Mood as representation of momentum
Open accessAbstract
Experiences affect mood, which in turn affects subsequent experiences. Recent studies suggest two specific principles. First, mood depends on how recent reward outcomes differ from expectations. Second, mood biases the way we perceive outcomes (e.g., rewards), and this bias affects learning about those outcomes. We propose that this two-way interaction serves to mitigate inefficiencies in the application of reinforcement learning to real-world problems. Specifically, we propose that mood represents the overall momentum of recent outcomes, and its biasing influence on the perception of outcomes 'corrects' learning to account for environmental dependencies. We describe potential dysfunctions of this adaptive mechanism that might contribute to the symptoms of mood disorders.
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While in typical experience, subjective valuation functions to fine-tune learning and regulate behaviour, the same mechanisms are in place in atypical experience such as the psychedelic state.
“While in typical experience, subjective valuation functions to fine-tune learning (Eldar et al. 2016) and regulate behaviour, we can see the same mechanisms in place in atypical experience such as the psychedelic state.”
From Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution (2021) · cites this paper as Eldar et al. 2016
Mood changes could reflect potential dysfunctions of adaptation to the environment and might contribute to symptoms of mood disorders.
“it is postulated that mood changes could reflect the potential dysfunctions of the adaption to our environment and that it might contribute to the symptoms of mood disorders [12,20,21].”
From Acceptance Mindfulness-Trait as a Protective Factor for Post-Natal Depression: A Preliminary Research (2022) · cites this paper as [12]
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Cites
Watkins, C., & Dayan, P. (1992) · Machine-mediated learning · doi:10.1023/a:1022676722315 · Cited by 14,277 · see citing works
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · see citing works
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083 · see citing works
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855 · see citing works
A Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method
Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004) · Science · doi:10.1126/science.1103572 · Cited by 3,422 · see citing works
Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B., Freedman, G., Murray, C. J. L., Vos, T., & Whiteford, H. A. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001547 · Cited by 3,115 · see citing works
Pattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,782 · see citing works
A wandering mind is an unhappy mind
Killingsworth, M. A., & Gilbert, D. T. (2010) · Science · doi:10.1126/science.1192439 · Cited by 2,575 · see citing works
Validity and reliability of the Experience-Sampling Method
CSIKSZENTMIHALYI, M., & LARSON, R. (1987) · The Journal of Nervous and Mental Disease · doi:10.1097/00005053-198709000-00004 · Cited by 2,544 · see citing works
Jordan, M. I. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9 · Cited by 2,449 · see citing works
Lottery winners and accident victims: is happiness relative?
Brickman, P., Coates, D., & Janoff-Bulman, R. (1978) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.36.8.917 · Cited by 2,225 · see citing works
Happiness Is a Stochastic Phenomenon
Lykken, D., & Tellegen, A. (1996) · Psychological Science · doi:10.1111/j.1467-9280.1996.tb00355.x · Cited by 1,719 · see citing works
Lottery winners and accident victims: Is happiness relative?
Brickman, P., Coates, D., & Janoff‐Bulman, R. (1978) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.36.8.917 · Cited by 1,707 · see citing works
Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans
Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J., & Frith, C. D. (2006) · Nature · doi:10.1038/nature05051 · Cited by 1,584 · see citing works
When More Pain Is Preferred to Less: Adding a Better End
Kahneman, D., Fredrickson, B. L., Schreiber, C. A., & Redelmeier, D. A. (1993) · Psychological Science · doi:10.1111/j.1467-9280.1993.tb00589.x · Cited by 1,502 · see citing works
Neuron-type-specific signals for reward and punishment in the ventral tegmental area
Cohen, J. Y., Haesler, S., Vong, L., Lowell, B. B., & Uchida, N. (2012) · Nature · doi:10.1038/nature10754 · Cited by 1,318 · see citing works
Diener, E., & Diener, C. I. (1996) · Psychological Science · doi:10.1111/j.1467-9280.1996.tb00354.x · Cited by 1,247 · see citing works
Sports Sentiment and Stock Returns
Edmans, A., Garcı́a, D., & Norli, Ø. (2007) · The Journal of Finance · doi:10.1111/j.1540-6261.2007.01262.x · Cited by 1,045 · see citing works
Opponent interactions between serotonin and dopamine
Daw, N. D., Kakade, S., & Dayan, P. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00052-7 · Cited by 844 · see citing works
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.018 · Cited by 809 · see citing works
Hare, T. A., O'Doherty, J., Camerer, C. F., Schultz, W., & Rangel, A. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.1309-08.2008 · Cited by 792 · see citing works
Decision Affect Theory: Emotional Reactions to the Outcomes of Risky Options
Mellers, B. A., Schwartz, A., Ho, K., & Ritov, I. (1997) · Psychological Science · doi:10.1111/j.1467-9280.1997.tb00455.x · Cited by 736 · see citing works
Objective Confirmation of Subjective Measures of Human Well-Being: Evidence from the U.S.A.
Oswald, A., & Wu, S. (2010) · Science · doi:10.1126/science.1180606 · Cited by 730 · see citing works
Reinforcement learning in the brain
Niv, Y. (2009) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2008.12.005 · Cited by 724 · see citing works
Halek, M., & Eisenhauer, J. G. (2001) · Journal of Risk & Insurance · doi:10.2307/2678130 · Cited by 708 · see citing works
Mood effects on subjective probability assessment
Wright, W. F., & Bower, G. H. (1992) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(92)90039-a · Cited by 681 · see citing works
The effect of positive feelings on risk taking: When the chips are down
Isen, A. M., & Patrick, R. (1983) · Organizational Behavior and Human Performance · doi:10.1016/0030-5073(83)90120-4 · Cited by 669 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Jahng, S., Wood, P. K., & Trull, T. J. (2008) · Psychological Methods · doi:10.1037/a0014173 · Cited by 593 · see citing works
Decision theory, reinforcement learning, and the brain
Dayan, P., & Daw, N. D. (2008) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/cabn.8.4.429 · Cited by 580 · see citing works
Weather, mood, and helping behavior: Quasi experiments with the sunshine samaritan.
