Abstract
Emotions are inherently complex – situated inside the brain while being influenced by conditions inside the body and outside in the world – resulting in substantial variation in experience. Most studies, however, are not designed to sufficiently sample this variation. In this paper, we discuss what could be discovered if emotion were systematically studied within persons ‘in the wild’, using biologically-triggered experience sampling: a multimodal and deeply idiographic approach to ambulatory sensing that links body and mind across contexts and over time. We outline the rationale for this approach, discuss challenges to its implementation and widespread adoption, and set out opportunities for innovation afforded by emerging technologies. Implementing these innovations will enrich method and theory at the frontier of affective science, propelling the contextually situated study of emotion into the future.
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Emotions are situated and multimodal: they unfold over time as the brain continuously converses with the body and the external world. Given this complexity, instances of the same emotion category (e.g., ‘anger’) are highly variable across individuals as well as within individuals across contexts. This variation is documented by a growing number of studies and meta-analyses of brain activity (e.g., Doyle et al., 2022; Westlin et al., 2023), peripheral physiological activity (e.g., Hoemann et al., 2020; Siegel et al., 2018), facial muscle movements (e.g., Barrett et al., 2019; Durán & Fernández-Dols, 2021) and other behaviors (e.g., Tsai et al., 2006; Wake et al., 2020). Variation also exists in affect (e.g., pleasantness, activation; e.g., Wilson-Mendenhall et al., 2015) and appraisals (e.g., novelty, control; e.g., Kuppens et al., 2003), and variability in all these is magnified by known individual and cultural differences (e.g., Hoemann et al., 2023; Mesquita, 2022). While few would deny the existence of variation in emotion by situation, person, and culture, studies are still rarely designed to look for it (Barrett, 2022). In this paper, we consider what might be discovered if emotion were systematically studied by sampling people deeply ‘in the wild’ using biologically-triggered experience sampling – a multimodal and idiographic approach to ambulatory sensing that links body and mind. We outline the rationale for this approach, propose opportunities for innovation with emerging technologies, and consider the challenges and possibilities this approach brings to the frontier of affective science.
Biologically-Triggered Experience Sampling
Instances of emotion arise as a complex ensemble of features. Some of these features are biological (e.g., physiological and chemical changes, skeletomotor movements), some are mental (e.g., goals, appraisals, affect), and some are contextual (e.g., environmental conditions, social interactions; Barrett, 2022). These features are in constant interplay with one another. For example, changes in immune function influence thoughts and feelings, which in turn influence interpersonal outcomes (for discussion and relevant references, see Barrett, 2017; Shaffer et al., 2022). Interrelationships such as these emerge as the brain coordinates and regulates the internal bodily systems to meet the predicted metabolic needs required to interface with an ever-changing external world – a process known as allostasis (Sterling, 2012). Allostasis is a brain-wide phenomenon (Barrett & Simmons, 2015; Kaplan & Zimmer, 2020; Kleckner et al., 2017; Sennesh et al., 2022; Sterling & Laughlin, 2015) and all experiences (e.g., perceptions, cognitions, emotions) are conditioned on it (Barrett, 2017; Ganzel et al., 2010). The central role of allostasis in emotion also provides an explanation for the observed variation in the features of these experiences within and across individuals: the activity of various bodily systems is being coordinated in a highly context-sensitive manner, to meet immediate situation-specific needs.
This variation has practical implications for study design, measurement, and analysis in affective science. Situation-dependent relationships between biological and mental features require methods that densely sample signals from multiple modalities (e.g., self-report, physiology, movement). However, most of what is known about emotion is based on instances elicited at a single point in time in lab-based settings via stimuli that do not reflect the complex variations and dynamics of the real world. Studies using experience sampling and other ambulatory sensing methods solve this problem by capturing momentary experiences over time and in daily life. In principle, these methods maximize the diversity of experiences that can be observed (Ibanez, 2022; Wilhelm & Grossman, 2010), and make it possible to characterize context- and/or person-specific patterns in features. Yet most experience sampling studies rely on randomly triggered prompts that are not guaranteed to capture instances of emotion or other allostatically-relevant changes in the body and may miss rarer, affectively intense instances.
