-3 showed no evidence that low intensity mood states influenced affective forecasts about various emotional events. It was argued, however, that higher intensity affective states, such as anxiety and anger, might exert influence over forecasts and decisions due to their similarities to other visceral states. Studies 4 and 5 revealed that anxiety, but not anger, leads to more pessimistic forecasts and decisions about anxiety provoking events. Moreover, the effects of anxiety on decisions were mediated by affective forecasts. In the following sections I review numerous possible mechanisms for this observed effect and consider why anxiety exerted an emotion-specific effect in the current studies.

Anxiety and Decisions: Evidence of Mediation Although much evidence suggests that matching the forecaster’s state at prediction to the state to be predicted can reduce empathy gaps in the prediction of visceral states, there has been little more than speculation about the mechanisms involved

(see Van Boven & Kane, in press). It has been argued that, like the intensity and durability biases in predictions of future feelings, empathy gaps are multiply determined

(Van Boven & Kane, in press). One suggested reason why people may underestimate the impact of visceral states on choices in the ‘heat of the moment’ is that visceral states may exert direct influences on behavior in the absence of conscious cognitive mediation

(Bechera, Damasio, Kimball, & Damasio, 1997; Bolles, 1975; Ledoux, 1996). Indeed, thirst, hunger, sexual and pain can influence people’s behavior without conscious awareness (Loewenstein, 1996). Neurophysiological research shows, for example, that lesions in the reward centers of the brain can induce a lack of interest in consumption without cognitive mediation (Bolles, 1975). Bechera et al. (1997) found that, in a gambling task, nonconscious biases can help guide behavior prior to the use of conscious knowledge in decisions involving risk. These direct, nonconscious influences on behavior provide people with little opportunity to learn about the relationship between visceral states and their behaviors and so exacerbate empathy gaps in prediction.

The results of Studies 4 and 5, however, do not support this conception, at least with regard to the effects of anxiety on predictions. The mediational analyses reported in these studies show no evidence of a direct effect of state anxiety on decisions, but rather an effect mediated by affective forecasts. This finding is largely consistent with another proposed cause of empathy gaps: the failure to appreciate the fact that visceral states focus attention on state-related information (Loewenstein, 1996; Van Boven & Kane, in press). When in a cold state, forecasters may fail to recognize that negative arousal

narrows attention and focuses attention on emotional attributes (e.g., Derryberry, 1993;

Derryberry & Read, 1998; Niedenthal et al., 1999) and inhibits attention to non-

emotional information (Fox, Russo, & Bowles, 2001; Fox, Russo, & Dutton, 2002). They

may also fail to appreciate that emotional states can facilitate the accessibility of emotion

congruent concepts (e.g., Bower, 1981). In essence, people in a cold state are not thinking

in the same manner as they would in a hot state. Emotion-related information does not

receive the same weight in cold decisions as it does in hot decisions. When in a state of

visceral arousal, or anxiety in this case, emotion-related information is in the forefront of awareness and thus receives substantial weight in judgments and behaviors, while information unrelated to the particular emotion or visceral state loses its value

(Loewenstein, 1996). In other words, anxious people are thinking in the present as they

would in a future anxiety-provoking situation. Their current anxiety may focus their

attention on anxiety-related information, leading to more pessimistic forecasts and thus a

decision to withdraw. Although Studies 4 and 5 do suggest that the influence of anxiety on decisions is mediated by affective forecasts, they do not provide definitive evidence

regarding the exact mechanism by which this indirect effect is produced. Let us now

consider a number of possible mechanisms for the effects observed in Studies 4 and 5.

Emotion-Congruent Effects on Memory and Attention

One possibility is that anxious individuals were more likely to recall negative or

emotionally threatening memories and use these in the construction of their affective

forecasts. Some research does show that anxious individuals have a tendency to bring

negative memories to mind (e.g., Bower, 1981; Burke & Mathews, 1992; Butler & Mathews, 1983; Butler & Mathews, 1987; Richards & Whittaker, 1990) and other work demonstrates that clinically anxious people show a memory advantage for emotionally threatening information (e.g., McNally, Foa, & Donnell, 1989; Cloitre & Liebowitz,

1991). Cloitre and Liebowitz (1991), for instance, showed that patients from disorder show better perceptual and semantic memory for threatening compared to positive or neutral words.

