Idit Shalev- Laboratory for Embodiment and Self-Regulation, Department of Psychology, Ariel University, Ariel, Israel
Research on emotion suggests that individuals regulate their emotions to attain hedonic or instrumental goals. However, little is known of emotion regulation under low emotional clarity. The theory of motivated cue integration (MCI) suggests that emotion regulation under low emotional clarity should be understood as dissociation between a high-level individual hierarchical system of goals and low level interoceptive and exteroceptive embodied cues. MCI conceptualizes low emotional clarity as the product of low access to signals of emotion that result in prediction error associated with mismatch between the current bodily state and the predicted state. This deficit in emotional processing could be understood as a problem of means substitution, suggesting that use of alternative multisensory data may facilitate situational evaluation. Based on this reasoning, a new perspective on emotion regulation under low emotional clarity is presented, according to which interchangeable attention to multisensory data associated with words, associations, and images may help in cue integration, enabling the creation of a link between concrete bodily cues, abstract mental representation, and a more accurate prediction. Based on the idea that emotional episodes are conceptualized as special types of goal-directed action episodes, this process will lead to the creation of broader integrative meaning, results in the development of emotion goal.
The Problem Of Low Emotional Clarity
Emotion regulation is an essential aspect of mental health and refers to processes that amplify, attenuate, or maintain the strength of emotional reactions (Gross, 1998; Davidson et al., 2000) such that individuals are capable of controlling their behavior (see Linehan, 1993; Melnick and Hinshaw, 2000) or accepting and valuing emotional responses (Zajonc, 1980; Schwarz, 1990; Linehan, 1993; Cole et al., 1994). To date, ample research on emotion regulation has mainly been focused on top-down strategies aimed to change current emotions into desired emotions (see Gross, 2015). For example, the instrumental approach of emotion regulation suggests that individual emotions are motivated by hedonic or instrumental benefits to promote goal pursuit (e.g., Tamir and Ford, 2009, 2012; Tamir and Bigman, 2018). Research sheds light on the differences between implicit and explicit regulation of emotion, indicating the extent of effort under explicit regulation of emotion (Gyurak et al., 2011; Koole and Rothermund, 2011). However, little is known about the transition mechanisms between low and high emotional clarity to establish an emotional goal and better regulatory control. There is evidence of the differentiation between six levels of regulatory control (Lane et al., 2000; Smith and Lane, 2015), indicating that the lowest level of regulatory control is the automatic level of somatic and visceral reflexes associated with body state‐ and behavioral-regulation (Longhurst, 2011; Strominger et al., 2012). In fact, only the sixth and highest level of regulation is associated with voluntary emotion regulation (Smith and Lane, 2015), with its activation depending on having an emotion goal and strategic goal-directed attempts to suppress or reappraise emotional responses. This level is associated with top-down voluntary control over cognition, attention, and behavior, which activates regions such as dorsolateral prefrontal cortex (DLPFC), the dorsomedial prefrontal cortex (DMPFC), the ventrolateral prefrontal cortex (VLPFC) and parts of the dorsal anterior cingulate cortex (dACC) and the posterior parietal cortex (Gross, 1998; Ochsner and Gross, 2005).
Emotional clarity is defined as the extent to which people unambiguously identify, label, and characterize their own emotions. A majority of theories consider awareness and clarity of emotions as the building blocks of emotion regulation (e.g., Salovey et al., 1995; Gratz and Roemer, 2004, 2008; Boden and Thompson, 2015). For example, the “process model” (Gross, 1998) suggests that appraisal of a situation involves assessment of cues associated with emotional response against top-down prevailing factors (e.g., goals, social, cultural and familial influences, personality, etc.,). Accordingly, there is evidence that individuals high in clarity of emotion have meta-emotional knowledge that facilitates efficient and effective selection and implementation of emotion regulation strategies which have the greatest likelihood of achieving emotion regulation goals (Barrett et al., 2001; Tamir et al., 2008; Gross and Jazaieri, 2014). By contrast, when clarity of emotion is low, people tend to not regulate emotions or to fail in effective selection and implementation of emotion regulation strategies (Boden et al., 2013). Research demonstrates that emotional clarity deficits are associated with symptoms of depression, social anxiety, borderline personality, binge eating, and alcohol use, indicating that emotional clarity deficits may be viewed as a transdiagnostic phenomenon with divergent pathways causing problems in controlling emotions ranging from condition to condition (Vine and Aldao, 2014). These deficits come into play especially in alexithymia (Murphy et al., 2018), viewed as a continuum which manifests as difficulty in identifying feelings and in distinguishing them from bodily sensations of emotional arousal, difficulty in describing one’s own feelings, and an externally oriented cognitive style, i.e., a focusing of one’s attention externally with little introspection or insight into one’s own feelings (Nemiah et al., 1976). Following this view, there is evidence that lower vagally mediated heart rate variability (vmHRV) resting is linked to enhanced emotional regulatory difficulties. Particularly, deficient in emotional clarity and impulse regulation, indicating that emotional regulation and autonomic regulation share neuronal networks within the brain (Williams et al., 2015). The influence of poor emotional clarity was also identified in psoriasis patients with high psychological distress. They were lower in emotional clarity than control and displayed less performance discrepancy under low vs. high cognitive load conditions (Panasiti et al., 2019).
