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 partners1, 2. 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 behaviour3. Recently however, neural signals have been observed during social exchange reminiscent of signals seen in associative paradigms4. 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 realised via the same associative processes previously established for learning other, simpler, features of the environment.

When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. Here, we investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions in magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice, and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.

Frequency-specific oscillations and phase-coupling of neuronal populations are essential mechanisms for the coordination of activity between brain areas during cognitive tasks. Therefore, the ongoing activity ascribed to the different functional brain networks should also be able to reorganise and coordinate via similar mechanisms. We develop a novel method for identifying large-scale phase-coupled network dynamics and show that resting networks in magnetoencephalography are well characterised by visits to short-lived transient brain states, with spatially distinct patterns of oscillatory power and coherence in specific frequency bands. Brain states are identified for sensory, motor networks and higher-order cognitive networks. The cognitive networks include a posterior alpha (8–12 Hz) and an anterior delta/theta range (1–7 Hz) network, both exhibiting high power and coherence in areas that correspond to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks have characteristic signatures in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales.

Neuroscientists are beginning to advance explanations of social behavior in terms of underlying brain mechanisms. Two distinct networks of brain regions have come to the fore. The first involves brain regions that are concerned with learning about reward and reinforcement. These same reward-related brain areas also mediate preferences that are social in nature even when no direct reward is expected. The second network focuses on regions active when a person must make estimates of another person's intentions. However, it has been difficult to determine the precise roles of individual brain regions within these networks or how activities in the two networks relate to one another. Some recent studies of reward-guided behavior have described brain activity in terms of formal mathematical models; these models can be extended to describe mechanisms that underlie complex social exchange. Such a mathematical formalism defines explicit mechanistic hypotheses about internal computations underlying regional brain activity, provides a framework in which to relate different types of activity and understand their contributions to behavior, and prescribes strategies for performing experiments under strong control.

Despite widespread interest in neural mechanisms of decision-making, most investigations focus on decisions between just two options. Here we adapt a biophysically plausible model of decision-making to predict how a key decision variable, the value difference signal-encoding how much better one choice is than another-changes with the value of a third, but unavailable, alternative. The model predicts a surprising failure of optimal decision-making: greater difficulty choosing between two options in the presence of a third very poor, as opposed to very good, alternative. Both investigation of human decision-making and functional magnetic resonance imaging-based measurements of value difference signals in ventromedial prefrontal cortex (vmPFC) bore out this prediction. The vmPFC signal decreased in the presence of low-value third alternatives, and vmPFC effect sizes predicted individual variation in suboptimal decision-making in the presence of multiple alternatives. The effect contrasts with that of divisive normalization in parietal cortex.

Many accounts of reward-based choice argue for distinct component processes that are serial and functionally localized. In this article, we argue for an alternative viewpoint, in which choices emerge from repeated computations that are distributed across many brain regions. We emphasize how several features of neuroanatomy may support the implementation of choice, including mutual inhibition in recurrent neural networks and the hierarchical organisation of timescales for information processing across the cortex. This account also suggests that certain correlates of value may be emergent rather than represented explicitly in the brain.

Naturalistic decision-making typically involves sequential deployment of attention to choice alternatives to gather information before a decision is made. Attention filters how information enters decision circuits, thus implying that attentional control may shape how decision computations unfold. We recorded neuronal activity from three subregions of the prefrontal cortex (PFC) while monkeys performed an attention-guided decision-making task. From the first saccade to decision-relevant information, a triple dissociation of decision- and attention-related computations emerged in parallel across PFC subregions. During subsequent saccades, orbitofrontal cortex activity reflected the value comparison between currently and previously attended information. In contrast, the anterior cingulate cortex carried several signals reflecting belief updating in light of newly attended information, the integration of evidence to a decision bound and an emerging plan for what action to choose. Our findings show how anatomically dissociable PFC representations evolve during attention-guided information search, supporting computations critical for value-guided choice.

SummaryAdaptive success in social animals depends on an ability to infer the likely actions of others. Little is known about the neural computations that underlie this capacity. Here, we show that the brain models the values and choices of others even when these values are currently irrelevant. These modeled choices use the same computations that underlie our own choices, but are resolved in a distinct neighboring medial prefrontal brain region. Crucially, however, when subjects choose on behalf of a partner instead of themselves, these regions exchange their functional roles. Hence, regions that represented values of the subject’s executed choices now represent the values of choices executed on behalf of the partner, and those that previously modeled the partner now model the subject. These data tie together neural computations underlying self-referential and social inference, and in so doing establish a new functional axis characterizing the medial wall of prefrontal cortex.