Cunningham, M. R. (1979) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.37.11.1947 · Cited by 552 · see citing works
A Dark Side of Happiness? How, When, and Why Happiness Is Not Always Good
Gruber, J., Mauss, I. B., & Tamir, M. (2011) · Perspectives on Psychological Science · doi:10.1177/1745691611406927 · Cited by 515 · see citing works
Mood-congruent judgment is a general effect.
Mayer, J. D., Gaschke, Y. N., Braverman, D. L., & Evans, T. W. (1992) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.63.1.119 · Cited by 499 · see citing works
Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis
Huys, Q. J., Pizzagalli, D. A., Bogdan, R., & Dayan, P. (2013) · Biology of Mood & Anxiety Disorders · doi:10.1186/2045-5380-3-12 · Cited by 480 · see citing works
Serotonin and dopamine: unifying affective, activational, and decision functions.
Cools, R., Nakamura, K., & Daw, N. D. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2010.121 · Cited by 464 · see citing works
A computational and neural model of momentary subjective well-being
Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1407535111 · Cited by 444 · see citing works
Self-citationReduced Reward Learning Predicts Outcome in Major Depressive Disorder
Vrieze, E., Pizzagalli, D. A., Demyttenaere, K., Hompes, T., Sienaert, P. A., de Boer, P., Schmidt, M. E., & Claes, S. J. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.10.014 · Cited by 443 · see citing works
Social and economic burden of mood disorders
Simon, G. E. (2003) · Biological Psychiatry · doi:10.1016/s0006-3223(03)00420-7 · Cited by 430 · see citing works
Mood and self-efficacy: Impact of joy and sadness on perceived capabilities
Kavanagh, D. J., & Bower, G. H. (1985) · Cognitive Therapy and Research · doi:10.1007/bf01173005 · Cited by 419 · see citing works
Serotonergic neurons signal reward and punishment on multiple timescales
Cohen, J. Y., Amoroso, M. W., & Uchida, N. (2015) · eLife · doi:10.7554/elife.06346 · Cited by 388 · see citing works
Emotional reactivity to daily events in major and minor depression.
Bylsma, L. M., Taylor-Clift, A., & Rottenberg, J. (2011) · Journal of Abnormal Psychology · doi:10.1037/a0021662 · Cited by 388 · see citing works
Psychiatric Comorbidity in Pathological Gambling: A Critical Review
Crockford, D. N., & el‐Guebaly, N. (1998) · The Canadian Journal of Psychiatry · doi:10.1177/070674379804300104 · Cited by 384 · see citing works
Neural prediction errors reveal a risk-sensitive reinforcement-learning process in the human brain
Niv, Y., Edlund, J. A., Dayan, P., & O'Doherty, J. P. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5498-10.2012 · Cited by 356 · see citing works
Which Comes First in Adolescence—Sex and Drugs or Depression?
Hallfors, D. D., Waller, M. W., Bauer, D. J., Ford, C. A., & Halpern, C. T. (2005) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2005.06.002 · Cited by 349 · see citing works
Linking nucleus accumbens dopamine and blood oxygenation
Knutson, B., & Gibbs, S. E. B. (2007) · Psychopharmacology · doi:10.1007/s00213-006-0686-7 · Cited by 337 · see citing works
Korn, C. W., Sharot, T., Walter, H., Heekeren, H. R., & Dolan, R. J. (2014) · Psychological Medicine · doi:10.1017/s0033291713001074 · Cited by 334 · see citing works
Bari, A., Theobald, D. E., Caprioli, D., Mar, A. C., Aidoo-Micah, A., Dalley, J. W., & Robbins, T. W. (2010) · Neuropsychopharmacology · doi:10.1038/npp.2009.233 · Cited by 325 · see citing works
Wang, X., & Krystal, J. H. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.018 · Cited by 309 · see citing works
Reimagining where we live: cultural placemaking and the \nlevelling up agenda
Lansley, A. (2022) · Research Repository (University of Gloucestershire) · Cited by 307 · see citing works
Reward Signals, Attempted Suicide, and Impulsivity in Late-Life Depression
Dombrovski, A. Y., Szanto, K., Clark, L., Reynolds, C. F., & Siegle, G. J. (2013) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2013.75 · Cited by 289 · see citing works
The role of potential loss in the influence of affect on risk-taking behavior
Arkes, H. R., Herren, L. T., & Isen, A. M. (1988) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(88)90011-8 · Cited by 279 · see citing works
State affective instability in borderline personality disorder assessed by ambulatory monitoring
Ebner‐Priemer, U., Kuo, J. R., Kleindienst, N., Welch, S. S., Reisch, T., Reinhard, I., Lieb, K., Linehan, M. M., & Bohus, M. (2007) · Psychological Medicine · doi:10.1017/s0033291706009706 · Cited by 255 · see citing works
'O Sole Mio: An Experimental Analysis of Weather and Risk Attitudes in Financial Decisions
Bassi, A., Colacito, R., & Fulghieri, P. (2012) · Review of Financial Studies · doi:10.1093/rfs/hht004 · Cited by 253 · see citing works
A normative perspective on motivation
NIV, Y., JOEL, D., & DAYAN, P. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.06.010 · Cited by 251 · see citing works
Self-citationNatural selection and the elusiveness of happiness
Nesse, R. M. (2004) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2004.1511 · Cited by 249 · see citing works
Depression: a decision-theoretic analysis
Huys, Q. J. M., Daw, N. D., & Dayan, P. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-033928 · Cited by 221 · see citing works
Serotonin and emotional processing: does it help explain antidepressant drug action?