Motivated cue integration (MCI) theory (Shalev, 2015, 2018, 2019) provides a perspective on the potential transformation of low emotional clarity into the creation of emotion goals. In what follows, I will first present the theory of MCI. I will then apply the principles of the theory on the problem of low emotional clarity. Based on the principles of the theory, I will finally propose therapeutic strategies to increase emotional clarity.
Theory of Motivated Cue Integration
The theory of MCI (Shalev, 2015, 2018, 2019) attests that individuals construe meaning by integrating high-level control processes and low-level cues, such that, on the one hand, active top-down goals influence attention to interoceptive and exteroceptive signals. On the other hand, the perceiver’s likelihood of drawing a specific inference is based on her unique system of goals, suggesting that active goals increase the accessibility of specific interoceptive and exteroceptive cues (Shalev, 2015).
According to MCI, this process occurs both in the bottom-up and the top-down directions through selective attention to multisensory information and affective signals, which, in turn, are integrated into meaning and result in action generation. Awareness of the association between high-level processes and low-level processes has been demonstrated in various studies. Based on the rationale that many voluntary actions aimed at ensuring homeostatic properties, Ainley and Tsakiris (2013) have shown that participants with higher levels of interoceptive awareness have demonstrated a greater intentional binding that reflects a stronger sense of agency. In addition, lower levels of interoceptive awareness were associated with greater self-other blurring during the enfacement illusion (Tajadura-Jiménez et al., 2012; Tajadura-Jiménez and Tsakiris, 2014). Additionally, Barrett et al. (2001) argued that persons with highly differentiated emotional experience could better regulate their emotions, pointing to the fact that a greater sensitivity to ongoing bodily changes will facilitate the regulation of emotional responses. This view is consistent with recent findings on the association between interoceptive ability and emotion regulation (Zamariola et al., 2019).
Low-Level Cues and the Creation of Meaning
The distinction between high-level and low-level processes has been illustrated in earlier emotion theories (James and Dennis, 1884; Damasio, 1994; Craig, 2004). For example, psychological theory of James and Dennis (1884) associated visceral-afferent feedback and emotional experience. The assumption that higher level of processing is grounded in the organism’s lower level sensory and motor experiences is also at the core of embodied cognition research (Barsalou, 1999, 2008; Meier et al., 2012; Winkielman et al., 2015), enabling new ways to conceptualize and understand emotional processes (Damasio, 1999; Bechara and Damasio, 2005; Niedenthal, 2007). There is evidence that embodied cognition is influenced by various sources of information including innate processes, personal history, and culture (Meier et al., 2012). The general idea is that exeroceptive contextual cues activate associated mental representations (e.g., Bargh and Morsella, 2010; Loersch and Payne, 2011), suggesting that activation automatically spreads from concepts driven by experiences in the physical world to their metaphorically-related social concepts (for reviews, see Williams et al., 2009; Meier et al., 2012). For example, research of the emotion-related associations between physical warmth/coldness and psychological warmth/coldness was heterogeneous, indicating that momentary motivational states can lead to different patterns of activation across different situations (Bargh and Shalev, 2012; Shalev, 2015).
This variability in findings and the challenge of replication (Shalev and Bargh, 2015) help explain why MCI emphasized the relevance of both internal and external cues. Research on interoception and psychopathology indicates various associations between psychopathology and over-sensitivity vs. under-sensitivity to interoceptive cues (Khalsa et al., 2018). Accordingly, recent developments in neuroscience research indicate the existence of two types of inputs: (1) exteroceptive inputs associated with the perception of the body from the outside, based on multisensory integration and (2) interoceptive inputs, defined as the sense of the internal physiological state that supports homeostatic regulation of the body, resulting in physiological integrity and associated affective states, drives, and emotions (Garfinkel et al., 2015).
