Midbrain Dopamine Neurons Signal Preference for Advance Information about Upcoming Rewards
Open accessAbstract
The desire to know what the future holds is a powerful motivator in everyday life, but it is unknown how this desire is created by neurons in the brain. Here we show that when macaque monkeys are offered a water reward of variable magnitude, they seek advance information about its size. Furthermore, the same midbrain dopamine neurons that signal the expected amount of water also signal the expectation of information, in a manner that is correlated with the strength of the animal's preference. Our data show that single dopamine neurons process both primitive and cognitive rewards, and suggest that current theories of reward-seeking must be revised to include information-seeking.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Stimuli that predict reward have an inherent aspect of motivational significance; a liquid-deprived monkey treats a cue indicating forthcoming liquid as a type of reward in itself, even when this information has no bearing on task performance.
“a liquid-deprived monkey treats a cue indicating forthcoming liquid as a type of reward in itself, even when this information has no bearing on task performance (BrombergMartin and Hikosaka, 2009).”
From Reward Changes Salience in Human Vision via the Anterior Cingulate (2010) · cites this paper as Bromberg-Martin and Hikosaka, 2009
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredWe used high-resolution (1-mm isotropic), rapid-repetition-time (1.378 s), accelerated ultrahigh-field (7T) fMRI to measure activity simultaneously across all five neuromodulatory nuclei and two interconnected cortical regions (AI and ACC).
“Here we exploit high-resolution (1-mm isotropic), rapid-repetition-time (1.378 s), accelerated ultrahigh-field (7T) functional magnetic resonance imaging (fMRI)27,28 to measure activity simultaneously across all five neuromodulatory nuclei and two interconnected cortical regions that project to them: the anterior insula (AI) and anterior cingulate cortex (ACC)3,22,29–33.”
From Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering (2026) · cites this paper as [27]
All the neuromodulatory systems with their origins in VTA, SN, DRN, LC and VSN have at one time or another been proposed to reflect uncertainty or the potential for information gain.
“all the neuromodulatory systems with their origins in the ventral tegmental area (VTA), substantia nigra (SN), dorsal raphe nucleus (DRN), locus coeruleus (LC) and ventral septal nucleus (VSN) have at one time or another been proposed to reflect uncertainty or the potential for information gain3,21–27”
From Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering (2026) · cites this paper as [27]
All major neuromodulatory systems have been linked to uncertainty processing, but identifying a specific or preeminent contribution has been difficult.
“All major neuromodulatory systems have at one time or another been linked to uncertainty processing3,21–27, but identifying a specific or preeminent contribution has been difficult.”
From Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering (2026) · cites this paper as [27]
Activity in individual neurons in both habenula and VTA reflects both the value of a stimulus in terms of the reward that it predicts and VoI.
“Activity in individual neurons in both habenula and VTA reflects both the value of a stimulus in terms of the reward that it predicts58–60 as well as VoI21,22,27,61”
From Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering (2026) · cites this paper as [27]
When actual reward was delivered, dopamine cells had excitatory and inhibitory responses to high or low rewards only if these rewards were unexpected, as expected from a prediction error term.
“When the actual reward was delivered (“Reward”) the cells again had excitatory and inhibitory responses to, respectively, high or low rewards, but only if these rewards were unexpected (“Rand”, but not “Info” conditions) precisely as expected from a prediction error term.”
From Attention, learning, and the value of information (2012) · cites this paper as Bromberg-Martin and Hikosaka, 2009
In an experiment where monkeys were initially uncertain about reward size, dopamine cells had transient excitatory response to a cue signaling larger-than-expected reward, transient inhibition to a cue signaling lower-than-expected reward, and nearly no response to a cue providing no new information.
“Dopamine cells had a transient excitatory response to a stimulus that signaled a larger-than expected reward (“Info-big”) and a transient inhibition to a stimulus that signaled a lower-than expected reward (“Info-small”), but had nearly no response to a stimulus that provided no new information (“Rand”, blue traces).”
