Affect Valuations Predict Emotional Response to Future Exam Success and Failure

doi:10.21203/rs.3.rs-3642438/v1

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Affect Valuations Predict Emotional Response to Future Exam Success and Failure

https://doi.org/10.21203/rs.3.rs-3642438/v1

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Does what a person desires to feel (affect valuation) predict their future affective reaction to salient life events? We tested this idea in the context of an exam, a salient achievement-oriented event for college students. One to two weeks prior to taking an exam, 180 university students rated their ideal affect, depression symptom severities, and provided affective forecasts for how they would react to exam failure and success. On the day when students received their exam scores, they rated their actual emotional response to the exam outcomes. Higher levels of ideal positive affect predicted greater positive affective responses to exam success. This prediction held even after controlling for affective forecasting, depression levels, and the exam score itself. Higher levels of ideal negative affect predicted greater negative affective reactions to exam failure, but did not survive parallel statistical controls. Results suggest the possibility that ideal affect performs motivational functions, particularly for positive affective states.

ideal affect

affective forecasting

depression

emotional response

cognition

How does what we ideally want to feel shape our motivation and behavior? Affect valuation theory (AVT) (Tsai, 2007, 2017) postulates that affective states can represent an important goal. According to this theory, affective states a person values (ideal affect) shape the person’s motivation to experience the desired emotions, as well as subsequent behavioral choices for activities that might increase the chances of bringing about the intended affective response. For example, a person who strongly values excitement may choose to attend a party with friends rather than watch a movie alone. During the party, such a person would likely feel more excitement than a person who prefers less excitement. Despite the theoretical salience of ideal affect in shaping motivated behaviors and subsequent emotional responses, little empirical work has examined the role of ideal affect in shaping downstream emotional responses.

The great body of research on ideal affect has often focused on between-culture differences in positive affect valuation (e.g., Ruby et al., 2012). Much less commonly, previous work has tested the direct predictive relationship between ideal affect and actual emotional responses. For example, baseline ideal affect predicted the emotional experiences reported in the following week (Scollon et al., 2009) and baseline desire for PA of college students predicted lower levels of psychological stress during the exam period (Millgram et al., 2019). In another ecological momentary assessment study, participants tended to be motivated to match their current emotional states with their ideal affect (Scheibe, et al., 2013). Although an important first step, these studies were uncontrolled, and did not examine reactivity to specific emotional events.

More directly, Chim et al. (2017) examined if ideal high arousal positive affect (HAP) and low arousal positive affect (LAP) predicted enjoyment of activities that elicited HAP and LAP, respectively. The results showed that the more a person valued LAP, the more the person enjoyed a LAP-related activity versus a HAP-related one. However, in this study, ideal HAP did not predict enjoyment of HAP-related activities. The authors explained that their null result may have been due to insufficiently powerful HAP-related activities in the study (e.g., amusement park rides and exercise). They suggested that, to examine the connection to ideal HAP, it might be useful to elicit HAP using target events relevant to personal achievement, such as an exam.

Therefore, the current study extended prior work by examining if ideal affect predicts emotional response to a college exam, a salient, relatively standardized, achievement-related event. Failure was operationalized by analyzing those students who achieved a lower exam grade than their expectation; success was operationalized by analyzing those students who achieved their expected grade or better.

To provide a more stringent test of ideal affect as a predictor of actual affect, we also modeled our hypothesis with three other variables. First, we assessed students’ forecasted affect about their reactions to the exam (Wilson & Gilbert, 2003). Presumably, forecasted affect would predict the student’s actual emotional response, as it is based on extensive past experiences, even if research shows that error is often present in these predictions (e.g., Gilbert et al., 1998). Second, we assessed self-reported depression symptoms, based on previous evidence that greater depression predicted stronger responses to failure (e.g., Abela, 2002) and weaker responses to success (e.g., Henriques & Davidson, 2000). Finally, we measured actual exam scores, reasoning that higher exam scores would predict stronger positive affect (PA) whereas lower exam grades would predict stronger negative affect (NA).