High-Level Goals, Emotion, and Action Generation
MCI suggests that the monitoring of the flow of interoceptive and exeroceptive cues, associated with words, mental images, accessible memories along with the individual’s goals, will contribute to the formation of integrative meaning (Shalev, 2018). Goals are defined as hierarchically organized cognitive representations of desired endpoints that affect evaluations, emotions, and behaviors (Fishbach and Ferguson, 2007). Several alternative means to the same goal potentially exist and could substitute for each other (Kruglanski et al., 2002). The degree and length of situational activation of individual’s goals are influenced by sources of motivational relevance regardless of representation content. For example, value relevance is the extent to which acting on a mental representation produces desired results and/or prevents unwanted results; control relevance is related to the efficacy with which the activated representation makes things happen; and truth relevance determines what is real (Eitam and Higgins, 2010).
Individual differences are conveyed by the unique associations that individuals make between goals and means of attainment (Shalev, 2015), the repeated coupling of sensory signals (Rescorla, 1985), and the strength of the association between particular physical sensations and psychological concepts such as the combination of homeostatic cues (e.g., temperature and dryness). In addition, situational demands, history, and psychiatric and neuropsychological conditions (e.g., cognitive flexibility) influence cue integration (Shalev, 2015). Recent findings indicate, for example, that avoidance behavior in social anxiety may be related to biased distance estimation (Givon-Benjio and Okon-Singer, 2020).
There is evidence that both emotional and non-emotional action tendencies are determined by high-level goal-directed processes, which differ only in the degree of control precedence that they have (Hommel et al., 2017). Accordingly, competition between different goal-directed processes results in action control loops in which the degree to which a given stimulus event is related to and discrete from a current goal is assessed (Hommel et al., 2017; Moors et al., 2017). This helps to explain that emotional episodes are conceptualized as special types of goal-directed action episodes (Hommel et al., 2017). Similarly, Solms (2014) suggested that emotion is extended to exteroception (i.e., contextualized: “I feel like that”) and transformed into a goal which comes into play through voluntary action.
Mci, Predictive Coding, and Low Emotional Clarity
According to the predictive coding model (e.g., Rao and Ballard, 1999; Clark, 2013; Spratling, 2017), emotional responses depend on the continuous updated process of prediction of internal signal causes. This process occurs because, to navigate the body in the world and minimize a free energy functional of internal states, the brain must discover information about the likely reasons for sensory cues (i.e., perception) without direct access to these causes (Friston, 2010). This process is also influenced by the need to minimize the cost of prediction error either by updating generative models or by performing actions to associate sensory states in line with predictions (Hutchinson and Barrett, 2019). For example, upon detecting physical or cognitive change, probabilistic inference on the reasons for sensory cues is computed according to Bayesian principles such that the brain may represent the concept of “anger” as having a 90% chance of accounting for that pattern, while representing “sadness” as having a 5% chance, happiness a 1% chance, and so forth (Barrett, 2017; Smith et al., 2017). From the perspective of interoceptive predictive coding models (Seth, 2013), interoceptive prediction error arises when the “actual” state of the body does not match the predicted state.
MCI suggests that dissociation between high-level personal goals and low-level multisensory data results in a prediction error. Under this condition, automatic emotion regulation of preferences does not contribute to relief of psychological distress. While automatic prediction may be efficient in different contexts, low emotional clarity requires increased awareness.
Based on this reasoning, according to MCI, low access to multisensory data could be viewed as a means substitution problem (Kruglanski et al., 2002), indicating that when goal progress via a prior means was unrecognized or thwarted, alternative means should be used. Following this view, alternative types of encoding (e.g., mental images and multisensory data) may increase access to the dissociated aspects of the psychological experience and clarify emotion. Supportive evidence exists for the possible substitutability of emotion and alternative types of encoding, indicating the association between multisensory data and mental images as alternative types of encoding (Holmes and Mathews, 2005). For example, research shows that mental images in different modalities are primarily sensory-perceptual representations. There is overlap between the brain areas activated during imagery and those involved in processing the equivalent sensory and perceptual events. For example, there is evidence that perceptual cues associated with homeostatic deficiency (e.g., words and visual images of dryness) influence self-regulation as if they were actual ones (Shalev, 2014, 2016; Halali et al., 2017). Likewise, neural processes involved in perceiving “real” events (Kosslyn et al., 2001) signaling danger or reward (Öhman and Mineka, 2001) and mental imagery areas are shared.
