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A Neural Computational Model of Incentive Salience
Open accessAbstract
Incentive salience is a motivational property with 'magnet-like' qualities. When attributed to reward-predicting stimuli (cues), incentive salience triggers a pulse of 'wanting' and an individual is pulled toward the cues and reward. A key computational question is how incentive salience is generated during a cue re-encounter, which combines both learning and the state of limbic brain mechanisms. Learning processes, such as temporal-difference models, provide one way for stimuli to acquire cached predictive values of rewards. However, empirical data show that subsequent incentive values are also modulated on the fly by dynamic fluctuation in physiological states, altering cached values in ways requiring additional motivation mechanisms. Dynamic modulation of incentive salience for a Pavlovian conditioned stimulus (CS or cue) occurs during certain states, without necessarily requiring (re)learning about the cue. In some cases, dynamic modulation of cue value occurs during states that are quite novel, never having been experienced before, and even prior to experience of the associated unconditioned reward in the new state. Such cases can include novel drug-induced mesolimbic activation and addictive incentive-sensitization, as well as natural appetite states such as salt appetite. Dynamic enhancement specifically raises the incentive salience of an appropriate CS, without necessarily changing that of other CSs. Here we suggest a new computational model that modulates incentive salience by integrating changing physiological states with prior learning. We support the model with behavioral and neurobiological data from empirical tests that demonstrate dynamic elevations in cue-triggered motivation (involving natural salt appetite, and drug-induced intoxication and sensitization). Our data call for a dynamic model of incentive salience, such as presented here. Computational models can adequately capture fluctuations in cue-triggered 'wanting' only by incorporating modulation of previously learned values by natural appetite and addiction-related states.
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“These results are consistent with the incentive salience theory, however, and highlight the dissociation that can occur between the predictive value of a cue, driven by cue learning (cue-reward association), and the attribution of cue ‘wanting’ (T. E. Robinson & Flagel, 2009; Zhang, Berridge, Tindell, Smith, & Aldridge, 2009).”
From Cued for Risk: Evidence for an Incentive Sensitization Framework to Explain the Interplay between Stress and Anxiety, Substance Abuse, and Reward Uncertainty in Disordered Gambling Behavior (2019) · cites this paper as Zhang, Berridge, Tindell, Smith, & Aldridge, 2009
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“The attribution of incentive salience is the product of previous experience (i.e., learned associations) interacting with an individual’s genetic propensity and neurobiological state 16,24–27.”
From A selective role for dopamine in stimulus-reward learning (2011) · cites this paper as [27]
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“Specifically, Zhang, Berridge, Tindell, Smith, and Aldridge (2009) suggested that a physiological drive state multiplies the value of a conditioned cue, without needing new learning to enhance the cue’s value.”
BackgroundFNDC5/irisin, a molecular target for boosting reward-related learning and motivation
Zsuga, J., Tajti, G., Papp, C., Juhász, B., & Gesztelyi, R. (2016) · Medical Hypotheses · doi:10.1016/j.mehy.2016.02.020 · Cited by 59
“Accordingly, the incentive salience value of a cue is the net of associative-learning derived prior knowledge concerning the relationship between the cue and the reward (unconditioned stimulus (UCS)) [11].”
BackgroundNeural Activity in the Ventral Pallidum Encodes Variation in the Incentive Value of a Reward Cue
Ahrens, A. M., Meyer, P., Ferguson, L. M., Robinson, T. E., & Aldridge, J. W. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.0736-16.2016 · Cited by 57
“, 2011; Tachibana and Hikosaka, 2012; Avila and Lin, 2014a, 2014b) and, importantly, this is the only brain region that has been shown to encode the predictive and incentive value of cues in a dissociable way (Tindell et al., 2005, 2009; Zhang et al., 2009; Smith et al., 2011).”
