Measuring Arousal: Promises and Pitfalls.

Measuring Arousal: Promises and Pitfalls - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Affect Sci . 2024 Dec 20;6(2):369–379. doi: 10.1007/s42761-024-00288-4 Measuring Arousal: Promises and Pitfalls Tess Reid 1, ✉ , Catie Nielson 2 , Jolie B Wormwood 1 Author information Article notes Copyright and License information 1 Department of Psychology, University of New Hampshire, Durham, NH 03824 USA 2 Department of Psychology, Northeastern University, Boston, MA 02115 USA Handling editor: Luiz Pessoa ✉ Corresponding author. Received 2024 Jul 2; Accepted 2024 Nov 26; Collection date 2025 Jun. © The Society for Affective Science 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. PMC Copyright notice PMCID: PMC12209062  PMID: 40605947 Abstract The concept of arousal is ubiquitous and has been suggested as a critical component of many mental and physical phenomena, including emotion, behavior, motivation, learning, attention, motor action, and neural activity. In this review, we discuss theoretical, empirical, and analytic challenges to defining and measuring arousal and suggest avenues for future research to address these challenges. We begin by contesting the typically implicit (but occasionally explicit) assumption in the literature that arousal is a unitary construct representing a generalized, organism-wide phenomenon—an assumption evidenced by the common use of a measure from one arousal domain (e.g., motor action or autonomic activity) as a proxy for arousal in another (e.g., subjective experience). We demonstrate that this assumption is not supported by empirical evidence, and that instead arousal is best conceptualized as multidimensional, comprising multiple factors that can manifest variably both within and across individuals. We then discuss the implications, promises, and pitfalls of adopting this perspective for future research on arousal. We argue that it necessitates employing novel methodological approaches, including highly multimodal data collection from individuals across multiple contexts, preferably in variable real-world settings. It also necessitates bringing innovative data-driven analytic techniques to bear. We conclude that future research stands to dramatically reshape the scientific understanding of arousal by conceptualizing it as multifaceted with a manifestation that is variable in the moment and by leveraging emerging technologies and analyses to improve its measurement. Keywords: Arousal, Peripheral physiology, Autonomic nervous system, Multi-modal, Ideographic, Data driven Measuring Arousal: Promises and Pitfalls The concept of arousal is ubiquitous, and it has been suggested as a critical component of many mental and physical phenomena, including emotion (e.g., Bliss-Moreau et al., 2020 ; Russell & Barrett, 1999 ), behavior (e.g., de Gee et al., 2017 ; Schachter & Singer, 1962 ), motivation (e.g., Hepach et al., 2019 ; Yerkes & Dodson, 1908 ), learning (e.g., Gray, 1977 ; Storbeck & Clore, 2008 ), attention (de Barbaro et al., 2017 ; Pribram & McGuinness, 1975 ), motor action (e.g., Burgess & Peever, 2013 )—including exercise (e.g., Lambourne & Tomporowski, 2010 )—and neural activity (e.g., Lang, 2014 ; Pfaff et al., 2008 ). Despite widespread usage, however, the term “arousal” remains ill-defined (Lindquist et al., 2016 ; Satpute et al., 2019 ). The present review provides a critical analysis of the current arousal literature, contesting the common conceptualization of arousal as a static, unitary construct. We first summarize evidence that contests this unitary view, showing that different measures or operationalizations of arousal (e.g., heart rate and sympathetic nervous system activity) vary in terms of their association with one another both across individuals in a given context and within the same individuals measured across contexts. Impelled by the demonstrable shortcomings of existing conceptualizations, we endorse an approach to studying arousal as multifaceted with a manifestation that is variable in the moment. Such an approach shifts the focus from whether or not a particular stimulus is arousing to the more nuanced questions of for whom, in which ways, and under what circumstances it is arousing. We conclude by discussing the methodological and analytic implications of this conceptual shift for the study of arousal and highlight the exciting and fruitful avenues for future research in affective science that it affords. Current Measurement and Conceptualizations: Evidence from Peripheral Physiology Arousal has been defined and operationalized across at least four different domains, including subjective arousal, behavioral or motoric arousal, peripheral physiological