Although several studies show that anxiety may promote both valence based and emotion specific selective memory effects, other research suggests that anxiety may be more often associated with biases of attention, not memory (e.g., Mathews & MacLeod,

1994). More specifically, anxiety is associated with an attentional bias towards threatening or emotionally negative information in the environment (Broadbent &

Broadbent, 1988; MacLeod & Mathews, 1988; Mogg & Bradley, 1988; Mogg, Mathews,

& Eysenck, 1992). Research utilizing attentional probe tasks, for example, presents individuals with stimulus arrays on computer screens that contain an emotionally negative or threatening word and a neutral word presented at different locations within the array. Subsequent to these exposures, participants are required to detect the presence of small dot probes at various locations on the screen. Anxious individuals are much faster at detecting the presence of dot probes at locations close to the previously presented negative word compared to probes presented in the vicinity of previously presented neutral words (e.g., MacLeod, Mathews, & Tata, 1986). Other work using dichotic listening tasks shows a similar bias towards threatening information even when this information is not directly attended to (Mathews & MacLeod, 1986).

In the context of Studies 4 and 5 in the current thesis, anxious individuals may have focused their attention on threats in the forecasting situation (threats including, fear

of negative evaluation, or fear of embarrassment for example) and so made pessimistic

forecasts. Research does show that the potential for criticism and negative evaluation that

often accompanies public speaking ranks among people’s worst (Arindell,

Pickersgill, Merckelbach, Ardon, & Cornet, 1991). Anxious individuals may thus have

been subject to a particular brand of focalism - emotional focalism. Not only might

anxious individuals have shown the typical attentional bias towards the focal event to the

exclusion of the periphery (Wilson et al., 2001), they may also have shown a further

attentional bias towards threatening or emotionally negative aspects of the focal event.

This attentional focus may explain why anxious individuals made more pessimistic

forecasts and decisions in the current studies.

Anxiety and Vividness

Another reason why anxious individuals may have made more pessimistic forecasts in Studies 4 and 5, is that they were able to imagine the public speaking engagement more vividly than participants in other conditions. Loewenstein (1996;

Loewenstein & Lerner, 2003; Loewenstein et al, 2001) suggests that vividness can have a significant impact on forecasts and behaviors and other research shows that anxiety can promote more vivid imagery of affective events (MacLeod, Tata, Kentish, & Jacobsen,

1997; Stöber, 2000). In a study by Stöber (2000), for example, individuals with high levels of trait anxiety generated representations of future negative events that were more detailed and vivid than those generated by individuals with high levels of trait .

The more vivid one’s mental representation of an event, the more intense will be one’s emotional reaction to that representation (Miller, Levin, Kozak, Cook, McLean, & Lang, 1987; Smith & Over, 1987; White, 1978). For example, people who can imagine events

more vividly salivate more while thinking about food (White, 1987) and are more sexually aroused when thinking about fantasies (Smith & Over, 1987). So in

Loewenstein’s (1996) words “…vividness may operate in part by intensifying immediate emotions associated with thinking about the outcome…”(p. 280). In the current studies, then, anxious individuals may have imagined the public speaking engagement more vividly than others, thereby increasing their current feelings of anxiety about that event, and thus biasing forecasts in a pessimistic fashion.

Anxiety and Appraisals

Although anxiety may have produced pessimistic forecasts via its influence on general cognitive processes such as memory, attention and vividness, another possibility is that the more specific appraisal tendencies associated with anxiety increased

in the current studies. As was discussed in Chapter 6, fear or anxiety arises from and

gives rise to appraisals of uncertainty and situational (as opposed to personal) control

(Lerner & Keltner, 2000; 2001; Smith & Ellsworth, 1985), whereas anger elicits the

opposite appraisal tendency (Lerner & Keltner, 2000; 2001). Consistent with these

appraisal themes, Lerner and Keltner and colleagues have shown that anger and

anxiety/fear can influence risk perception in appraisal congruent ways (Lerner, Goldberg,

& Tetlock, 1998; Lerner & Gonzalez, 2005; Lerner, Gonzalez, Small, & Fischhoff, 2003;

Lerner & Keltner, 2000, 2001). In general, anxiety/fear has been linked to pessimistic

risk assessments, while anger has been associated with increased optimism.

