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Cited by 299Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology StudiesMemory and Neural Mechanisms

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A Neural Computational Model of Incentive Salience

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Zhang, Jun, Berridge, Kent C., Tindell, Amy J., Smith, Kyle S., Aldridge, J. Wayne·2009·PLoS Computational Biology
DOI 10.1371/journal.pcbi.1000437PMC PMC2703828
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Abstract

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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Figure 1 Simulations of dynamic shifts in incentive salience. All are induced by changes in physiological state after learning a CS-UCS association in an initial state (Eqn 3). The left column is for multiplicative mechanisms (Eqn 3a), while the right column is for additive mechanisms (Eqn 3b). The top row is for shifts after learning a Pavlovian association with a reward UCS (e.g., sucrose taste), and the bottom row is for shifts after learning with an aversive UCS (e.g., intense salt taste). Initial learning is assumed to proceed by a Rescorla-Wagner type of rule initially [20] (t = 0,1,…,10) as described by the equation V(t) = A (1−exp (−t/τ)), with asymptote A = 1.3 for appetitive reward and A = −1.3 for aversive reward, and the time constant τ = 3. At time step t = 11, a new motivation manipulation is introduced, such as by a shift in a physiological state relevant to the reward. The change in incentive salience occurs as indicated by the arrows, either multiplicatively (V*κ) or additively (V+log κ), where κ (for illustrative purpose) takes the values 5,4,2,1, 0.7, 0.2, 0.1. See Eqn (3a,3b) of the manuscript. Colored lines in the upper-left panel describes Experiment 2 (drug sensitization and acute amphetamine administration), while the lower-right panel depicts Experiment 1 (salt appetite). Note that additive modulation can reverse reward valence, while multiplicative modulation maintains the original reward valence and changes only magnitude.
Figure 1 Simulations of dynamic shifts in incentive salience. All are induced by changes in physiological state after learning a CS-UCS association in an initial state (Eqn 3). The left column is for multiplicative mechanisms (Eqn 3a), while the right column is for additive mechanisms (Eqn 3b). The top row is for shifts after learning a Pavlovian association with a reward UCS (e.g., sucrose taste), and the bottom row is for shifts after learning with an aversive UCS (e.g., intense salt taste). Initial learning is assumed to proceed by a Rescorla-Wagner type of rule initially [20] (t = 0,1,…,10) as described by the equation V(t) = A (1−exp (−t/τ)), with asymptote A = 1.3 for appetitive reward and A = −1.3 for aversive reward, and the time constant τ = 3. At time step t = 11, a new motivation manipulation is introduced, such as by a shift in a physiological state relevant to the reward. The change in incentive salience occurs as indicated by the arrows, either multiplicatively (V*κ) or additively (V+log κ), where κ (for illustrative purpose) takes the values 5,4,2,1, 0.7, 0.2, 0.1. See Eqn (3a,3b) of the manuscript. Colored lines in the upper-left panel describes Experiment 2 (drug sensitization and acute amphetamine administration), while the lower-right panel depicts Experiment 1 (salt appetite). Note that additive modulation can reverse reward valence, while multiplicative modulation maintains the original reward valence and changes only magnitude.CC0 · PubMed Central
Figure 2 Natural salt appetite dynamically enhances incentive salience of a salt CS. Neural coding of CS ‘wanting’ illustrated by firing in ventral pallidum neurons (A). Ordinarily neurons in ventral pallidum that code CS for rewards fire to onset of an auditory tone CS that previously predicted infusion of sucrose solution into the rat's mouth (right column) but not to a CS for intense salt solution (A). A novel salt appetite state causes the neurons to fire as vigorously to the CS for salt as to the CS for sucrose while responses to sucrose cues persist unchanged (A). From [12]. Translation of enhanced CS incentive salience into action during salt appetite (B). When measured behaviorally, a novel salt appetite state causes rats to avidly consume a specific solution containing a gustatory CS (bitter or sour) that previously was paired with intense salt. Ordinarily the rats would not prefer to consume either CS solution [10],[50]. In all cases, the rats had not yet retasted actual salt UCS when they showed new ‘wanting’ of the CS.