arousal, and central nervous system arousal. Across these domains, a variety of specific measures have been suggested to have a one-to-one mapping to the construct of arousal (see Table 1 ). Even if not intended, labelling these disparate measures as reflecting “arousal” implies that they are potentially interchangeable measures of the same latent construct. Indeed, some researchers have explicitly argued for a construct of generalized arousal (e.g., Duffy, 1957 ; Gray, 1970 ; Pfaff et al., 2008 ). For example, Pfaff and colleagues (2008) define arousal as unitary and organism-wide, i.e., as “a primitive, fundamental force that fuels cortical, autonomic and endocrine arousal in a manner that activates behavior” (Pfaff et al., 2008 ; p. 11). The conceptualization of arousal as unitary and organism-wide has already met with strong criticism (Aston-Jones & Cohen, 2005 ; Berridge & Waterhouse, 2003 ; Mandler, 1975 ; Robbins, 1997 ; Satpute et al., 2019 ), and yet the idea of generalized arousal has persisted in both the design and execution of studies and in how researchers interpret and integrate their findings with existing knowledge. Table 1. Example measures of arousal across domains Domain Example measures Example papers Autonomic nervous system • Increased electrodermal activity (EDA, eccrine sweat gland activity) • Picard et al., 2016 (tonic) • Gavazzeni et al., 2008 (phasic) • Increased heart rate or decreased heart period or interbeat interval • Konvalinka et al., 2011 (HR) • Decreased pre-ejection period (PEP) • Yim et al., 2015 • Increased pupil dilation + • Wang et al., 2018 Central nervous system • More activity in the locus coeruleus–noradrenergic system • Berridge & Waterhouse, 2003 • Greater EEG oscillation amplitude • Stenberg, 1992 • Greater ERP amplitudes • Lithari et al., 2010 • Single-neuron activity • Pfaff et al., 2008 • Increased thalamic and frontomedial activity measured via fMRI • Anders et al., 2004 Endocrine • Increased salivary alpha amylase (sAA) • Carr et al., 2016 • Increased hypocretin • Winsky-Sommerer et al., 2005 ( wakeful arousal ) Behavior • Changes in reaction time • Sagaspe et al., 2011 (increased) • Eason et al., 1969 (decreased) • Increased motoric activity (e.g., finger movement, smiling, fidgeting) • Malmo, 1957 • Schachter & Singer, 1962 • Increased computer mouse movement • Grimes et al., 2013 • Gait speed changes • Bouman et al., 2015 (increased) • Delbaere et al., 2009 (decreased) Subjective experience • Self-reported experience of arousal • Hoemann et al., 2020 • Self-assessment manikin (SAM) • Bradley & Lang, 1994 Open in a new tab + Also used as an indicator of central nervous system activity in some studies (e.g., Laeng et al., 2012 ; van der Wel & van Steenbergen, 2018 ) To demonstrate how empirical findings are inconsistent with a conceptualization of arousal as unitary and organism-wide, we focus here on autonomic nervous system (ANS) activity given the abundance of arousal research in this area and its relevance to affective science, though the points made are not unique to this measurement domain. In the domain of peripheral physiology, researchers have routinely operationalized arousal using one or more measures of ANS activity (e.g., Bradley et al., 2001 ; Lang et al., 1993 ; Picard et al., 2016 ). For example, many studies have used heart rate (or its inverse, heart period) as a measure of arousal (e.g., Konvalinka et al., 2011 ; Marx & Soler-Baillo, 2005 ). However, heart rate is multiply determined, meaning that a given heart rate change can be caused by different patterns of activation and withdrawal in the two branches of the ANS (Iwata & LeDoux, 1988 ): the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). Despite the enduring view that the SNS and PNS operate reciprocally, it is evident that they can (and frequently do) act independently of one another (see Berntson et al., 1991 , 1993 , 1994 ; Morrison, 2001 ; Hoemann et al., 2020 ). For example, while an increase in heart rate can be caused by an increase in SNS activity and a withdrawal in PNS activity (e.g., reciprocal response), it could also be caused mainly by SNS activation in isolation or by PNS withdrawal in isolation or some other combination of activity across these branches. That is, the multidetermined nature of heart rate undermines its utility as a measure of arousal, necessarily implying that it does not consistently covary with other physiological activity thought to index arousal (e.g., SNS activity). In an attempt to achieve greater physiological or autonomic specificity, some researchers have focused on measures that more precisely reflect only SNS activity to assess arousal (e.g., Bradley et al., 2008 ; Picard et al., 2016 ). For example, electrodermal activity (EDA, activity of the eccrine sweat glands) has often been used to operationalize arousal (e.g., Bradley et al., 2001 ; Fisher et al., 2010 ; Lang, 2014 ; Lang et al., 1993 ; Picard et al., 2016 ) on the premise that eccrine sweat gland activity is controlled exclusively via sympathetic innervation (Boucsein, 2012 ). However, even measures of EDA recorded simultaneously across regions of the body do not always covary (Picard et al., 2016 ; Van Dooren et al., 2012 ). In fact, EDA activity recorded from the fingers shows low correlation with simultaneous EDA activity recorded from the neck or back (Van Dooren et al., 2012 ), and even EDA recorded bilaterally from the same body part (e.g., wrist) frequently diverges (e.g., right and left wrist; Picard et al., 2016 ). Moreover, there are several other SNS measures used to index arousal, including pre-ejection period (PEP, an inverse estimate of sympathetic activity reflected in cardiac contractility, e.g., Kelsey, 1991 ), salivary alpha-amylase (sAA, e.g., Carr et al., 2016 ; Beltzer et al., 2014 ), and pupil dilation (e.g., Bradley et al., 2008 ; Urai et al., 2017 ), and changes in EDA do not always covary with changes in these other SNS measures (e.g., PEP: Wormwood et al., 2019 ; Tomaka et al., 1993 ; sAA: Fisher et al., 2010 ; pupil dilation: Wang et al., 2018 ; Leuchs et al., 2018 ; Jentsch et al., 2020 ). Likewise, these SNS measures also do not consistently covary with one another (e.g., PEP and sAA: Nagy et al., 2015 ; Yim et al., 2015 ; e.g., sAA and pupil dilation: Nielsen & Mather, 2015 ; Warren et al., 2019 ), further illustrating the idea that SNS activity can be region or organ specific (Berntson et al., 1991 ). This lack of concordance between various ANS measures suggests that even if arousal can be assessed with ANS measures, there is no system-wide, generalized arousal that is driving changes in such measures. Critically, although we focus here on examples using ANS measures, this same lack of covariation across measures has also been documented in other measurement domains (see, e.g., neural: Satpute et al., 2019 , and behavioral: Malmo, 1957 ). Another indicator that two operationalizations are reliable and valid measures of the same underlying construct is that their association is consistent over time and across contexts. That is, two measures of arousal, even if from different domains, should covary not just in a given context but across contexts as well. However, the empirical evidence also demonstrates a significant lack of consistent covariation among potential measures of arousal across contexts. For example, different physical contexts (e.g., one posture vs. another or a moving person vs. one that is still) result in variability in the relationship between measures of arousal across domains, even for the same person. This is because physical demands (e.g., in different tasks) have an impact on the individual’s goals and metabolic needs, which in turn affect physiology in disparate ways (see Obrist, 1968 ; Obrist et al., 1970 ). Illustrative studies demonstrate how posture affects CNS measures of arousal such that participants demonstrated greater cortical asymmetry (using an EEG-based measures of cortical asymmetry as a measure of cortical arousal) with an upright or forward-leaning posture during viewing of evocative stimuli (e.g., an anger provocation vs. pictures of food) compared to lying down, despite no reported differences in subjective experience (Harmon-Jones & Peterson, 2009 ; see also Price & Harmon-Jones, 2010 ; Harmon-Jones et al., 2011 ). Thus, even for the same measure of arousal (cortical asymmetry), there appears to be inconsistency in its activation across different physical contexts and in its relation to subjective experiences of arousal. Even in a relatively constricted physical context such as high intensity exercise, the relationship between physiological and self-reported arousal can vary depending on within-individual (e.g., glycogen loaded vs. glycogen depleted, Hartman et al., 2018 ) and between-individual differences (for review, see Ekkekakis et al., 2011 ). Just as physical contexts can impact measures of physiological arousal and their associations with other measures of arousal, so too can different psychological contexts. Some researchers have assumed that contexts which are normed as “high arousal” push individuals towards consistent goals (e.g., of alarm or escape), thereby leading to highly covarying physiological activity and subjective experience (e.g., Brown et al., 2020 ; Mauss et al., 2004 ). However, even in highly evocative situations, an individual’s psychological context (e.g., goals, level of motivation, cognitive