The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Associative learning of social value
Open accessAbstract
Our decisions are guided by information learnt from our environment. This information may come via personal experiences of reward, but also from the behaviour of social partners. Social learning is widely held to be distinct from other forms of learning in its mechanism and neural implementation; it is often assumed to compete with simpler mechanisms, such as reward-based associative learning, to drive behaviour. Recently, neural signals have been observed during social exchange reminiscent of signals seen in studies of associative learning. Here we demonstrate that social information may be acquired using the same associative processes assumed to underlie reward-based learning. We find that key computational variables for learning in the social and reward domains are processed in a similar fashion, but in parallel neural processing streams. Two neighbouring divisions of the anterior cingulate cortex were central to learning about social and reward-based information, and for determining the extent to which each source of information guides behaviour. When making a decision, however, the information learnt using these parallel streams was combined within ventromedial prefrontal cortex. These findings suggest that human social valuation can be realized by means of the same associative processes previously established for learning other, simpler, features of the environment.
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“While computational studies of decision-making about rewards and punishments show that participants adapt their rate of learning about action-outcome contingencies in response to changes in environmental volatility 8–10”
From Adults with autism overestimate the volatility of the sensory environment (2017) · cites this paper as [10]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Optimal integration of opinions, whether from human or artificial agents, should consider the following factors: their judgments, their confidence in the correctness of their opinions, and the average objective accuracy of the agents in similar situations in the past.”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [44]
Experimental — accuracy not yet measuredIn the citing paper
“A common neural structure observed in studies involving social and non-social reinforcers is the human striatum”
From The social brain and reward: Social information processing in the human striatum (2014) · cites this paper as [3]
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“Finally, ventromedial prefrontal cortex appears to integrate information concerning advice from others, the validity of the advice, and the value of decision options 3, 52.”
From The social brain and reward: Social information processing in the human striatum (2014) · cites this paper as [3]
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Cites
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“1, Methods and Supplementary Information): (1) the reward magnitude of each option (generated randomly at each trial); (2) the likely correct response (blue or green) based on their own experience of rewards on each option; and (3) the confederate’s advice, and how trustworthy the confederate currently was.”
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Schurz, M., Radua, J., Aichhorn, M., Richlan, F., & Perner, J. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.01.009 · Cited by 1,656
A common role of insula in feelings, empathy and uncertainty
Singer, T., Critchley, H. D., & Preuschoff, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.05.001 · Cited by 1,270
Frontal cortex and reward-guided learning and decision-making
Rushworth, M. F. S., Noonan, M. P., Boorman, E. D., Walton, M. E., & Behrens, T. E. (2011) · Neuron · doi:10.1016/j.neuron.2011.05.014 · Cited by 1,111
Self-citationMechanisms of Social Cognition
Frith, C. D., & Frith, U. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-120710-100449 · Cited by 973
Haber, S. N. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.1/shaber · Cited by 922
Metadata
- Authors
- Behrens, T. E. J., Hunt, L. T., Woolrich, M. W., & Rushworth, M. F. S.
- Affiliations
- John Radcliffe Hospital (GB)
- Corresponding author
- Behrens, Timothy E. J.
- Venue
- Nature
- Published
- 2008-11
- Volume
- 456
- Issue
- 7219
- Pages
- 245-249
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC2605577
- OpenAlex
- W2056448794
- Cited by
- 925
- References
- 34
- Citations by year
- 2026: 42 · 2025: 52 · 2024: 36 · 2023: 58 · 2022: 49 · 2021: 69 · 2020: 54 · 2019: 65 · 2018: 54 · 2017: 62 · 2016: 64 · 2015: 53 · 2014: 52 · 2013: 62 · 2012: 56
- FWCI
- 16.59
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Keywords
- social valuation, social learning, associative learning, reward learning, decision-making, advice taking, anterior cingulate cortex, ventromedial prefrontal cortex, social exchange, computational models of learning
- MeSH terms
- Adult, Female, Humans, Learning, Male, Middle Aged, Reward, Social Behavior, Models, Statistical, Prefrontal Cortex
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