Biologically-triggered experience sampling addresses this gap by assessing mental and contextual features at moments when there are notable changes in ongoing biological activity. By initiating sampling based on these shifts in bodily conditions, this approach can more selectively target instances of emotion as they occur throughout daily life. In our own proof-of-concept study (Hoemann et al., 2020), continuous electrocardiography (ECG), impedance cardiography (ICG), and accelerometry were collected and used to trigger prompts whenever an integrated smartphone app detected sustained increases or decreases in the time interval between heartbeats (interbeat interval; IBI) in the absence of major movement or posture change. At each prompt, participants reported on their current experience, including freely-generated emotion labels and rated valence and arousal. Consistent with past work showing pervasive variation in emotion, our unsupervised machine learning analyses revealed patterns of physiological change that varied across individuals and that mapped in a many-to-many relationship with emotion words and affect ratings (Hoemann et al., 2020). Other recent studies have examined triggering prompts based on increases in electrodermal activity (EDA; Van Halem et al., 2020), decreases in heart rate variability (HRV; Schwerdtfeger & Rominger, 2021), and motoric features such as posture and gait (Giurgiu et al., 2020; Kanning et al., 2021).
Opportunities for Innovation
Relative to existing lab-based and ambulatory approaches, biologically-triggered experience sampling generates longitudinal, within-person data sets that integrate multiple modalities around biological changes of interest. The field has much to gain from this approach, especially as new measures become available and easier to use. In this section, we highlight two opportunities for innovation, where biologically-triggered experience sampling can make a substantive contribution to understanding emotion.
First, increasing the number of modalities sampled will make it possible to model emotion in higher dimensionality, and answer fundamental questions about how biological, mental, and contextual features are related over time. Our initial implementation of biologically-triggered experience sampling, for example, could be augmented by existing and emerging ambulatory measures to track meaningful emotional changes in daily life. Table 1 provides example technologies and the feature(s) monitored by each. These additions can also push forward translational and clinical research. Because the brain continually performs allostasis and all experience relies on this basic process, allostatic dysregulation is coming to be understood as a transdiagnostic vulnerability to mental and physical disorders. Consistent with this hypothesis, growing evidence demonstrates that symptoms of major depressive disorder (e.g., distress, context insensitivity, motor retardation) are associated with persistent problems in energy regulation (Shaffer et al., 2022; see also Tian et al., 2023). Coupling ambulatory sensing technologies that assess immunologic and metabolic function (Table 1) with those assessing peripheral physiological (e.g., cardiovascular) activity can further the longitudinal and situated study of brain-body health.
Second, innovations in biologically-triggered experience sampling offer a unique opportunity to create and test real-time, person- and context-specific interventions, which can revolutionize the detection and management of stress or even clinical symptomology. Recent studies have combined EDA with skin temperature data to predict stress responses in situ (Kyriakou et al., 2019), used HRV to identify moments when there may be heightened psychological vulnerability (Schwerdtfeger & Rominger, 2021), and tracked self-reported stress in everyday life alongside smartphone-estimated blood pressure (Gordon & Mendes, 2021). These innovations enable targeted sampling of stressful experiences, and can also inform the delivery of just-in-time adaptive interventions (e.g., Nahum-Shani et al., 2018; Schneider et al., 2023). Using biological signals to push content when people are exhibiting increased physiological arousal can supply tools when they may be most effective. Such interventions could ultimately scaffold people’s flexible application of emotion regulatory strategies, by helping them use the right tool for the situation (Blanke et al., 2020; Kalokerinos et al., 2019). If content included information about ongoing biological activity, this may enhance skills such as emotional granularity (i.e., emotion differentiation) by making people aware of features they can use to distinguish different types of emotion experiences in different contexts (Hoemann, Nielson et al., 2021).
Challenges and Possibilities
Biologically-triggered experience sampling involves practical, methodological and pragmatic challenges. Table 2 outlines considerations for key decision points in study design and data analysis. More generally, a multimodal and idiographic approach to ambulatory sensing requires an interdisciplinary research team, well-organized pipelines for processing, integrating and curating data, and participants who are willing to be intensively sampled and maybe even interested in the results (e.g., Gordon & Mendes, 2021). It also requires augmenting the funding mechanisms that support this research vision, creating different incentive structures within the practice of science, and changing the ways we train the next generation of scientists. The scale of these challenges requires system-level innovation and so requires buy-in from the field writ large.