load, attentiveness, fatigue, desire for social cohesion) can vary widely, either due to task factors or individual differences. For example, even when attempting to hold subjective (normative) arousal constant, Bradley et al. ( 2001 ) observed that relationships between physiological variables were not stable. Specifically, there were different physiological patterns across multiple measures (i.e., EDA, heart rate, and startle blink) when participants viewed negative, high arousal pictures, dependent on the semantic content of the pictures (i.e., pictures of an attacking person vs. a mutilated body; Bradley et al., 2001 ). It is also well-established that even in highly arousing motivated performance tasks (e.g., mental math, speech delivery), relationships between ANS measures (e.g., whether PEP increases are associated with CO increases/TPR decreases or not) vary across individuals as a function of their appraisals of their own coping resources in relation to the perceived demands of the task (e.g., Quigley et al., 2002 ; Tomaka et al., 1993 ). Relatedly, covariance among ANS measures during motivated performance tasks can also change within people over time (e.g., Khalaf et al., 2020 ). The implications of these concerns for the ecological validity of arousal research are emphasized in a recent study utilizing ambulatory physiological monitoring in daily life that found evidence of substantial within-person variability in the relationships between measures of cardiac physiology and self-reported arousal across evocative, ecologically valid situations (Hoemann et al., 2020 ). This suggests that psychological factors, in addition to physical contexts, have a significant impact on the relationships among different physiological measures of arousal. An Alternative Approach to Defining and Measuring Arousal The commonly assumed “unitary view” of arousal arises from an (often) implicit notion that a measure from any arousal domain can serve as an equally good proxy measure of a latent state of arousal. This traditional conceptualization of arousal assumes that a measure of arousal from any one of a number of measurement domains can “stand in” for the latent construct or concept. Translating this conceptualization to a measurement model, this approach is akin to what is called an “effect indicator” model, which assumes that changes in each (and any) measure are causally driven by the latent construct (i.e., experiencing an instance of “arousal” causes coordinated changes in all measures of arousal). As a result, each measure is an equally good proxy for the construct because they are causally related to its occurrence. Consistent with the unitary view of arousal, this model assumes that measures of arousal will share variance in a given context and will covary relatively consistently across contexts. However, the body of empirical evidence reviewed above illustrates that the assumptions underlying this traditional conceptualization of arousal and its accompanying measurement model do not appear to hold. Measures of arousal do not consistently covary within one arousal domain (e.g., ANS measures) or across domains (e.g., ANS and subjective measures of arousal), nor do observed patterns of covariance within or across domains necessarily remain the same within individuals over time; the strength and direction of these relationships vary as a function of the physical and psychological context. Taken together, these findings illustrate the need for the field to adopt a new conceptual understanding of arousal and establish a measurement model consistent with this shift. An alternative measurement model, the “causal indicator” statistical model, arises from a very different view of how measures can be used to understand a construct like arousal (Bollen & Lennox, 1991 , see also Barrett, 2011 ). The basic assumption of a causal indicator model is that a latent construct (here, arousal) is constructed from multiple independent components or features, and that none of the individual components or features is either necessary or sufficient for defining the latent construct (Bollen & Lennox, 1991 ). Causal indicator models are commonly used to assess emergent latent constructs such as socioeconomic status (SES), where SES as a construct is viewed as arising or emerging from one of multiple possible patterns of indicators, such as lower parental income, lower education, and poor neighborhood quality (Bollen & Lennox, 1991 ). Similarly, arousal might be viewed as arising or emerging from one of multiple possible patterns of indicators, such as increased subjective arousal and electrodermal activity or decreased pre-ejection period and faster reaction time. The data presented