Harmer, C. J. (2008) · Neuropharmacology · doi:10.1016/j.neuropharm.2008.06.036 · Cited by 219 · see citing works
Dopaminergic Modulation of Decision Making and Subjective Well-Being
Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0702-15.2015 · Cited by 218 · see citing works
Sports, Games, and Play: Social and Psychological Viewpoints
Gonzales, J. L. (1990) · Sociology of Sport Journal · doi:10.1123/ssj.7.2.195 · Cited by 211 · see citing works
The Affective Consequences of Expected and Unexpected Outcomes
Shepperd, J. A., & McNulty, J. K. (2002) · Psychological Science · doi:10.1111/1467-9280.00416 · Cited by 202 · see citing works
Somerville, L. H., Wagner, D. D., Wig, G. S., Moran, J. M., Whalen, P. J., & Kelley, W. M. (2013) · Cerebral Cortex · doi:10.1093/cercor/bhr373 · Cited by 196 · see citing works
Brown, L. K., Tolou‐Shams, M., Lescano, C. M., Houck, C. D., Zeidman, J. A., Pugatch, D. L., & Lourie, K. J. (2006) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2006.01.015 · Cited by 194 · see citing works
Interaction between emotional state and learning underlies mood instability
Eldar, E., & Niv, Y. (2015) · Nature Communications · doi:10.1038/ncomms7149 · Cited by 185 · see citing works
The affective control of thought: Malleable, not fixed.
Huntsinger, J. R., Isbell, L. M., & Clore, G. L. (2014) · Psychological Review · doi:10.1037/a0037669 · Cited by 158 · see citing works
Why wasn't O.J. convicted? Emotional coherence in legal inference
Thagard, P. R. (2003) · Cognition & Emotion · doi:10.1080/0269993024400002 · Cited by 149 · see citing works
(2012) · Psychology Press eBooks · doi:10.4324/9780203728376-13 · Cited by 143 · see citing works
The dorsomedial striatum encodes net expected return, critical for energizing performance vigor
Wang, A. Y., Miura, K., & Uchida, N. (2013) · Nature Neuroscience · doi:10.1038/nn.3377 · Cited by 128 · see citing works
Pathological Gambling and Depression
Becoña, E., Lorenzo, M. D. C., & Fuentes, M. J. A. (1996) · Psychological Reports · doi:10.2466/pr0.1996.78.2.635 · Cited by 105 · see citing works
Marsella, S., & Gratch, J. S. (2002) · Adaptive Agents and Multi-Agents Systems · doi:10.1145/544741.544821 · Cited by 105 · see citing works
Are computational models of any use to psychiatry?
Huys, Q. J. M., Moutoussis, M., & Williams, J. H. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.03.001 · Cited by 102 · see citing works
Ghosts, UFOs, and magic: Positive affect and the experiential system.
King, L. A., Burton, C. M., Hicks, J. A., & Drigotas, S. M. (2007) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.92.5.905 · Cited by 97 · 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
Stress increases aversive prediction error signal in the ventral striatum
Robinson, O. J., Overstreet, C. M., Charney, D. R., Vytal, K. E., & Grillon, C. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1213923110 · Cited by 91 · see citing works
Huntsinger, J. R. (2012) · Journal of Experimental Psychology General · doi:10.1037/a0027709 · Cited by 82 · see citing works
Measuring Beliefs and Rewards: A Neuroeconomic Approach*
Caplin, A., Dean, M. R. W., Glimcher, P. W., & Rutledge, R. B. (2010) · The Quarterly Journal of Economics · doi:10.1162/qjec.2010.125.3.923 · Cited by 63 · see citing works
Neural correlates of affective priming effects based on masked facial emotion: An fMRI study
Suslow, T., Kugel, H., Ohrmann, P., Stuhrmann, A., Grotegerd, D., Redlich, R., Bauer, J., & Dannlowski, U. (2012) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2012.09.008 · Cited by 58 · see citing works
Advanced Reinforcement Learning
Daw, N. D. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-416008-8.00016-4 · Cited by 44 · see citing works
Parker, R. M., Paul, E. S., Burman, O. H. P., Browne, W. J., & Mendl, M. (2014) · Behavioural Brain Research · doi:10.1016/j.bbr.2014.07.048 · Cited by 43 · see citing works
Genetic Determinants of Financial Risk Taking
Kuhnen, C. M., & Chiao, J. Y. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004362 · Cited by 35 · see citing works
Statistical Models of Conditioning
Dayan, P., & Long, T. W. (1997) · Cited by 34 · see citing works
Describing men, describing women: Sex membership salience and numerical distinctiveness
Pichevin, M.-F., & Hurtig, M.-C. (1996) · European Journal of Social Psychology · doi:10.1002/(sici)1099-0992(199607)26:4 · Cited by 33 · see citing works
Does happiness induce a rosy outlook
Headey, B., & Veenhoven, R. (1989) · RePub (Erasmus University Rotterdam) · Cited by 22 · see citing works
The effects of induced mood on irrational thoughts and views of the world
Madigan, R. J., & Bollenbach, A. K. (1986) · Cognitive Therapy and Research · doi:10.1007/bf01177818 · Cited by 19 · see citing works
Impact of emotional context congruency on decision making under ambiguity.
Aïte, A., Borst, G., Moutier, S., Varescon, I., Brown, I., Houdé, O., & Cassotti, M. (2013) · Emotion · doi:10.1037/a0031345 · Cited by 18 · see citing works
Affective facilitation and inhibition of cultural influences on reasoning
Koo, M., Clore, G. L., Kim, J. M., et al. (2011) · Cognition & Emotion · doi:10.1080/02699931.2011.613920 · Cited by 13 · see citing works
Flagged first, then by citations
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Modeling the Predictive Social Mind
Tamir, D., & Thornton, M. A. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.12.005 · Cited by 286 · see citing works
“More generally, psychiatric disorders might be effectively characterized by disordered state transition experiences rather than by only over- or underabundance of particular states [40,41].”