In sum, we examined if ideal affect predicts emotional response of PA and NA to an exam outcome, and if it provides incremental prediction over other variables that might plausibly account for affective reactions.

We expected to find the following:

Higher levels of ideal positive affect (ideal PA) will predict greater actual PA response to exam success.
The predictive relationship of ideal PA to success response will remain significant even after controlling for forecasted PA, levels of depression, and actual exam scores.
Higher levels of ideal HAP will predict greater actual PA response to exam success, and even when controlling for forecasted PA, levels of depression, actual exam scores, and ideal LAP.

Incidentally, we could also test an opposing hypothesis for ideal PA—that higher levels of ideal PA would predict smaller PA response to an exam success. The opposing hypotheses is based on some evidence that excessively valuing positive emotions may have paradoxical effects (Ford & Mauss, 2014). By contrast, due to limited empirical work to guide predictions for NA, we took an exploratory approach to our analyses of ideal negative affect (ideal NA) and reactivity to exam failure.

Sampling

Although there were no precise matches in the literature using our predictors and outcomes on which to base sample size estimates, we determined sample size based on Scollon et al. (2009)’s study in which the correlations between baseline ideal affect and affective states were measured. With the effect size of r = .30, the estimated sample size was 82 (G*Power 3 software, α = .05, two-tailed, power set at .80). Considering the potential drop-out rate (presumably 20%) and the equal chance of exam success/failure, we aimed to recruit approximately 200 students.

Participants

Participants were students enrolled in an introductory psychology course of a university in the United States. One hundred ninety-two students participated in the Time 1 (T1) and 185 students completed the Time 2 survey (T2). At T2, one student was excluded for not taking an exam and four students were excluded due to missing data, leaving 180 students for the current study. All 180 students reported that the upcoming exam results were more than somewhat important (above 3 on 5 Likert scale; 1, not at all; 5 extremely important). Participants were mostly females (74.5%), the mean age was 18.80 years (SD = 1.43), and 52.2% Caucasians, 17.9% Hispanic, 8.7% African Americans, 8.5% Asians, and 13% others. All the participants had four to five exams for the course. The third exam was chosen for data collection, so that participants would have a basis for realistic expectations about their grades.

Instruments

Ideal Affect

Ideal PA and NA were measured using a modified Affect Valuation Index (Tsai et al., 2006) with 10 item scales (e.g., Pekrun et al., 2004). The ideal PA items were: enthusiastic, satisfied, excited, joy, elated, relief, calm, at rest, serene, happy. The ideal NA items were: depressed, shame, disappointed, fearful, sluggish, anger, sad, nervous, lonely, boredom. Participants were asked how much they ideally want to feel these emotions in a typical week, on a 5 Likert scale (1, not at all; 5, extremely). Our focal analyses of ideal HAP and ideal LAP focused on a subset of these items. Ideal HAP was the mean of enthusiastic, excited, elated, and joy, based on Fredrickson (1998). Ideal LAP was the mean of relief, calm, at rest and serene, based on Chim et al. (2017).

Forecasted Affect

Our analyses of forecasted affect used the same affective states as the analyses of ideal affect. Participants were asked how much they would feel each emotion if 1) their actual score was similar or better (exam success) and 2) worse (exam failure) than their prediction, on a 5 Likert scale (1, not at all; 5, extremely).

Actual Emotional Response

PA and NA responses likewise used the same set of emotions. On the day when the exam results came out (T2), participants were asked how much they felt each emotion when thinking of their exam score on a 5 Likert scale (1, not at all; 5, extremely).

Depression

Depression was assessed with the Inventory for Depression Diagnosis (the IDD; Zimmerman & Coryell, 1987), a 21 item scale (α=84 in the present sample). The item regarding suicidality was removed due to institutional review board concern. Higher scores on the IDD indicate higher levels of depression during the last two weeks. In the current study, participants’ mean depression score was 14.9 (SD = 8.70; range 1–63), which is relatively high in a college student sample (e.g., Haaga et al., 1993; M = 8.5).