In numerous studies Lerner and colleagues (e.g., Lerner & Gonzalez, 2005;

Lerner & Keltner, 2001) have shown not only that anger and fear have opposite influences on risk preferences and judgments, but also that these effects are mediated by

relevant appraisal dimensions. Lerner and Keltner (2001), for example, showed that anger

produced more optimistic risk estimates than did anxiety because anger promoted

appraisals of control about the future. In another study, Lerner and Gonzalez (2005)

showed that the effects of anxiety and anger on probability ratings of future events were

mediated by appraisals of control. Participants in this study were induced into angry or

fearful affective states and then asked to rate the likelihood of various positive and

negative events happening to them in the future. Again, because anger is associated with

increased of personal control, angry participants were more optimistic about

their futures than were fearful participants. In a mediational analysis these researchers

demonstrated that anxiety produced appraisals of less control, which in turn promoted more pessimistic assessments of life expectations. The direct effect of anxiety on probability judgments disappeared once appraisals of control were considered as a mediator.

Although Studies 4 and 5 demonstrated that state anxiety led to more pessimistic forecasts and subsequent decisions, no direct measures of control or certainty appraisals were taken and so the role of appraisals in accounting for the observed effects needs further attention in future research. Further, the results of Studies 4 and 5 are only partially supportive of an appraisal approach to emotions and affective forecasting.

Consistent with Lerner and colleagues’ work on anxiety/fear and risk, participants in both

Studies 4 and 5 were more pessimistic in their forecasts about a public speaking engagement. Contrary to their findings, however, angry participants did not differ from controls in either their affective forecasts or decisions. In addition, happy individuals were not more optimistic than controls in Studies 4 and 5. Happiness, much like anger, is associated with appraisals of certainty and individual control and thus should also have produced optimistic forecasts and decisions (Lerner & Keltner, 2001; Smith & Ellsworth,

1985). One possibility for this discrepancy in findings is the relevance of the induced emotions to forecast made in Studies 4 and 5.

The Relevance of Anxiety to Affective Forecasts

One reason why happiness and anger may not have influenced forecasts and

decisions about public speaking is that these emotions are irrelevant to a public speaking episode. For most people public speaking is an anxiety-arousing event and so most people would readily associate feelings of anxiety with public speaking. If someone is thinking about a public speech and currently experiencing incidental anxiety, those feelings of anxiety are readily attributable to the imagined public speaking episode.

Happiness and anger, on the other hand, are not emotions that are typically associated with public speaking and so have little informative value for the forecasts made in

Studies 4 and 5. In short, state anxiety influenced forecasts (perhaps via appraisals or some other mechanism) because it was perceived as informative to the forecast at hand.

Anger and happiness were not perceived as informative and so exerted no influence on forecasts.

Much research does suggest that emotions only influence judgments to the extent that those emotions are relevant or informative to the judgment (see Schwarz & Clore,

1996 for a review). As discussed earlier, one way that transient affective states may be rendered uninformative for judgment is that other judgment-relevant information be available (Schwarz et al., 1987). Another possibility, however, is that current emotional states may be rendered irrelevant to judgments if those emotions bear no relation to the judgmental target. Although anger, happiness and anxiety all have implications for general risk judgments (e.g., Lerner & Keltner, 2000, 2001; Nygren, Isen, Taylor, &

Dulin, 1996), anger and happiness seem to possess little relationship to public speaking episodes and so may have been rendered irrelevant to such forecasts. This reasoning is supported by work that demonstrates that affective influences on judgments can be rather emotion specific and often depend on a match between the experienced affective state and the judgment to be made (Butler & Mathews, 1987; Constans & Mathews, 1993).

Butler and Mathews (1987), for example, showed that state anxiety increases perceived risks for future anxiety-related events, but not for other negative, non-anxiety related events. In this study, participants experiencing anxiety just before sitting an exam rated the likelihood of various future events. Results showed that anxious individuals rated anxiety-provoking events (e.g., “The next exam you sit will be an unusually hard one”) as more likely compared to other negative, non-anxiety provoking risks (e.g., “If you borrowed a friend’s tape recorder you would damage it accidentally”).