Human experience emerges amid dynamically changing signal arrays in the brain, in the body, and from the world. Sampling and modeling this landscape are challenging, but necessary to understand the nature of emotion. While each of the innovations outlined above adds incremental value to the study of emotion in the wild, their real power will be realized from implementing them in concert. A robust, generalizable science of emotion requires sampling individuals across multiple situations in daily life beyond the standardized and restricted experiences evoked in the lab, measuring deeply by simultaneously monitoring mental (e.g., affect, appraisals), biological (e.g., metabolic activity, posture), and contextual features (e.g., ambient temperature, social interaction), and leveraging analytic approaches that capture complex yet reliable patterns of feature variation. Such an approach is required if we are to determine how different features structure the variation in emotion, allowing affective science to reveal generalities rather than presume them.
References
Barrett, L. F. (2017). The theory of constructed emotion: An active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(1), 1–23. https://doi.org/10.1093/scan/nsw154
Barrett, L. F. (2022). Context reconsidered: Complex signal ensembles, relational meaning, and population thinking in psychological science. American Psychologist, 77(8), 894–920. https://doi.org/10.1037/amp0001054
Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest, 20(1), 1–68. https://doi.org/10.1177/1529100619832930
Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews Neuroscience, 16(7), 419–429. https://doi.org/10.1038/nrn3950
Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1994). Autonomic cardiac control, I: Estimation and validation from pharmacological blockades. Psychophysiology, 31(6), 572–585. https://doi.org/10.1111/j.1469-8986.1994.tb02350.x
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
Blanke, E. S., Brose, A., Kalokerinos, E. K., Erbas, Y., Riediger, M., & Kuppens, P. (2020). Mix it to fix it: Emotion regulation variability in daily life. Emotion, 20(3), 473–485. https://doi.org/10.1037/emo0000566
Blei, D. M., & Jordan, M. I. (2006). Variational inference for Dirichlet process mixtures. Bayesian Analysis, 1(1), 121–143. https://doi.org/10.1214/06-ba104
Bleichner, M. G., & Debener, S. (2017). Concealed, unobtrusive ear-centered EEG acquisition: CEEGrids for transparent EEG. Frontiers in Human Neuroscience, 11, 163. https://doi.org/10.3389/fnhum.2017.00163
Bradley, M. M., & Lang, P. J. (2000). Measuring emotion: Behavior, feeling, and physiology. In R. D. Lane & L. Nadel (Eds.), Cognitive neuroscience of emotion (pp. 242–276). Oxford University Press.
Coco, M. I., Mønster, D., Leonardi, G., Dale, R., & Wallot, S. (2021). Unidimensional and multidimensional methods for recurrence quantification analysis with crqa. The R Journal, 13(1), 143–165. https://doi.org/10.32614/RJ-2021-062
Dillen, N., Ilievski, M., Law, E., Nacke, L. E., Czarnecki, K., & Schneider, O. (2020). Keep calm and ride along: Passenger comfort and anxiety as physiological responses to autonomous driving styles. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3313831.3376247
Doyle, C. M., Lane, S. T., Brooks, J. A., Wilkins, R. W., Gates, K. M., & Lindquist, K. A. (2022). Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion. Social Cognitive and Affective Neuroscience, 17(11), 995–1006. https://doi.org/10.1093/scan/nsac028
Durán, J. I., & Fernández-Dols, J.-M. (2021). Do emotions result in their predicted facial expressions? A meta-analysis of studies on the co-occurrence of expression and emotion. Emotion, 21(7), 1550–1569. https://doi.org/10.1037/emo0001015
Fan, M., Chou, C.-A., Yen, S.-C., & Lin, Y. (2019). A network-based multimodal data fusion approach for characterizing dynamic multimodal physiological patterns (arXiv:1901.00877). arXiv. http://arxiv.org/abs/1901.00877 . Accessed 4 Mar 2023.
Ganzel, B. L., Morris, P. A., & Wethington, E. (2010). Allostasis and the human brain: Integrating models of stress from the social and life sciences. Psychological Review, 117(1), 134–174.