Procedure

One to two weeks before the exam (Time, T1), participants attended an in-person lab study. After providing informed consent, participants were instructed about each questionnaire and told they would receive text messages with the follow-up study link when their exam score was released (Time, T2) and should complete it within 24 hours after they received the text message. To give participants a specific goal, they were asked to report their expected exam score for the following exam. Then, participants completed the IDD, ideal affect and affective forecasting questionnaires. At T2, participants received text messages with the online study link using Qualtrics software. In the online survey, participants were asked to report their exam score and state whether it was similar to/higher than, or lower than their expectations. Next, they were asked to rate their emotional response to the score. To check if participants were responding without reading, there was a mock question of selecting number 3. All the participants correctly chose the number 3.

Exam success was referred to when students reported having achieved their expected grade or better (88 students), whereas exam failure was when students reportedly achieved a lower grade than their expectation (92 students). Table 1 shows the detailed characteristics of the sample per event. Students who had exam success and failure did not differ in terms of sex, ethnicity ratio, age, depression, nor on any ideal affect nor affective forecasting score. Participants with exam success achieved higher exam scores than participants with exam failures, t(178) = 7.73, p < .001. As expected, those who succeeded in the exam reported experiencing more PA, t(178) = 15.19, p < .001, and less NA, t(178) = -8.68, p < .001, than those who failed.

Table 1

*Demographic Characteristics, Mean and Standard Deviations of Variables Per Event*
	Positive event	Negative event
	Mean (SD)	Mean (SD)	t/ χ^2* (p)
Sample size (female; %)	88 (62; 70.5)	92 (73; 79.3)	1.897 (.168) ^*
Caucasian (%)	56.8	46.7	4.419 (.491) ^*
Age	18.72 (1.58)	18.89 (1.31)	− .803 (.423)
IDD total score	14.78 (9.76)	15.13 (7.69)	− .265 (.791)
Ideal PA	4.00 (0.54)	3.99 (0.49)	.144 (.886)
Ideal NA	1.37 (0.47)	1.34 (0.41)	.524 (.601)
Forecasted PA to success	4.00 (0.60)	4.03 (0.58)	− .354 (.724)
Forecasted NA to failure	2.91 (0.75)	3.03 (0.81)	-1.041 (.299)
Exam score at T2	87.23 (8.70)	76.13 (10.43)	7.731 (< .001)
PA response	3.42 (0.90)	1.60 (0.69)	15.192 (< .001)
NA response	1.34 (0.60)	2.33 (0.88)	-8.682 (< .001)
Note: IDD, the Inventory for Depression Diagnosis; PA, positive affect; NA, negative affect.

PA Response to Exam Success

Correlations among variables are displayed in Table 2. To examine whether ideal PA predicted positive affective responses to exam success, a simple regression analysis was run with ideal PA as an IV. Consistent with our first hypothesis, students who had higher level of ideal PA exhibited greater PA responses to exam success, F(1, 86) = 7.73, p = .007, R² = .083, β = .28, t = 2.78, p = .007.

Table 2

*Correlations among Variables in an Exam Success (Lower Left-Hand Side) And an Exam Failure (Upper Right-Hand Side).*
	1	2	3	4	5	6	7	8
1. Depression		− .057	− .031	− .100	.381^**	− .141	.298^*	− .258^*
2. Ideal PA	.074		− .561^**	.236^*	− .135	.049	− .239^*	.272^**
3. Ideal NA	.053	− .736^**		.191	.242^*	.049	.296^**	− .159
4. Forecasted PA to an exam success	.085	.292^**	− .102		.352^**	.194	.124	.262^*
5. Forecasted NA to an exam failure	.471^**	.092	.103	.343^**		− .271^**	.584^**	− .216^*
6. PA response	− .252^*	.283^**	− .387^**	.181	.035		− .498^**	.435^**
7. NA response	.408^**	− .085	.319^**	− .039	.258^*	− .636^**		− .523^**
8. Exam score	− .185	.146	.382^**	− .064	− .254^*	.509^**	− .507^**
Note: Depression, IDD total score; PA, positive affect; NA, negative affect; , p < .05; *, p < .001.