Mci and the Creation of Emotion Goal
To increase clarity of emotion, a moment-to-moment self-regulation should be based on tracking accessible multisensory data and creation of association between selective perceptual cues and individual need, suggesting that low emotional clarity results in self-regulation failure. While in predetermined situations, individuals are governed by automatic reflexive responses, if the person wants to make plausible decisions in unfamiliar circumstances, this will be achieved through a sort of integration of past experience and current alternatives in affective evaluation (Solms, 2014). Accordingly, emotional clarity enables complex organisms to identify, monitor, and handle deviations from homeostatic settlement points in uncertain contexts through integrative comprehension and voluntary action. Such change underpins learning from experience (Solms and Friston, 2018).
Although most emotion-regulating approaches address individual maladaptive feelings, MCI proposes treating emotion control as a function of contextual meaning. The general idea is that low emotional clarity caused low access to multisensory data. Therefore, awareness of sensation techniques may support the re-creation of meaning. To address this goal, the individual should first identify a situation or event that caused discomfort and invest selective attention in the physical sensations, associations, and mental images associated with this event. Techniques involve awareness-of-sensation to peripheral cues (e.g., mindfulness and experiential approach), enabling emergence of new details which may reconstruct the emotional experience. Gendlin (2012) suggested that a positive shift in psychological experience emerges from tracking the changes of bodily experience. As such, in the practice of focusing, the focuser assigns words, mental images, or phrases that express the present sensory experience. The focuser ranges from feeling to verbal association and returning to other physical sensations that arise during the process. After remaining throughout the interaction, a shift is generated and a greater understanding of the essence of the problem or action should be taken. Attention to perceptual cues results in the identification of a specific word or image that carries meaning or reveals an individual’s unconscious personal purpose. Once relief has been achieved, the focuser labels items that she wants to memorize from the process or to formulate an integrative image of the experience.
Clinicians and neuropsychologists can train individuals to use awareness-of-sensation techniques to increase emotional clarity by integrating peripheral multisensory data and creating associations between perceptual cues, symbols, and the individual’s personal needs to create an emotion regulation goal. Integrating peripheral cues in the moment-by-moment creation of meaning may bridge the gap between research on higher level emotion regulation (Suri et al., 2018) and various conditions that hinder personal goal accessibility. There is evidence that use of brief awareness of sensation enhances aspects of emotional processing such as emotional intensity, emotional memory, and emotional attention bias (Guendelman et al., 2017; Wu et al., 2019). Research indicates that mindfulness influences interpersonal emotional reactions through an experiential process, while altering the subjective and physiological experience of emotions, and also biasing interpersonal behavioral patterns (Grecucci et al., 2015). Likewise, there is evidence that when expecting negative images, prefrontal and right insular activations correlated negatively with trait-mindfulness, indicating that more attentive individuals need fewer regulatory resources to attenuate emotional arousal (Lutz et al., 2014). Future research should further study the relations between higher level individual goals and lower level interoceptive and exteroceptive signals to increase emotional clarity and to shed light on individual’s needs. Integrating high-level processes and low-level signals may diagnose various psychological conditions and add to the state-of-the-art self-regulation research.
Author Contributions
The author confirms being the sole contributor of this work and has approved it for publication.
Funding
This research was funded by Ariel University Research Authority, grant number RA1900000095 to IS.
Conflict of Interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
The author thanks the editor and reviewers for their helpful comments.
References
Ainley, V., and Tsakiris, M. (2013). Body conscious? Interoceptive awareness, measured by heartbeat perception, is negatively correlated with self-objectification. PLoS One 8:e55568. doi: 10.1371/journal.pone.0055568
Bargh, J., and Morsella, E. (2010). “Unconscious behavioral guidance systems” in Then a miracle occurs: Focusing on behavior in social psychological theory and research. eds. C. Agnew, D. E. Carlston, W. G. Graziano, and J. R. Kelly (New York, NY: Oxford University Press), 89–118.
Bargh, J. A., and Shalev, I. (2012). The substitutability of physical and social warmth in daily life. Emotion 12, 154–162. doi: 10.1037/a0023527
Barrett, L. F. (2017). How emotions are made: The secret life of the brain. Houghton Mifflin Harcourt.
