In everyday life, we have to decide whether it is worth exerting effort to obtain rewards. Effort can be experienced in different domains, with some tasks requiring significant cognitive demand and others being more physically effortful. The motivation to exert effort for reward is highly subjective and varies considerably across the different domains of behaviour. However, very little is known about the computational or neural basis of how different effort costs are subjectively weighed against rewards. Is there a common, domain-general system of brain areas that evaluates all costs and benefits? Here, we used computational modelling and functional magnetic resonance imaging (fMRI) to examine the mechanisms underlying value processing in both the cognitive and physical domains. Participants were trained on two novel tasks that parametrically varied either cognitive or physical effort. During fMRI, participants indicated their preferences between a fixed low-effort/low-reward option and a variable higher-effort/higher-reward offer for each effort domain. Critically, reward devaluation by both cognitive and physical effort was subserved by a common network of areas, including the dorsomedial and dorsolateral prefrontal cortex, the intraparietal sulcus, and the anterior insula. Activity within these domain-general areas also covaried negatively with reward and positively with effort, suggesting an integration of these parameters within these areas. Additionally, the amygdala appeared to play a unique, domain-specific role in processing the value of rewards associated with cognitive effort. These results are the first to reveal the neurocomputational mechanisms underlying subjective cost–benefit valuation across different domains of effort and provide insight into the multidimensional nature of motivation.

Recognising and representing one's self as distinct from others is a fundamental component of self-awareness. However, current theories of self-recognition are not embedded within global theories of cortical function and therefore fail to provide a compelling explanation of how the self is processed. We present a theoretical account of the neural and computational basis of selfrecognition that is embedded within the free-energy account of cortical function. In this account one's body is processed in a Bayesian manner as the most likely to be "me". Such probabilistic representation arises through the integration of information from hierarchically organised unimodal systems in higher-level multimodal areas. This information takes the form of bottom-up "surprise" signals from unimodal sensory systems that are explained away by top-down processes that minimise the level of surprise across the brain. We present evidence that this theoretical perspective may account for the findings of psychological and neuroimaging investigations into self-recognition and particularly evidence that representations of the self are malleable, rather than fixed as previous accounts of self-recognition might suggest.

The anterior cingulate cortex (ACC) is implicated in a broad range of behaviors and cognitive processes, but it has been unclear what contribution, if any, the ACC makes to social behavior. We argue that anatomical and functional evidence suggests that a specific sub-region of ACC—in the gyrus (ACCg)—plays a crucial role in processing social information. We propose that the computational properties of the ACCg support a contribution to social cognition by estimating how motivated other individuals are and dynamically updating those estimates when further evidence suggests they have been erroneous. Notably this model, based on vicarious motivation and error processing, provides a unified account of neurophysiological and neuroimaging evidence that the ACCg is sensitive to costs, benefits, and errors during social interactions. Furthermore, it makes specific, testable predictions about a key mechanism that may underpin variability in socio-cognitive abilities in health and disease.

SummarySpeed-accuracy trade-off is an intensively studied law governing almost all behavioral tasks across species. Here we show that motivation by reward breaks this law, by simultaneously invigorating movement and improving response precision. We devised a model to explain this paradoxical effect of reward by considering a new factor: the cost of control. Exerting control to improve response precision might itself come at a cost—a cost to attenuate a proportion of intrinsic neural noise. Applying a noise-reduction cost to optimal motor control predicted that reward can increase both velocity and accuracy. Similarly, application to decision-making predicted that reward reduces reaction times and errors in cognitive control. We used a novel saccadic distraction task to quantify the speed and accuracy of both movements and decisions under varying reward. Both faster speeds and smaller errors were observed with higher incentives, with the results best fitted by a model including a precision cost. Recent theories consider dopamine to be a key neuromodulator in mediating motivational effects of reward. We therefore examined how Parkinson’s disease (PD), a condition associated with dopamine depletion, alters the effects of reward. Individuals with PD showed reduced reward sensitivity in their speed and accuracy, consistent in our model with higher noise-control costs. Including a cost of control over noise explains how reward may allow apparent performance limits to be surpassed. On this view, the pattern of reduced reward sensitivity in PD patients can specifically be accounted for by a higher cost for controlling noise.

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.