Next, we performed a hierarchical regression analysis to determine whether ideal PA continued to predict responses to exam success even after controlling for other likely predictors. Thus, we entered depression severity, forecasted PA to exam success, and actual exam scores into our model, which now explained about 33.5% of variance in PA response. Importantly, when ideal PA was entered this model, it was a significant predictor, and the R² change was significant, explaining approximately 37.2% of variance in PA response. In sum, the results supported our second hypothesis, with greater ideal PA predicting greater PA response to exam success, even after controlling for other variables. Full details of the regressions are presented in Table 3. Finally, to control the possible effect of ideal NA due to possible multicollinearity with ideal PA, we conducted a stepwise multiple regression with PA response as a DV and depression, forecasted PA, exam score, ideal PA and ideal NA as IVs. The results showed that ideal PA remained significant, β = .24, t = 2.74, p = .007.

Table 3

*Hierarchical Regression Analysis of Predictors of Positive Emotional Response to Exam Success*
Predictors	B	SEB	95% CI	β	t	p	R²	∆ R²	∆F	p
Model 1							.335	.335	14.120	< .001
Depression	− .018	.008	− .035, .182	− .198	-2.181	.032
Forecasted PA	.285	.133	.020, .550	.191	2.136	.036
Exam score	.051	.009	.032, .069	.490	5.398	< .001
Model 2							.372	.037	4.844	.031
Depression	− .020	.008	− .036, − .003	− .215	-2.414	.018
Forecasted PA	.194	.137	− .078, .466	.130	1.417	.160
Exam score	.047	.009	.029, .066	.455	5.047	< .001
Ideal PA	.335	.152	.032, .638	.204	2.201	.031
Note: Depression, IDD total score; F-PA, forcasted positive affect, ; PA, positive affect.

Further, following Chim et al. (2017), a hierarchical regression analysis examined whether ideal HAP predicts responses to exam success, again controlling for other likely predictors. Our first model with depression severity, forecasted PA to exam success, actual exam scores, and ideal LAP as IVs explained about 35.3% of variance in PA response. Importantly, when ideal HAP was entered, ideal HAP and the R² change were both significant, explaining approximately 38.5% of variance in PA response. Thus, results supported our third hypothesis that greater ideal HAP would predict greater PA responses to exam success, even after controlling for other variables. Full details of the analysis are presented in Table 4.

Table 4

*Hierarchical Regression Analysis of Ideal High Arousal Positive Affect Predicting Positive Emotional Response to Exam Success*
Predictors	B	SEB	95% CI	β	t	p	R²	∆ R²	∆F	p
Model 1							.353	.353	11.307	< .001
Depression	− .019	.008	− .036, − .003	.211	-2.326	.022
Forecasted PA	.228	.138	− .046, .502	.153	1.656	.102
Exam score	.049	.009	.030, .067	.469	5.141	< .001
Ideal LAP	.182	.122	− .060, .425	.139	1.497	.138
Model 2							.385	.032	4.303	.041
Depression	− .021	.008	− .037, − .004	− .226	-2.537	.013
Forecasted PA	.176	.137	− .097, .449	.118	1.280	.204
Exam score	.048	.009	.029, .066	.460	5.132	< .001
Ideal LAP	.071	.131	− .189, .332	.054	.545	.587
Ideal HAP	.291	.140	.012, .570	.207	2.074	.041
Note: Depression, IDD total score; F-PA, forcasted positive affect; LAP, low arousal positive affect; HAP, high arousal positive affect.

NA Response to Exam Failure

A simple regression analysis was run with ideal NA as an IV. In this exploratory analysis, higher levels of ideal NA predicted stronger NA response to poor exam performance, F(1, 90) = 8.64, p = .004, R² = .088, β = .29, t = 2.94, p = .004. Next, a hierarchical regression analysis was performed to examine if ideal NA could predict NA response even after controlling for depression, forecasted NA to exam failure, and actual exam scores. This model explained about 50.7% of variance in NA response. After entering ideal NA, the R² change was not significant. Thus, ideal NA did not survive covariation when other predictors were included in the model. Instead, higher levels of forecasted NA and lower exam scores predicted greater NA response to exam failure. Full details of the analyses are presented in Table 5.