Barrett, L. F., Gross, J., Christensen, T. C., and Benvenuto, M. (2001). Knowing what you’re feeling and knowing what to do about it: mapping the relation between emotion differentiation and emotion regulation. Cognit. Emot. 15, 713–724. doi: 10.1080/02699930143000239
Barsalou, L. W. (1999). Perceptual symbol systems. Behav. Brain Sci. 22, 577–609. doi: 10.1017/S0140525X99002149
Barsalou, L. W. (2008). Grounded cognition. Annu. Rev. Psychol. 59, 617–645. doi: 10.1146/annurev.psych.59.103006.093639
Bechara, A., and Damasio, A. R. (2005). The somatic marker hypothesis: a neural theory of economic decision. Games Econ. Behav. 52, 336–372. doi: 10.1016/j.geb.2004.06.010
Boden, M. T., and Thompson, R. J. (2015). Facets of emotional awareness and associations with emotion regulation and depression. Emotion 15, 399–410. doi: 10.1037/emo0000057
Boden, M. T., Westermann, S., McRae, K., Kuo, J., Alvarez, J., Kulkarni, M. R., et al. (2013). Emotion regulation and posttraumatic stress disorder: a prospective investigation. J. Soc. Clin. Psychol. 32, 296–314. doi: 10.1521/jscp.2013.32.3.296
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36, 181–204. doi: 10.1017/S0140525X12000477
Cole, P. M., Michel, M. K., and Teti, L. O. D. (1994). The development of emotion regulation and dysregulation: a clinical perspective. Monogr. Soc. Res. Child Dev. 59, 73–100. doi: 10.1111/j.1540-5834.1994.tb01278.x
Craig, A. D. (2004). Human feelings: why are some more aware than others? Trends Cogn. Sci. 8, 239–241. doi: 10.1016/j.tics.2004.04.004
Damasio, A. R. (1999). The feeling of what happens: body and emotion in the making of consciousness. Nature 401. doi: 10.1176/appi.ps.51.12.1579
Damasio, A. R. (1994). Descartes’ error: Emotion, rationality and the human brain. New York: G. P. Putnam’s Sons.
Davidson, R. J., Jackson, D. C., and Kalin, N. H. (2000). Emotion, plasticity, context, and regulation: perspectives from affective neuroscience. Psychol. Bull. 126, 890–909. doi: 10.1037/0033-2909.126.6.890
Eitam, B., and Higgins, E. T. (2010). Motivation in mental accessibility: relevance of a representation (ROAR) as a new framework. Soc. Personal. Psychol. Compass 4, 951–967. doi: 10.1111/j.1751-9004.2010.00309.x
Fishbach, A., and Ferguson, M. J. (2007). “The goal construct in social psychology” in Social psychology: Handbook of basic principles. 2nd Edn. New York, US: Guilford Press, 490–515.
Friston, K. (2010). The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138. doi: 10.1038/nrn2787
Garfinkel, S. N., Seth, A. K., Barrett, A. B., Suzuki, K., and Critchley, H. D. (2015). Knowing your own heart: distinguishing interoceptive accuracy from interoceptive awareness. Biol. Psychol. 104, 65–74. doi: 10.1016/j.biopsycho.2014.11.004
Gendlin, E. T. (2012). Focusing-oriented psychotherapy: A manual of the experiential method. Guilford Press.
Givon-Benjio, N., and Okon-Singer, H. (2020). Biased estimations of interpersonal distance in non-clinical social anxiety. J. Anxiety Disord. 69:102171. doi: 10.1016/j.janxdis.2019.102171
Gratz, K. L., and Roemer, L. (2004). Multidimensional assessment of emotion regulation and dysregulation: development, factor structure, and initial validation of the difficulties in emotion regulation scale. J. Psychopathol. Behav. Assess. 26, 41–54. doi: 10.1023/B:JOBA.0000007455.08539.94
Gratz, K. L., and Roemer, L. (2008). The relationship between emotion dysregulation and deliberate self-harm among female undergraduate students at an urban commuter university. Cogn. Behav. Ther. 37, 14–25. doi: 10.1080/16506070701819524
Grecucci, A., De Pisapia, N., Thero, D. K., Paladino, M. P., Venuti, P., and Job, R. (2015). Baseline and strategic effects behind mindful emotion regulation: behavioral and physiological investigation. PLoS One 10:e0116541. doi: 10.1371/journal.pone.0116541
Gross, J. J. (1998). The emerging field of emotion regulation: an integrative review. Rev. Gen. Psychol. 2, 271–299. doi: 10.1037/1089-2680.2.3.271
Gross, J. J. (2015). Emotion regulation: current status and future prospects. Psychol. Inq. 26, 1–26. doi: 10.1080/1047840X.2014.940781