Ghaffari, R., Yang, D. S., Kim, J., Mansour, A., Wright, J. A., Model, J. B., Wright, D. E., Rogers, J. A., & Ray, T. R. (2021). State of sweat: Emerging wearable systems for real-time, noninvasive sweat sensing and analytics. ACS Sensors, 6(8), 2787–2801. https://doi.org/10.1021/acssensors.1c01133
Giurgiu, M., Niermann, C., Ebner-Priemer, U., & Kanning, M. (2020). Accuracy of sedentary behavior–triggered ecological momentary assessment for collecting contextual information: Development and feasibility study. JMIR MHealth and UHealth, 8(9), e17852. https://doi.org/10.2196/17852
Gordon, A. M., & Mendes, W. B. (2021). A large-scale study of stress, emotions, and blood pressure in daily life using a digital platform. Proceedings of the National Academy of Sciences, 118(31), e2105573118. https://doi.org/10.1073/pnas.2105573118
Haddad, M., Hermassi, S., Aganovic, Z., Dalansi, F., Kharbach, M., Mohamed, A. O., & Bibi, K. W. (2020). Ecological validation and reliability of hexoskin wearable body metrics tool in measuring pre-exercise and peak heart rate during shuttle run test in professional handball players. Frontiers in Physiology, 11, 957. https://doi.org/10.3389/fphys.2020.00957
Harari, G. M., Müller, S. R., Aung, M. S., & Rentfrow, P. J. (2017). Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences, 18, 83–90. https://doi.org/10.1016/j.cobeha.2017.07.018
Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Estimating time-varying mixed graphical models in high-dimensional data. Journal of Statistical Software, 93(8), 1–46. https://doi.org/10.18637/jss.v093.i08
Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020). Context-aware experience sampling reveals the scale of variation in affective experience. Scientific Reports, 10, 12459. https://doi.org/10.1038/s41598-020-69180-y
Hoemann, K., Khan, Z., Kamona, N., Dy, J., Barrett, L. F., & Quigley, K. S. (2021). Investigating the relationship between emotional granularity and cardiorespiratory physiological activity in daily life. Psychophysiology, 58(6), e13818. https://doi.org/10.1111/psyp.13818
Hoemann, K., Nielson, C., Yuen, A., Gurera, J. W., Quigley, K. S., & Barrett, L. 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
Hoemann, K., Lee, Y., Kuppens, P., Gendron, M., & Boyd, R. L. (2023). Emotional granularity is associated with daily experiential diversity. Affective Science. https://doi.org/10.1007/s42761-023-00185-2
Ibanez, A. (2022). The mind’s golden cage and cognition in the wild. Trends in Cognitive Sciences, 26(12), 1031–1034. https://doi.org/10.1016/j.tics.2022.07.008
Kalokerinos, E. K., Erbas, Y., Ceulemans, E., & Kuppens, P. (2019). Differentiate to regulate: Low negative emotion differentiation is associated with ineffective use but not selection of emotion-regulation strategies. Psychological Science, 30(6), 863–879. https://doi.org/10.1177/0956797619838763
Kanning, M., Niermann, C., Ebner-Primer, U., & Giurgiu, M. (2021). The context matters - not all prolonged sitting bouts are equally related to momentary affective states: An ambulatory assessment with sedentary-triggered E-diaries. International Journal of Behavioral Nutrition and Physical Activity, 18(1), 106. https://doi.org/10.1186/s12966-021-01170-3
Kaplan, H. S., & Zimmer, M. (2020). Brain-wide representations of ongoing behavior: A universal principle? Current Opinion in Neurobiology, 64, 60–69. https://doi.org/10.1016/j.conb.2020.02.008
Kappeler-Setz, C., Gravenhorst, F., Schumm, J., Arnrich, B., & Tröster, G. (2013). Towards long term monitoring of electrodermal activity in daily life. Personal and Ubiquitous Computing, 17(2), 261–271. https://doi.org/10.1007/s00779-011-0463-4
Kleckner, I. R., Zhang, J., Touroutoglou, A., Chanes, L., Xia, C., Simmons, W. K., Quigley, K. S., Dickerson, B. C., & Barrett, L. F. (2017). Evidence for a large-scale brain system supporting allostasis and interoception in humans. Nature Human Behaviour, 1(5), 0069. https://doi.org/10.1038/s41562-017-0069
Kuppens, P., Van Mechelen, I., Smits, D. J., & De Boeck, P. (2003). The appraisal basis of anger: Specificity, necessity and sufficiency of components. Emotion, 3(3), 254–269. https://doi.org/10.1037/1528-3542.3.3.254
Kyriakou, K., Resch, B., Sagl, G., Petutschnig, A., Werner, C., Niederseer, D., Liedlgruber, M., Wilhelm, F. H., Osborne, T., & Pykett, J. (2019). Detecting moments of stress from measurements of wearable physiological sensors. Sensors, 19(17), Article 17. https://doi.org/10.3390/s19173805
Liao, Y., & Schembre, S. (2018). Acceptability of continuous glucose monitoring in free-living healthy individuals: Implications for the use of wearable biosensors in diet and physical activity research. JMIR MHealth and UHealth, 6(10), e11181. https://doi.org/10.2196/11181
Lodewyckx, T., Tuerlinckx, F., Kuppens, P., Allen, N. B., & Sheeber, L. (2011). A hierarchical state space approach to affective dynamics. Journal of Mathematical Psychology, 55(1), 68–83. https://doi.org/10.1016/j.jmp.2010.08.004
Ma, Q., Mermelstein, R. J., & Hedeker, D. (2022). A shared-parameter location-scale mixed model to link the responsivity in self-initiated event reports and the event-contingent Ecological Momentary Assessments. Statistics in Medicine, 41(10), 1780–1796. https://doi.org/10.1002/sim.9328
Mesquita, B. (2022). Between us: How cultures create emotions. Norton.
