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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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BackgroundNeurocomputational Theories of Homeostatic Control
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Mannella, F., Mirolli, M., & Baldassarre, G. (2016) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2016.00181 · Cited by 33
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BackgroundACT-RΦ: A cognitive architecture with physiology and affect
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“The VP appears to also encode the purely predictive aspects of a cue; that is, the cognitive associations that are learned during CS-US pairings, which may or may not be accompanied by a heightened incentive-motivational state (Tindell et al., 2005; Zhang et al., 2009; Smith et al., 2011).”
BackgroundSelf-citationPromises and Threats with Conditionals and Disjunctions
van Rooij, R., & Franke, M. (2012) · De Gruyter eBooks · doi:10.1515/9781614511601.69 · Cited by 26
“But also in more on-the-spot decision making: the salience of choice options matters especially when these have a strong desire-raising component (compare the discussion of “incentive salience” by Zhang et al. (2009) and references therein).”
Iglesias, A. G., Chiu, A. S., Wong, J. W. H., Campus, P., Li, F. R., Liu, Z., Bhatti, J. K., Patel, S. A., Deisseroth, K. A., Akil, H., et al. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0848-23.2023 · Cited by 32
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“The VP has also been shown to specifically encode the incentive value of a cue in a way that can be experimentally dissociated from reward prediction (Smith et al. 2011; Tindell et al. 2005; Zhang et al. 2009).”
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Effects of nicotine self-administration on incentive salience in male Sprague Dawley rats
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“Goal-directed behavior is regulated by two factors: the incentive value of the goal (reward) and the internal drive (physiological state) of an agent (Berridge, 2004; Zhang et al., 2009).”
BackgroundA mathematical model of reward-mediated learning in drug addiction
Chou, T., & D'Orsogna, M. R. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0082997 · Cited by 25
Modeling the effects of motivation on choice and learning in the basal ganglia
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“[38] predicts different patterns in dopaminergic teaching signals.”
BackgroundMethodResultInfluentialSubjective reward value of visual sexual stimuli is coded in human striatum and orbitofrontal cortex
Klein, S., Kruse, O., Markert, C., León, I. T., Strahler, J., & Stark, R. M. (2020) · Behavioural Brain Research · doi:10.1016/j.bbr.2020.112792 · Cited by 23
A Computational View on the Nature of Reward and Value in Anhedonia
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Experimental predictions drawn from a computational model of sign-trackers and goal-trackers
Lesaint, F., Sigaud, O., Clark, J. J., Flagel, S. B., & Khamassi, M. (2014) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2014.06.001 · Cited by 22
Neuroanatomical Abnormalities in Older Depressed Adults With Apathy: A Systematic Review
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“Indeed, a computational model of dopamine-mediated incentive salience attribution proposed by the same research group (Zhang et al. 2009; see also Berridge 2012) does not account for the “reboosting” process (it was an explicit choice of the authors).”
The interpretation of behavior-model correlations in unidentified cognitive models
van Maanen, L., & Miletić, S. (2020) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01783-y · Cited by 23
Neural Networks With Motivation
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Single caudate neurons encode temporally discounted value for formulating motivation for action
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A critique of motivation constructs to explain higher-order behavior: We should unpack the black box
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Towards an incentive salience model of intertemporal choice
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“Comparing the incentive salience model (equation 2) with Laibson’s (1997) quasi-hyperbolic discounting model (equation 1), it becomes obvious that both models have essentially the same functional form (Zhang et al., 2009).”
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“The computational version of the incentive salience component of the model (Zhang et al. 2009) calculates the decision utility of a stimulus S at time t with the formula: Ṽ(St) = rt x κ + γV(St+1), where γ is a temporal discount factor.”
