Emotion and decision-making: affect-driven belief systems in anxiety and depression

Paulus, Martin P.; Yu, Angela J.

doi:10.1016/j.tics.2012.07.009

Cited by 287Neural and Behavioral Psychology StudiesAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesDecision-Making and Behavioral Economics

Emotion and decision-making: affect-driven belief systems in anxiety and depression

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Paulus, Martin P., Yu, Angela J.·2012·Trends in Cognitive Sciences

DOI 10.1016/j.tics.2012.07.009PMC PMC3446252

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Abstract

Emotion processing and decision-making are integral aspects of daily life. However, our understanding of the interaction between these constructs is limited. In this review, we summarize theoretical approaches that link emotion and decision-making, and focus on research with anxious or depressed individuals to show how emotions can interfere with decision-making. We integrate the emotional framework based on valence and arousal with a Bayesian approach to decision-making in terms of probability and value processing. We discuss how studies of individuals with emotional dysfunctions provide evidence that alterations of decision-making can be viewed in terms of altered probability and value computation. We argue that the probabilistic representation of belief states in the context of partially observable Markov decision processes provides a useful approach to examine alterations in probability and value representation in individuals with anxiety and depression, and outline the broader implications of this approach.

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Recent models of interoception and emotion incorporate Bayesian concepts (e.g. predictive coding), wherein the brain makes sense of dynamic changes in a wealth of viscerosensory information by predicting likely causes of those changes.
“Thus, recent models of interoception and emotion incorporate Bayesian concepts (e.g.predictive coding), wherein the brain makes sense of dynamic changes in a wealth of viscerosensory information by predicting likely causes of those changes: prior interoceptive experiences are deployed as predictions of probable interoceptive experience, to yield 'prediction errors', i.e., the difference between an”
From Physiological feelings (2019) · cites this paper as Paulus and Yu, 2012
The “Bayesian models of decision-making and control” section describes Bayesian models that link beliefs with observations and conceptualize emotions as influencing that process (page unavailable). It does not describe predictive coding of changing viscerosensory information, inferred causes of those changes, or interoceptive prediction errors; searches for those terms found no supporting passage in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measured
In predictive processing terms, there may be an over-precision of negative predictions about the future in anxiety, coupled with an inability to down-regulate error signals related to ambiguous stimuli.
“In predictive processing terms, there may be an over-precision (49, 62, 63) of negative predictions about the future, coupled with an inability to down-regulate error signals related to ambiguous stimuli.”
From Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders (2026) · cites this paper as [63]
In “A conceptual framework for the influence of affect-driven belief systems on decision-making in anxiety and depression” (page unavailable), the paper suggests that anxious individuals may over-represent low-probability, high-threat outcomes. That supports a bias toward negative possibilities, but it does not specifically describe over-precise predictions about the future or an inability to down-regulate error signals for ambiguous stimuli. (LLM, conf=0.81)
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Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs.
Doshi‐Velez, F., Pineau, J., & Roy, N. (2008) · PubMed · doi:10.1901/jaba.2008.301-256 · Cited by 59 · see citing works
The Format in Which Uncertainty Information Is Presented Affects Decision Biases
Gottlieb, D., Weiss, T. R., & Chapman, G. B. (2007) · Psychological Science · doi:10.1111/j.1467-9280.2007.01883.x · Cited by 59 · see citing works
Performance benefits of depression: Sequential decision making in a healthy sample and a clinically depressed sample.