above suggest that the construct of arousal appears to be more consistent with a causal indicator approach, since specific patterns of covariance among measures thought to be associated with higher arousal also show considerable variability across measurement domains and even within individuals across different contexts. Given that no one measure of arousal can “stand in” for another, there is a pressing need to shift to a conceptual and measurement approach to the study of arousal that is more akin to a causal indicator or emergent approach—a framework that provides a better mapping to existing data. For researchers interested in arousal, adopting this new approach will require multimodal measurements of the components or features that comprise arousal across domains (which we will call indicators from here forward), including autonomic physiological, self-report, behavioral, and central nervous system measures. This approach would mirror that of researchers interested in other emergent constructs, like SES, who must measure a multitude of indicators to get a proxy measure for the underlying construct; they do not use any one individual indicator as a stand-in for the latent construct (e.g., education alone is not used as a proxy for SES). This conceptual approach also emphasizes that our language matters—use of a single word, like arousal, strongly (if implicitly) reinforces the idea that any and all measures can veridically index an assumedly singular underlying construct. As such, if a researcher’s goal or measurement approach is narrower, e.g., the assessment of sympathetic arousal or self-reported arousal (both of which should be clearly operationalized), then it is important to describe the construct of interest accordingly and use terminology more closely tied to the variable measured instead of using “arousal” as a broad catch-all (e.g., the way a researcher studying the influence of education level would refer to this variable as education level and not SES). Thus, we join other affective scientists in calling for a move away from use of the broader term “arousal” without a clear and specific operationalization (Barrett & Bliss-Moreau, 2009 ; Blascovich et al., 1992 ; Colombetti & Harrison, 2018 ; Robbins, 1997 ; Robbins & Everitt, 1995 ; Satpute et al., 2019 ) and towards conceptualizing arousal as a heterogenous, multidimensional construct. Future Directions: Promises and Pitfalls This conceptualization of arousal as an emergent construct involving inherent variability in the strength of various indicators within and across individuals has practical implications for measurement and analysis of arousal in affective science. We propose a novel framework for future research that leverages technological and methodological advances to assess arousal more rigorously than was previously possible. We recommend that researchers (1) collect data across multiple modalities and contexts, (2) use existing or novel data analytic techniques that can uncover sources of variation and the structure of the variation within these multimodal data, and (3) pursue intensive idiographic data collection. We expand these recommendations and provide relevant examples below of recent research that is beginning to utilize this approach. Multimodal measurement is critical for assessing arousal as an emergent, multidimensional construct since there is variability in the pattern of indicators that would be indicative of heightened arousal both across and within individuals. In our own recent work using a novel physiologically triggered experience sampling paradigm, we recorded a number of physiological and physical measures (i.e., electrocardiogram, impedance cardiogram, electrodermal activity, movement) for 8 h per day while participants went about their daily lives for 2 weeks. During the experience sampling period, participants responded to brief experience sampling prompts and completed a single behavioral task trial on a smartphone whenever a notable and sustained change in interbeat interval was detected, which occurred in the absence of movement (Hoemann et al., 2020 ). Results showed individual differences in the number and types of patterns of physiological responses, which demonstrated little correspondence with self-report arousal or indeed any of our self-report measures (e.g., affective valence, emotion). By leveraging multimodal measurement techniques and an unsupervised clustering approach, this work begins to illuminate the dynamic relationships among different measures of physiological activity and self-reported affective experience across a variety of everyday contexts. In so doing, this work makes an important contribution to ongoing theoretical debates in