InfluentialA-learning: A new formulation of associative learning theory
Ghirlanda, S., Lind, J., & Enquist, M. (2020) · Psychonomic Bulletin & Review · doi:10.3758/s13423-020-01749-0 · Cited by 20
Misdeed of the need: towards computational accounts of transition to addiction
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“[61] where the previously learned expected rewards were modulated by an ad”
20 Years of Aberrant Salience in Psychosis: What Have We Learned?
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Doesn't everybody jaywalk? On codified rules that are seldom followed and selectively punished
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“Wanting” versus “needing” related value: An fMRI meta‐analysis
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“…selection), not the activational one, means that a needed stimulus must still become ‘wanted’, by altering mesolimbic dopamine reactivity and encountering a relevant reward predicting cue (Zhang et al., 2009), in order to have full motivational value (Bindra, 1974; Toates, 1994; Berridge, 2004).”
BackgroundResultInfluentialTheory-driven computational models of drug addiction in humans: Fruitful or futile?
Lim, T. V., & Ersche, K. D. (2023) · Addiction Neuroscience · doi:10.1016/j.addicn.2023.100066 · Cited by 16
Towards a Neuroscience of Well-Being: Implications of Insights from Pleasure Research
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“Conscious wanting includes conscious desires for incentives or cognitive goals, while learning includes the updating of explicit and cognitive predictions (Friston and Kiebel 2009; Zhang et al. 2009).”
BackgroundSelf-citationTommaso, M. D., Mastropasqua, T., & Turatto, M. (2018) · Learning and Motivation · doi:10.1016/j.lmot.2018.01.001 · Cited by 15
Probing the role of reward expectancy in Pavlovian-instrumental transfer
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“…striatum (Rescorla and Solomon 1967; Bindra 1968; Taylor and Robbins 1984; Kelley and Delfs 1991; Dickinson and Balleine 1994; Dickinson et al. 2000; Wyvell and Berridge 2000; Niv et al. 2007; Zhang et al. 2009; Shin et al. 2010; Flagel et al. 2011; Duchesne and Boye 2013; Wassum et al. 2013).”
BackgroundVentral Caudate and Anterior Insula Recruitment During Value Estimation of Passionate Rewarding Cues
Duarte, I. C., Coelho, G. F., Brito‐Costa, S., Cayolla, R. R., Afonso, S., & Castelo-Branco, M. S. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00678 · Cited by 13
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Medial Parietal Cortex Activation Related to Attention Control Involving Alcohol Cues
Gladwin, T. E., Schulte, M. H. J., Ridderinkhof, K. R., & Wiers, R. W. H. J. (2013) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2013.00174 · Cited by 13
“The test is routinely used to screen”
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Multicomponential affective processes modulating food-seeking behaviors
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Appetitive Needs and Addiction
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“…Autoshaping · Pavlovian conditioning · Reinforcement schedules · Mathematical model · Price equation Pavlovian responses are more complex than sometimes assumed, requiring learning, motivation, expectation, and even emotion (e.g., Rescorla & Wagner, 1972; Stout et al., 2003; Zhang et al., 2009).”
BackgroundRange-Adaptive Value Representation in Different Stages of Schizophrenia: A Proof of Concept Study
Wang, L.-L., Lam, C. Y. T., Huang, J., Cheung, E. F. C., Lui, S. S. Y., & Chan, R. C. K. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab099 · Cited by 12
Effort-motivated behavior resolves paradoxes in appetitive conditioning
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A Hybrid Cognitive Architecture with Primal Affect and Physiology
Dancy, C. L. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2906162 · Cited by 10
“[61] to integrate SEEKING/wanting behavior into the existing ACT-R procedural memory system.”
BackgroundContextual cues facilitate dynamic value encoding in the mesolimbic dopamine system
Fraser, K. M., Collins, V. L., Wolff, A. R., Ottenheimer, D. J., Bornhoft, K. N., Pat, F., Chen, B. J., Janak, P. H., & Saunders, B. T. (2025) · Current Biology · doi:10.1016/j.cub.2024.12.031 · Cited by 15
Computational Mechanisms of Osmoregulation: A Reinforcement Learning Model for Sodium Appetite
Uchida, Y., Hikida, T., & Yamashita, Y. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.857009 · Cited by 9
“For example, classical drive reduction theory (Hull, 1943) assumes that the discrepancy from the optimal state drives behavior to reduce the discrepancy, while incentive salience theory (Zhang et al., 2009; Berridge, 2012) assumes that the “incentive” to consume sodium switches depending on the internal sodium state.”