Figure 2 Natural salt appetite dynamically enhances incentive salience of a salt CS. Neural coding of CS ‘wanting’ illustrated by firing in ventral pallidum neurons (A). Ordinarily neurons in ventral pallidum that code CS for rewards fire to onset of an auditory tone CS that previously predicted infusion of sucrose solution into the rat's mouth (right column) but not to a CS for intense salt solution (A). A novel salt appetite state causes the neurons to fire as vigorously to the CS for salt as to the CS for sucrose while responses to sucrose cues persist unchanged (A). From [12]. Translation of enhanced CS incentive salience into action during salt appetite (B). When measured behaviorally, a novel salt appetite state causes rats to avidly consume a specific solution containing a gustatory CS (bitter or sour) that previously was paired with intense salt. Ordinarily the rats would not prefer to consume either CS solution [10],[50]. In all cases, the rats had not yet retasted actual salt UCS when they showed new ‘wanting’ of the CS.CC0 · PubMed Central
Figure 3 Selective amplification of CS incentive salience (not CS prediction or UCS hedonic impact) by transient amphetamine intoxication and more permanent drug sensitization. Experimental design of the serial CS1/CS2/UCS procedure, and effects of sensitization and amphetamine on neuronal firing profiles in ventral pallidum (A). The relative rank-ordering of neuronal responses to CS1/CS2/UCS is defined as the “profile” of a neuron; it can be represented mathematically as the angle of a vector in a two dimensional space, where the two axes represent two orthogonal contrasts formed from the three responses (B). The computation is such that this angular value indexing a response profile exists in a continuum which 1) exhausts all possible firing patterns (i.e., relative orders in firing rates to these three types of stimuli); and 2) guarantees that nearby values represents similar firing patterns. Temporal difference error-coding implies maximal response to CS1 which has the greatest prediction, whereas value-coding implies maximal firing to UCS which has the highest hedonic value. By contrast, incentive-coding implies maximal firing to CS2 that has the greatest motivational impact as it immediately precedes the actual reward. The data panel shows firing in control condition contrasted to the combination of amphetamine plus sensitization (C). The summary arrow panel shows the averaged neuronal response for each group of rats, illustrating the additive increments produced by sensitization, amphetamine and combination of both (D). Data from [13].
Figure 3 Selective amplification of CS incentive salience (not CS prediction or UCS hedonic impact) by transient amphetamine intoxication and more permanent drug sensitization. Experimental design of the serial CS1/CS2/UCS procedure, and effects of sensitization and amphetamine on neuronal firing profiles in ventral pallidum (A). The relative rank-ordering of neuronal responses to CS1/CS2/UCS is defined as the “profile” of a neuron; it can be represented mathematically as the angle of a vector in a two dimensional space, where the two axes represent two orthogonal contrasts formed from the three responses (B). The computation is such that this angular value indexing a response profile exists in a continuum which 1) exhausts all possible firing patterns (i.e., relative orders in firing rates to these three types of stimuli); and 2) guarantees that nearby values represents similar firing patterns. Temporal difference error-coding implies maximal response to CS1 which has the greatest prediction, whereas value-coding implies maximal firing to UCS which has the highest hedonic value. By contrast, incentive-coding implies maximal firing to CS2 that has the greatest motivational impact as it immediately precedes the actual reward. The data panel shows firing in control condition contrasted to the combination of amphetamine plus sensitization (C). The summary arrow panel shows the averaged neuronal response for each group of rats, illustrating the additive increments produced by sensitization, amphetamine and combination of both (D). Data from [13].CC0 · PubMed Central
Figure 4 Behavioral confirmation of dynamic amplification of cue-triggered by amphetamine activation of mesolimbic systems. Transient ‘wanting’ comes and goes with the cue (A). Amphetamine microinjection in nucleus accumbens dynamically magnifies ‘wanting’ for sugar reward – but only in presence of reward cue (CS+). Cognitive expectations and ordinary wanting are not altered (reflected in baseline lever pressing in absence of cue and during irrelevant cue, CS−) (B). From [72].
Figure 4 Behavioral confirmation of dynamic amplification of cue-triggered by amphetamine activation of mesolimbic systems. Transient ‘wanting’ comes and goes with the cue (A). Amphetamine microinjection in nucleus accumbens dynamically magnifies ‘wanting’ for sugar reward – but only in presence of reward cue (CS+). Cognitive expectations and ordinary wanting are not altered (reflected in baseline lever pressing in absence of cue and during irrelevant cue, CS−) (B). From [72].CC0 · PubMed Central

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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.
ORCID
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
DOI
10.1371/journal.pcbi.1000437
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
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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