BackgroundMedial and orbital frontal cortex in decision-making and flexible behavior
Klein-Flügge, M. C., Bongioanni, A., & Rushworth, M. F. S. (2022) · Neuron · doi:10.1016/j.neuron.2022.05.022 · Cited by 244 · see citing works
Deeply Felt Affect: The Emergence of Valence in Deep Active Inference
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021) · Neural Computation · doi:10.1162/neco_a_01341 · Cited by 246 · see citing works
“More recent models have suggested that mood reflects the recent history of reward prediction errors, which serves the function of biasing perception of future reward (Eldar et al., 2016; Eldar & Niv, 2015).”
BackgroundDistorted Cognitive Processes in Major Depression: A Predictive Processing Perspective
Κube, T., Schwarting, R. K. W., Rozenkrantz, L., Glombiewski, J. A., & Rief, W. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.07.017 · Cited by 238 · see citing works
The self in context: brain systems linking mental and physical health
Koban, L., Gianaros, P. J., Hedy Kober, A., & Wager, T. D. (2021) · Nature reviews. Neuroscience · doi:10.1038/s41583-021-00446-8 · Cited by 241 · see citing works
Modeling psychopathology: From data models to formal theories.
Haslbeck, J. M. B., Ryan, O., Robinaugh, D. J., Waldorp, L., & Borsboom, D. (2021) · Psychological Methods · doi:10.1037/met0000303 · Cited by 222 · see citing works
Anhedonia in depression: biological mechanisms and computational models
Cooper, J. A., Arulpragasam, A. R., & Treadway, M. T. (2018) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.01.024 · Cited by 188 · see citing works
“measures of reward reactivity, learning, and decision-making to emotional experience and mood states that may be disrupted in mood disorders [14,15].”
Advances in the computational understanding of mental illness
Huys, Q. J. M., Browning, M. D., Paulus, M. P., & Frank, M. J. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0746-4 · Cited by 184 · see citing works
Carruthers, P. (2018) · Philosophy and Phenomenological Research · doi:10.1111/phpr.12395 · Cited by 177 · see citing works
“…the positive valence deriving from good things higher than would otherwise be expected, thus strengthening the evaluative-learning signal and allowing one to adjust to changes in the distribution of goods faster than would be possible through evaluative conditioning alone (Eldar et al., 2016).”
Animal affect and decision-making
Mendl, M., & Paul, E. S. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.01.025 · Cited by 161 · see citing works
Positive affect, surprise, and fatigue are correlates of network flexibility
Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-00425-z · Cited by 158 · see citing works
Algorithms for survival: a comparative perspective on emotions
Bach, D. R., & Dayan, P. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2017.35 · Cited by 144 · see citing works
Juechems, K., & Summerfield, C. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.07.012 · Cited by 147 · see citing works
Perry, A., Roberts, G. M. P., Mitchell, P. B., & Breakspear, M. J. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0267-2 · Cited by 146 · see citing works
Lewis, C. A., Kimmig, A.-C. S., Zsido, R. G., Jank, A., Derntl, B., & Sacher, J. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1095-z · Cited by 118 · see citing works
The geometry of domain-general performance monitoring in the human medial frontal cortex
Fu, Z., Beam, D. E., Chung, J. M., Reed, C. M., Mamelak, A. N., Adolphs, R., & Rutishauser, U. (2022) · Science · doi:10.1126/science.abm9922 · Cited by 102 · see citing works
Social anxiety is characterized by biased learning about performance and the self.
Koban, L., Schneider, R., Ashar, Y. K., Andrews‐Hanna, J. R., Landy, L. N., Moscovitch, D. A., Wager, T. D., & Arch, J. J. (2017) · Emotion · doi:10.1037/emo0000296 · Cited by 103 · see citing works
Advances in modeling learning and decision-making in neuroscience
Collins, A. G. E., & Shenhav, A. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01126-y · Cited by 104 · see citing works
“Complementary research has examined how these changes in affective states feed back into one ’ s estimates of future outcomes, resulting in biases in learning and choice [229 – 231].”
BackgroundNeuro-computational account of how mood fluctuations arise and affect decision making
Vinckier, F., Rigoux, L., Oudiette, D., & Pessiglione, M. (2018) · Nature Communications · doi:10.1038/s41467-018-03774-z · Cited by 101 · see citing works
“Yet in an auto-correlated environment, the same effect would be adaptive 16 .”
BackgroundMood Instability and Reward Dysregulation—A Neurocomputational Model of Bipolar Disorder
Mason, L., Eldar, E., & Rutledge, R. B. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.3163 · Cited by 100 · see citing works
Signed and unsigned reward prediction errors dynamically enhance learning and memory
Rouhani, N., & Niv, Y. (2021) · eLife · doi:10.7554/elife.61077 · Cited by 105 · see citing works
Levy, I., & Schiller, D. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.11.007 · Cited by 95 · see citing works
A simple model for learning in volatile environments
Piray, P., & Daw, N. D. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007963 · Cited by 104 · see citing works
Rethinking post-traumatic stress disorder – A predictive processing perspective
Κube, T., Berg, M., Kleim, B., & Herzog, P. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.04.014 · Cited by 101 · see citing works
Play in predictive minds: A cognitive theory of play.
Andersen, M. M., Kiverstein, J., Miller, M., & Roepstorff, A. (2023) · Psychological Review · doi:10.1037/rev0000369 · Cited by 122 · see citing works
Chronic and Acute Stress Promote Overexploitation in Serial Decision Making
Lenow, J., Constantino, S., Daw, N., & Phelps, E. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3618-16.2017 · Cited by 83 · see citing works
“In fact, it is possible that psychological-level accounts of mood and appraisal can actually be more precisely understood in terms of computational-level mechanisms that entail assessing the average reward within environments (Huys et al., 2015; Eldar et al., 2016).”