BackgroundBlunted Expected Reward Value Signals in Binge Alcohol Drinkers
Tolomeo, S., Baldacchino, A., & Steele, J. D. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.2157-21.2022 · Cited by 11
“Similar learning models have been proposed for addiction (McClure et al., 2003; Zhang et al., 2009; Berridge, 2012).”
BackgroundCinotti, F., Marchand, A. R., Roesch, M. R., Girard, B., & Khamassi, M. (2019) · Psychopharmacology · doi:10.1007/s00213-019-05323-y · Cited by 10
Reward-Bases: Dopaminergic Mechanisms for Adaptive Acquisition of Multiple Reward TypesPreprint
Millidge, B., Song, Y.-H., Lak, A., Walton, M., & Bogacz, R. (2023) · bioRxiv · doi:10.1101/2023.05.09.540067 · Cited by 7
“We also simulated the ‘homeostatic model’ of Zhang et al. (2009), which modulates the expected instantaneous rewards on the next state according to physiological state (see Methods for details) and which has been proposed as a model of the instant reward revaluation observed in animals.”
MethodFlexible control of Pavlovian-instrumental transfer based on expected reward value.
Marshall, A. T., Halbout, B., Munson, C., Hutson, C., & Ostlund, S. B. (2023) · Journal of Experimental Psychology Animal Learning and Cognition · doi:10.1037/xan0000348 · Cited by 10
“The current findings are less compatible with existing theories of incentive learning (Dayan & Balleine, 2002; McClure et al., 2003; Zhang et al., 2009) that assume that the motivational properties of reward-predictive cues are directly linked to their state value – i.”
BackgroundResultZhang, J., Berridge, K. C., & Aldridge, J. W. (2011) · Springer eBooks · doi:10.1007/978-1-4614-0751-5_7 · Cited by 9
“Two specific forms of r were postulated in Zhang et al. (2009): an additive mechanism and a multiplicative mechanism.”
BackgroundMethodSelf-citationPeripheral Signals Modifying Food Reward
Menzies, J. R. W., Skibicka, K. P., Egecioglu, E., Leng, G., & Dickson, S. L. (2011) · Handbook of experimental pharmacology · doi:10.1007/978-3-642-24716-3_6 · Cited by 9
“However, in the field of motivation and reward, testable ideas are now beginning to be developed on how neuronal activity can be modelled as an information processing system whose output is the implementation of behaviour (Anselme 2010; Zhang et al. 2009).”
BackgroundThe behavioral pharmacology of anorexigenic drugs in nonhuman primates
Foltin, R. W. (2012) · Behavioural Pharmacology · doi:10.1097/fbp.0b013e3283566aa0 · Cited by 9
“A consistent finding in studies using rodents is that drugs that increase dopamine, such as amphetamine and cocaine, also increase the incentive value of stimuli paired with reinforcement (Robinson and Berridge, 1993; Zhang et al., 2009; Smith et al., 2011).”
BackgroundTemptation, self-control, and inter-temporal choice
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Smith, B. J., & Read, S. J. (2021) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-021-00953-2 · Cited by 9
The causal role between phasic midbrain dopamine signals and learning
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“More recently, a number of computational studies have added another layer of complexity to their models by incorporating the idea of incentive motivation as a way to better capture the role of dopamine in reward learning (McClure et al., 2003; Niv, 2007; Zhang et al., 2009; Morita et al., 2013).”