affective science concerning the nature of emotions (for discussions, see Siegel et al., 2018 ; Lindquist et al., 2013 ). Specifically, these findings are inconsistent with theoretical perspectives on emotion that posit emotions to have consistent and distinct underlying patterns of autonomic nervous system activity and affective properties (e.g., subjective feelings of valence and arousal) that distinguish experiences of a given emotion from experiences of another. Consistent with constructionist views of emotions (Barrett, 2017 ), findings instead suggest substantial variability in subjective and physiological indicators of arousal both between and within individuals, even for instances labeled by the same emotion word. This paradigm is one of several recently used to collect deeply multimodal data, either in the lab or in the world (Kyriakou et al., 2019 ; Busscher et al., 2020 ; Ahn, 2019 ; Paul et al., 2019 ; Dillen et al., 2020 ; Susam et al., 2021 ; Neijts et al., 2015 ; for review, see De Geus & Gevonden, 2023 ), demonstrating that the technology now exists to support greater depth and breadth of multimodal measurement than was previously possible. For example, tools now exist to support a wide variety of multimodal measures relevant to the study of arousal, such as real-world physiology and eye movement (e.g., Dillen et al., 2020 ), vocalizations and self-report (e.g., Timmons et al., 2017 ), experience sampling and genetic markers (Neijts et al., 2015 ), and simultaneous central nervous system measures (e.g., fMRI and EEG, Valdes-Sosa et al., 2009 ; for reviews, see Zhang et al., 2020 ; Tulay et al., 2019 ). Moreover, given the variability in the strength of association among indicators of arousal across contexts, future research on arousal should seek to sample and understand this variability in more detail. This would require studies that sample multimodal data across a large variety of contexts, yielding richer data for examining variability by increasing the range of different contexts that participants encounter. Although there are several methods for collecting data across multiple contexts, ambulatory monitoring—often with participants going about their daily lives—is perhaps the most important tool for this type of sampling. In recent years, intensive multimodal, within-person ambulatory data collection has become more feasible, as researchers develop new methods and devices for unobtrusive yet sensitive measurements of daily life in freely moving people (e.g., Goodwin et al., 2008 ; Picard & Healey, 1997 ; Sano & Picard, 2013 ; Wilhelm & Grossman, 2010 ). Collecting data during daily life can maximize the potential range of participant experiences and consequently enhance the potential range of observed physiological changes. For example, Wilhelm & Grossman ( 2010 ) present ambulatory monitoring data for one participant showing physiological responses nearly an order of magnitude larger than what was achieved during a standard in-lab stress task. Daily life monitoring can also include contexts that cannot be ethically and/or practically constructed in a lab-based setting. For example, during our ambulatory physiology and experience sampling study (Hoemann et al., 2020 ), participants reported all of the following naturally occurring events: was reunited with their spouse after being apart for several years, had a near-miss accident while driving a car, discovered that a love interest reciprocated their feelings, found out their housing plans had fallen through at the last minute, faced a difficult situation while working in a rape crisis center, spent the day with a parent who visits infrequently, got an expensive parking ticket, and learned of the death of a close relative. In addition to these spontaneous events, researchers can leverage ambulatory monitoring to sample in other extreme or out of ordinary contexts, such as in high-stress occupations (e.g., police work; Baldwin et al., 2019 ; Kelley et al., 2019 ) or experiences (e.g., flying in an airplane for aerophobics; Wilhelm & Roth, 1998 ). Large multimodal, multi-context studies of arousal, like those we have proposed here, can also benefit from employing data-driven, or unsupervised, analytic techniques, as they can be used to discover latent structure in the variation across contexts and measures, rather than looking only for patterns of covariance among potential indicators of arousal that were proposed or stipulated a priori. Multiple authors have argued that stipulation assumes that we know what the patterns of importance are. As William James ( 1994 , p. 206) pointed out, “Surely there is no definite affection of ‘anger’ in an ‘entitative’ sense.” He and many others since have argued