Neural implementation of computational mechanisms underlying the continuous trade-off between cooperation and competition.

Social interactions evolve continuously. Sometimes we cooperate, sometimes we compete, while at other times we strategically position ourselves somewhere in between to account for the ever-changing social contexts around us. Research on social interactions often focuses on a binary dichotomy between competition and cooperation, ignoring people's evolving shifts along a continuum. Here, we develop an economic game - the Space Dilemma - where two players change their degree of cooperativeness over time in cooperative and competitive contexts. Using computational modelling we show how social contexts bias choices and characterise how inferences about others' intentions modulate cooperativeness. Consistent with the modelling predictions, brain regions previously linked to social cognition, including the temporo-parietal junction, dorso-medial prefrontal cortex and the anterior cingulate gyrus, encode social prediction errors and context-dependent signals, correlating with shifts along a cooperation-competition continuum. These results provide a comprehensive account of the computational and neural mechanisms underlying the continuous trade-off between cooperation and competition.

The anatomy of apathy: A neurocognitive framework for amotivated behaviour.

Apathy is a debilitating syndrome associated with many neurological disorders, including several common neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, and focal lesion syndromes such as stroke. Here, we review neuroimaging studies to identify anatomical correlates of apathy, across brain disorders. Our analysis reveals that apathy is strongly associated with disruption particularly of dorsal anterior cingulate cortex (dACC), ventral striatum (VS) and connected brain regions. Remarkably, these changes are consistent across clinical disorders and imaging modalities. Review of the neuroimaging findings allows us to develop a neurocognitive framework to consider potential mechanisms underlying apathy. According to this perspective, an interconnected group of brain regions - with dACC and VS at its core - plays a crucial role in normal motivated behaviour. Specifically we argue that motivated behaviour requires a willingness to work, to keep working, and to learn what is worth working for. We propose that deficits in any one or more of these processes can lead to the clinical syndrome of apathy, and outline specific approaches to test this hypothesis. A richer neurobiological understanding of the mechanisms underlying apathy should ultimately facilitate development of effective therapies for this disabling condition.

Contributions of the Medial Prefrontal Cortex to Social Influence in Economic Decision-Making.

Economic decisions are guided by highly subjective reward valuations (SVs). Often these SVs are over-ridden when individuals conform to social norms. Yet, the neural mechanisms that underpin the distinct processing of such normative reward valuations (NVs) are poorly understood. The dorsomedial and ventromedial portions of the prefrontal cortex (dmPFC/vmPFC) are putatively key regions for processing social and economic information respectively. However, the contribution of these regions to economic decisions guided by social norms is unclear. Using functional magnetic resonance imaging and computational modeling we examine the neural mechanisms underlying the processing of SVs and NVs. Subjects (n = 15) indicated either their own economic preferences or made similar choices based on a social norm-learnt during a training session. We found that that the vmPFC and dmPFC make dissociable contributions to the processing of SV and NV. Regions of the dmPFC processed "only" the value of rewards when making normative choices. In contrast, we identify a novel mechanism in the vmPFC for the coding of value. This region signaled both subjective and normative valuations, but activity was scaled positively for SV and negatively for NV. These results highlight some of the key mechanisms that underpin conformity and social influence in economic decision-making.

Reinforcement learning signals in the anterior cingulate cortex code for others' false beliefs.

The ability to recognise that another's belief is false is a hallmark of our capacity to understand others' mental states. It has been suggested that the computational and neural mechanisms that underpin learning about others' mental states may be similar to those that underpin first-person Reinforcement Learning (RL). In RL, unexpected decision-making outcomes constitute prediction errors (PE), which are coded for by neurons in the Anterior Cingulate Cortex (ACC). Does the ACC signal the PEs (false beliefs) of others about the outcomes of their decisions? We scanned subjects using fMRI while they monitored a third-person's decisions and similar responses made by a computer. The outcomes of the trials were manipulated, such that the actual outcome was unexpectedly different from the predicted outcome on 1/3 of trials. We examined activity time-locked to privileged information which indicated the actual outcomes only to subjects. Activity in the gyral ACC was found when the outcomes of the third-person's decisions were unexpectedly positive. Activity in the sulcal ACC was found when the third-person's or computer's outcomes were unexpectedly positive. We suggest that a property of the ACC is that it codes PEs, with a portion of the gyral ACC specialised for processing the PEs of others.

The anterior cingulate cortex: monitoring the outcomes of others' decisions.

The ability to attribute mental states to others and understand the basis of their decisions is essential for human social interaction. A controversial theory states that this is achieved by simulating another's information processing in one's own neural circuits. The anterior cingulate cortex (ACC) is known to play an important role in the registration of discrepancies between the predicted and actual outcomes of decisions (prediction errors).When positive and negative feedback fails altogether, the failure may also signal errors in the prediction that the outcome of that decision would be informative and guide future decisions. Does the ACC signal that an outcome is unexpectedly uninformative? When an outcome directed to others is uninformative, do we understand their mental states by simulating them in the circuits of the ACC in our own brain? The aim of our study was to test for these two possibilities in the human brain with event-related fMRI. We tested whether the ACC processes errors in the prediction of informative feedback and whether the ACC is also activated when scanned subjects process the same outcomes of another's decisions. We show that each is processed by a separate subregion of the ACC.

Cerebellar information processing in relapsing-remitting multiple sclerosis (RRMS).

Recent research has characterized the anatomical connectivity of the cortico-cerebellar system - a large and important fibre system in the primate brain. Within this system, there are reciprocal projections between the prefrontal cortex and Crus II of the cerebellar cortex, which both play important roles in the acquisition and execution of cognitive skills. Here, we propose that this system also plays a particular role in sustaining skilled cognitive performance in patients with Relapsing-Remitting Multiple Sclerosis (RRMS), in whom advancing neuropathology causes increasingly inefficient information processing. We scanned RRMS patients and closely matched healthy subjects while they performed the Paced Auditory Serial Addition Test (PASAT), a demanding test of information processing speed, and a control task. This enabled us to localize differences between conditions that change as a function of group (group-by-condition interactions). Hemodynamic activity in some patient populations with CNS pathology are not well understood and may be atypical, so we avoided analysis strategies that rely exclusively on models of hemodynamic activity derived from the healthy brain, using instead an approach that combined a 'model-free' analysis technique (Tensor Independent Component Analysis, TICA) that was relatively free of such assumptions, with a post-hoc 'model-based' approach (General Linear Model, GLM). Our results showed group-by-condition interactions in cerebellar cortical Crus II. We suggest that this area may have in role maintaining performance in working memory tasks by compensating for inefficient data transfer associated with white matter lesions in MS.