BackgroundSuboptimal choice by pigeons is eliminated when key-pecking behavior is replaced by treadle-pressing
González-Torres, R., Flores, J., & Orduña, V. (2020) · Behavioural Processes · doi:10.1016/j.beproc.2020.104157 · Cited by 7
Qualitative Approaches to the Study of Substance and Behavioral Addictions
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Kalhan, S., Garrido, M. I., Hester, R., & Redish, A. D. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.032 · Cited by 9
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“Considering the importance of both reinforcement learning and addictive motivation in GD, both of these theories, or attempts to converge them (McClure et al., 2003; Zhang et al., 2009), may have important implications for gambling scholars (for a review see, Linnet, 2014).”
BackgroundIndividual concerns modulate reward-related learning and behaviors involving sexual outcomes.
Stussi, Y., Sennwald, V., Pool, E. R., Delplanque, S., Brosch, T., Bianchi‐Demicheli, F., & Sander, D. (2021) · Motivation Science · doi:10.1037/mot0000249 · Cited by 6
Can the incentive-sensitization theory of addiction incorporate addiction to opioid drugs?
Robinson, T. E., & Berridge, K. C. (2026) · Psychopharmacology · doi:10.1007/s00213-025-07001-8 · Cited by 6
“…that, “with some tweaking the architecture of the Michigan model (Berridge 2012 ; Robinson and Berridge 1993) and its computational version (Dayan and Berridge 2014; Zhang et al. 2009 ) can accommodate most of our find - ings, except for the critical role that this model attributes to dopamine”.”
Self-citationCraving money? Evidence from the laboratory and the field
Payzan-LeNestour, É., & Doran, J. S. (2024) · Science Advances · doi:10.1126/sciadv.adi5034 · Cited by 7
“3, which is equivalent to the cached Pavlovian learned value acquired through “temporal difference” (TD) reinforcement learning as proposed in the literature [e.g., ( 5 , 30 , 31 )].”
BackgroundMethodPanayi, M. C., Khamassi, M., & Killcross, S. (2021) · Behavioral Neuroscience · doi:10.1037/bne0000454 · Cited by 6
“Indeed, this is reflected in the models we have considered by the surprisingly strong MB contributions to behaviour in simple Pavlovian tasks, consistent with other RL models of Pavlovian approach behaviour (Dayan & Berridge, 2014; Lesaint et al., 2014; Zhang et al., 2009).”
MethodRobinson, M. J. F., & Berridge, K. C. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.26091-1 · Cited by 6
Self-citationConsidering the Influence of the Pavlovian System on Behavior: Appraisal and Value Representation
Sennwald, V., Pool, E. R., & Sander, D. (2017) · Psychological Inquiry · doi:10.1080/1047840x.2017.1259951 · Cited by 6
“For a reward-associated stimulus to trigger a motivational state (i.e., wanting), it has to be relevant for the current concerns of the individual (Pool et al., 2016; Robinson & Berridge, 2013; Tindell, Smith, Pecina, Berridge, & Aldridge, 2006; Zhang et al., 2009).”
Needing: An Active Inference Process for Physiological Motivation
Bosulu, J., Pezzulo, G., & Hétu, S. (2024) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_02209 · Cited by 5
“This tendency exists irrespective of reward prediction (Berridge, 2023; Smith & Read, 2022; Zhang, Berridge, Tindell, Smith, & Aldridge, 2009).”
BackgroundAssociation between depressive symptom clusters and food attentional bias
Hawkins, M. A. W., Vrany, E. A., Cyders, M. A., Ciciolla, L. M., Wells, T. T., & Stewart, J. C. (2018) · Eating Behaviors · doi:10.1016/j.eatbeh.2018.07.002 · Cited by 5
Substance and Behavioral Addictions Assessment Instruments
Grigsby, T. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.012 · Cited by 6
Neural network models of personality structure and dynamics
Read, S. J., & Miller, L. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819200-9.00004-1 · Cited by 5
van den Ende, M. W. J., Epskamp, S., van der Maas, H. L. J., Lees, M., & Sloot, P. M. A. (2021) · doi:10.31234/osf.io/h7tr9 · Cited by 5
“Zhang, Berridge, Tindell, Smith, and Aldridge (2009) model incentive salience based on an adjusted reinforcement model.”