von Helversen, B., Wilke, A., Johnson, T., Schmid, G., & Klapp, B. F. (2011) · Journal of Abnormal Psychology · doi:10.1037/a0023238 · Cited by 54 · see citing works
Obsessive-compulsive behaviour: a disorder of decision-making
Sachdev, P. S., & Malhi, G. S. (2005) · Australian & New Zealand Journal of Psychiatry · doi:10.1111/j.1440-1614.2005.01680.x · Cited by 54 · see citing works
Allocating conservation resources between areas where persistence of a species is uncertain
McDonald‐Madden, E., Chadès, I., Mccarthy, M. A., Linkie, M., & Possingham, H. P. (2011) · Ecological Applications · doi:10.1890/09-2075.1 · Cited by 52 · see citing works
Decision making in depression: differences in decisional conflict between healthy and depressed individuals
van Randenborgh, A., de Jong‐Meyer, R., & Hüffmeier, J. (2009) · Clinical Psychology & Psychotherapy · doi:10.1002/cpp.651 · Cited by 48 · see citing works
Regret and the negative evaluation of decision outcomes in major depression
Chase, H. W., Camille, N., Michael, A., Bullmore, E. T., Robbins, T. W., & Sahakian, B. J. (2010) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.10.3.406 · Cited by 37 · see citing works
Reinforcement learning with limited reinforcement
Doshi, F., Pineau, J., & Roy, N. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390189 · Cited by 24 · see citing works
Preferences Under Risk: Content-Dependent Behavior and Psychological Processing
Kusev, P., & van Schaik, P. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00269 · Cited by 22 · see citing works
Inconsistency in Risk Preferences: A Psychophysical Anomaly
Vlaev, I. (2011) · Frontiers in Psychology · doi:10.3389/fpsyg.2011.00304 · Cited by 8 · see citing works

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Uncertainty and anticipation in anxiety: an integrated neurobiological and psychological perspective
Grupe, D. W., & Nitschke, J. B. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3524 · Cited by 2,007 · see citing works
Anxious individuals have difficulty learning the causal statistics of aversive environments
Browning, M., Behrens, T. E., Jocham, G., O'Reilly, J. X., & Bishop, S. J. (2015) · Nature Neuroscience · doi:10.1038/nn.3961 · Cited by 476 · see citing works
Interoception and drug addiction
Paulus, M. P., & Stewart, J. L. (2014) · Neuropharmacology · doi:10.1016/j.neuropharm.2013.07.002 · Cited by 281 · see citing works
Self-citation
Reinforcement learning in depression: a review of computational research
Chen, C., Takahashi, T., Nakagawa, S., Inoue, T., & Kusumi, I. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.05.005 · Cited by 233 · see citing works
Physiological feelings
Pace-Schott, E. F., Amole, M. C., Aue, T., Balconi, M., Bylsma, L. M., Critchley, H., Demaree, H. A., Friedman, B. H., Gooding, A. E. K., Gosseries, O., et al. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.05.002 · Cited by 221 · see citing works
“, the difference between an expected internal state and afferent interoceptive data (Barrett and Simmons, 2015; Paulus and Yu, 2012; Saxe and Houlihan, 2017; Seth and Friston, 2016).”
Modeling Trait Anxiety: From Computational Processes to Personality
Raymond, J. G., Steele, J. D., & Seriès, P. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00001 · Cited by 203 · see citing works
Enhanced Risk Aversion, But Not Loss Aversion, in Unmedicated Pathological Anxiety
Charpentier, C. J., Aylward, J., Roiser, J. P., & Robinson, O. J. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.12.010 · Cited by 198 · see citing works
Decision Making: From Neuroscience to Psychiatry
Lee, D. (2013) · Neuron · doi:10.1016/j.neuron.2013.04.008 · Cited by 169 · see citing works
The future is here: A review of foresight systems in anxiety and depression
Miloyan, B., Pachana, N. A., & Suddendorf, T. (2014) · Cognition and Emotion · doi:10.1080/02699931.2013.863179 · Cited by 160 · see citing works
Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation
Klein-Flügge, M. C., Kennerley, S. W., Saraiva, A. C., Penny, W. D., & Bestmann, S. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004116 · Cited by 163 · see citing works
A model for learning based on the joint estimation of stochasticity and volatility
Piray, P., & Daw, N. D. (2021) · Nature Communications · doi:10.1038/s41467-021-26731-9 · Cited by 153 · see citing works
Top-down and bottom-up factors in threat-related perception and attention in anxiety
Sussman, T. J., Jin, J., & Mohanty, A. (2016) · Biological Psychology · doi:10.1016/j.biopsycho.2016.08.006 · Cited by 132 · see citing works
Preclinical animal anxiety research – flaws and prejudices
Ennaceur, A., & Chazot, P. L. (2016) · Pharmacology Research & Perspectives · doi:10.1002/prp2.223 · Cited by 128 · see citing works
Are Executive Functioning Deficits Concurrently and Predictively Associated with Depressive and Anxiety Symptoms in Adolescents?