Table 5

*Hierarchical Regression Analysis of Predictors of Negative Emotional Response to Exam Failure*
Predictors	B	SEB	95% CI	β	t	p	R²	∆ R²	∆F	p
Model 1							.507	.507	30.106	< .001
Depression	.000	.010	− .019, .019	.002	.029	.977
Forecasted NA	.538	.089	.361, .715	.494	6.044	< .001
Exam score	− .035	.007	− .049, − .022	− .416	-5.315	< .001
Model 2							.520	.014	2.447	.121
Depression	.003	.010	− .016, .022	.024	.283	.788
Forecasted NA	.501	.091	.320, .683	.460	5.486	< .001
Exam score	− .034	.007	− .047, − .021	− .398	-5.078	< .001
Ideal NA	.260	.166	− .070, .590	.122	1.564	.121
Note: Depression, IDD total score; F-NA, forcasted negative affect; NA, negative affect.

In this study, ideal affect predicted future emotional response, using a specific achievement-related event, performance on an exam. In a more rigorous test, we examined the incremental predictive power of ideal affect relative to other variables that might plausibly relate to students’ response to exam outcome. Three main findings stood out. First, higher levels of ideal PA alone predicted greater PA response to exam success. Second, ideal PA continued to predict PA responses to exam success even after controlling for the students’ forecasted affective reactions to success, depression levels, and the students’ actual exam scores. Third, higher levels of ideal HAP predicted greater PA response to exam success even after covariates were included in the model. By contrast, analyses of ideal NA were less conclusive: Although higher levels of ideal NA predicted larger greater NA response to exam failures, but this effect did not survive when similar statistical controls were applied.

These findings have theoretical significance. That ideal PA, and to a lesser extent, ideal NA, predicted affective response to exam outcomes, are consistent with aspects of AVT. Specifically, it meshes with the idea that ideal affect marks an emotional goal that people will pursue, shaping behaviors as well as subsequent emotional reactions to events related to this goal (Tsai, 2007). For example, it is plausible that a person who has a higher valuation of PA and considers an exam important would likely try harder to get good grades, knowing that exam success will bring them PA. Indeed, previous studies showed that those who find certain emotions useful (value the emotions) tend to be motivated to engage in activities that are likely to cause the valued emotions (e.g., Tamir et al., 2007). While this result fits with AVT, it is in tension with findings that extreme levels of ideal happiness are associated with deleterious effects, such as disappointment and higher reported levels of negative affect (e.g., Ford & Mauss, 2014). One possibility is that ideal PA only confers benefits within a certain range, or within specific contexts. This study cannot resolve this issue, given the major differences in design and in how ideal affect was measured across investigations (e.g., ideal PA in general vs. ‘extreme’ happiness valuation).

Notably, ideal PA had additive predictive explanatory value for PA response to exam success, even after accounting for forecasted feelings, depression and actual exam performance. This result is striking, considering the other control variables’ seemingly more proximal relationship to PA responses. Affective forecasts are the closest to the outcome since they are participants’ specific expectations concerning response to the outcome in question. By contrast, ideal affect concerns general valuation, unconnected to any specific outcome. Further, depression is one of the most-studied variables known to influence both positive and negative response (Bylsma et al., 2008). Finally, it is obvious that better actual exam performance should predict the degree of PA to the exam outcome. In this respect, it is remarkable that ideal PA remained a predictor of PA response even after controlling for these variables.

Our focal analyses of ideal HAP and LAP also extended Chim et al. (2016). In that study, the authors found that higher levels of ideal LAP predicted greater enjoyment of LAP-eliciting activities. However, the same result did not hold for ideal HAP. The authors suggested that HAP-eliciting events should involve personal achievements such as an exam success. Using an achievement-related target event, we found that only ideal HAP, not ideal LAP, predicted PA response to an exam success (again after controlling for relevant variables). Thus, consistent with Chim et al. (2017)’s reasoning, personal achievement may be particularly related to ideal HAP, and ideal HAP in these contexts will predict how people will react when goals are achieved.

