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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Computational studies of reward and punishment decision-making show that people adjust how quickly they learn action-outcome contingencies when environmental volatility changes.
“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]
The paper’s Abstract (page unavailable) says the optimal learning rate depends on volatility: faster in volatile conditions and slower in stable ones. It also reports that participants used model-tracked reward information in their choices. The study concerns reward outcomes and social advice, however, not punishment decision-making, so it does not support that part of the cited sentence. (LLM, conf=0.85)
Experimental — accuracy not yet measuredOptimal integration of opinions should consider judgments, confidence, and average objective accuracy of agents.
“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]
The paper’s Abstract (page unavailable) says optimal choices in its task combine the likely correct action with the probability that a human confederate’s advice is correct, learned from past outcomes. It does not address an agent’s expressed confidence in a particular opinion or establish the same rule for artificial agents. (LLM, conf=0.86)
Experimental — accuracy not yet measuredA common neural structure observed in studies involving social and non-social reinforcers is the human striatum.
“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]
Ventromedial prefrontal cortex appears to integrate information concerning advice from others, the validity of the advice, and the value of decision options.
“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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- 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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