Han, G., Helm, J. L., Iucha, C., Zahn‐Waxler, C., Hastings, P. D., & Klimes‐Dougan, B. (2015) · Journal of Clinical Child & Adolescent Psychology · doi:10.1080/15374416.2015.1041592 · Cited by 119 · see citing works
The Iowa Gambling Task in depression – what have we learned about sub-optimal decision-making strategies?
Must, A., Horváth, S., Németh, V. L., & Janka, Z. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00732 · Cited by 109 · see citing works
The influence of subcortical shortcuts on disordered sensory and cognitive processing.
McFadyen, J., Dolan, R. J., & Garrido, M. I. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0287-1 · Cited by 91 · see citing works
State anxiety biases estimates of uncertainty and impairs reward learning in volatile environments
Hein, T. P., de Fockert, J., & Ruiz, M. H. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117424 · Cited by 99 · see citing works
Anticipatory Anxiety Disrupts Neural Valuation during Risky Choice
Engelmann, J. B., Meyer, F., Fehr, E., & Ruff, C. C. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2880-14.2015 · Cited by 95 · see citing works
Neural Basis of Emotional Decision Making in Trait Anxiety
Xu, P., Gu, R., Broster, L. S., Wu, R., Dam, N. T. V., Jiang, Y., Fan, J., & Luo, Y. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1253-13.2013 · Cited by 97 · see citing works
The influence of depression symptoms on exploratory decision-making
Blanco, N. J., Otto, A. R., Maddox, W. T., Beevers, C. G., & Love, B. C. (2013) · Cognition · doi:10.1016/j.cognition.2013.08.018 · Cited by 93 · see citing works
The Affective Bases of Risk Perception: Negative Feelings and Stress Mediate the Relationship between Mental Imagery and Risk Perception
Sobków, A., Traczyk, J., & Zaleśkiewicz, T. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00932 · Cited by 93 · see citing works
Effects of anxiety and mood disorders on craving and substance use among patients with substance use disorder: An ecological momentary assessment study
Fatseas, M., Serre, F., Swendsen, J., & Auriacombe, M. (2018) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2018.03.008 · Cited by 89 · see citing works
Decision Making: a Theoretical Review
Morelli, M., Casagrande, M., & Forte, G. (2021) · Integrative Psychological and Behavioral Science · doi:10.1007/s12124-021-09669-x · Cited by 102 · see citing works
Sex differences in emotion-related cognitive processes in irritable bowel syndrome and healthy control subjects
Labus, J. S., Gupta, A., Coveleskie, K., Tillisch, K., Kilpatrick, L. A., Jarcho, J. M., Feier, N. A., Bueller, J. A., Stains, J., Smith, S. E., et al. (2013) · Pain · doi:10.1016/j.pain.2013.06.024 · Cited by 86 · see citing works
Down But Not Yet Out: Depression, Political Efficacy, and Voting
Bernardi, L., Mattila, M., Papageorgiou, A., & Rapeli, L. (2022) · Political Psychology · doi:10.1111/pops.12837 · Cited by 83 · see citing works
Greater decision uncertainty characterizes a transdiagnostic patient sample during approach-avoidance conflict: a computational modelling approach
Smith, R. S., Kirlić, N., Stewart, J. L., Touthang, J., Kuplicki, R., Khalsa, S. S., Feinstein, J. S., Paulus, M. P., & Aupperle, R. L. (2021) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.200032 · Cited by 78 · see citing works
Self-citation
Cooperation of the vestibular and cerebellar networks in anxiety disorders and depression
Hilber, P., Cendelín, J., Gall, A. L., Machado, M., Tůma, J., & Besnard, S. (2018) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2018.10.004 · Cited by 81 · see citing works
Research Paper: Executive Functions in Students With Depression, Anxiety, and Stress Symptoms
Ajilchi, B., & Nejati, V. (2017) · Basic and Clinical Neuroscience Journal · doi:10.18869/nirp.bcn.8.3.223 · Cited by 79 · see citing works
Anxiety, avoidance, and sequential evaluation
Zorowitz, S., Momennejad, I., & Daw, N. (2019) · bioRxiv · doi:10.1162/cpsy_a_00026 · Cited by 64 · see citing works
Reading Emotion From Mouse Cursor Motions: Affective Computing Approach
Yamauchi, T., & Xiao, K. (2017) · Cognitive Science · doi:10.1111/cogs.12557 · Cited by 70 · see citing works
Cognitive control in depression and anxiety: out of control?