Monti, A., Porciello, G., Panasiti, M. S., & Aglioti, S. M. (2021). Gut markers of bodily self-consciousness (p. 2021.03.05.434072). bioRxiv. https://doi.org/10.1101/2021.03.05.434072
Nabian, M., Yin, Y., Wormwood, J., Quigley, K. S., Barrett, L. F., & Ostadabbas, S. (2018). An open-source feature extraction tool for the analysis of peripheral physiological data. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1–11. https://doi.org/10.1109/JTEHM.2018.2878000
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52(6), 446–462. https://doi.org/10.1007/s12160-016-9830-8
Obrist, P. A., Webb, R. A., Sutterer, J. R., & Howard, J. L. (1970). The cardiac-somatic relationship: Some reformulations. Psychophysiology, 6(5), 569–587. https://doi.org/10.1111/j.1469-8986.1970.tb02246.x
Rahman, M. M., Xu, X., Nathan, V., Ahmed, T., Ahmed, M. Y., McCaffrey, D., Kuang, J., Cowell, T., Moore, J., Mendes, W. B., & Gao, J. A. (2022). Detecting physiological responses using multimodal earbud sensors. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 01–05. https://doi.org/10.1109/EMBC48229.2022.9871569
Rominger, C., & Schwerdtfeger, A. R. (2022). Feelings from the heart part II: Simulation and validation of static and dynamic HRV decrease-trigger algorithms to detect stress in firefighters. Sensors, 22(8), Article 8. https://doi.org/10.3390/s22082925
Schneider, S., Junghaenel, D. U., Smyth, J. M., Fred Wen, C. K., & Stone, A. A. (2023). Just-in-time adaptive ecological momentary assessment (JITA-EMA). Behavior Research Methods, 1–19. https://doi.org/10.3758/s13428-023-02083-8
Scholz, L., Ortiz Perez, A., Bierer, B., Eaksen, P., Wöllenstein, J., & Palzer, S. (2017). Miniature low-cost carbon dioxide sensor for mobile devices. IEEE Sensors Journal, 17(9), 2889–2895. https://doi.org/10.1109/JSEN.2017.2682638
Schwerdtfeger, A. R., & Rominger, C. (2021). Feelings from the heart: Developing HRV decrease-trigger algorithms via multilevel hyperplane simulation to detect psychosocially meaningful episodes in everyday life. Psychophysiology, 58(11), e13914. https://doi.org/10.1111/psyp.13914
Sennesh, E., Theriault, J., Brooks, D., van de Meent, J.-W., Barrett, L. F., & Quigley, K. S. (2022). Interoception as modeling, allostasis as control. Biological Psychology, 167, 108242. https://doi.org/10.1016/j.biopsycho.2021.108242
Shaffer, C., Westlin, C., Quigley, K. S., Whitfield-Gabrieli, S., & Barrett, L. F. (2022). Allostasis, action, and affect in depression: Insights from the theory of constructed emotion. Annual Review of Clinical Psychology, 18(1), 553–580. https://doi.org/10.1146/annurev-clinpsy-081219-115627
Siegel, E. H., Sands, M. K., Van den Noortgate, W., Condon, P., Chang, Y., Dy, J., Quigley, K. S., & Barrett, L. F. (2018). Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories. Psychological Bulletin, 144(4), 343–393. https://doi.org/10.1037/bul0000128
Snippe, E., Smit, A. C., Kuppens, P., Burger, H., & Ceulemans, E. (2023). Recurrence of depression can be foreseen by monitoring mental states with statistical process control. Journal of Psychopathology and Clinical Science, 132, 145–155. https://doi.org/10.1037/abn0000812
Sterling, P. (2012). Allostasis: A model of predictive regulation. Physiology and Behavior, 106(1), 5–15. https://doi.org/10.1016/j.physbeh.2011.06.004
Sterling, P., & Laughlin, S. (2015). Principles of neural design. MIT Press.