From Attention, learning, and the value of information (2012) · cites this paper as Bromberg-Martin and Hikosaka, 2009
A recent study using an information choice task proposed that the cognitive type of reliability is encoded in midbrain dopaminergic cells, but the findings remain open to alternative interpretations.
“While a recent study using an “information choice task” proposed that this type of reliability is encoded in midbrain dopaminergic cells (Bromberg-Martin and Hikosaka, 2009), the findings remain open to alternative interpretations.”
From Attention, learning, and the value of information (2012) · cites this paper as Bromberg-Martin and Hikosaka, 2009
Dopamine neurons had two types of responses on the information choice task: a prediction error response at the time of the actual cue, and an earlier response at the time of selection that was slightly stronger for informative than uninformative cues.
“Dopamine neurons (Fig. 3B) had two types of responses on the task. At the time of the actual information (marked “Cue”) in Fig. 3B, the neurons emitted the customary prediction error response which, as described above, was excitatory for a “good news” (big reward) cue and inhibitory for a “bad news” (small reward) cue. Of particular interest however was a response that preceded the actual cue and”
From Attention, learning, and the value of information (2012) · cites this paper as Bromberg-Martin and Hikosaka, 2009
In the information choice task, reward outcomes were on average equal and fixed in all conditions, so monkeys could not increase their physical rewards with a specific choice.
“Notably, the reward outcomes themselves were on average equal and fixed in all conditions, so that monkeys could not increase their physical rewards with a specific choice.”
From Attention, learning, and the value of information (2012) · cites this paper as Bromberg-Martin and Hikosaka, 2009
Monkeys reliably selected the informative target, suggesting they had an intrinsic preference for information.
“Nevertheless, monkeys reliably selected the informative target suggesting that they had an intrinsic preference for information.”
From Attention, learning, and the value of information (2012) · cites this paper as Bromberg-Martin and Hikosaka, 2009
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486 · see citing works
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500 · see citing works
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Theory of games and economic behavior
Garcia, S. R., & Miller, S. J. (2019) · American Mathematical Society eBooks · doi:10.1090/mbk/121/32 · Cited by 16,777 · see citing works
(1966) · Medical Entomology and Zoology · Cited by 10,293 · 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
Signal detection theory and psychophysics
Ingleby, D. (1967) · Journal of Sound and Vibration · doi:10.1016/0022-460x(67)90197-6 · Cited by 8,616 · see citing works
Holroyd, C. B., & Coles, M. G. H. (2002) · Psychological Review · doi:10.1037/0033-295x.109.4.679 · Cited by 4,027 · see citing works
Theory of Games and Economic Behavior
Hawkins, D. (1945) · Philosophy of Science · doi:10.1086/286866 · Cited by 3,970 · see citing works
Dopamine, learning and motivation.
Wise, R. A. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1406 · Cited by 3,379 · cited 2× · see citing works
“Information Signals in the Dopaminergic Reward System Dopamine neuron activity is thought to teach the brain to seek basic goals like food and water, reinforcing and punishing actions by adjusting synaptic connections between neurons in cortical and subcortical brain structures (Wise, 2004; Montague et al., 2004).”