that folk categories of psychological phenomena, while they may be “real” in that they are socially constructed, may not provide the basis for how one psychological phenomenon can be distinguished from another. Further, an unsupervised analytic approach provides a strategy for assessing what underlying features of psychological phenomena, along with their physical features, structure the observed variance. This approach also can help to lay bare the philosophical commitments we use when we design studies to assess arousal and determine how these commitments align (or do not) with patterns in the data. For example, in a recent paper (Wormwood et al., 2019 ), we applied an unsupervised machine learning approach to physiological data collected during a standard motivated performance task (i.e., mental math portion modified from the Trier Social Stress Task; Kirschbaum et al., 1993 ). This task is often used to classify participants into two groups with disparate patterns of affective responding: those who feel threatened by an upcoming task (i.e., they rate the task as stressful and are not confident in their ability to cope with it) and those who feel challenged (i.e., they rate the task as stressful but are confident in their ability to cope with it; Tomaka et al., 1993 , 1997 ). Using a data-driven approach, we found that three groups could be distinguished, including a group that was characterized predominantly by a lack of electrodermal responsiveness, an autonomic measure that is not typically included in the set of threat-challenge physiological measures (Wormwood et al., 2019 ). It was through the multimodal assessment of arousal and unsupervised analytic approach that a previously undetected pattern of affective reactivity during a motivated performance task was identified. This finding is a clear demonstration of how adopting research practices that reflect an emergent, multidimensional conceptualization of arousal can expand and enrich our understanding of individual differences in affective experience beyond what is possible under the unitary assumption of arousal. The conceptualization of arousal as emergent and multidimensional also has implications for how researchers approach attempts to predict arousal in context. Specifically, it suggests that algorithms built on a limited number of potential indicators of arousal measured over a limited number of contexts will almost certainly be mis-specified for predicting heightened arousal in the real world, failing for many individuals and in many contexts. Successful models will require individual tailoring to identify the potential indicators of arousal or pattern of indicators that are relevant for a given person in a given context. Building such models necessitates that researchers take a deeply idiographic approach, including measuring across multiple days and contexts and recording multiple measures for each participant. With this recommendation, we echo calls made by other researchers for the development of more extensive (i.e., more measures, more time points) idiographic datasets (Caldwell et al., 2008 ; Dubois & Adolphs, 2016 ; Madan, 2021 ) and point readers to important past and ongoing work that has utilized such an approach (e.g., Kappeler-Setz et al., 2013 ; Picard et al., 2016 ; Poldrack et al., 2015 ; Thammasan et al., 2020 ; Van Doren et al., 2021 ). Where possible, these datasets should be made publicly available. An example of such a dataset is Poldrack’s 18 months of near daily fMRI recordings of his own brain (Poldrack et al., 2015 ), from which he and his colleagues showed immense variability in brain connectivity over time, as well as its relationship to other variables (e.g., gene expression). Other longitudinal, idiographic datasets include the UK Biobank (e.g., Sudlow et al., 2015 ), LEMON study of aging (Babayan et al., 2019 ), and ongoing ABCD adolescent study (Casey et al., 2018 ). Such deep idiographic measurement will be critical for clinical applications of research on the role of arousal in the realm of occupational stress or psychopathology, such as long-term, individualized measurement and training interventions for those in high-stress occupations (e.g., police officers, Andersen et al., 2016 ) and for diagnosis and intervention to enable a more personalized medicine approach to enhancing mental health care (e.g., for depression, Scangos et al., 2021 ; anxiety disorders, Kritikos et al., 2021 ; schizophrenia, Bauer et al., 2020 ). Despite recent technological and data analytic advances, there remain significant barriers to collecting and analyzing large idiographic, multimodal, longitudinal datasets. First, such data collection efforts are both time- and resource-intensive