In this study, greater ideal NA predicted stronger NA responses to an exam failure. One possible interpretation is that people may match their current emotional experience with their ideal affect, regardless of the valence of affect, and even when the outcome is disappointing. Caution is warranted since other competing variables contributed more significantly to NA response to an exam failure, than ideal NA. Specifically, higher levels of forecasted NA, and lower exam score significantly predicted greater NA response to an exam failure, explaining approximately 50% of the variance. Since previous literature mainly focused on errors in affective forecasting (e.g., Gilbert, et al., 1998), we know less about the contexts in which affective forecasts are an accurate predictor emotional response (i.e., are people more accurate at forecasting PA than NA?).

Our study has limitations. Firstly, it could not determine why ideal affect might influence emotional response. The AVT literature proposed two mechanisms that can be tested in future work: behavioral efforts and emotion regulation. Ideal affect can motivate people to pursue a behavior that can bring about their desired emotional state (Millgram et al., 2019; Zhou et al., 2023). Individuals who generally wanted to feel less PA in daily life were less likely to increase their reactions to happy images in a laboratory task (Millgram et al., 2019). A person with higher levels of ideal PA might put more effort in studying in order to achieve a positive event that can bring about their desired emotional state. In the event of exam success, the person may feel happier due to the sense of achievement through greater behavioral efforts, compared to those with low levels of ideal PA. Alternatively, it is also possible that ideal affect may influence emotional response via motivation to match their emotions with ideal affect (Tamir & Gutentag, 2017). For example, a person with lower levels of ideal NA may down-regulate her or his NA response to exam failure to match with their ideal NA states. Future studies should investigate which of the two mechanisms work to explain the influence of ideal PA and PA on affective response. Secondly, although we controlled for a number of variables, we could not include all variables for practical reasons. Most notably, trait affect and cultural orientation (e.g., individualism vs. collectivism) were not assessed, and these should be included in future work.

Despite these limitations, our study was novel in demonstrating that the value that people attach to positive affect can predict how strongly they react to achievement-related events, namely success on an exam, an important and relatively standardized context. Further, results from this study revealed that ideal HAP as well as ideal LAP can predict emotional response to a positive event when the event is achievement-related, which could not be tested in Chim et al (2017). Finally, these results may have implications for education. Since high levels of ideal HAP predict greater PA response to success, ideal HAP might be associated with how much a person values good grades (exam success), which subsequently drives greater efforts to study. Future work is needed to explicitly test these ideas.

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Compliance with Ethical Standards: The current study procedure was approved by the institutional review board of the University of South Florida (Pro 00034232).

Informed Consent: Informed consent was obtained from all participants included in this study.

Funding: No funding was received for conducting this study.

Data Availability Statement: Data used in this study are openly available at https://osf.io/xv7kr/?view_only=2fb90d7c6dbd4b15ba1c75453901c266

Author Contribution: Conceptualization, Methodology, Formal analysis, and Writing-original draft [Sunkyung Yoon]; Conceptualization, Writing-review and editing, and Supervision [Jonathan Rottenberg].

Acknowledgement: None.

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¹Additionally, we examined whether ideal PA predicts PA response to exam failure. Firstly, a simple regression analysis was conducted with ideal PA as an IV. The result showed that ideal PA did not predict PA response to exam failure, F(1, 90) = .21, p = .641. The regression of ideal NA predicting NA response to exam success was not run due to lacking variance on NA response in exam success with kurtosis of 8.52.

No competing interests reported.

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Affect Valuations Predict Emotional Response to Future Exam Success and Failure

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Abstract

Introduction

Methods

Sampling

Participants

Instruments

Ideal Affect

Forecasted Affect

Actual Emotional Response

Depression

Procedure

Results

PA Response to Exam Success

NA Response to Exam Failure

Discussion

Declarations

References

Footnote

Additional Declarations

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