BackgroundDecodability of Reward Learning Signals Predicts Mood Fluctuations
Eldar, E., Roth, C., Dayan, P., & Dolan, R. J. (2018) · Current Biology · doi:10.1016/j.cub.2018.03.038 · Cited by 84 · see citing works
“The finding of multiple timescales adds to previous theoretical accounts of mood as reflecting changes in the availability of reward [3, 26], suggesting that fast and slow changes in mood track short-term and long-term changes in this availability.”
A mechanistic account of serotonin’s impact on mood
Michely, J., Eldar, E., Martin, I. M., & Dolan, R. J. (2020) · Nature Communications · doi:10.1038/s41467-020-16090-2 · Cited by 72 · see citing works
Moutoussis, M., Shahar, N., Hauser, T., & Dolan, R. (2017) · Computational Psychiatry · doi:10.1162/cpsy_a_00014 · Cited by 68 · see citing works
“Practice has therefore already been helped at the level of psychoeducation, which is central to therapy (Moutoussis, Eldar et al., 2016).”
BackgroundNon-invasive vagus nerve stimulation boosts mood recovery after effort exertion
Ferstl, M., Teckentrup, V., Lin, W. M., Kräutlein, F., Kühnel, A., Klaus, J., Walter, M., & Kroemer, N. B. (2021) · Psychological Medicine · doi:10.1017/s0033291720005073 · Cited by 79 · see citing works
Gygax, L. (2017) · Ethology · doi:10.1111/eth.12655 · Cited by 80 · see citing works
The computational challenge of social learning
FeldmanHall, O., & Nassar, M. R. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.09.002 · Cited by 75 · see citing works
Schiller, D., Yu, A. N. C., Alia‐Klein, N., Becker, S., Cromwell, H. C., Dolcos, F., Eslinger, P. J., Frewen, P., Kemp, A. H., Pace-Schott, E. F., et al. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105450 · Cited by 86 · see citing works
“…of positive momentum, and similarly, it can occur in the opposite direction where negative mood represents diminishing reward availability (Eldar et al., 2016) To capture the temporal dynamics of mood, rather than static one-off measurements, studies have used repeated, momentary…”
InfluentialComparisons Inform Me Who I Am: A General Comparative-Processing Model of Self-Perception
Nexhmedin Morina, B. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620966788 · Cited by 73 · see citing works
Anxiety and depression: A top‐down, bottom‐up model of circuit function
LeDuke, D. O., Borio, M., Miranda, R. L., & Tye, K. M. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14997 · Cited by 76 · see citing works
“20 The metaphorical narrative surrounding mood is akin to the geometry of attractor state landscapes.”
BackgroundEmotions as ComputationsPreprint
Emanuel, A., & Eldar, E. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104977 · Cited by 83 · see citing works
Affect and Decision Making: Insights and Predictions from Computational Models
Roberts, I. D., & Hutcherson, C. A. C. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.04.005 · Cited by 76 · see citing works
“For example, to understand the impact of affect, which evolves and persists over time [126], it is critical to account for temporal and contextual factors that extend beyond the immediate choice.”
BackgroundTemporal dynamics of real-world emotion are more strongly linked to prediction error than outcome.
Villano, W. J., Otto, A. R., Ezie, C. E. C., Gillis, R., & Heller, A. S. (2020) · Journal of Experimental Psychology General · doi:10.1037/xge0000740 · Cited by 69 · see citing works
“…w2 PA t jPEj (1) NA(t) w0 w1 NA t joutcomej w2 NA t jPEj (2) As prior work suggests that the influence of events on mood state decay over time (Eldar et al., 2016; Rutledge et al., 2014), we employed forgetting factors ( PA, NA) to decay the influence of model parameters as time following…”
BackgroundMethodPsychiatric Illnesses as Disorders of Network Dynamics
Durstewitz, D., Huys, Q. J. M., & Koppe, G. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.01.001 · Cited by 80 · see citing works
Dynamic computational phenotyping of human cognition
Schurr, R., Reznik, D., Hillman, H., Bhui, R., & Gershman, S. J. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01814-x · Cited by 73 · see citing works
“In another study, induction of positive or negative mood throughout the task was associated with biased reward perception 61,62 .”
A model of mood as integrated advantage.
Bennett, D., Davidson, G., & Niv, Y. (2021) · Psychological Review · doi:10.1037/rev0000294 · Cited by 69 · see citing works
Emotion regulation across the life span
Helion, C., Krueger, S. M., & Ochsner, K. N. (2019) · Handbook of clinical neurology · doi:10.1016/b978-0-12-804281-6.00014-8 · Cited by 67 · see citing works
“…striatum is a subcortical brain region inner-vated with dopaminergic neurons and involved in tracking the subjective value of stimuli, signaling the presence of/expectation of reward, and encoding errors and outcomes of such predictions (Knutson et al., 2001; Phan et al., 2002; Eldar et al., 2016).”
BackgroundScholl, J., & Klein-Flügge, M. C. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.09.050 · Cited by 62 · see citing works
“found that people with high mood instability (a risk factor for bipolar disorder), valued monetary rewards differently as a result of an incidental event (large win or loss) that affected their current mood [182,183].”
Amemori, K., Amemori, S., Gibson, D. J., & Graybiel, A. M. (2018) · Neuron · doi:10.1016/j.neuron.2018.07.022 · Cited by 65 · see citing works
“In computational modeling studies (Bakic et al., 2014; Eldar et al., 2016), efforts have been made to define quantitatively subjectivemood-like states persistently influencing value-based decision-making (Eldar and Niv, 2015).”
Grahek, I., Schaller, M., & Tackett, J. L. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620966794 · Cited by 64 · see citing works
“, 2019), emotion (Eldar et al., 2016), the relationship between emotion and cognition (Grahek et al.”