Gross, J. J., and Jazaieri, H. (2014). Emotion, emotion regulation, and psychopathology: an affective science perspective. Clin. Psychol. Sci. 2, 387–401. doi: 10.1177/2167702614536164
Guendelman, S., Medeiros, S., and Rampes, H. (2017). Mindfulness and emotion regulation: insights from neurobiological, psychological, and clinical studies. Front. Psychol. 8:220. doi: 10.3389/fpsyg.2017.00220
Gyurak, A., Gross, J. J., and Etkin, A. (2011). Explicit and implicit emotion regulation: a dual-process framework. Cognit. Emot. 25, 400–412. doi: 10.1080/02699931.2010.544160
Halali, E., Meiran, N., and Shalev, I. (2017). Keep it cool: temperature priming effect on cognitive control. Psychol. Res. 81, 343–354. doi: 10.1007/s00426-016-0753-6
Holmes, E. A., and Mathews, A. (2005). Mental imagery and emotion: a special relationship? Emotion 5, 489–497. doi: 10.1037/1528-3542.5.4.489
Hommel, B., Moors, A., Sander, D., and Deonna, J. (2017). Emotion meets action: towards an integration of research and theory. Emot. Rev. 9, 295–298. doi: 10.1177/1754073916689379
Hutchinson, J. B., and Barrett, L. F. (2019). The power of predictions: an emerging paradigm for psychological research. Curr. Dir. Psychol. Sci. 28, 280–291. doi: 10.1177/0963721419831992
James, W., and Dennis, W. (1884). “What is emotion?” in Readings in the history of psychology. ed. W. Dennis (New York: Appleton-Century-Crofts), 290.
Khalsa, S. S., Adolphs, R., Cameron, O. G., Critchley, H. D., Davenport, P. W., Feinstein, J. S., et al. (2018). Interoception and mental health: a roadmap. Biol. Psychiatry. Cogn. Neurosci. Neuroimaging 3, 501–513. doi: 10.1016/j.bpsc.2017.12.004
Koole, S. L., and Rothermund, K. (2011). “I feel better but I don’t know why”: the psychology of implicit emotion regulation. Cognit. Emot. 25, 389–399. doi: 10.1080/02699931.2010.550505
Kosslyn, S. M., Ganis, G., and Thompson, W. L. (2001). Neural foundations of imagery. Nat. Rev. Neurosci. 2, 635–642. doi: 10.1038/35090055
Kruglanski, A. W., Shah, J. Y., Pierro, A., and Mannetti, L. (2002). When similarity breeds content: need for closure and the allure of homogeneous and self-resembling groups. J. Pers. Soc. Psychol. 83, 648–662. doi: 10.1037/0022-3514.83.3.648
Lane, R. D., Nadel, L., and Kaszniak, A. W. (2000). “Epilogue: The future of emotion research from the perspective of cognitive neuroscience” in Cognitive neuroscience of emotion. eds. R. D. Lane and L. Nadel (New York: Oxford University Press), 407–410.
Linehan, M. M. (1993). Skills training manual for treating borderline personality disorder. New York, US: Guilford Press.
Loersch, C., and Payne, B. K. (2011). The situated inference model: an integrative account of the effects of primes on perception, behavior, and motivation. Perspect. Psychol. Sci. 6, 234–252. doi: 10.1177/1745691611406921
Longhurst, J. (2011). “Regulation of autonomic function by visceral and somatic afferents” in Central regulation of autonomic functions. 2nd Edn. eds. I. J. Llewellyn-Smith and A. J. M. Verberne (New York, NY, USA: Oxford University Press), 161–179.
Lutz, J., Herwig, U., Opialla, S., Hittmeyer, A., Jäncke, L., Rufer, M., et al. (2014). Mindfulness and emotion regulation—an fMRI study. Soc. Cogn. Affect. Neurosci. 9, 776–785. doi: 10.1093/scan/nst043
Meier, B. P., Schnall, S., Schwarz, N., and Bargh, J. A. (2012). Embodiment in social psychology. Top. Cogn. Sci. 4, 705–716. doi: 10.1111/j.1756-8765.2012.01212.x
Melnick, S. M., and Hinshaw, S. P. (2000). Emotion regulation and parenting in AD/HD and comparison boys: linkages with social behaviors and peer preference. J. Abnorm. Child Psychol. 28, 73–86. doi: 10.1023/A:1005174102794
Moors, A., Boddez, Y., and De Houwer, J. (2017). The power of goal-directed processes in the causation of emotional and other actions. Emot. Rev. 9, 310–318. doi: 10.1177/1754073916669595
Murphy, J., Catmur, C., and Bird, G. (2018). Alexithymia is associated with a multidomain, multidimensional failure of interoception: evidence from novel tests. J. Exp. Psychol. Gen. 147, 398–408. doi: 10.1037/xge0000366
Nemiah, J. C., Freyberger, H. J., and Sifneos, P. E. (1976). “Alexithymia: A view of the psychosomatic process” in Modern trends in psychosomatic medicine. Vol. 3. ed. O. W. Hill (London: Buttersworth), 430–439.
