We examine whether the accuracy of people’s predictions about their future emotions is related to the ways they experience their emotions throughout daily life, including individual differences in emotional granularity, emotional complexity, emotional intelligence, and emodiversity. Participants predicted how they would feel while viewing a series of evocative pictures based on descriptions; 1 week later they rated their felt emotion while viewing the pictures. Participants also completed a 7-day experience sampling protocol in which they rated their current emotions throughout the day, and these data were used to derive individual difference measures. Results revealed less prediction error for those higher in emotional intelligence and negative emotional granularity (for negative emotions only), but greater error for those higher in emodiversity. Findings shed light on individual differences in affective forecasting accuracy, a skill that has important implications for people’s daily choices and well-being.
AllenV. D.RahmanN.WeissmanA.MacCannC.LewisC.RobertsR. D. (2015). The Situational Test of Emotional Management–Brief (STEM-B): Development and validation using item response theory and latent class analysis. Personality and Individual Differences, 81, 195–200. https://doi.org/10.1016/j.paid.2015.01.053
2.
AllenV. D.WeissmanA.HellwigS.MacCannC.RobertsR. D. (2014). Development of the situational test of emotional understanding–brief (STEU-B) using item response theory. Personality and Individual Differences, 65, 3–7. https://doi.org/10.1016/j.paid.2014.01.051
3.
BarrettL. F. (2004). Feelings or words? Understanding the content in self-report ratings of experienced emotion. Journal of Personality and Social Psychology, 87(2), 266–281. https://doi.org/10.1037/0022-3514.87.2.266
4.
BarrettL. F.GrossJ.ChristensenT. C.BenvenutoM. (2001). Knowing what you’re feeling and knowing what to do about it: Mapping the relation between emotion differentiation and emotion regulation. Cognition & Emotion, 15(6), 713–724. https://doi.org/10.1080/02699930143000239
5.
CarstensenL. L.PasupathiM.MayrU.NesselroadeJ. R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology, 79(4), 644–655. https://doi.org/10.1037/0022-3514.79.4.644
6.
CarstensenL. L.TuranB.ScheibeS.RamN.Ersner-HershfieldH.Samanez-LarkinG. R.BrooksK. P.NesselroadeJ. R. (2011). Emotional experience improves with age: Evidence based on over 10 years of experience sampling. Psychology and Aging, 26(1), 21–33. https://doi.org/10.1037/a0021285
7.
CattellR. B.CattellA. K. S.RhymerR. M. (1947). P-technique demonstrated in determining psychophysiological source traits in a normal individual. Psychometrika, 12(4), 267–288. https://doi.org/10.1007/BF02288941
8.
DemiralpE.ThompsonR. J.MataJ.JaeggiS. M.BuschkuehlM.BarrettL. F.EllsworthP. C.DemiralpM.Hernandez-GarciaL.DeldinP. J.GotlibI. H.JonidesJ. (2012). Feeling blue or turquoise? Emotional differentiation in major depressive disorder. Psychological Science, 23(11), 1410–1416. https://doi.org/10.1177/0956797612444903
9.
DunnE. W.BrackettM. A.Ashton-JamesC.SchneidermanE.SaloveyP. (2007). On emotionally intelligent time travel: Individual differences in affective forecasting ability. Personality and Social Psychology Bulletin, 33(1), 85–93. https://doi.org/10.1177/0146167206294201
10.
GilbertD. T.WilsonT. D. (2000). Miswanting: Some problems in the forecasting of future affective states. In ForgasJ. P. (Ed.), Feeling and thinking: The role of affect in social cognition (pp. 178–197). Cambridge University Press.
11.
GrühnD.LumleyM. A.DiehlM.Labouvie-ViefG. (2013). Time-based indicators of emotional complexity: Interrelations and correlates. Emotion, 13(2), 226–237. https://psycnet.apa.org/doi/10.1037/a0030363
12.
HalpernJ.ArnoldR. M. (2008). Affective forecasting: An unrecognized challenge in making serious health decisions. Journal of General Internal Medicine, 23(10), 1708–1712. https://doi.org/10.1007/s11606-008-0719-5
13.
HoemannK.KhanZ.FeldmanM. J.NielsonC.DevlinM.DyJ.Feldman-BarettL.WormwoodJ. B.QuigleyK. S. (2020). Context-aware experience sampling reveals the scale of variation in affective experience. Scientific Reports, 10(1), 12459. https://doi.org/10.1038/s41598-020-69180-y
14.
HoemannK.NielsonC.YuenA.GureraJ. W.QuigleyK. S.BarrettL. F. (2021). Expertise in emotion: A scoping review and unifying framework for individual differences in the mental representation of emotional experience. Psychological Bulletin, 147(11), 1159–1183. https://doi.org/10.1037/bul0000327
15.
HoergerM.ChapmanB. P.EpsteinR. M.DubersteinP. R. (2012). Emotional intelligence: A theoretical framework for individual differences in affective forecasting. Emotion, 12(4), 716–725. https://doi.org/10.1037/a0026724
16.
LangP. J.BradleyM. M.CuthbertB. N. (2008). International affective picture system (IAPS): Affective ratings of pictures and instruction manual (Technical report A-8). University of Florida, Gainesville.
17.
LarsenR. J.EidM. (2008). Ed Diener and the science of subjective well-being. In EidM.LarsenR. J. (Eds.), The science of subjective well-being (pp. 1–13). The Guilford Press.
18.
LindquistK. A.BarrettL. F. (2008). Emotional complexity. In LewisM.Haviland-JonesJ. M.BarrettL. F. (Eds.), Handbook of emotions (pp. 513–530). The Guilford Press.
OngA. D.BensonL.ZautraA. J.RamN. (2018). Emodiversity and biomarkers of inflammation. Emotion, 18(1), 3–14. https://doi.org/10.1037/emo0000343
21.
OngA. D.BergemanC. S. (2004). The complexity of emotions in later life. The Journals of Gerontology Series B, 59(3), 117–122. https://doi.org/10.1093/geronb/59.3.P117
22.
PondR. S.Jr.KashdanT. B.DeWallC. N.SavostyanovaA.LambertN. M.FinchamF. D. (2012). Emotion differentiation moderates aggressive tendencies in angry people: A daily diary analysis. Emotion, 12(2), 326–337. https://doi.org/10.1037/a0025762
23.
QuoidbachJ.GruberJ.MikolajczakM.KoganA.KotsouI.NortonM. I. (2014). Emodiversity and the emotional ecosystem. Journal of Experimental Psychology: General, 143(6), 2057–2066. https://doi.org/10.1037/a0038025
24.
ReichJ. W.ZautraA. J.DavisM. (2003). Dimensions of affect relationships: Models and their integrative implications. Review of General Psychology, 7(1), 66–83. https://doi.org/10.1037/1089-2680.7.1.66
25.
SkardS.KnudsenE. S.SjåstadH.ThorbjørnsenH. (2021). How virtual reality influences travel intentions: The role of mental imagery and happiness forecasting. Tourism Management, 87, 104360. https://doi.org/10.1016/j.tourman.2021.104360
26.
TabachnickB. G.FidellL. S. (2018). Using multivariate statistics (7th ed.). Pearson.
27.
WarrinerA. B.KupermanV.BrysbaertM. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45, 1191–1207. https://doi.org/10.3758/s13428-012-0314-x
28.
WilsonT. D.GilbertD. T. (2003). Affective forecasting. In ZannaM. P. (Ed.), Advances in experimental social psychology (Vol. 35, pp. 345–411). Elsevier Academic Press. https://doi.org/10.1016/S0065-2601(03)01006-2
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