BackgroundOn Valence: Imperative or Representation of Value?
Carruthers, P. (2021) · The British Journal for the Philosophy of Science · doi:10.1086/714985 · Cited by 65 · see citing works
Roberts, C., Sahakian, B. J., & Robbins, T. W. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.09.001 · Cited by 61 · see citing works
A computational perspective on the roles of affect in cognitive control
Grahek, I., Musslick, S., & Shenhav, A. (2020) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2020.02.001 · Cited by 62 · see citing works
“Positive affect can influence the subjective value of outcomes even when it is not tied to performance on a task (Clore et al., 2001; Eldar et al., 2016; Isen et al., 1988), for instance when an individual is induced to feel good by a performance-noncontingent reward (Eldar & Niv, 2015; van…”
BackgroundMomentary subjective well-being depends on learning and not reward
Blain, B., & Rutledge, R. B. (2020) · eLife · doi:10.7554/elife.57977 · Cited by 61 · see citing works
“This result might imply a role for mood in learning in line with influential proposals (Eldar et al., 2016; Eldar and Niv, 2015).”
ResultBakker, J. M., Goossens, L., Kumar, P., Lange, I., Michielse, S., Schruers, K. R. J., Bastiaansen, J. A., Lieverse, R., Marcelis, M. C., van Amelsvoort, T., et al. (2018) · Psychological Medicine · doi:10.1017/s0033291718003446 · Cited by 57 · see citing works
Paul, K., & Pourtois, G. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx010 · Cited by 58 · see citing works
“Both results fit with previous research on mood congruent expectations (Mayer et al., 1992; Loewenstein and Lerner, 2003; Sharot et al., 2011; Eldar et al., 2016), but they extend it substantially by revealing for the first time which neurophysiological mechanism (i.”
Biased belief updating in depression
Kube, T. (2023) · Clinical Psychology Review · doi:10.1016/j.cpr.2023.102298 · Cited by 61 · see citing works
Kiverstein, J. D., Miller, M., & Rietveld, E. (2020) · Neuroscience of Consciousness · doi:10.1093/nc/niaa003 · Cited by 56 · see citing works
“comes from work showing that low serotonin makes people insensitive to negative outcomes (Bari et al. 2010; Eldar et al. 2016).”
BackgroundInfluentialDynamics of amygdala connectivity in bipolar disorders: a longitudinal study across mood states
Rey, G., Bolton, T. A. W., Gaviria, J., Piguet, C., Preti, M. G., Favre, S., Aubry, J., Ville, D. V. D., & Vuilleumier, P. O. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01038-x · Cited by 55 · see citing works
Transdiagnostic computations of uncertainty: towards a new lens on intolerance of uncertainty
Sandhu, T. R., Xiao, B., & Lawson, R. P. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105123 · Cited by 51 · see citing works
Thornton, M. A., & Tamir, D. (2020) · Cortex · doi:10.1016/j.cortex.2019.12.012 · Cited by 53 · see citing works
Brunstrom, J. M., & Cheon, B. K. (2018) · Physiology & Behavior · doi:10.1016/j.physbeh.2018.02.038 · Cited by 46 · see citing works
Active inference models do not contradict folk psychology
Smith, R. S., Ramstead, M. J. D., & Kiefer, A. B. (2022) · Synthese · doi:10.1007/s11229-022-03480-w · Cited by 50 · see citing works
“It is noteworthy in this context that other studies have also found that positive and negative reward prediction errors are associated with positive and negative changes in mood, respectively (Eldar &Niv, 2015; Eldar et al., 2016, 2018; Mason et al., 2017; Rutledge et al., 2014).”
ResultExplicit nonconceptual metacognition
Carruthers, P. (2020) · Philosophical Studies · doi:10.1007/s11098-020-01557-1 · Cited by 46 · see citing works
A highly replicable decline in mood during rest and simple tasks
Jangraw, D. C., Keren, H., Sun, H., Bedder, R. L., Rutledge, R. B., Pereira, F., Thomas, A. G., Pine, D. S., Zheng, C. Y., Nielson, D. M., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01519-7 · Cited by 55 · see citing works
Between Pleasure and Contentment: Evolutionary Dynamics of Some Possible Parameters of Happiness
Gao, Y., & Edelman, S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153193 · Cited by 45 · see citing works
“Note that coping with environmental changes does not necessarily require learning in the phenotype [49], although such an ability (as in, for instance, reinforcement learning [21, 36]) may make for smarter and more efficient agents [48].”
BackgroundResultSerotonin modulates asymmetric learning from reward and punishment in healthy human volunteers
Michely, J., Eldar, E., Erdman, A., Martin, I. M., & Dolan, R. J. (2022) · Communications Biology · doi:10.1038/s42003-022-03690-5 · Cited by 43 · see citing works
“…variability in human learning asymmetries, it is tempting to speculate that serotonergic agents may act preferentially in the striatum and prefrontal cortex to alter the relative degree of impact from positive and negative outcomes (Macoveanu, 2014; Eldar et al., 2016; Lefebvre et al., 2017).””
BackgroundMethodResultInfluentialSelf-citationPleasure, Reward Value, Prediction Error and Anhedonia
Kieslich, K., Valton, V., & Roiser, J. P. (2022) · Current topics in behavioral neurosciences · doi:10.1007/7854_2021_295 · Cited by 49 · see citing works
The brain represents people as the mental states they habitually experience
Thornton, M. A., Weaverdyck, M. E., & Tamir, D. (2019) · Nature Communications · doi:10.1038/s41467-019-10309-7 · Cited by 42 · see citing works
Real-world unexpected outcomes predict city-level mood states and risk-taking behavior
Otto, A. R., & Eichstaedt, J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206923 · Cited by 37 · see citing works
Deane, G. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab024 · Cited by 38 · see citing works
“While in typical experience, subjective valuation functions to fine-tune learning (Eldar et al. 2016) and regulate behaviour, we can see the same mechanisms in place in atypical experience such as the psychedelic state.”