BackgroundResultInfluentialSelf-citationThe Virtual Personalities Neural Network Model: Neurobiological Underpinnings
Read, S. J., Brown, A. D., Wang, P., & Miller, L. C. (2018) · Personality Neuroscience · doi:10.1017/pen.2018.6 · Cited by 55
“According to Berridge (Berridge, 2007, 2012; Zhang et al., 2009) the degree of WANTING is a multiplicative function of the relevant Environment cues and Interoceptive cues.”
Stress, Motivation, and the Gut–Brain Axis: A Focus on the Ghrelin System and Alcohol Use Disorder
Morris, L. S., Voon, V., & Leggio, L. (2018) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.13781 · Cited by 57
“D1 receptor activation in the “direct pathway” of the basal ganglia is implicated in reward-related incentive learning that directs approach behaviors for certain stimuli (“wanting”) (Beninger and Miller, 1998; Zhang et al., 2009).”
Incentive salience attribution under reward uncertainty: A Pavlovian model
Anselme, P. (2014) · Behavioural Processes · doi:10.1016/j.beproc.2014.10.016 · Cited by 57
The practice of poetry and the psychology of well-being
Croom, A. M. (2014) · Journal of Poetry Therapy · doi:10.1080/08893675.2015.980133 · Cited by 61
Reed, D. D., Naudé, G. P., Gelino, B. W., & Amlung, M. T. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.011 · Cited by 48
Cocaine addiction as a homeostatic reinforcement learning disorder.
Keramati, M., Durand, A., Girardeau, P., Gutkin, B., & Ahmed, S. H. (2017) · Psychological Review · doi:10.1037/rev0000046 · Cited by 44
The incentive salience of courtship vocalizations: Hormone-mediated ‘wanting’ in the auditory system
Maney, D. L. (2013) · Hearing Research · doi:10.1016/j.heares.2013.04.011 · Cited by 47
The Prevention and Treatment of Gambling Disorders: Some Art, Some Science
Derevensky, J. L. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.024 · Cited by 45
Considering the Overlap and Nonoverlap of Compulsivity, Impulsivity, and Addiction
Blum, A. W., & Grant, J. E. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.038 · Cited by 44
Neurobiological Foundations of Behavioral Addictions
Vaccaro, A. G., & Potenza, M. N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.016 · Cited by 45
Neurobiology of Substance Addictions
Christie, N. C., & Bechara, A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.015 · Cited by 44
Sensitization of Incentive Salience and the Transition to Addiction
Warlow, S. M., Baumgartner, H. M., Freeland, C. M., Naffziger, E. E., Olney, J. J., Berridge, K. C., & Robinson, M. J. F. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.006 · Cited by 46
“However, it is important to note that the intensity of “wanting” triggered by a predictive cue not only depends on the cue’s incentive value, but also the current dopamine-related brain state of the individual (Zhang et al., 2009; Berridge & Robinson, 2016).”
BackgroundSelf-citationStriatal dopamine integrates cost, benefit, and motivation
Eshel, N., Touponse, G. C., Wang, A. R., Osterman, A. K., Shank, A. N., Groome, A. M., Taniguchi, L., Pinto, D. F. C., Tucciarone, J., Bentzley, B. S., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.038 · Cited by 51
Mindfulness-Based Interventions Applied to Addiction Treatments
Kechter, A., & Black, D. S. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.040 · Cited by 43
Multiple Memory Systems, Addiction, and Health Habits: New Routes for Translational Science
Stacy, A. W., Pike, J. R., & Lee, A. Y. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.017 · Cited by 43
Tanning as an Addiction: The State of the Research and Implications for Intervention
Miller, K. A., & Mays, D. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.037 · Cited by 43
Schulte, E. M., Schiestl, E. T., & Gearhardt, A. N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.035 · Cited by 43
Behavioral Economics and Addictive Disorders
Jarmolowicz, D. P., & Schneider, T. D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.005 · Cited by 42
Prevention and Treatment of Compulsive Buying Disorder
Galimov, A., & Black, D. W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.027 · Cited by 42
Blum, K., Roy, A. K., Podesta, A., Modestino, E. J., Steinberg, B., Gondré-Lewis, M. C., Baron, D. A., Thanos, P. K., Lott, L., Badgaiyan, S., et al. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.031 · Cited by 42
Prevention and Treatment of “Food Addiction”
Davis, C., & Mason, A. E. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.023 · Cited by 42
Human Neurobiological Approaches to Hedonically Motivated Behaviors
Burger, K. S., Shearrer, G. E., & Sadler, J. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.009 · Cited by 42
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Griffiths, M. D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.034 · Cited by 42
Dopamine Receptor-Specific Contributions to the Computation of Value
Burke, C. J., Soutschek, A., Weber, S. C., Beharelle, A. R., Fehr, E., Haker, H., & Tobler, P. N. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.302 · Cited by 41
“This finding is in line with a multiplicative rather than an additive effect of the drug on value processing (Zhang et al, 2009) and suggests that it may be important to Neuropsychopharmacology use a large range of possible outcomes.”
