Moving through our environment generates multiple changes in my sensations. But I do not experience the environment as changing. My conscious perceptual experience is of a stable environment through which I move. This perception is created by intricate neural computations that automatically take account of my movements. The stable environment that I experience is independent of my actions. As a result, I experience it as objective: a set of facts about the world that constrain my movements. Because it is objective I expect that it will also constrain the movements of others in the same way, whether these are rocks rolling down a hill or animals foraging for food. This experience of objectivity creates a shared understanding of the world that enhances our interactions with others. Our perceptual experiences, while personal, are shaped by our model of the world, and since others are modelling the same world, their models will be very similar. Interactions with others will further increase this similarity. The models create a form of common knowledge. This common knowledge is an inherent feature of our basic conscious perception, even when we're not actively reflecting on or deliberately sharing our experiences. The common knowledge created by our conscious perception of the world enables the coordination of behaviour which is a critical precursor for the evolution of cooperative behaviour.

Autism has influenced social-cognitive neuroscience in important ways. It has provided the impetus to look for the brain basis of mentalizing and encouraged the search for the brain bases of other social abilities. A fundamental aspect of social interaction is the ability to predict what other agents are going to do. We propose a hierarchy of three worlds—the world of objects, the world of agents, and the world of ideas—that respectively present their own challenges and solutions to such predictions. The world of ideas provides a direct interface between individual minds and other minds (i.e., culture). We highlight the power of culture to change subjective experiences and the power of subjective experiences to influence culture. The example of autism shows these mutual influences at work. These influences have led to dramatic changes in the concept of “autism” since its first use in child psychiatry.

Bargaining is a fundamental social behavior in which individuals often accept unfair offers. Traditional behavioral models, based solely on choice data, typically interpret this acceptance as simple reward-maximization. However, the suppression of emotions such as inequity aversion or pride may also play a critical role in this decision. Incorporating response time alongside choice data provides a means to quantify participants' internal conflict in suppressing these emotions and deciding to accept unfair offers. In this study, we conducted functional magnetic resonance imaging (fMRI) of the ultimatum game, where participants decided within 10 seconds whether to accept or reject monetary distribution offers from a proposer. Using the drift diffusion model (DDM), we quantified decision-making dynamics based on both choice and response time. Participants who suppressed disadvantageous inequity (DI)-driven rejection (reflected by a lower DDM weight for DI) exhibited heightened dorsal anterior cingulate cortex (dACC) activity in response to DI. Functional connectivity analysis revealed a negative correlation between the dACC and the ventrolateral prefrontal cortex (vlPFC) when DI was large, which encoded both the rejection rates, and the response times associated with accepting DI offers. Furthermore, vlPFC activity was significantly correlated with amygdala activity during high DI conditions, specifically encoding response time for accepting DI offers but not rejection rates. Importantly, these findings could not be captured using standard value-based models that rely solely on choice data. Our results underscore the dACC's critical role in mediating the suppression of emotional responses to DI, enabling the acceptance of unfair offers in a dynamic bargaining process. ### Competing Interest Statement The authors have declared no competing interest.

I suggest that shared knowledge is an inherent feature of conscious experience, even when we're not actively reflecting on or sharing our experiences. When we navigate the physical world, we perceive ourselves as existing within a stationary environment independent of our actions. This perception arises from intricate neural computations that take account of our movements as well as sensory input. This shared understanding of the world is essential for successful social interaction, as we assume that others perceive the same reality. Our subjective experiences, while personal, are shaped by our shared understanding of the world, which becomes a form of common knowledge.

The terminology used in discussions on mental state attribution is extensive and lacks consistency. In the current paper, experts from various disciplines collaborate to introduce a shared set of concepts and make recommendations regarding future use.

Individuals must regularly choose between prosocial and proself behaviors. While past neuroscience research has revealed the neural foundations for prosocial behaviors, many studies have oversimplified proself behaviors, viewing them merely as a reward-maximization process. However, recent behavioral evidence suggests that response times for proself behaviors are often slower than those for prosocial behaviors, suggesting a more complex interdependence between prosocial and proself neural computations. To address this issue, we conducted an fMRI experiment with the ultimatum game, where participants were requested to accept (money distributed as proposed) or reject (both sides receive none) offers of money distribution. In the decisions, the participants could maximize self-interest by accepting the offer (i.e., proself), while by rejecting it, they could punish unfair proposers and promote the “equity” social norm (i.e., prosocial). We constructed a drift diffusion model (DDM) that considers both behavioral choices and response times and used the DDM parameters in our fMRI analysis. We observed that participants who suppressed inequity-driven rejection behaviors displayed heightened dACC activity in response to disadvantageous inequity. Importantly, our functional connectivity analysis demonstrated that the dACC exhibited negative functional connectivity with the amygdala when unfair offers were presented. Furthermore, the PPI connectivity encoded the average reaction time for accepting unfair offers (i.e., proself behaviors). Considering that the amygdala also responded to disadvantageous inequity in these experiments and previous studies, these results show that the top-down control of prosocial motives (i.e., aversion to disadvantageous inequity) plays a key role in implementing proself behaviors.### Competing Interest StatementThe authors have declared no competing interest.