BackgroundTurner, R. M. (1978) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(78)90073-3 · Cited by 2,269 · see citing works
Discrete coding of reward probability and uncertainty by dopamine neurons
Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003) · Science · doi:10.1126/science.1077349 · Cited by 2,085 · see citing works
Learning the value of information in an uncertain world
Behrens, T. E. J., Woolrich, M. W., Walton, M. E., & Rushworth, M. F. S. (2007) · Nature Neuroscience · doi:10.1038/nn1954 · Cited by 2,053 · see citing works
By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism
Frank, M. J., Seeberger, L. C., & O'Reilly, R. C. (2004) · Science · doi:10.1126/science.1102941 · Cited by 1,964 · see citing works
Temporal Resolution of Uncertainty and Dynamic Choice Theory
Kreps, D. M., & Porteus, E. L. (1978) · Econometrica · doi:10.2307/1913656 · Cited by 1,753 · see citing works
Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450 · see citing works
Two types of dopamine neuron distinctly convey positive and negative motivational signals
Matsumoto, M., & Hikosaka, O. (2009) · Nature · doi:10.1038/nature08028 · Cited by 1,378 · see citing works
Self-citationMidbrain dopamine neurons encode a quantitative reward prediction error signal
Bayer, H. M., & Glimcher, P. W. (2005) · Neuron · doi:10.1016/j.neuron.2005.05.020 · Cited by 1,304 · see citing works
Multiple reward signals in the brain
Schultz, W. (2000) · Nature Reviews Neuroscience · doi:10.1038/35044563 · Cited by 1,303 · see citing works
Lateral habenula as a source of negative reward signals in dopamine neurons
Matsumoto, M., & Hikosaka, O. (2007) · Nature · doi:10.1038/nature05860 · Cited by 1,292 · see citing works
Self-citationAn Introduction to the Bootstrap.
Kimber, A., Efron, B., & Tibshirani, R. J. (1994) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348146 · Cited by 1,246 · see citing works
Miller, S. M. (1987) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.52.2.345 · Cited by 1,142 · see citing works
Honig, W. K., & Staddon, J. E. R. (2022) · Routledge eBooks · doi:10.4324/9781003256670 · Cited by 970 · see citing works
Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events
Horvitz, J. C. (2000) · Neuroscience · doi:10.1016/s0306-4522(00)00019-1 · Cited by 967 · see citing works
Computational roles for dopamine in behavioural control
Montague, P. R., Hyman, S. E., & Cohen, J. D. (2004) · Nature · doi:10.1038/nature03015 · Cited by 966 · see citing works
Miller, S. M. (1987) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.52.2.345 · Cited by 843 · see citing works
Psychological Expected Utility Theory and Anticipatory Feelings
Caplin, A., & Leahy, J. V. (2001) · The Quarterly Journal of Economics · doi:10.1162/003355301556347 · Cited by 812 · see citing works
The short-latency dopamine signal: a role in discovering novel actions?
Redgrave, P., & Gurney, K. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn2022 · Cited by 762 · see citing works
Associative learning mediates dynamic shifts in dopamine signaling in the nucleus accumbens
Day, J. J., Roitman, M. F., Wightman, R. M., & Carelli, R. M. (2007) · Nature Neuroscience · doi:10.1038/nn1923 · Cited by 710 · see citing works
Roesch, M. R., Calu, D. J., & Schoenbaum, G. (2007) · Nature Neuroscience · doi:10.1038/nn2013 · Cited by 597 · see citing works
Risky business: the neuroeconomics of decision making under uncertainty
Platt, M. L., & Huettel, S. A. (2008) · Nature Neuroscience · doi:10.1038/nn2062 · Cited by 556 · see citing works
Neuronal Activity Related to Reward Value and Motivation in Primate Frontal Cortex
Roesch, M. R., & Olson, C. R. (2004) · Science · doi:10.1126/science.1093223 · Cited by 535 · see citing works
Addiction as a Computational Process Gone Awry
Redish, A. D. (2004) · Science · doi:10.1126/science.1102384 · Cited by 534 · see citing works
Anterior Cingulate: Single Neuronal Signals Related to Degree of Reward Expectancy
Shidara, M., & Richmond, B. J. (2002) · Science · doi:10.1126/science.1069504 · Cited by 515 · see citing works
The role of observing responses in discrimination learning. Part I.