ResultLopez‐Paniagua, D., & Seger, C. A. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00008 · Cited by 41
“Extensions to reinforcement learning are an active area of research (Zhang, Berridge, Tindell, Smith, & Aldridge, 2009 ).”
BackgroundDeep and beautiful. The reward prediction error hypothesis of dopamine
Colombo, M. (2013) · Studies in History and Philosophy of Science Part C Studies in History and Philosophy of Biological and Biomedical Sciences · doi:10.1016/j.shpsc.2013.10.006 · Cited by 46
Piray, P., Keramati, M., Dezfouli, A., Lucas, C., & Mokri, A. (2010) · Neural Computation · doi:10.1162/neco_a_00009 · Cited by 40
“Previously, individual susceptibility to drug addiction was not addressed explicitly by abstract computational (Dayan, 2009; Dezfouli et al., 2009; Redish, 2004; Redish et al., 2007; Redish & Johnson, 2007; Zhang et al., 2009) or multilevel circuit models of addiction (Gutkin et al.”
Loss of Specificity in Basal Ganglia Related Movement Disorders
Bronfeld, M., & Bar‐Gad, I. (2011) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2011.00038 · Cited by 39
“Another parameter that may affect the pattern of BG output is dopamine, which is often regarded as a reinforcement signal indicating the relative saliency or importance of various inputs (Schultz, 2007; Joshua et al., 2009a; Zhang et al., 2009).”
A Reinforcement Learning Theory for Homeostatic Regulation
Keramati, M., & Gutkin, B. S. (2011) · City Research Online (City University London) · Cited by 39
Schomaker, J., Walper, D., Wittmann, B. C., & Einhäuser, W. (2017) · Vision Research · doi:10.1016/j.visres.2017.02.003 · Cited by 38
Generalization in neural networks: A broad survey
Rohlfs, C. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128701 · Cited by 51
Metadata
- Authors
- Zhang, J., Berridge, K. C., Tindell, A. J., Smith, K. S., & Aldridge, J. W.
- Affiliations
- University of Michigan (US)
- McGovern Institute for Brain Research (US)
- Corresponding author
- Berridge, Kent C.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2009-07-17
- Volume
- 5
- Issue
- 7
- Pages
- e1000437
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2703828
- OpenAlex
- W2097552217
- Cited by
- 299
- References
- 98
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 5 · 2023: 12 · 2022: 15 · 2021: 19 · 2020: 57 · 2019: 53 · 2018: 17 · 2017: 11 · 2016: 11 · 2015: 11 · 2014: 15 · 2013: 23 · 2012: 17
- FWCI
- 4.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- PLoS eBooks
- Funders
- National Institutes of Health
- National Institute of Mental Health (MH-63649, R01MH063649)
- National Institute on Drug Abuse (R01DA017752, DA-017752, DA015188, R01DA015188)
- Air Force Office of Scientific Research
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- incentive salience, computational model, want, physiological state, temporal difference learning, predictive value, addiction, salt appetite, motivation
- MeSH terms
- Algorithms, Animals, Appetite, Behavior, Animal, Brain, Conditioning, Classical, Dopamine, Learning, Limbic System, Models, Neurological, Motivation, Reward, Rats
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