A deep dive into the social mind-brain, examining the processes we share with other social animals and illuminating those that are uniquely human. What Makes Us Social? is a scholarly but accessible exploration of the underlying processes that make humans the most social species on the planet. Chris and Uta Frith, pioneers in the field of cognitive neuroscience, review the many forms of social behavior that we humans share with other animals and examine the special form that only humans possess, including its dark side. These uniquely human abilities allow us to reflect on our behavior and share these reflections with other people, which in turn enables us to reason why we do things and to exert some control over our automatic behaviors. As a result, we can learn cooperatively with others and create and value cultural artifacts that survive through the generations. Going beyond how we come to know ourselves and understand the minds of others, Frith and Frith investigate how we adapt mutually to make social interactions work. This book stands out in its application of a computational framework—one that lies at the intersection of psychology and artificial intelligence—to key concepts of social cognition, such as empathy, trust, group identity, and reputation management. Ultimately, What Makes Us Social? is a profound examination of the ways we communicate, cooperate, share, and compete with other humans and how these capabilities define us as a species.

This paper concerns the distributed intelligence or federated inference that emerges under belief-sharing among agents who share a common world—and world model. Imagine, for example, several animals keeping a lookout for predators. Their collective surveillance rests upon being able to communicate their beliefs—about what they see—among themselves. But, how is this possible? Here, we show how all the necessary components arise from minimising free energy. We use numerical studies to simulate the generation, acquisition and emergence of language in synthetic agents. Specifically, we consider inference, learning and selection as minimising the variational free energy of posterior (i.e., Bayesian) beliefs about the states, parameters and structure of generative models, respectively. The common theme—that attends these optimisation processes—is the selection of actions that minimise expected free energy, leading to active inference, learning and model selection (a.k.a., structure learning). We first illustrate the role of communication in resolving uncertainty about the latent states of a partially observed world, on which agents have complementary perspectives. We then consider the acquisition of the requisite language—entailed by a likelihood mapping from an agent's beliefs to their overt expression (e.g., speech)—showing that language can be transmitted across generations by active learning. Finally, we show that language is an emergent property of free energy minimisation, when agents operate within the same econiche. We conclude with a discussion of various perspectives on these phenomena; ranging from cultural niche construction, through federated learning, to the emergence of complexity in ensembles of self-organising systems.

Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.

Our conscious experience is determined by a combination of top-down processes (e.g., prior beliefs) and bottom-up processes (e.g., sensations). The balance between these two processes depends on estimates of their reliability (precision), so that the estimate considered more reliable is given more weight. We can modify these estimates at the metacognitive level, changing the relative weights of priors and sensations. This enables us, for example, to direct our attention to weak stimuli. But there is a cost to this malleability. For example, excessive weighting of top-down processes, as in schizophrenia, can lead to perceiving things that are not there and believing things that are not true. It is only at the top of the brain's cognitive hierarchy that metacognitive control becomes conscious. At this level, our beliefs concern complex, abstract entities with which we have limited direct experience. Estimates of the precision of such beliefs are more uncertain and more malleable. However, at this level, we do not need to rely on our own limited experience. We can rely instead on the experiences of others. Explicit metacognition plays a unique role, enabling us to share our experiences. We acquire our beliefs about the world from our immediate social group and from our wider culture. And the same sources provide us with better estimates of the precision of these beliefs. Our confidence in our high-level beliefs is heavily influenced by culture at the expense of direct experience.

There is growing interest in the relationship been AI and consciousness. Joseph LeDoux and Jonathan Birch thought it would be a good moment to put some of the big questions in this area to some leading experts. The challenge of addressing the questions they raised was taken up by Kristin Andrews, Nicky Clayton, Nathaniel Daw, Chris Frith, Hakwan Lau, Megan Peters, Susan Schneider, Anil Seth, Thomas Suddendorf, and Marie Vanderkerckhoeve.