Ochsner, K. N., and Gross, J. J. (2005). The cognitive control of emotion. Trends Cogn. Sci. 9, 242–249. doi: 10.1016/j.tics.2005.03.010
Öhman, A., and Mineka, S. (2001). Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol. Rev. 108, 483–522. doi: 10.1037/0033-295X.108.3.483
Panasiti, M. S., Ponsi, G., Monachesi, B., Lorenzini, L., Panasiti, V., and Aglioti, S. M. (2019). Cognitive load and emotional processing in psoriasis: a thermal imaging study. Exp. Brain Res. 237, 211–222. doi: 10.1007/s00221-018-5416-y
Rao, R. P. N., and Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79–87. doi: 10.1038/4580
Rescorla, R. A. (1985). “Pavlovian conditioning analogues to Gestalt perceptual principles” in Affect, conditioning, and cognition: Essays on the determinants of behavior. eds. F. R. Brush and J. B. Overmier (Hillsdale, NJ: Erlbaum), 113–130.
Salovey, P., Mayer, J. D., Golman, S. L., Turvey, C., and Palfai, T. P. (1995). “Emotional attention, clarity, and repair: Exploring emotional intelligence using the Trait Meta-Mood Scale” in Emotion, disclosure, and health. ed. J. W. Pennebaker (Washington, DC: American Psychological Association), 125–154.
Schwarz, N. (1990). “Feelings as information: Informational and motivational functions of affective states” in Handbook of motivation and cognition. Foundations of social behavior. Vol. 2. eds. E. T. Higgins and R. M. Sorrentino (New York, NY: Guilford Press), 527–561.
Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends Cogn. Sci. 17, 565–573. doi: 10.1016/j.tics.2013.09.007
Shalev, I. (2014). Implicit energy loss: embodied dryness cues influence vitality and depletion. J. Consum. Psychol. 24, 260–270. doi: 10.1016/j.jcps.2013.09.011
Shalev, I. (2015). The architecture of embodied cue integration: insight from the “motivation as cognition” perspective. Front. Psychol. 6:658. doi: 10.3389/fpsyg.2015.00658
Shalev, I. (2016). Pictorial and mental arid landscape images reduce the motivation to change negative habits. J. Environ. Psychol. 45, 30–39. doi: 10.1016/j.jenvp.2015.11.005
Shalev, I. (2018). Using motivated cue integration theory to understand a moment-by-moment transformative change: a new look at the focusing technique. Front. Hum. Neurosci. 12:307. doi: 10.3389/fnhum.2018.00307
Shalev, I. (2019). Motivated cue integration in alexithymia: improving interoception and emotion information processing by awareness-of-sensation techniques. Front. Psychiatry 10:329. doi: 10.3389/fpsyt.2019.00329
Shalev, I., and Bargh, J. (2015). On the association between loneliness and physical warmth-seeking through bathing: reply to Donnellan et al. (2014) and three further replications of Bargh and Shalev (2012) study 1. Emotion 15, 120–123. doi: 10.1037/emo0000014
Smith, R., and Lane, R. D. (2015). The neural basis of one’s own conscious and unconscious emotional states. Neurosci. Biobehav. Rev. 57, 1–29. doi: 10.1016/j.neubiorev.2015.08.003
Smith, R., Thayer, J. F., Khalsa, S. S., and Lane, R. D. (2017). The hierarchical basis of neurovisceral integration. Neurosci. Biobehav. Rev. 75, 274–296. doi: 10.1016/j.neubiorev.2017.02.003
Solms, M. (2014). A neuropsychoanalytical approach to the hard problem of consciousness. J. Integr. Neurosci. 13, 173–185. doi: 10.1142/S0219635214400032
Solms, M., and Friston, K. (2018). How and why consciousness arises: some considerations from physics and physiology. J. Conscious. Stud. 25, 202–238.