BackgroundPositive affect as a computational mechanism
Eldar, E., Pessiglione, M., & van Dillen, L. F. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.01.007 · Cited by 40 · see citing works
Iigaya, K., Jolivald, A., Jitkrittum, W., Gilchrist, I. D., Dayan, P., Paul, E. S., & Mendl, M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165840 · Cited by 39 · see citing works
“First, negative mood states may reflect previous experience of an elevated rate of negative events, and act as a Bayesian prior that the probability of future negative events is high [9, 38, 39].”
BackgroundHigher well-being is related to reduced affective reactivity to positive events in daily life.
Rueschkamp, J. M. G., Kuppens, P., Riediger, M., Blanke, E. S., & Brose, A. (2018) · Emotion · doi:10.1037/emo0000557 · Cited by 40 · see citing works
Housing and personality effects on judgement and attention biases in dairy cows
Kremer, L., Bus, J. D., Webb, L. E., Bokkers, E. A. M., ENGEL, B., van der Werf, J. T. N., Schnabel, S., & van Reenen, C. G. (2021) · Scientific Reports · doi:10.1038/s41598-021-01843-w · Cited by 37 · see citing works
Mechanisms for survival: vagal control of goal-directed behavior
Teckentrup, V., & Kroemer, N. B. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.11.001 · Cited by 40 · see citing works
The affective gradient hypothesis: An affect-centered account of motivated behavior
Shenhav, A. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.08.003 · Cited by 43 · see citing works
Jones, S., Paul, E. S., Dayan, P., Robinson, E. S. J., & Mendl, M. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.05.044 · Cited by 36 · see citing works
“…negative emotions) make more negative and pessimistic judgements about ambiguous or future events than happier people [9,10], and on theoretical arguments that such a ff ect-related changes in decisions about ambiguity have adaptive value and hence are likely to be observed across species [11 – 13].”
A translational perspective on the anti-anhedonic effect of ketamine and its neural underpinnings
Pulcu, E., Guinea, C., Cowen, P. J., Murphy, S. E., & Harmer, C. J. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01183-1 · Cited by 35 · see citing works
Bushby, E. V., Friel, M., Goold, C. M., Gray, H. E., Smith, L., & Collins, L. (2018) · Frontiers in Veterinary Science · doi:10.3389/fvets.2018.00193 · Cited by 36 · see citing works
Jones, S., Neville, V., Higgs, L., Paul, E. S., Dayan, P., Robinson, E. S. J., & Mendl, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-30571-x · Cited by 35 · see citing works
Cognitive emotional processing across mood disorders
Panchal, P., Kaltenboeck, A., & Harmer, C. J. (2019) · CNS Spectrums · doi:10.1017/s109285291800130x · Cited by 37 · see citing works
“Therefore, mood is proposed to reflect the “overall momentum of recent outcomes,” such that biases work to account for environmental contingencies.(80) As demonstrated by Eldar and Niv(79) above, this interaction can become dysfunctional in the case of mood instability, contributing to the development and maintenance…”
Dissociable effects of reward magnitude on fronto‐medial theta and FRN during performance monitoring
Paul, K., Vassena, E., Severo, M. C., & Pourtois, G. (2019) · Psychophysiology · doi:10.1111/psyp.13481 · Cited by 36 · see citing works
“In this earlier study, we interpreted these results as reflecting an optimistic bias created by positive mood, leading to an overestimation of the likelihood of positive events (Eldar, Rutledge, Dolan, & Niv, 2016; Loewenstein & Lerner, 2003; Mayer, Gaschke, Braverman, & Evans, 1992).”
ResultA Neurocomputational Model for Intrinsic Reward
Chew, B., Blain, B., Dolan, R. J., & Rutledge, R. B. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0858-20.2021 · Cited by 40 · see citing works
“Recent studies demonstrate that computational approaches can quantify consistent relationships between subjective feelings and value-based decision-making (20, 21, 29, 30), including in relation to individual social preferences (31).”
BackgroundState representation in mental illness
Radulescu, A., & Niv, Y. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.03.011 · Cited by 33 · see citing works
Ross, M., Garland, A., Harlander-Matauschek, A., Kitchenham, L., & Mason, G. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-48351-6 · Cited by 33 · see citing works
Computational models of subjective feelings in psychiatryPreprint
Kao, C.-H., Feng, G., Hur, J. K., Jarvis, H., & Rutledge, R. B. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.105008 · Cited by 46 · see citing works
“A recent proposal also argues that mood represents the momentum of rewards in an environment (Bennett et al., 2022; Blain and Rutledge, 2020; Eldar et al., 2016; Eldar and Niv, 2015).”
BackgroundEmociones académicas y aprendizaje de biología, una asociación duradera
de Alda, J. A. G. O., Merino, J. M., Gómez, F. J. M., Jiménez, V. M., & Gallego, R. E. (2019) · Enseñanza de las Ciencias Revista de investigación y experiencias didácticas · doi:10.5565/rev/ensciencias.2598 · Cited by 32 · see citing works
Bliss‐Moreau, E., & Rudebeck, P. H. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.06.024 · Cited by 32 · see citing works
A general motivational architecture for human and animal personality
Giudice, M. D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104967 · Cited by 34 · see citing works
“…— variously described as the probability of encountering threats vs. opportunities, the momentum of recent outcomes, the progress (or lack thereof) toward the individual ’ s goals, and the expected success of future actions (e.g., Eldar et al., 2016; Nesse, 2004; Nettle and Bateson, 2012).”