Other work with clinical populations also demonstrates that anxiety influences processing and judgment to the extent that the judgmental target is deemed relevant to the experience of anxiety (see Mathews & MacLeod, 1994 for a review). Work with patients, for example, shows that interference on color-naming tasks obtains only for words that are specifically related to panic disorder (Ehlers, Margraf, Davies, &

Roth, 1988; McNally, Riemann, Louro, Lukach, & Kim, 1992). In these studies, words related to disease, collapse or imminent death, which are typical fears of those with panic disorder (Clark, 1986), show interference on color naming tasks, but other emotional words do not. Other studies conducted with social phobics demonstrate greatest

interference effects with socially threatening words (, Rapee, Heimberg, &

Dombeck, 1990). More generally, research with clinical populations shows color naming interference effects for words related to patients’ specific concerns (Cooper & Fairburn,

1992; Foa, Feske, Murdock, Kozak, & McCarthy, 1991, and see Mathews & MacLeod,

1994 for a review). Presumably, felt anxiety for these patients is viewed as relevant and attributable to their specific fears and not generalizable across all negative events. With regard to the current studies then, due to the widely held belief that feelings of anxiety are

relevant to thinking about and engaging in public speaking, participants may have

attributed any experienced anxiety to the mental image of the event and so made more

pessimistic forecasts. Anger and happiness, on the other hand, which are not perceived as

related to public speaking, may have been deemed irrelevant and so more easily

discounted from the forecasting process leaving forecasts and decisions untouched.

Another, more general point regarding the relevance of the emotions induced in

the current studies to the forecasts made, relates to the temporal orientation of the

induced emotions. Anxiety is an example of an anticipatory or future-directed emotion,

an emotion that is elicited by contemplating the consequences of a decision (Bagozzi,

Baumgartner & Pieters, 1998; Lerner & Loewenstein, 2003). Anxiety is experienced as a

result of imagining some uncertain or possibly threatening future event (Loewenstein et

al., 2001). Anger, happiness and sadness, on the other hand are not anticipatory emotions

in the sense that anxiety is. Anger is typically experienced as a result of perceptions of

injustice or unfair treatment (Smith & Ellsworth, 1985). Both happiness and sadness are

also examples of affective responses to events that have already happened (Ortony, Clore, & Collins, 1988; Smith & Ellsworth, 1985). In light of this distinction, one may expect

anticipatory emotions, such as anxiety or hope, to exert more impact on forecasts because

such emotions are specifically relevant to future events. If one is currently experiencing

anxiety it may be easier to attribute such feelings to representations of future events,

because when one typically feels anxiety that anxiety relates to a future event. If one

currently feels sad or angry, emotions typically experienced about things past, such

feelings may be deemed irrelevant to an imagined future event due to a mismatch

between the temporal qualities of the emotion and the imagined event. Future research

may pursue this question with a focus on distinguishing the effects of anticipatory

emotions, such as anxiety and hope, from other emotions whose temporal references are

the past or present.

Limitations and Future Directions

Although I have considered various limitations and future directions throughout

the course of this chapter, I will now address a number of additional and more general

limitations of the current thesis and propose some related avenues of future research. One

limitation of Studies 4 and 5 pertains to the generalizability of these results. Both studies

included the same induction of anxiety and the same affective forecast. Two questions

thus arise. One: does state anxiety lead to more pessimistic forecasts and decisions for events other than public speaking? And two: will another manipulation of state anxiety produce similar biases in forecasts and decisions?

The answer to the first question has been partially presented earlier in this chapter.

While some evidence does suggest that affective states can have similar effects across

judgmental domains (e.g., Johnson & Tversky, 1983), other work shows that emotion- congruence effects may be more pronounced for judgments whose emotional qualities

match those of the induced state (see Mathews and MacLeod, 1994 for a review). In

addressing this question with regard to anxiety and affective forecasts, future work will

need to consider (1) whether state anxiety produces pessimistic forecasts and decisions

about other anxiety provoking events (such as social criticism or negative evaluation, for

example) and (2) whether state anxiety influences forecasts for other, non-anxiety

inducing affective events (such as winning the lottery or taking a vacation).

The second concern with generalizability pertains to the method of affect

induction. In the present studies, movies were used to induce affective states and the question arises: will other inductions produce the same effects as observed here?

