The assessment of emotional clarity via response times to emotion items: shedding light on the response process and its relation to emotion regulation strategies

ABSTRACT

Researchers have begun to use response times (RTs) to emotion items as an indirect measure of emotional clarity. Our first aim was to scrutinise the properties of this RT measure in more detail than previously. To be able to provide recommendations as to whether (and how) emotional intensity – as a possible confound – should be controlled for, we investigated the specific form of the relation between emotional intensity and RTs to emotion items. In particular, we assumed an inverted U-shaped relation at the item level. Moreover, we analysed the RT measure’s convergent validity with respect to individuals’ confidence in their emotion ratings. As a second aim, we compared the predictive validity of emotional clarity measures (RT measure, self-report) with respect to daily emotion regulation. The results of three experience sampling studies showed that the association between emotional intensity and RT followed an inverted U shape. RT was in part related to confidence. Emotional clarity measures were unrelated to reappraisal. There was some evidence that lower emotional clarity was related to a greater use of suppression. The findings highlight that emotional intensity and squared emotional intensity should be controlled for when using the RT measure of emotional clarity in future research.

KEYWORDS:

• Emotional clarity
• response time
• inverted U-shaped time effect
• confidence
• emotion regulation

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. We additionally investigated the RT to binary responses. RT to yes/no responses was positively related to RT to Likert items. The relations between RT to yes/no responses with confidence and emotion regulation were largely the same as for RT to Likert items.

2. First, we excluded RTs that were higher than M + 3 SDs across all RTs of all participants (Sample 1: 1.7% out of 4690 RTs; Sample 2: 1.3% out of 6745 RTs; Sample 3: 0.7% out of 8896 RTs). Next, we computed person-specific cut-off values (intra-individual M + 3 SDs) and excluded RTs that were higher than a participant's cut-off value (Sample 1: 1.9% out of 4609 RTs; Sample 2: 2.3% out of 6656 RTs; Sample 3: 2.3% out of 8831 RTs).

3. Before computing the median across setting items, RT outliers (larger than interindividual M + 3 SDs) were deleted.

4. When group-mean (i.e. person-mean) centring was used instead of grand-mean centring to estimate the “pure” within-persons relation between emotional intensity and RT (see Enders & Tofighi, 2007), very similar results, which led to the same conclusions, emerged.

5. In a few models, convergence problems occurred when random slopes were specified for all predictors, which sometimes happens “when we try to estimate too many random (variance) components that are actually close or equal to zero” (Hox, 2010, p. 42). In this case, we used a forward (instead of a backward) strategy to test the significance of random slopes (i.e. each random slope term was tested separately against the random intercept model).

6. To test whether distributional characteristics of the RTs such as skewness were responsible for the curvilinear relation between emotional intensity and RT (cf. Cohen, Cohen, West, & Aiken, 2003, p. 250), the same multilevel models were also specified with log-transformed RTs and rank-transformed RTs as the dependent variables. In both cases, the inverted U-shaped relation between emotional intensity and RT held.

7. One reason for the differences between Sample 3 and the other samples might be the item branching used in Sample 3 (where the intensity item was presented only when the previous item, asking whether a specific emotion was experienced, was answered affirmatively). Compared with the other two samples that used a nonbranching item format, the frequency of very weak emotional intensity was very small, and thus, the estimation of the left part of the curve may have been less precise than in Samples 1 and 2. However, future research should more closely examine the effects that item branching has on both the response itself (e.g. in terms of the reported intensity levels) and the response time.

8. Additionally, we investigated whether there were changes in RT and emotion regulation over the study time. Multilevel growth curve models revealed that in all three samples, RT to positive and negative emotion items decreased over study time with a stronger decrease at the beginning of the studies. This is plausible because participants get more familiar with the handling of the handheld devices or smartphones and after a few occasions they know which questions are included in the questionnaire. In contrast, emotion regulation did not change over study time. This could be interpreted to show that the repeated assessment of momentary emotional experiences did not act as an emotion regulation intervention in people's daily life.

9. To test whether RT and emotional intensity interacted in the prediction of emotion regulation, we specified moderated multilevel models. There were no significant interactions (|b| ≤ 0.07, |t| ≤ 1.15, p > .05).

Additional information

Funding

Study 1 was supported by the German Research Foundation [grant number LI-1827/1-2].