Wyckoff, L. B. (1952) · Psychological Review · doi:10.1037/h0053932 · Cited by 507 · see citing works
Midbrain dopamine neurons encode decisions for future action
Morris, G., Nevet, A., Arkadir, D., Vaadia, E., & Bergman, H. (2006) · Nature Neuroscience · doi:10.1038/nn1743 · Cited by 503 · see citing works
Unix-based multiple-process system, for real-time data acquisition and control
Hays, A. V., Richmond, B. J., & Optican, L. M. (1982) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · Cited by 497 · see citing works
Dopamine: generalization and bonuses
Kakade, S. M., & Dayan, P. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00048-5 · Cited by 477 · see citing works
Influence of Reward Delays on Responses of Dopamine Neurons
Kobayashi, S., & Schultz, W. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.1600-08.2008 · Cited by 431 · see citing works
Processing of Temporal Unpredictability in Human and Animal Amygdala
Herry, C., Bach, D. R., Esposito, F., Di Salle, F., Perrig, W. J., Scheffler, K., Lüthi, A., & Seifritz, E. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5218-06.2007 · Cited by 401 · see citing works
Risk-sensitive neurons in macaque posterior cingulate cortex
McCoy, A. N., & Platt, M. L. (2005) · Nature Neuroscience · doi:10.1038/nn1523 · Cited by 380 · see citing works
Intrinsically Motivated Learning of Hierarchical Collections of Skills
Barto, A. G., Singh, S. P., & Chentanez, N. (2004) · Cited by 376 · see citing works
Schultz, W., & Romo, R. (1990) · Journal of Neurophysiology · doi:10.1152/jn.1990.63.3.607 · Cited by 334 · cited 1× · see citing works
“Consistent with previous studies of reward coding (Schultz and Romo, 1990; Kawagoe et al., 2004; Roesch et al., 2007; Matsumoto and Hikosaka, 2007), we observed similar neural coding of behav-”
ResultThe temporal precision of reward prediction in dopamine neurons
Fiorillo, C. D., Newsome, W. T., & Schultz, W. (2008) · Nature Neuroscience · doi:10.1038/nn.2159 · Cited by 310 · see citing works
March, J. G. (1996) · Psychological Review · doi:10.1037/0033-295x.103.2.309 · Cited by 307 · see citing works
Observing and conditioned reinforcement
Dinsmoor, J. A. (1983) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00017969 · Cited by 307 · see citing works
Coding of Predicted Reward Omission by Dopamine Neurons in a Conditioned Inhibition Paradigm
Tobler, P. N., Dickinson, A., & Schultz, W. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-32-10402.2003 · Cited by 300 · see citing works
Correlated Coding of Motivation and Outcome of Decision by Dopamine Neurons
Satoh, T., Nakai, S., Sato, T., & Kimura, M. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-30-09913.2003 · Cited by 298 · see citing works
Joshua, M., Adler, A., Mitelman, R., Vaadia, E., & Bergman, H. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.3839-08.2008 · Cited by 273 · see citing works
Prefrontal Coding of Temporally Discounted Values during Intertemporal Choice
Kim, S., Hwang, J., & Lee, D. (2008) · Neuron · doi:10.1016/j.neuron.2008.05.010 · Cited by 241 · see citing works
Kawagoe, R., Takikawa, Y., & Hikosaka, O. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00721.2003 · Cited by 158 · see citing works
Self-citationNiv, Y., Joel, D., Meilijson, I., & Ruppin, E. (2002) · Adaptive Behavior · doi:10.1177/10597123020101001 · Cited by 157 · see citing works
Naloxone attenuates rat’s preference for signaled shock
Fanselow, M. S. (1979) · Physiological Psychology · doi:10.3758/bf03326621 · Cited by 156 · see citing works
Choosing between predictable and unpredictable shock conditions: Data and theory.
Badia, P., Harsh, J., & Abbott, B. B. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.5.1107 · Cited by 153 · see citing works
Neural Dissociation of Delay and Uncertainty in Intertemporal Choice
Luhmann, C. C., Chun, M. M., Yi, D.-J., Lee, D., & Wang, X. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.5058-08.2008 · Cited by 150 · see citing works
Temporal Discounting Predicts Risk Sensitivity in Rhesus Macaques
Hayden, B. Y., & Platt, M. L. (2007) · Current Biology · doi:10.1016/j.cub.2006.10.055 · Cited by 147 · see citing works
The acquisition of observing responses in the absence of differential external reinforcement.
