2013
DOI: 10.1523/jneurosci.3095-12.2013
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Neural Correlates of Reinforcement Learning and Social Preferences in Competitive Bidding

Abstract: In competitive social environments, people often deviate from what rational choice theory prescribes, resulting in losses or suboptimal monetary gains. We investigate how competition affects learning and decision-making in a common value auction task. During the experiment, groups of five human participants were simultaneously scanned using MRI while playing the auction task. We first demonstrate that bidding is well characterized by reinforcement learning with biased reward representations dependent on social… Show more

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Cited by 77 publications
(89 citation statements)
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References 48 publications
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“…Consistent with this view, several types of social decisions reviewed above have been associated with responses in regions outside the classic valuation circuitry (such as the dlPFC 113 , TPJ 11,114 and dmPFC 70,75 ) that may in principle provide input that is relevant for the construction of uniquely social values. This possibility has been formally tested with connectivity analyses 38,45,57,58,67,69,74,113,115 , which have revealed that different types of social decision making and learning involve functional coupling between BOLD responses in 'classic' valuation regions and BOLD responses in regions outside the reward circuitry (for example, in the TPJ or dmPFC). However, the specificity of these effects in social contexts remains to be established by direct comparisons of how social versus non-social decisions may change patterns of connectivity, and how the strength of this connectivity relates to behavioural performance.…”
Section: Adaptive Codingmentioning
confidence: 99%
“…Consistent with this view, several types of social decisions reviewed above have been associated with responses in regions outside the classic valuation circuitry (such as the dlPFC 113 , TPJ 11,114 and dmPFC 70,75 ) that may in principle provide input that is relevant for the construction of uniquely social values. This possibility has been formally tested with connectivity analyses 38,45,57,58,67,69,74,113,115 , which have revealed that different types of social decision making and learning involve functional coupling between BOLD responses in 'classic' valuation regions and BOLD responses in regions outside the reward circuitry (for example, in the TPJ or dmPFC). However, the specificity of these effects in social contexts remains to be established by direct comparisons of how social versus non-social decisions may change patterns of connectivity, and how the strength of this connectivity relates to behavioural performance.…”
Section: Adaptive Codingmentioning
confidence: 99%
“…Recent studies have shown that the functional connectivities between the vmPFC and several regions including frontal, temporal, and parietal regions play a critical role in the processing of subjective value (Hare et al, 2010;Janowski et al, 2013;Smith et al, 2014;van den Bos et al, 2013).…”
Section: Discussionmentioning
confidence: 99%
“…Haruno et al [2014] 62 parametric analysis, positive correlation with disadvantageous level 3.05 Kirk et al [2011] 40 disadvantageous outcomes > equal outcomes 3.53 Kirk et al [2016] 50 parametric analysis, positive correlation with disadvantageous level 0.68 Lindner et al [2014] 30 Performed worse > performed better 5.71 Roalf [2010] 27 disadvantageous outcomes > equal outcomes 3.69 Sanfey et al [2003] 19 disadvantageous outcomes > equal outcomes 3.20 Servaas et al [2015] 114 disadvantageous outcomes > equal outcomes 9.20 van den Bos et al [2013] 40 Self-not-won/other-won > Self-won/others-lost 3.28 White et al [2013] 20 parametric analysis, positive correlation with disadvantageous level 0.09 White et al [2014] 21 parametric analysis, positive correlation with disadvantageous level 1.85 Wu et al [2014a,b] 18 parametric analysis, negative correlation with subjective utility…”
Section: 22mentioning
confidence: 99%