The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Predictability Modulates Human Brain Response to Reward
Open accessAbstract
Certain classes of stimuli, such as food and drugs, are highly effective in activating reward regions. We show in humans that activity in these regions can be modulated by the predictability of the sequenced delivery of two mildly pleasurable stimuli, orally delivered fruit juice and water. Using functional magnetic resonance imaging, the activity for rewarding stimuli in both the nucleus accumbens and medial orbitofrontal cortex was greatest when the stimuli were unpredictable. Moreover, the subjects' stated preference for either juice or water was not directly correlated with activity in reward regions but instead was correlated with activity in sensorimotor cortex. For pleasurable stimuli, these findings suggest that predictability modulates the response of human reward regions, and subjective preference can be dissociated from this response.
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“Many early fMRI studies into prediction errors used model-free accounts in Pavlovian paradigms and located prediction errors in striatal BOLD (Berns et al., 2001; O’Doherty, 2004; O’Doherty et al., 2003; Haruno et al., 2004).”
From Goals and Habits in the Brain (2013) · cites this paper as Berns et al., 2001
Experimental — accuracy not yet measuredIn the citing paper
“As the OFC has been implicated in prior studies of prediction error learning (Berns et al., 2001; Takahashi et al., 2009), we selected this region as an a priori structure in which to search for learning signals.”
From Behavioral and Neural Properties of Social Reinforcement Learning (2011) · cites this paper as Berns et al., 2001
In the citing paper
“Non-human primate and human imaging studies have implicated the ventral striatum and orbital frontal cortex (OFC) in prediction error signaling (Schultz et al., 1997; Berns et al., 2001; Fiorillo et al., 2003; McClure et al., 2003).”
From Behavioral and Neural Properties of Social Reinforcement Learning (2011) · cites this paper as Berns et al., 2001
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Cites
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Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.
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Metadata
- Authors
- Berns, G. S., McClure, S. M., Pagnoni, G., & Montague, P. R.
- Affiliations
- Emory University (US)
- Baylor College of Medicine (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2001-04-15
- Volume
- 21
- Issue
- 8
- Pages
- 2793-2798
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6762527
- OpenAlex
- W2096684870
- Cited by
- 711
- References
- 48
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 23 · 2023: 11 · 2022: 12 · 2021: 17 · 2020: 19 · 2019: 18 · 2018: 26 · 2017: 17 · 2016: 35 · 2015: 26 · 2014: 32 · 2013: 35 · 2012: 37
- FWCI
- 9.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by-nc-sa)
- VTechWorks (Virginia Tech) (submittedVersion)
- Funders
- National Institute on Drug Abuse (K08 DA00367, K08DA000367, R01-DA11723, R01DA011723)
- 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
- reward, predictability, nucleus accumbens, medial orbitofrontal cortex, sensorimotor cortex, subjective preference, food reward
- MeSH terms
- Adolescent, Adult, Brain, Brain Mapping, Computer Simulation, Corpus Striatum, Deglutition, Food Preferences, Frontal Lobe, Hippocampus, Humans, Magnetic Resonance Imaging, Nucleus Accumbens, Reward, Somatosensory Cortex, Stimulation, Chemical, Taste, Neural Networks, Computer, Prefrontal Cortex, Intuition
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