Prokasy, W. F. (1956) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0046740 · Cited by 146 · see citing works
Behavioral and Neural Changes after Gains and Losses of Conditioned Reinforcers
Seo, H., & Lee, D. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.4726-08.2009 · Cited by 137 · see citing works
Hope: An empirical study of attitude toward the timing of uncertainty resolution
Chew, S. H., & Ho, J. L. Y. (1994) · Journal of Risk and Uncertainty · doi:10.1007/bf01064045 · Cited by 127 · see citing works
The stimulus conditions which follow learned responses.
Perkins, C. C. (1955) · Psychological Review · doi:10.1037/h0040520 · Cited by 125 · see citing works
Feature-Based Anticipation of Cues that Predict Reward in Monkey Caudate Nucleus
Lauwereyns, J., Takikawa, Y., Kawagoe, R., Kobayashi, S., Koizumi, M., Coe, B. C., Sakagami, M., & Hikosaka, O. (2002) · Neuron · doi:10.1016/s0896-6273(02)00571-8 · Cited by 123 · see citing works
Self-citationLiving with uncertainty: attractiveness and resolution timing
Lovallo, D. P., & Kahneman, D. J. (2000) · Journal of Behavioral Decision Making · doi:10.1002/(sici)1099-0771(200004/06)13:2 · Cited by 122 · see citing works
Influences of Rewarding and Aversive Outcomes on Activity in Macaque Lateral Prefrontal Cortex
Kobayashi, S., Nomoto, K., Watanabe, M., Hikosaka, O., Schultz, W., & Sakagami, M. (2006) · Neuron · doi:10.1016/j.neuron.2006.08.031 · Cited by 109 · see citing works
Self-citationToward a quantitative theory of secondary reinforcement.
Wyckoff, L. B. (1959) · Psychological Review · doi:10.1037/h0046882 · Cited by 106 · see citing works
Anxiety and Decision Making with Delayed Resolution of Uncertainty
Wu, G. (1999) · Theory and Decision · doi:10.1023/a:1004990410083 · Cited by 105 · see citing works
Gormezano, I. (2013) · Psychology Press eBooks · doi:10.4324/9780203772515 · Cited by 86 · see citing works
Lieberman, D. A. (1972) · Learning and Motivation · doi:10.1016/0023-9690(72)90030-6 · Cited by 86 · see citing works
Roper, K. L., & Zentall, T. R. (1999) · Learning and Motivation · doi:10.1006/lmot.1999.1030 · Cited by 78 · see citing works
Experimental Testing of Intrinsic Preferences for NonInstrumental Information
Eliaz, K., & Schotter, A. (2007) · American Economic Review · doi:10.1257/aer.97.2.166 · Cited by 72 · see citing works
Stressor predictability and rat brain noradrenaline metabolism
Tsuda, A., Ida, Y., Satoh, H., Tsujimaru, S., & Tanaka, M. (1989) · Pharmacology Biochemistry and Behavior · doi:10.1016/0091-3057(89)90198-6 · Cited by 57 · see citing works
Observing behavior in monkeys (Macaca arctoides): Support for the information hypothesis
Schrier, A. M., Thompson, C. R., & Spector, N. R. (1980) · Learning and Motivation · doi:10.1016/0023-9690(80)90006-5 · Cited by 50 · see citing works
STIMULUS‐PRODUCING RESPONSES IN CHIMPANZEES1
Kelleher, R. T. (1958) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.1958.1-87 · Cited by 46 · see citing works
The Role of S− in Human Observing Behavior: Bad News Is Sometimes Better Than No News
Lieberman, D. A., Cathro, J. S., Nichol, K., & Watson, E. (1997) · Learning and Motivation · doi:10.1006/lmot.1997.0951 · Cited by 42 · see citing works
The Resolution of Uncertainty: An Experimental Study
Ahlbrecht, M., & Weber, M. (1996) · Cited by 41 · see citing works
Infrequent reward facilitates observing responses in rats
McMichael, J. S., Lanzetta, J. T., & Driscoll, J. M. (1967) · Psychonomic Science · doi:10.3758/bf03330648 · Cited by 36 · see citing works
Steiner, J. (1970) · Quarterly Journal of Experimental Psychology · doi:10.1080/14640747008401937 · Cited by 31 · see citing works
Conditions affecting acquisition of observing responses in the absence of differential reward.