Paulus, M. P. (2014) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2014.12.003 · Cited by 71 · see citing works
Self-citation
Treatment approaches for interoceptive dysfunctions in drug addiction
Paulus, M. P., Stewart, J. L., & Haase, L. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00137 · Cited by 69 · see citing works
Self-citation
The Psychological Impact of the COVID-19 Pandemic in Remote Learning in Higher Education
Godoy, L. D., Falcoski, R. M., Incrocci, R. M., Versuti, F. M., & Padovan‐Neto, F. E. (2021) · Education Sciences · doi:10.3390/educsci11090473 · Cited by 70 · see citing works
An experimental study on an adaptive e-learning environment based on learner’s personality and emotion
Fatahi, S. (2019) · Education and Information Technologies · doi:10.1007/s10639-019-09868-5 · Cited by 67 · see citing works
Impairments in goal-directed actions predict treatment response to cognitive-behavioral therapy in social anxiety disorder
Alvares, G. A., Balleine, B. W., & Guastella, A. J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094778 · Cited by 63 · see citing works
The Role of Behavioral Frictions in Health Insurance Marketplace Enrollment and Risk: Evidence from a Field Experiment
Domurat, R., Menashe, I., & Yin, W. (2019) · The American Economic Review · doi:10.1257/aer.20190823 · Cited by 60 · see citing works
Deficits of unconscious emotional processing in patients with major depression: An ERP study
Zhang, D., He, Z., Chen, Y., & Wei, Z. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.03.056 · Cited by 65 · see citing works
Emotionally Aversive Cues Suppress Neural Systems Underlying Optimal Learning in Socially Anxious Individuals
Piray, P., Ly, V., Karin Roelofs, A., Cools, R., & Toni, I. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1394-18.2018 · Cited by 61 · see citing works
A Decision Architecture for Safety Computations
Tashjian, S. M., Zbozinek, T. D., & Mobbs, D. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.01.013 · Cited by 60 · see citing works
Categorization=decision making+generalization
Seger, C. A., & Peterson, E. J. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.03.015 · Cited by 59 · see citing works
Z*-numbers: Augmented Z-numbers for machine-subjectivity representation
Banerjee, R., & Pal, S. K. (2015) · Information Sciences · doi:10.1016/j.ins.2015.06.026 · Cited by 57 · see citing works
Predicting decisional conflict: Anxiety and depression in shared decision making
Köther, A. K., Alpers, G. W., Büdenbender, B., Lenhart, M., Michel, M. S., & KRIEGMAIR, M. C. (2020) · Patient Education and Counseling · doi:10.1016/j.pec.2020.10.037 · Cited by 56 · see citing works
Mental Capacity, Decision-Making and Emotion Dysregulation in Severe Enduring Anorexia Nervosa
van Elburg, A., Danner, U. N., Sternheim, L. C., Lammers, M., & Elzakkers, I. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.545317 · Cited by 55 · see citing works
Executive control suppresses pupillary responses to aversive stimuli.
Cohen, N., Moyal, N., & Henik, A. (2015) · Biological Psychology · doi:10.1016/j.biopsycho.2015.09.006 · Cited by 50 · see citing works
Neural loss aversion differences between depression patients and healthy individuals: A functional MRI investigation
Pammi, V. S. C., Rajesh, P. P. G., Kesavadas, C., Mary, P. R., Seema, S., Radhakrishnan, A., & Sitaram, R. (2015) · The Neuroradiology Journal · doi:10.1177/1971400915576670 · Cited by 51 · see citing works
Impaired decision making and delayed memory are related with anxiety and depressive symptoms in acromegaly
Crespo, I., Vives, A. S., Valassi, E., Pires, P., Webb, S. M., & Resmini, E. (2015) · Endocrine · doi:10.1007/s12020-015-0634-6 · Cited by 49 · see citing works
Depression and political predispositions: Almost blue?