Although some research suggests that affect infusion effects are equivalent across

methods of induction (e.g., Mayer et al., 1992), other work suggests that the semantic

content of particular affect inductions can account for various observed instances of

affect infusion (see Wyer, Clore, & Isbell, 1999 for a review). As such, it is important to

replicate the results of the current studies with other methods of affect induction.

A more general limitation of the current studies is that the results are

underdetermined. Future work should address this issue by examining the validity of

several of the explanations offered for the present results. One possible line of future

research may address the role of episodic versus semantic knowledge in affective

forecasting. If it is indeed the case that mood exerted no effects in Studies 1-3 due to the employment of semantic knowledge in forecasts, future work might include forecasts that require the utilization of episodic knowledge and examine the impact of mood on such forecasts. One obvious approach to such a question would be to specify that people make forecasts either by considering past instances in which they have felt the same way or by

considering how a particular event typically makes people feel. One might expect affect

infusion into forecasts made with the first, episodic-based strategy, but not into forecasts

made via the second, semantic-based procedure.

Alternatively, one could use a less direct manipulation of the memory strategy

used for making forecasts. Recent work by Robinson and Clore (2002b), for instance,

shows that people can be prompted to use either episodic or semantic memory to make

judgments in different situations. If, for example, people are asked to retrospectively

estimate the frequency of a particular emotional experience over a short time period (e.g.,

the past few hours or days) they use episodic memory. If they are asked to make similar

estimates over longer time frames, semantic memory forms the basis of estimation. One

possibility for future research then is to have people make forecasts of various future

events using retrospective estimates of their emotional experiences from short or long

time frames as the basis of their forecasts. One might expect that people who base their

forecasts on the consideration of their emotional experiences of the past week (and thus

utilize episodic memory) may be more susceptible to mood congruence effects than those

who base their forecasts on longer retrospective time frames.

A related line of inquiry could pursue the possible dissociation between the effects of affect on subjective probability estimates and affective forecasts. It has been suggested that affect may influence judgments of probability in a manner different to judgments of value or (Nygren et al., 1996). Future work may address this issue in affective forecasting. While low intensity positive and negative moods may indeed bias probability estimates in mood congruent ways (e.g., Wright & Bower, 1992), the current thesis suggests that these congruence effects do not extend to predictions of the affective

value of future affective states. Given that both predicted utility and subjective

probability both play large roles in numerous models of social decision making (e.g.,

, Kahneman & Tversky, 1979), examining the differential and possibly

dissociated influences of affect on different elements of the decision making process

would be a valuable enterprise for future research.

Other future work might consider the exact mechanism through which state

anxiety exerts its effects on affective forecasts and decisions. If, for instance, anxiety

influences forecasts and decisions via its informational value as a future-oriented

emotional state, misattribution manipulations might ameliorate its effect. Research does

show that if people can attribute their current affective states to an irrelevant source these

affective states no longer infuse judgments (e.g., Schwarz & Clore, 1983). So if

forecasters can attribute their feelings of anxiety to an irrelevant source these feelings

may no longer produce pessimistic forecasts and decisions. Research by Sheppard et al

(2005) does indeed suggest that anxiety may indeed influence forecasts via its

informational value. Still other work could examine the role of appraisal tendencies in

forecasts of anxiety by assessing whether anxious individuals’ appraisals of increased uncertainty and loss of control mediate the effect of anxiety on forecasts and decisions.

More generally, future work should assess the extent to which state and trait variables interact to influence affective forecasts. Although the role of Need for

Cognition was considered as part of the assessment of the AIM in the current thesis, other state-trait interactions may be important for clarifying the role of transient emotions in affective forecasting. Research from clinical populations shows, for instance, that attentional effects in emotional processing are most pronounced when the emotional

stimuli are in a domain of great concern to the subject (see Mathews & MacLeod, 1994

for a review). So while state anxiety may, on average, make people’s forecasts about anxiety provoking events more pessimistic, this may be especially pronounced for people

who also suffer trait anxiety about a particular class of events. Other research shows that the effects of state anxiety on attentional biases are most pronounced for individuals who have high trait anxiety (see Mineka, Rafaeli, & Yovel, 2003 for a review). Future research could also examine whether similar state-trait anxiety interactions exist for forecasting and decisional biases.

Practical Implications

In general, the results of the current studies highlight the importance of considering the role of emotions in everyday affective forecasting and decision making.