Mitchell, K. M., Perkins, N. P., & Perkins, C. C. (1965) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0022578 · Cited by 25 · see citing works
Observing Responses and Uncertainty Reduction
Steiner, J. (1967) · Quarterly Journal of Experimental Psychology · doi:10.1080/14640746708400063 · Cited by 25 · see citing works
Neural Representation of Information Measure in the Primate Premotor Cortex
Nakamura, K. (2006) · Journal of Neurophysiology · doi:10.1152/jn.01326.2005 · Cited by 21 · see citing works
Woods, J., & Winger, G. (2002) · Psychopharmacology · doi:10.1007/s00213-002-1201-4 · Cited by 20 · see citing works
Role of Food Deprivation in the Acquisition of the Observing Response
Wehling, H. E., & Prokasy, W. F. (1962) · Psychological Reports · doi:10.2466/pr0.1962.10.2.399 · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Dopamine in motivational control: rewarding, aversive, and alerting
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010) · Neuron · doi:10.1016/j.neuron.2010.11.022 · Cited by 2,396 · see citing works
Self-citationNeuronal Reward and Decision Signals: From Theories to Data
Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126 · see citing works
Active inference: a process theory
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017) · Neural Computation · doi:10.1162/neco_a_00912 · Cited by 1,156 · see citing works
The Psychology and Neuroscience of Curiosity
Kidd, C., & Hayden, B. Y. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.010 · Cited by 1,026 · see citing works
Excitatory transmission from the amygdala to nucleus accumbens facilitates reward seeking
Stuber, G. D., Sparta, D. R., Stamatakis, A. M., van Leeuwen, W. A., Hardjoprajitno, J. E., Cho, S., Tye, K. M., Kempadoo, K. A., Zhang, F., Deisseroth, K., et al. (2011) · Nature · doi:10.1038/nature10194 · Cited by 878 · see citing works
Information-seeking, curiosity, and attention: computational and neural mechanisms
Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.001 · Cited by 784 · see citing works
Active inference and epistemic value
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015) · Cognitive Neuroscience · doi:10.1080/17588928.2015.1020053 · Cited by 731 · see citing works
Reward Changes Salience in Human Vision via the Anterior Cingulate
Hickey, C., Chelazzi, L., & Theeuwes, J. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.1026-10.2010 · Cited by 606 · see citing works
Learning to reinforcement learn
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016) · arXiv · doi:10.48550/arxiv.1611.05763 · Cited by 579 · see citing works
Towards a neuroscience of active sampling and curiosity
Gottlieb, J., & Oudeyer, P.-Y. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0078-0 · Cited by 494 · see citing works
Reward processing by the lateral habenula in normal and depressive behaviors
Proulx, C. D., Hikosaka, O., & Malinow, R. (2014) · Nature Neuroscience · doi:10.1038/nn.3779 · Cited by 461 · see citing works
Self-citationBrain reward circuitry beyond the mesolimbic dopamine system: a neurobiological theory
Ikemoto, S. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2010.02.001 · Cited by 454 · see citing works
Hayden, B. Y., Heilbronner, S. R., Pearson, J. M., & Platt, M. L. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.4652-10.2011 · Cited by 379 · see citing works
Toward a neurobiology of delusions
Corlett, P. R., Taylor, J. R., Wang, X.-J., Fletcher, P. C., & Krystal, J. H. (2010) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2010.06.007 · Cited by 394 · see citing works
Dopamine, affordance and active inference
Friston, K. J., Shiner, T., FitzGerald, T., Galea, J. M., Adams, R., Brown, H., Dolan, R. J., Moran, R., Stephan, K. E., & Bestmann, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002327 · Cited by 381 · see citing works
The neuromodulator of exploration: A unifying theory of the role of dopamine in personality
DeYoung, C. G. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00762 · Cited by 387 · see citing works
Selection history: How reward modulates selectivity of visual attention.
