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Scientific Understanding of Consciousness |
Social Decisions in the Ventromedial Prefrontal Cortex
Nature 456, 245-249 (13 November 2008) Associative learning of social value Timothy E. J. Behrens, Laurence T. Hunt, Mark W. Woolrich & Matthew F. S. Rushworth FMRIB Centre, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK (paraphrase) 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. Dopamine neurons in the ventral tegmental area (VTA) code reward prediction errors. Similar signals are reported in the dopaminoceptive striatum and even in the VTA itself, when specialized strategies are used in human fMRI studies. fMRI correlates of the learning rate in the reward domain have been reported in anterior cingulate sulcus (ACCs). If humans can learn from social information in a similar fashion, it should be possible to detect signals that co-vary with the same computational parameters, but in the social domain. We performed a similar analysis for prediction errors on reward information (reward minus expected reward). We found a significant effect of reward prediction error in the ventral striatum, the ventromedial prefrontal cortex, and anterior cingulate sulcus. As in the social domain, we observed significant effects of all three elements of the reward prediction error. The volatility of confederate advice correlated with BOLD signal in a circumscribed region in the adjacent ACC gyrus (ACCg) Learning about reward probability from vicarious and personal experiences recruits distinct neural systems, but subjects combine information across both sources when making decisions. A ventromedial portion of the prefrontal cortex (VMPFC) has been shown to code such an expected value signal for the chosen action during decision making. We computed two probabilities of reward on the subject's chosen option: one based only on experience and one based only on confederate advice. BOLD signal in the VMPFC was significantly correlated with both probabilities. However, there was subject variability in whether the VMPFC signal better reflected the reward probability based on outcome history or on social information. The extent to which the VMPFC data reflected each source of information (at the time of the decision) was predicted by the ACCs/ACCg response to outcome/social volatility (at the time when the outcomes were witnessed) Here, we have shown that the weighting assigned to social information is subject to learning and continual update via associative mechanisms. We use techniques that predict behaviour when learning from personal experiences to show that similar mechanisms explain behaviour in a social context. Furthermore, we demonstrate fundamental similarities between the neural encoding of key parameters for reward-based and social learning. Despite using similar mechanisms, distinct anatomical structures code learning parameters in the two domains. However, information from both is combined in ventromedial prefrontal cortex when making a decision. By comparing the two sources of information, we find that social prediction error signals similar to those reported in dopamine neurons for reward-based learning are coded in the MTG, STS/TPJ and DMPFC. BOLD signal fluctuations in these regions are often seen in social tasks, and in tasks which involve the attribution of motive to stimuli. Such activations have been thought critical in studies of the theory of mind. That these regions should code quantitative prediction and prediction error signals about a confederate lends more weight to the argument that social evaluation mechanisms are able to rely on simple associative processes. (end of paraphrase)
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