Bernardi, L. (2020) · Party Politics · doi:10.1177/1354068820930391 · Cited by 47 · see citing works
Web-browsing patterns reflect and shape mood and mental health
Kelly, C., & Sharot, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02065-6 · Cited by 47 · see citing works
High trait anxiety is associated with attenuated feedback-related negativity in risky decision making
Takács, Á., Kóbor, A., Janacsek, K., Honbolygó, F., Csépe, V., & Németh, D. (2015) · Neuroscience Letters · doi:10.1016/j.neulet.2015.06.022 · Cited by 46 · see citing works
Gender, Exposure to News, Knowledge About Climate Change, and Prosociality Predict Climate Anxiety Scores in Mexican Students
Ramírez-López, A. S., Rosetti, M., & Poma, A. (2022) · Ecopsychology · doi:10.1089/eco.2022.0049 · Cited by 44 · see citing works
Computational processes of simultaneous learning of stochasticity and volatility in humans
Piray, P., & Daw, N. D. (2024) · Nature Communications · doi:10.1038/s41467-024-53459-z · Cited by 45 · see citing works
A neural and behavioral trade-off between value and uncertainty underlies exploratory decisions in normative anxiety
Aberg, K. C., Toren, I., & Paz, R. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01363-z · Cited by 44 · see citing works
Trait anxiety impairs cognitive flexibility when overcoming a task acquired response and a preexisting bias
Wilson, C. G., Nusbaum, A. T., Whitney, P. J., & Hinson, J. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204694 · Cited by 44 · see citing works
Increased inflammation predicts nine-year change in major depressive disorder diagnostic status.
Zainal, N. H., & Newman, M. G. (2021) · Journal of Abnormal Psychology · doi:10.1037/abn0000716 · Cited by 41 · see citing works
Hemispheric Asymmetries in Striatal Reward Responses Relate to Approach–Avoidance Learning and Encoding of Positive–Negative Prediction Errors in Dopaminergic Midbrain Regions
Aberg, K. C., Doell, K. C., & Schwartz, S. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1859-15.2015 · Cited by 42 · see citing works
Smartphone-based prenatal education for parents with preterm birth risk factors
Kim, U. O., Barnekow, K., Ahamed, S. I., Dreier, S., Jones, C., Taylor, M., Hasan, M. K., & Basir, M. (2018) · Patient Education and Counseling · doi:10.1016/j.pec.2018.10.024 · Cited by 40 · see citing works
Dopamine, depressive symptoms, and decision-making: the relationship between spontaneous eye blink rate and depressive symptoms predicts Iowa Gambling Task performance
Byrne, K. A., Norris, D. D., & Worthy, D. A. (2015) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-015-0377-0 · Cited by 37 · see citing works
Understanding the neural code of stress to control anhedonia
Xia, F., Fascianelli, V., Vishwakarma, N., Ghinger, F. G., Kwon, A., Gergues, M. M., Lalani, L. K., Fusi, S., & Kheirbek, M. A. (2024) · Nature · doi:10.1038/s41586-024-08241-y · Cited by 38 · see citing works
Depression and attitudes to change in referendums: The case of Brexit
Bernardi, L., & Johns, R. (2020) · European Journal of Political Research · doi:10.1111/1475-6765.12398 · Cited by 37 · see citing works
Beta coherence within human ventromedial prefrontal cortex precedes affective value choices
Lipsman, N., Kaping, D., Westendorff, S., Sankar, T., Lozano, A. M., & Womelsdorf, T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.104 · Cited by 37 · see citing works
Psychiatric Neural Networks and Precision Therapeutics by Machine Learning
Komatsu, H., Watanabe, E., & Fukuchi, M. (2021) · Biomedicines · doi:10.3390/biomedicines9040403 · Cited by 37 · see citing works
State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward-based learning
Hein, T. P., & Ruiz, M. H. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118895 · Cited by 37 · see citing works
The Role of the Cerebellar and Vestibular Networks in Anxiety Disorders and Depression: the Internal Model Hypothesis