Failing, M., & Theeuwes, J. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1380-y · Cited by 345 · see citing works
“Other research suggests that primates strategically search for the reward-associated cue under such circumstances (Bromberg-Martin & Hikosaka, 2009).”
Dissociation of neural networks for anticipation and consumption of monetary and social rewards
Rademacher, L., Krach, S., Kohls, G., Irmak, A., Gründer, G., & Spreckelmeyer, K. N. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.089 · Cited by 347 · see citing works
A computational framework for the study of confidence in humans and animals
Kepecs, A., & Mainen, Z. F. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0037 · Cited by 305 · see citing works
Lateral habenula neurons signal errors in the prediction of reward information
Bromberg-Martin, E. S., & Hikosaka, O. (2011) · Nature Neuroscience · doi:10.1038/nn.2902 · Cited by 275 · see citing works
Self-citationDüzel, E., Bunzeck, N., Guitart-Masip, M., & Düzel, S. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2009.08.006 · Cited by 249 · see citing works
Medial 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
Attention, learning, and the value of information
Gottlieb, J. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.034 · Cited by 241 · see citing works
“shown in Figure 3B, in an experiment in which monkeys were initially uncertain about the size of a reward and at the time marked ‘‘Cue’’ received a visual signal that conveyed information about the expected reward (Bromberg-Martin and Hikosaka, 2009).”
BackgroundMethodResultInfluentialDopamine neurons respond to errors in the prediction of sensory features of expected rewards
Takahashi, Y. K., Batchelor, H. M., Liu, B., Khanna, A., Morales, M., & Schoenbaum, G. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.025 · Cited by 200 · see citing works
Model-based predictions for dopamine
Langdon, A. J., Sharpe, M. J., Schoenbaum, G., & Niv, Y. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.10.006 · Cited by 161 · see citing works
Learning from Action: Reconsidering Movement Signaling in Midbrain Dopamine Neuron Activity
Coddington, L. T., & Dudman, J. T. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.036 · Cited by 150 · see citing works
Eitam, B., Kennedy, P. M., & Tory Higgins, E. (2013) · Experimental Brain Research · doi:10.1007/s00221-012-3370-7 · Cited by 141 · see citing works
Kim, U., & Lee, T. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08030.x · Cited by 133 · see citing works
A neural network for information seeking
White, J. K., Bromberg-Martin, E. S., Heilbronner, S. R., Zhang, K., Pai, J., Haber, S. N., & Monosov, I. E. (2019) · Nature Communications · doi:10.1038/s41467-019-13135-z · Cited by 129 · see citing works
Self-citationI control therefore I do: Judgments of agency influence action selection
Karsh, N., & Eitam, B. (2015) · Cognition · doi:10.1016/j.cognition.2015.02.002 · Cited by 125 · see citing works
Dopaminergic basis for signaling belief updates, but not surprise, and the link to paranoia
Nour, M. M., Dahoun, T., Schwartenbeck, P., Adams, R. A., FitzGerald, T. H. B., Coello, C., Wall, M. B., Dolan, R. J., & Howes, O. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1809298115 · Cited by 101 · see citing works
Play, Curiosity, and Cognition
Chu, J., & Schulz, L. E. (2020) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-070120-014806 · Cited by 93 · see citing works
Transient activation of midbrain dopamine neurons by reward risk
Fiorillo, C. D. (2011) · Neuroscience · doi:10.1016/j.neuroscience.2011.09.037 · Cited by 90 · see citing works
Neural circuitry of information seeking
Bromberg-Martin, E. S., & Monosov, I. E. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.07.006 · Cited by 89 · see citing works