Tian, Y. E., Di Biase, M. A., Mosley, P. E., Lupton, M. K., Xia, Y., Fripp, J., Breakspear, M., Cropley, V., & Zalesky, A. (2023). Evaluation of brain-body health in individuals with common neuropsychiatric disorders. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2023.0791
Tsai, J. L., Levenson, R. W., & McCoy, K. (2006). Cultural and temperamental variation in emotional response. Emotion, 6(3), 484–497. https://doi.org/10.1037/1528-3542.6.3.484
Van Halem, S., Roekel, E., Kroencke, L., Kuper, N., & Denissen, J. (2020). Moments that matter? On the complexity of using triggers based on skin conductance to sample arousing events within an experience sampling framework. European Journal of Personality, 34(5), 794–807. https://doi.org/10.1002/per.2252
Wake, S., Wormwood, J., & Satpute, A. B. (2020). The influence of fear on risk taking: A meta-analysis. Cognition and Emotion, 34(6), 1143–1159. https://doi.org/10.1080/02699931.2020.1731428
Westlin, C., Theriault, J. E., Katsumi, Y., Nieto-Castanon, A., Kucyi, A., Ruf, S. F., Brown, S. M., Pavel, M., Erdogmus, D., Brooks, D. H., Quigley, K. S., Whitfield-Gabrieli, S., & Barrett, L. F. (2023). Improving the study of brain-behavior relationships by revisiting basic assumptions. Trends in Cognitive Sciences, 27(3), 246–257. https://doi.org/10.1016/j.tics.2022.12.015
Wilhelm, F. H., & Grossman, P. (2010). Emotions beyond the laboratory: Theoretical fundaments, study design, and analytic strategies for advanced ambulatory assessment. Biological Psychology, 84(3), 552–569. https://doi.org/10.1016/j.biopsycho.2010.01.017
Wilson-Mendenhall, C. D., Barrett, L. F., & Barsalou, L. W. (2015). Variety in emotional life: Within-category typicality of emotional experiences is associated with neural activity in large-scale brain networks. Social Cognitive and Affective Neuroscience, 10(1), 62–71. https://doi.org/10.1093/scan/nsu037
Wrzus, C., & Neubauer, A. B. (2022). Ecological momentary assessment: A meta-analysis on designs, samples, and compliance across research fields. Assessment, 30(3), 825–846. https://doi.org/10.1177/10731911211067538
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K.H. was supported by the Research Foundation – Flanders (FWO; 12A3923N). K.S.Q., L.F.B., and J.B.W. were supported by the U.S. Army Research Institute for the Behavioral and Social Sciences (W911NF-16-1-019). K.S.Q. and L.F.B. were also supported by the National Cancer Institute (R01 CA258269-01), the National Institute of Mental Health (R01 MH113234, R01 MH109464), the National Institute on Aging (R01AG071173), and the Unlikely Collaborators Foundation. The views, opinions, and/or findings contained in this review are those of the authors and shall not be construed as an official Department of the Army position, policy, or decision, unless so designated by other documents, nor do they necessarily reflect the views of the Unlikely Collaborators Foundation.
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Hoemann, K., Wormwood, J.B., Barrett, L.F. et al. Multimodal, Idiographic Ambulatory Sensing Will Transform our Understanding of Emotion. Affec Sci 4, 480–486 (2023). https://doi.org/10.1007/s42761-023-00206-0
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DOI: https://doi.org/10.1007/s42761-023-00206-0