Hilber, P. (2022) · The Cerebellum · doi:10.1007/s12311-022-01400-9 · Cited by 38 · see citing works
The quiet virtues of sadness: A selective theoretical and interpretative appreciation of its potential contribution to wellbeing
Lomas, T. (2018) · New Ideas in Psychology · doi:10.1016/j.newideapsych.2018.01.002 · Cited by 35 · see citing works
Enhancing the psychometric properties of the Iowa gambling task using full generative modeling
Sullivan-Toole, H., Haines, N., Dale, K., & Olino, T. M. (2022) · Computational Psychiatry · doi:10.5334/cpsy.89 · Cited by 35 · see citing works
Patient Experiences of Web-Based Cognitive Behavioral Therapy for Heart Failure and Depression: Qualitative Study
Lundgren, J., Johansson, P., Jaarsma, T., Andersson, G., & Kärner Köhler, A. (2018) · Journal of Medical Internet Research · doi:10.2196/10302 · Cited by 33 · see citing works
Hyper-responsivity to losses in the anterior insula during economic choice scales with depression severity
Engelmann, J. B., Berns, G. S., & Dunlop, B. W. (2017) · Psychological Medicine · doi:10.1017/s0033291717001428 · Cited by 33 · see citing works
Altered subjective reward valuation among drug-deprived heavy marijuana users: Aversion to uncertainty.
Hefner, K. R., Starr, M. J., & Curtin, J. J. (2015) · Journal of Abnormal Psychology · doi:10.1037/abn0000106 · Cited by 32 · see citing works
Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans
Hein, T. P., Gong, Z., Ivanova, M., Fedele, T., Nikulin, V. V., & Ruiz, M. H. (2023) · Communications Biology · doi:10.1038/s42003-023-04628-1 · Cited by 32 · see citing works
Modelling consumer choice through the random regret minimization model: An application in the food domain
Biondi, B., van der Lans, I. A., Mazzocchi, M., Fischer, A. R. H., van Trijp, J. C. M., & Camanzi, L. (2018) · Food Quality and Preference · doi:10.1016/j.foodqual.2018.12.008 · Cited by 32 · see citing works
Trait anxiety and probabilistic learning: Behavioral and electrophysiological findings
Jiang, D., Zhang, D., Chen, Y., He, Z., Gao, Q., Gu, R., & Xu, P. (2017) · Biological Psychology · doi:10.1016/j.biopsycho.2017.10.010 · Cited by 29 · see citing works
Environmental Beliefs and Pro-Environmental Behavioral Intention of an Environmentally Themed Exhibition Audience: The Mediation Role of Exhibition Attachment
Li, X., Yu, R., & Su, X. (2021) · SAGE Open · doi:10.1177/21582440211027966 · Cited by 31 · see citing works
Behavioral preference in sequential decision‐making and its association with anxiety
Zhang, D., & Gu, R. (2018) · Human Brain Mapping · doi:10.1002/hbm.24016 · Cited by 31 · see citing works
Intolerance of uncertainty in children with attention-deficit/hyperactivity disorder
Gramszlo, C., Fogleman, N. D., Rosen, P. J., & Woodruff‐Borden, J. (2017) · ADHD Attention Deficit and Hyperactivity Disorders · doi:10.1007/s12402-017-0244-7 · Cited by 28 · see citing works
Analysis of hand kinematics reveals inter-individual differences in intertemporal decision dynamics
Calluso, C., Committeri, G., Pezzulo, G., Lepora, N. F., & Tosoni, A. (2015) · Experimental Brain Research · doi:10.1007/s00221-015-4427-1 · Cited by 31 · see citing works
Gut Signals and Gut Feelings: Science at the Interface of Data and Beliefs
Holzer, P. D. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.929332 · Cited by 29 · see citing works
Bayesian Models of Individual Differences
Powell, G., Meredith, Z., McMillin, R., & Freeman, T. C. A. (2016) · Psychological Science · doi:10.1177/0956797616665351 · Cited by 26 · see citing works
Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning
Aberg, K. C., Doell, K. C., & Schwartz, S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0166675 · Cited by 28 · see citing works
Modeling of agent-based complex network under cyber-violence
Huang, C., Hu, B., Jiang, G., & Yang, R. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.03.066 · Cited by 27 · see citing works