Self-citationDopamine neurons learn to encode the long-term value of multiple future rewards
Enomoto, K., Matsumoto, N., Nakai, S., Satoh, T., Sato, T. K., Ueda, Y., Inokawa, H., Haruno, M., & Kimura, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1014457108 · Cited by 83 · see citing works
Dopamine neuron ensembles signal the content of sensory prediction errors
Stalnaker, T. A., Howard, J. D., Takahashi, Y. K., Gershman, S. J., Kahnt, T., & Schoenbaum, G. (2019) · eLife · doi:10.7554/elife.49315 · Cited by 74 · see citing works
Intrinsic motivations and open-ended development in animals, humans, and robots: an overview
Baldassarre, G., Stafford, T., Mirolli, M., Redgrave, P., Ryan, R. M., & Barto, A. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00985 · Cited by 73 · see citing works
Nicotinic receptors in the ventral tegmental area promote uncertainty-seeking
Naudé, J., Tolu, S., Dongelmans, M., Torquet, N., Valverde, S., Rodriguez, G., Pons, S., Maskos, U., Mourot, A., Marti, F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4223 · Cited by 61 · see citing works
Approach-Induced Biases in Human Information Sampling
Hunt, L. T., Rutledge, R. B., Malalasekera, W. M. N., Kennerley, S. W., & Dolan, R. J. (2016) · PLOS Biology · doi:10.1371/journal.pbio.2000638 · Cited by 60 · see citing works
Clark, L., & Zack, M. (2023) · Addictive Behaviors · doi:10.1016/j.addbeh.2023.107626 · Cited by 59 · see citing works
Curiosity: primate neural circuits for novelty and information seeking
Monosov, I. E. (2024) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00784-9 · Cited by 44 · see citing works
A neural mechanism for conserved value computations integrating information and rewards
Bromberg-Martin, E. S., Feng, Y.-Y., Ogasawara, T., White, J. K., Zhang, K., & Monosov, I. E. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01511-4 · Cited by 43 · see citing works
Self-citationGendron, M., Mesquita, B., & Barrett, L. F. (2020) · Culture, Mind, and Brain · doi:10.1017/9781108695374.010 · Cited by 20 · see citing works
Addressing altered anticipation as a transdiagnostic target through computational psychiatry
Sepúlveda, P., Aitsahalia, I., Kumar, K., Atkin, T., & Iigaya, K. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2025.02.014 · Cited by 3 · see citing works
D’Ambrogio, S., Grohn, J., Khalighinejad, N., Mattar, M. G., Hunt, L., & Rushworth, M. F. S. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02342-9 · Cited by 1 · see citing works
- Authors
- Bromberg-Martin, E. S., & Hikosaka, O.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Hikosaka, Okihide
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2009-07
- Volume
- 63
- Issue
- 1
- Pages
- 119-126
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC2723053
- OpenAlex
- W2167125619
- Cited by
- 548
- References
- 83
- Citations by year
- 2026: 36 · 2025: 26 · 2024: 43 · 2023: 44 · 2022: 28 · 2021: 34 · 2020: 80 · 2019: 41 · 2018: 29 · 2017: 27 · 2016: 28 · 2015: 27 · 2014: 17 · 2013: 21 · 2012: 24
- FWCI
- 9.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Eye Institute
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- midbrain dopaminergic neurons, information seeking, reward anticipation, reward magnitude, preferences, variable rewards, water reward, reward seeking, macaque, single-unit recording
- MeSH terms
- Action Potentials, Animals, Behavior, Animal, Conditioning, Operant, Cues, Decision Making, Discrimination Learning, Dopamine, Eye Movements, Macaca mulatta, Male, Mesencephalon, Neurons, Neuropsychological Tests, Photic Stimulation, Reinforcement Schedule, Reward, Statistics as Topic, Signal Detection, Psychological
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 ›
Are you an author, or do you see a problem with this page? Request a review