BackgroundSearch for solutions, learning, simulation, and choice processes in suicidal behavior
Dombrovski, A. Y., & Hallquist, M. N. (2021) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1561 · Cited by 31 · see citing works
“…standard RL models tend to focus solely on learning from experience about a single action or cue, there is increasing evidence that learning signals for a given action generalize to others and that this generalization is a governing process in the unfolding of mood (Eldar et al., 2016, 2018).”
BackgroundOversampled and undersolved: Depressive rumination from an active inference perspective
Berg, M., Feldmann, M., Kirchner, L., & Κube, T. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104873 · Cited by 32 · see citing works
Smith, R. S., Varshney, L. R., Nagayama, S., Kazama, M., Kitagawa, T., Shunsuke, M., & Ishikawa, Y. (2022) · International Journal of Wellbeing · doi:10.5502/ijw.v12i4.2659 · Cited by 31 · see citing works
Grèzes, J., Erblang, M., Vilarem, E., Quiquempoix, M., Beers, P. V., Guillard, M., Sauvet, F., Mennella, R., & Rabat, A. (2021) · SLEEP · doi:10.1093/sleep/zsab186 · Cited by 28 · see citing works
“The second hypothesis arises from past studies suggesting an impact of total SD [38], as well as of mood [35, 36], on the valuation process at the time of choice during economic decisions.”
Does reconsolidation occur in natural settings? Memory reconsolidation and anxiety disorders
Fernández, R. S., Pedreira, M. E., & Boccia, M. M. (2017) · Clinical Psychology Review · doi:10.1016/j.cpr.2017.08.004 · Cited by 28 · see citing works
Raoult, C. M. C., Moser, J., & Gygax, L. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.02197 · Cited by 29 · see citing works
“Also, the manipulations have not usually been shaped by the aim of producing mismatches that are viewed as the cause of mood changes by Eldar et al. (2016).”
BackgroundInfluentialThe temporal representation of experience in subjective mood
Keren, H., Zheng, C. Y., Jangraw, D. C., Chang, K. K., Vitale, A. D., Rutledge, R. B., Pereira, F., Nielson, D. M., & Stringaris, A. K. (2021) · eLife · doi:10.7554/elife.62051 · Cited by 29 · see citing works
“have focused on event value (either in terms of expectations or outcomes or both) (Eldar et al., 2016; Katsimerou et al., 2014; Russell et al., 1989; Rutledge et al., 2014; Vinckier et al., 2018; Watson and Tellegen, 1985) as influences on subjective reports of well-being, while neglecting the importance of time.”
BackgroundMethodInfluentialSynthesising boredom: a predictive processing approach
Darling, T. (2023) · Synthese · doi:10.1007/s11229-023-04380-3 · Cited by 29 · see citing works
“A “feeling of grip” arises through an agent’s error-dynamics (i.e. rates of relative to expected error-reduction) (Kiverstein et al., 2019 ), with mood informing how we find ourselves in the world at a given time, creating a sense of direction and momentum (Eldar et al., 2016).”
Brain response patterns to economic inequity predict present and future depression indices
Tanaka, T., Yamamoto, T., & Haruno, M. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0207-1 · Cited by 28 · see citing works
Carruthers, P., & Williams, D. M. (2022) · Cognition · doi:10.1016/j.cognition.2022.105117 · Cited by 71 · see citing works
Neave, H. W., Jensen, E. H., Durrenwachter, M., et al. (2023) · Journal of Dairy Science · doi:10.3168/jds.2023-24085 · Cited by 26 · see citing works
Distorted learning from local metacognition supports transdiagnostic underconfidence
Katyal, S., Huys, Q. J. M., Dolan, R., & Fleming, S. M. (2025) · Nature Communications · doi:10.1038/s41467-025-57040-0 · Cited by 23 · see citing works
“Such transfer would be consistent with global con fi dence re fl ecting a slowly changing cognitive state with its own dynamics, akin to mood 26 .”
Belief updating in bipolar disorder predicts time of recurrence
Ossola, P., Garrett, N., Sharot, T., & Marchesi, C. (2020) · eLife · doi:10.7554/elife.58891 · Cited by 24 · see citing works
“Hence manic or depressive symptoms arise only in the absence of the other (Bonsall et al., 2015; Bonsall et al., 2012; Chang and Chou, 2018; Daugherty et al., 2009; Eldar et al., 2018; Eldar et al., 2016; Eldar and Niv, 2015; Mason et al., 2017; Nana, 2009; Steinacher and Wright, 2013).”
BackgroundPharmacologically-induced stress has minimal impact on judgement and attention biases in sheep
Monk, J. E., Belson, S., & Lee, C. (2019) · Scientific Reports · doi:10.1038/s41598-019-47691-7 · Cited by 24 · see citing works
Søholm, U., Broadley, M. M., Zaremba, N., Divilly, P., Nefs, G., MAHMOUDI, Z., de Galan, B. E., Pedersen‐Bjergaard, U., Brennan, A., Pollard, D. J., et al. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-051651 · Cited by 25 · see citing works
- Authors
- Eldar, E., Rutledge, R. B., Dolan, R. J., & Niv, Y.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- Princeton University (US)
- Corresponding author
- Eldar, Eran
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 20
- Issue
- 1
- Pages
- 15-24
- ISSN
- 1364-6613 (ISSN-L)
- PMC
- PMC4703769
- OpenAlex
- W2194932295
- Cited by
- 383
- References
- 84
- Citations by year
- 2026: 29 · 2025: 51 · 2024: 52 · 2023: 60 · 2022: 41 · 2021: 41 · 2020: 35 · 2019: 31 · 2018: 18 · 2017: 20 · 2016: 5
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Trends in Cognitive Sciences (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Army Research Office (W911NF-14-1-0101)
- Max-Planck-Gesellschaft
- Wellcome Trust (095844/Z/11/Z)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- mood, momentum, reinforcement learning, reward anticipation, learning bias, environmental dependency, mood disorders
- MeSH terms
- Affect, Humans, Learning, Reinforcement, Psychology, Reward
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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