Reinforcement learning theory powerfully characterizes how we learn to benefit ourselves. In this theory, prediction errors-the difference between a predicted and actual outcome of a choice-drive learning. However, we do not operate in a social vacuum. To behave prosocially we must learn the consequences of our actions for other people. Empathy, the ability to vicariously experience and understand the affect of others, is hypothesized to be a critical facilitator of prosocial behaviors, but the link between empathy and prosocial behavior is still unclear. During functional magnetic resonance imaging (fMRI) participants chose between different stimuli that were probabilistically associated with rewards for themselves (self), another person (prosocial), or no one (control). Using computational modeling, we show that people can learn to obtain rewards for others but do so more slowly than when learning to obtain rewards for themselves. fMRI revealed that activity in a posterior portion of the subgenual anterior cingulate cortex/basal forebrain (sgACC) drives learning only when we are acting in a prosocial context and signals a prosocial prediction error conforming to classical principles of reinforcement learning theory. However, there is also substantial variability in the neural and behavioral efficiency of prosocial learning, which is predicted by trait empathy. More empathic people learn more quickly when benefitting others, and their sgACC response is the most selective for prosocial learning. We thus reveal a computational mechanism driving prosocial learning in humans. This framework could provide insights into atypical prosocial behavior in those with disorders of social cognition.reinforcement learning theory | prosocial behavior | empathy | reward | subgenual anterior cingulate cortex P rosocial behaviors, namely, social behaviors or actions intended to benefit others, are a fundamental but poorly understood aspect of social interaction (1). To behave prosocially, animals need to learn about the consequences that their actions can have for others. In reinforcement learning theory (RLT), prediction errors (PEs)-differences between expected and actual outcomesdrive learning (2). RLT provides a powerful framework for understanding how animals learn to obtain rewards for themselves (3). However, the processes by which animals learn to make choices that benefit others are unknown. Here we use RLT to characterize prosocial learning, combining functional magnetic resonance imaging (fMRI) and detailed computational modeling of behavior.Studies using economic games, moral judgments, or charity donation tasks have consistently reported activity in the ventral striatum, posterior regions of the subgenual cingulate cortex/basal forebrain (hereinafter referred to as sgACC), dorsal anterior cingulate cortex (dACC), and dorsolateral prefrontal cortex (DLPFC) during prosocial behavior (4-7). Each of these regions receives input from midbrain dopaminergic neurons (8), and these cortical regions all project to the ventral ...

SummaryProsocial acts – those that are costly to ourselves but benefit others – are a central component of human co-existence1–3. While the financial and moral costs of prosocial behaviours are well understood4–6, everyday prosocial acts do not typically come at such costs. Instead, they require effort. Here, using computational modelling of an effort-based task we show that people are prosocially apathetic. They are less willing to choose to initiate highly effortful acts that benefit others compared to benefitting themselves. Moreover, even when choosing to initiate effortful prosocial acts, people show superficiality, exerting less force into actions that benefit others than themselves. These findings replicated, were present when the other was anonymous or not, and when choices were made to earn rewards or avoid losses. Importantly, the least prosocially motivated people had higher subclinical levels of psychopathy and social apathy. Thus, although people sometimes ‘help out’, they are less motivated to benefit others and sometimes ‘superficially prosocial’, which may characterise everyday prosociality and its disruption in social disorders.

Apathy is a debilitating but poorly understood disorder characterized by a reduction in motivation. As well as being associated with several brain disorders, apathy is also prevalent in varying degrees in healthy people. Whilst many tools have been developed to assess levels of apathy in clinical disorders, surprisingly there are no measures of apathy suitable for healthy people. Moreover, although apathy is commonly comorbid with symptoms of depression, anhedonia and fatigue, how and why these symptoms are associated is unclear. Here we developed the Apathy-Motivation Index (AMI), a brief self-report index of apathy and motivation. Using exploratory factor analysis (in a sample of 505 people), and then confirmatory analysis (in a different set of 479 individuals), we identified subtypes of apathy in behavioural, social and emotional domains. Latent profile analyses showed four different profiles of apathy that were associated with varying levels of depression, anhedonia and fatigue. The AMI is a novel and reliable measure of individual differences in apathy and might provide a useful means of probing different mechanisms underlying sub-clinical lack of motivation in otherwise healthy individuals. Moreover, associations between apathy and comorbid states may be reflective of problems in different emotional, social and behavioural domains.