Emotions and caring mediate the relationship between knowledge of sustainability and willingness to pay for greener aviation
Ragbir, N. K., Rice, S. C., Winter, S. R., & Choy, E. C. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101491 · Cited by 26 · see citing works
Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms
Flannery, J. S., Riedel, M. C., Bottenhorn, K. L., Poudel, R., Salo, T., Hill-Bowen, L. D., Laird, A. R., & Sutherland, M. T. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00763-7 · Cited by 25 · see citing works
The computational structure of consummatory anhedonia
Hall, A., Browning, M. D., & Huys, Q. J. M. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.006 · Cited by 29 · see citing works
Experimentally induced and real-world anxiety have no demonstrable effect on goal-directed behaviour
Gillan, C. M., Vaghi, M. M. S., Hezemans, F. H., Grothe, S. V. G., Dafflon, J., Brühl, A. B., Savulich, G., & Robbins, T. W. (2020) · Psychological Medicine · doi:10.1017/s0033291720000203 · Cited by 29 · see citing works
Anxiety and Depression in Metastatic Cancer: A Critical Review of Negative Impacts on Advance Care Planning and End-of-Life Decision Making With Practical Recommendations
Arch, J. J., Bright, E. E., Finkelstein, L. B., Fink, R. M., Mitchell, J. L., Andorsky, D. J., & Kutner, J. S. (2023) · JCO Oncology Practice · doi:10.1200/op.23.00287 · Cited by 25 · see citing works
Cooperative versus competitive influences of emotion and cognition on decision making: A primer for psychiatry research
Chick, C. F. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.01.048 · Cited by 24 · see citing works
A fuzzy cognitive map model to calculate a user's desirability based on personality in e-learning environments
Fatahi, S., & Moradi, H. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.041 · Cited by 24 · see citing works
Shared Decision Making during the COVID-19 Pandemic
Köther, A. K., Siebenhaar, K. U., & Alpers, G. W. (2021) · Medical Decision Making · doi:10.1177/0272989x211004147 · Cited by 22 · see citing works
Impaired probabilistic reversal learning in anxiety: Evidence from behavioral and ERP findings
Xia, L., Xu, P., Yang, Z.-Y., Gu, R.-L., & Zhang, D.-D. (2021) · NeuroImage: Clinical · doi:10.1016/j.nicl.2021.102751 · Cited by 18 · see citing works
The relationship between intolerance of uncertainty in chiropractic students and their treatment intervention choices
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Authors: Paulus, M. P., & Yu, A. J.
Affiliations: University of California San Diego (US)
Corresponding author: Paulus, Martin P.
ORCID: Paulus, Martin P.
Yu, Angela J.
Venue: Trends in Cognitive Sciences
Publisher: Elsevier BV
Published: 2012-09
Volume: 16
Issue: 9
Pages: 476-483
ISSN: 1364-6613 (ISSN-L)
DOI: 10.1016/j.tics.2012.07.009
PMC: PMC3446252
OpenAlex: W2072888936
Cited by: 287
References: 110
Citations by year: 2026: 18 · 2025: 16 · 2024: 28 · 2023: 23 · 2022: 21 · 2021: 39 · 2020: 29 · 2019: 16 · 2018: 15 · 2017: 23 · 2016: 16 · 2015: 19 · 2014: 10 · 2013: 13 · 2012: 1
FWCI: 1.09
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Trends in Cognitive Sciences (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
TUbilio (Technical University of Darmstadt) (submittedVersion)
Funders: National Institute on Drug Abuse (R01DA016663, R01DA018307, R03DA030440, P20DA027843, R01DA027797)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Keywords: emotional decision-making, anxiety, depression, Bayesian decision making, valence and arousal, partially observable Markov decision processes, belief states, emotional dysfunction, mood disorders
MeSH terms: Affect, Anxiety Disorders, Arousal, Bayes Theorem, Decision Making, Depressive Disorder, Emotions, Humans

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Emotion and decision-making: affect-driven belief systems in anxiety and depression

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