for research staff and participants. Successful data collection requires attentive and highly skilled research staff to troubleshoot issues that can arise and data collection at nonstandard work hours (e.g., as in sleep studies). Such studies also require high participant compliance and conscientiousness (e.g., in ambulatory studies, participants need to recharge devices and tolerate wearing them over weeks to months). In addition, these projects can be quite expensive to implement, including costs for substantial participant remuneration, research staff salaries, the purchase and upkeep of equipment, and potentially additional data storage and/or computer processing capabilities. Second, large amounts of data are generated from such studies, which introduces complexities during and after data collection in storing, processing, organizing, and integrating across data sources. Third, unsupervised analyses require large datasets to enable reliable clustering and are not very tolerant of sparse data. However, one way to supplement (or even use) a sparser dataset is to pair data with computational simulation, which can be used to test hypotheses without the need to collect large or expensive datasets or can be used in combination with existing datasets to mine their full potential. For example, Allen et al. ( 2022 ) used simulated psychophysics to test the relationship between various parameters, such as a simulated subjects’ expectation (i.e., prior) of how subjectively arousing a stimulus would be and the subject’s subsequent simulated cardiac changes. They found that varying the precision of a simulated subject’s prior for how arousing a stimulus would be impacted the extent of cardiac acceleration demonstrated in response to the actual stimulus, and that arousal-based priors were considered less precise when occurring in systole (in line with Garfinkel & Critchley, 2016 ; Garfinkel et al., 2014 ). However, while computational simulation can be a helpful tool, we emphasize that intensive data collection efforts are necessary for studying arousal in a truly idiographic, multimodal, and cross-contextual way. It is critical that researchers and funding agencies invest in the development and deployment of such research programs and associated methodologies. Conclusion Research on arousal can provide vital insights into phenomena of interest to affective scientists from a multitude of sub-disciplines within psychology (e.g., emotion, learning, attention, clinical) as well as researchers in other fields of study. As we have argued here, moving forward, affective science must take into account evidence demonstrating that arousal is not a unitary construct, that it must be measured across multiple measures and contexts, and that researchers should be specific in their operationalizations when discussing arousal or potential indicators of arousal. In applying these caveats to arousal research, we recommend a more multidimensional conceptualization of arousal that is inclusive of expected variability in the indicators of arousal across individuals and contexts. Finally, to adequately address this new definition, we encourage affective science research that is idiographic, multimodal, and cross-contextual and that employs data analysis techniques capable of discovering structure within variation. Ultimately, this research has the potential to transform our understanding of arousal in both basic affective science and applied settings, improving our understanding of human experience more generally. Acknowledgements We would like to thank Drs. Karen Quigley and Lisa Barrett for their input and support during the preparation of this manuscript. Additional Information Funding Preparation of this article was supported in part by the US Army Research Institute for the Behavioral and Social Sciences (W911NF-16–1-019). The views, opinions, and/or findings contained in this article 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. Competing Interests The authors declare no competing interests. Data Availability No datasets were generated or analysed during the current study. Author Contributions All authors (Tess Reid, Catie Nielson, Jolie B. Wormwood) contributed to the writing, preparation, and review of the manuscript. Ethical Approval Declaration not applicable. Conflict of Interest Not applicable. Informed Consent Not applicable. References Ahn, S. Y. (2019). Using multimodal analysis to explore language learner identity construction. Visualising Multilingual Lives. More Than Words , 134–150. Allen, M., Levy, A., Parr, T., & Friston, K. J. (2022). In the body’s eye: The computational anatomy of interoceptive inference. 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