Spratling, M. W. (2017). A hierarchical predictive coding model of object recognition in natural images. Cogn. Comput. 9, 151–167. doi: 10.1007/s12559-016-9445-1
Strominger, N. L., Demarest, R. J., and Laemle, L. B. (2012). “Reflexes and muscle tone” in Noback’s human nervous system, Seventh edition: Structure and function. Science Springer and Business Media. Totowa, NJ: Humana Press, 143–154.
Suri, G., Sheppes, G., Young, G., Abraham, D., McRae, K., and Gross, J. J. (2018). Emotion regulation choice: the role of environmental affordances. Cognit. Emot. 32, 963–971. doi: 10.1080/02699931.2017.1371003
Tajadura-Jiménez, A., Longo, M. R., Coleman, R., and Tsakiris, M. (2012). The person in the mirror: using the enfacement illusion to investigate the experiential structure of self-identification. Conscious. Cogn. 21, 1725–1738. doi: 10.1016/j.concog.2012.10.004
Tajadura-Jiménez, A., and Tsakiris, M. (2014). Balancing the “inner” and the “outer” self: interoceptive sensitivity modulates self–other boundaries. J. Exp. Psychol. Gen. 143:736. doi: 10.1037/a0033171
Tamir, M., and Bigman, Y. E. (2018). Expectations influence how emotions shape behavior. Emotion 18, 15–25. doi: 10.1037/emo0000351
Tamir, M., and Ford, B. Q. (2009). Choosing to be afraid: preferences for fear as a function of goal pursuit. Emotion 9, 488–497. doi: 10.1037/a0015882
Tamir, M., and Ford, B. Q. (2012). When feeling bad is expected to be good: emotion regulation and outcome expectancies in social conflicts. Emotion 12, 807–816. doi: 10.1037/a0024443
Tamir, M., Mitchell, C., and Gross, J. J. (2008). Hedonic and instrumental motives in anger regulation: research report. Psychol. Sci. 19, 324–328. doi: 10.1111/j.1467-9280.2008.02088.x
Vine, V., and Aldao, A. (2014). Impaired emotional clarity and psychopathology: a transdiagnostic deficit with symptom-specific pathways through emotion regulation. J. Soc. Clin. Psychol. 33, 319–342. doi: 10.1521/jscp.2014.33.4.319
Williams, D. P., Cash, C., Rankin, C., Bernardi, A., Koenig, J., and Thayer, J. F. (2015). Resting heart rate variability predicts self-reported difficulties in emotion regulation: a focus on different facets of emotion regulation. Front. Psychol. 6:261. doi: 10.3389/fpsyg.2015.00261
Williams, L. E., Huang, J. Y., and Bargh, J. A. (2009). The scaffolded mind: higher mental processes are grounded in early experience of the physical world. Eur. J. Soc. Psychol. 39, 1257–1267. doi: 10.1002/ejsp.665
Winkielman, P., Niedenthal, P., Wielgosz, J., Eelen, J., and Kavanagh, L. C. (2015). “Embodiment of cognition and emotion” in APA handbook of personality and social psychology. Vol. 1. eds. M. Mikulincer and P. R. Shaver (Washington, DC: American Psychological Association), 151–175.
Wu, R., Liu, L. -L., Zhu, H., Su, W. -J., Cao, Z. -Y., Zhong, S. -Y., et al. (2019). Brief mindfulness meditation improves emotion processing. Front. Neurosci. 13:1074. doi: 10.3389/fnins.2019.01074
Zajonc, R. B. (1980). Feeling and thinking: preferences need no inferences. Am. Psychol. 35, 151–175. doi: 10.1037/0003-066X.35.2.151
Keywords: motivated cue-integration, emotion regulation, emotional clarity, awareness of sensation techniques, self-regulation, multisensory, interoception
Citation: Shalev I (2020) Motivated Cue-Integration and Emotion Regulation: Awareness of the Association Between Interoceptive and Exteroceptive Embodied Cues and Personal Need Creates an Emotion Goal. Front. Psychol. 11:1630. doi: 10.3389/fpsyg.2020.01630
Edited by:
Alessia Celeghin, University of Turin, ItalyReviewed by:
Andrea Zaccaro, University of Pisa, ItalyGiorgia Ponsi, Sapienza University of Rome, Italy
Federica Vellante, University of Studies G. d’Annunzio Chieti and Pescara, Italy
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*Correspondence: Idit Shalev, c2hhbGV2LmlkaXRAZ21haWwuY29t