Adrenergic manipulation inhibits pavlovian conditioned approach behaviors
Pasquariello, K. Z., Han, M., Unal, C., & Meyer, P. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.10.037 · Cited by 4
The Theory of Hedonic Motivation
Williams, D. M. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316275047.017 · Cited by 3
Behavioral regulation relies on interacting forces and predictive models
Read, S. J., & Miller, L. C. (2023) · Journal of Personality · doi:10.1111/jopy.12815 · Cited by 4
Prevention and Treatment of Sex Addiction
Reid, R. C., Grubbs, J. B., & Kraus, S. W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.025 · Cited by 4
Personality and Sexual Offending; Non-Sexual Motivators and Disinhibition in Context
Hoberman, H. M. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2416-5_8 · Cited by 4
LikeWant: A new methodology to measure implicit wanting for flavors and fragrances
Cereghetti, D. M., Chillà, C., Porcherot, C., Sander, D., Cayeux, I., & Delplanque, S. (2019) · Food Quality and Preference · doi:10.1016/j.foodqual.2019.103829 · Cited by 5
Doesn’t everybody jaywalk? On codified rules that are seldom followed and selectively punishedPreprint
Wylie, J., & Gantman, A. P. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/yfsed · Cited by 4
Koob, G. F., Arends, M. A., & Moal, M. L. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-386937-1.00004-0 · Cited by 3
Gaming Disorder and Its Treatment
Griffiths, M. D., Kuss, D. J., & Pontes, H. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.029 · Cited by 6
A neurocomputational account of multi-line electronic gambling machines
Peters, J. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.12.009 · Cited by 3
Treatment of Alcohol, Tobacco, and Other Drug (ATOD) Misuse
Sussman, S. Y., & Bolshakova, M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.022 · Cited by 3
The Role of Culture in Addiction
Hoffman, B. R., & Unger, J. B. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.018 · Cited by 3
Reward Processing in Depression
Chen, C., & Takahashi, T. (2017) · Wiley eBooks · doi:10.1002/9781119159193.ch5 · Cited by 4
A Neural Network Model of Motivated Decision‐Making in Everyday Social Behavior
Read, S. J., & Miller, L. C. (2019) · doi:10.1002/9781119485001.ch7 · Cited by 3
Recent Opioid Use Impedes Range Adaptation in Reinforcement Learning in Human Addiction
Gueguen, M. C. M., Anlló, H., Bonagura, D., Kong, J., Hafezi, S., Palminteri, S., & Konova, A. B. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.12.005 · Cited by 3
Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process
Ilin, R., Zhang, J., PERLOVSKY, L. I., & Kozma, R. (2013) · Cognitive Neurodynamics · doi:10.1007/s11571-013-9262-0 · Cited by 2
“At least two separate motivational systems are involved: ‘‘wanting’’ associated with mesolimbic dopamine activation and ‘‘liking’’ associated with nucleus accumbens opioid activation (Berridge and Robinson 1998, 2003; Berridge 2004; Tindell et al. 2005; Zhang et al. 2009).”
BackgroundSelf-citationTheories of compulsive drug use: A brief overview of learning and motivation processes
Foreman, L. M., Baetu, I., Rego, J., Collins‐Praino, L. E., & Moustafa, A. A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816979-7.00007-8 · Cited by 3
Passionate Love Addiction: An Evolutionary Survival Mechanism That Can Go Terribly Wrong
Bolshakova, M., Fisher, H. E., Aubin, H., & Sussman, S. Y. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.026 · Cited by 2
Philosophical Issues in the Addictions
Glackin, S. N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.007 · Cited by 2
(2020) · Cambridge University Press eBooks · doi:10.1017/9781108632591.020 · Cited by 2
Neural Networks With Motivation
Shuvaev, S. A., Tran, N. B., Stephenson‐Jones, M., Li, B., & Koulakov, A. A. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.609316 · Cited by 20
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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