When people imagine the consequences of everyday events or decide whether or not to do something potentially anxiety-provoking they should be aware that their current feeling states (at least state anxiety) could influence their decisions. Some health related- judgments, for instance, require people to make decisions about taking tests that may promote anxiety. Research has found that people’s fear of negative test outcomes can often dissuade them from taking such tests. Sieff et al. (1999) showed that people typically overestimate the distress they will feel about a positive HIV test result (i.e. a result indicating they have the virus) and that such feelings may prevent people from seeking a test. Studies 4 and 5 in the current thesis suggest that transient feelings of anxiety may exacerbate this tendency even further. If people are currently anxious (even as a result of incidental anxiety due to a movie they have just seen), they may be even more pessimistic about their feelings about a positive result and so be even less likely to take a test. In such situations where there are clear benefits to selecting one decision outcome (such as taking a HIV test in this case), people need to be aware that incidental fear or anxiety can bias their forecasts and decisions, often leading to sub-optimal choices.

The findings of Studies 4 and 5 may also have implications for understanding anxiety disorders in clinical populations. While caution must be exercised when generalizing from normal to clinical populations, this work does add to the body of work attesting to cognitive and decision biases in anxiety disorders. Reviews by Mathews and

MacLeod (1994) and Mineka et al. (2003) show that anxious individuals do have certain cognitive biases including memory and attentional biases for negative or threatening information. The current studies shed light on a possible reason why people with various anxiety disorders display avoidant behaviors. Social phobics, for instance, shy away from social interaction because they perceive the consequences of such interaction as extremely anxiety provoking. If, however, social phobics were able to forecast their future anxiety from a currently cold state, they may generate less pessimistic forecasts and may be more likely to engage in social interaction. This implies that not only should treatment programs for social anxiety attempt to change cognitions about social interaction, they should also try to build an appreciation in the patient of the role of current anxiety in shaping cognitions and decisions about the future.

Conclusions

The current thesis joins a growing body of work that highlights the benefits of taking an emotion-specific approach to the study of the role of emotions in social judgment and decision making (e.g., Lerner & Keltner, 2000; 2001). The results of the

current thesis suggest that while anxiety may in fact lead to more pessimistic forecasts

and decisions in the domain of public speaking, other affective states may not. Not only does the current project show that emotions of the same valence can exert different effects on affective forecasting and decision making, it also confirms the mediating role that affective forecasts play in affective decision-making. Although numerous questions regarding the exact mechanism through which anxiety exerts its effects and the exact reason why other emotions exert no effects need to be answered, this work provides a sound first step in the examination of the effects of transient affective states on affective forecasting and the consequences of such effects for the decision making process.

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Appendix A

Table A Confidence intervals for reading time differences between high and low elaboration conditions (Study 1) 99% Standardized Confidence Interval Item Receive high distinction (0.30, 1.07) Meet person at party (-0.04, 0.74)* Work bonus (0.10, 0.85) Fail assignment (0.14, 0.91) Receive a gift (0.32, 1.09) Lose your wallet (0.10, 0.87) Get fired (0.29, 1.06) Break-up (-0.03, 0.74)* Chocolate (-0.01, 0.76)* Find $2 (0.23, 0.98) Lose $2 (0.14, 0.91) Caught in the rain (0.28, 1.05) Note. Asterisks indicate items that were excluded from subsequent analyses. Appendix B

Negative Feedback Passage (Study 2) You provide limited coverage of the general subject area and make a reasonable attempt at using research and literature in supporting your ideas. However, you failed to cover some issues that were of relevance to the question. Your emphasis on theoretical points is

generally ambiguous, and some further discussion of the practical side of the issue would

have been beneficial. Your arguments sometimes lack logical coherence, but you do

express yourself reasonably well. You make some interesting points although these could

have been elaborated upon. You need also to be careful about your attention to detail.

Ambiguously Valenced Feedback Passage (Study 2) You provide reasonable coverage of the general subject area and make some use of recent

research and literature in supporting your ideas. However, you failed to cover some

issues that were of relevance to the question. Your emphasis on theoretical points is

sensible, although some further discussion of the practical side of the issue would have

been beneficial. Your argument is generally logical and sound, although your expression

is sometimes convoluted and imprecise. And while you do make some interesting and

novel suggestions, your attention to detail is sometimes lax.