The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Expectation Modulates Neural Responses to Pleasant and Aversive Stimuli in Primate Amygdala
Open accessAbstract
Animals and humans learn to approach and acquire pleasant stimuli and to avoid or defend against aversive ones. However, both pleasant and aversive stimuli can elicit arousal and attention, and their salience or intensity increases when they occur by surprise. Thus, adaptive behavior may require that neural circuits compute both stimulus valence--or value--and intensity. To explore how these computations may be implemented, we examined neural responses in the primate amygdala to unexpected reinforcement during learning. Many amygdala neurons responded differently to reinforcement depending upon whether or not it was expected. In some neurons, this modulation occurred only for rewards or aversive stimuli, but not both. In other neurons, expectation similarly modulated responses to both rewards and punishments. These different neuronal populations may subserve two sorts of processes mediated by the amygdala: those activated by surprising reinforcements of both valences-such as enhanced arousal and attention-and those that are valence-specific, such as fear or reward-seeking behavior.
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“These findings suggest that OT modulates context-specific processing associated with both negative and positive valence in the amygdala, consistent with the encoding of both positive and negative motivational values by the primate amygdala neurons (Belova et al., 2007; Paton et al., 2006).”
From Oxytocin and social cognition in rhesus macaques: Implications for understanding and treating human psychopathology (2014) · cites this paper as Belova et al., 2007
In the citing paper
“In addition to being essential for aversively motivated learning, plasticity in the LA is also required for several forms of appetitively motivated learning (43–45) and a number of studies have found that separate populations of amygdala neurons mediate aversive and appetitive processing (46–48).”
From Optical activation of lateral amygdala pyramidal cells instructs associative fear learning (2010) · cites this paper as [48]
Citations
Cites
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334
(1966) · Medical Entomology and Zoology · Cited by 10,293
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
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
LeDoux, J. E. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.155 · Cited by 8,444
Rescorla, R. A. (1972) · Medical Entomology and Zoology · Cited by 7,268
Classical conditioning II: Current research and theory.
Black, A. H., & Prokasy, W. F. (1972) · Appleton-Century-Crofts eBooks · Cited by 4,449
Contributions of the Amygdala to Emotion Processing: From Animal Models to Human Behavior
Phelps, E. A., & LeDoux, J. E. (2005) · Neuron · doi:10.1016/j.neuron.2005.09.025 · Cited by 3,203 · cited 2×
“…to support processes such as attention, attention-based learning, arousal, and the enhancement of memory formation, all of which have been shown to involve the amygdala (Holland and Gallagher, 1999, 2006; Kensinger and Corkin, 2004; McGaugh, 2004; Oya et al., 2005; Phelps and LeDoux, 2005).”
Pearce, J. M., & Hall, G. (1980) · Psychological Review · doi:10.1037/0033-295x.87.6.532 · Cited by 3,023
A theory of attention: Variations in the associability of stimuli with reinforcement
Mackintosh, N. J. (1975) · Psychological Review · doi:10.1037/h0076778 · Cited by 2,733
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St. Onge, J. R. (2012) · Open Collections · doi:10.14288/1.0072501 · Cited by 1
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Redolar-Ripoll, E. (2012) · Revista Argentina de Ciencias del Comportamiento ( RACC ) · doi:10.32348/1852.4206.v4.n2.5174 · Cited by 1
Yamanaka, K., & Waki, H. (2021) · Research Square · doi:10.21203/rs.3.rs-109253/v2 · Cited by 1
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Heilbronner, S. R. (2012) · DukeSpace (Duke University) · Cited by 1
Uncertainty coding in the reward system
Chung, H.-K., Tobler, P. N., & Weber, E. U. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-820480-1.00013-9 · Cited by 2
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Rabinak, C. A. (2009) · Deep Blue (University of Michigan)
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Godoy, J. C. (2012) · Redalyc (Universidad Autónoma del Estado de México) · doi:10.30882/1852.4206.v4.n2.5168
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Hernandez, C. M., Orsini, C. A., Labiste, C. C., Wheeler, A.-R., Eyck, T. W. T., Bruner, M. M., Sahagian, T. J., Harden, S. W., Frazier, C. J., Setlow, B., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/556894
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McFadyen, J. (2019) · The University of Queensland · doi:10.14264/uql.2019.269
Novaes, L. S. (2019) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.42.2019.tde-18022019-144217
Yamanaka, K., & Waki, H. (2020) · doi:10.21203/rs.3.rs-109253/v1
Sladky, R., Riva, F., Rosenberger, L. A., van Honk, J., & Lamm, C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.03.442429
Frank, M. J. (2010) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.3286956.2972054
Sah, P. (2010) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.5316956.5264054
Häusser, M., & London, M. (2010) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.4592958.5101055
Frank, M. J. (2010) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.7340957.7601055
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Doya, K. (2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.013 · Cited by 1
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Doya, K. (2023) · Cambridge University Press eBooks · doi:10.1017/9781108755610.026
Kim, D., Wang, Z., Sakagami, M., Sasaki, Y., & Watanabe, T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.13.566726
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Tu, G., Wen, P., Halawa, A., & Takehara‐Nishiuchi, K. (2024) · eLife · doi:10.7554/elife.102986.1
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Kuraoka, K., & Nakamura, K. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0093-24.2025 · Cited by 1
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Tallot, L. (2015) · theses.fr (ABES) · doi:10.70675/21dbbecbze3bcz4e26z92adz6c438f2bee10
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Kouskoff, V. (2021) · theses.fr (ABES) · doi:10.70675/a8f4e479z2341z4f88zaf5bz91e7451d469d
Peter, A. S., Kim, G., & DiCarlo, J. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.22.726311 · Cited by 1
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Metadata
- Authors
- Belova, M. A., Paton, J. J., Morrison, S. E., & Salzman, C. D.
- Affiliations
- Columbia University (US)
- New York State Psychiatric Institute (US)
- Corresponding author
- Salzman, C. Daniel
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2007-09
- Volume
- 55
- Issue
- 6
- Pages
- 970-984
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC2042139
- OpenAlex
- W2133622401
- Cited by
- 349
- References
- 65
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 10 · 2023: 15 · 2022: 11 · 2021: 17 · 2020: 15 · 2019: 18 · 2018: 16 · 2017: 28 · 2016: 17 · 2015: 32 · 2014: 22 · 2013: 23 · 2012: 29
- FWCI
- 10.85
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 2146 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Alliance for Research on Schizophrenia and Depression
- National Institute on Drug Abuse
- National Eye Institute
- National Science Foundation
- National Institute of Mental Health
- Eunice Kennedy Shriver National Institute of Child Health and Human Development
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- amygdala, expectations, surprise, reinforcement learning, reward, punishment, aversive stimuli, stimulus valence, stimulus intensity, arousal, attention, fear, reward seeking
- MeSH terms
- Amygdala, Animals, Arousal, Data Interpretation, Statistical, Learning, Macaca mulatta, Neurons, Psychomotor Performance, Reinforcement, Psychology, ROC Curve, Excitatory Postsynaptic Potentials
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