Uta Frith and Chris Frith spoke at the Royal Institution around the launch of their 'graphic biography' Two Heads, written with their son Alex Frith and illustrated by Daniel Locke.

To survive, all animals need to predict what other agents are going to do next. We review neural mechanisms involved in the steps required for this ability. The first step is to determine whether an object is an agent, and if so, how sophisticated it is. This involves brain regions carrying representations of animate agents. The movements of the agent can then be anticipated in the short term based solely on physical constraints. In the longer term, taking into account the agent's goals and intentions is useful. Observing goal directed behaviour activates the neural action observation network, and predicting future goal directed behaviour is helped by the observer's own action generating mechanisms. Intentions are critically important in determining actions when interacting with other agents, as several intentions can lie behind an action. Here, interpretation is helped by prior beliefs about the agent and the brain's mentalising system is engaged. Biologically-constrained computational models of action recognition exist, but equivalent models for understanding intentional agents remain to be developed.

Scientific thinking about the minds of humans and other animals has been transformed by the idea that the brain is Bayesian. A cornerstone of this idea is that agents set the balance between prior knowledge and incoming evidence based on how reliable or 'precise' these different sources of information are - lending the most weight to that which is most reliable. This concept of precision has crept into several branches of cognitive science and is a lynchpin of emerging ideas in computational psychiatry - where unusual beliefs or experiences are explained as abnormalities in how the brain estimates precision. But what precisely is precision? In this Primer we explain how precision has found its way into classic and contemporary models of perception, learning, self-awareness, and social interaction. We also chart how ideas around precision are beginning to change in radical ways, meaning we must get more precise about how precision works.

Perceptual decision-making employs a range of higher order metacognitive processes. Two of the most important of these are perceptual awareness; or the clarity with which one reports seeing a perceptual stimulus, and response confidence; or the certainty one has about the correctness of one's own perceptual categorizations. We used a novel false feedback paradigm to investigate the relationships between these two processes. We asked people to perform a standard psychophysical detection task. We used feedback to selectively intervene either on our participants’ trust in their own perceptual awareness of the stimulus, or on their confidence in their own responses. We measured the effects of these interventions on response accuracy; on reports of perceptual awareness; and on response confidence. We found that by undermining people's trust in their awareness of the sensory stimulus, we could reliably reduce their accuracy on the task. We suggest that the reason this occurred is that people came to rely less on evidence from their senses when making perceptual decisions. We conclude by suggesting that there is a not a one-to-one mapping between content in conscious experience and how that content is used in perceptual decision making, and that one's perception of the reliability of content also plays a role.

In this Primer, Daniel Yon and Chris Frith explain ‘precision’ – a key concept in Bayesian models of the mind and brain. The idea of precision is central to current thinking across the cognitive sciences, but in recent years ideas about precision have begun to change. This raises important questions about precisely how precision works.

When a glass is lifted from a tray, there is a challenge for the waiter. He must quickly compensate for the reduction in the weight of the tray to keep it balanced. This compensation is easily achieved if the waiter lifts the glass himself. Because he has, himself, initiated the action, he can predict the timing and the magnitude of the perturbation of the tray and respond (via the holding hand) accordingly. In this study, we examined coordination when either one or two people hold the tray while either one of them or a third person removes the glass. Our results show that there is exquisite coordination between the two people holding the tray. We suggest that this coordination depends upon the haptic link provided by the rigid platform that both people are holding. We conclude that the guest at a reception should not lift his drink from the waiter's tray until they have the waiter's attention but, if too thirsty to wait, should lend a hand holding the tray.

Metacognition - the ability to represent, monitor and control ongoing cognitive processes - helps us perform many tasks, both when acting alone and when working with others. While metacognition is adaptive, and found in other animals, we should not assume that all human forms of metacognition are gene-based adaptations. Instead, some forms may have a social origin, including the discrimination, interpretation, and broadcasting of metacognitive representations. There is evidence that each of these abilities depends on cultural learning and therefore that cultural selection might shape human metacognition. The cultural origins hypothesis is a plausible and testable alternative that directs us towards a substantial new programme of research.

Schizophrenia is a neurodevelopmental psychiatric disorder thought to result from synaptic dysfunction that affects distributed brain connectivity, rather than any particular brain region. While symptomatology is traditionally divided into positive and negative symptoms, abnormal social cognition is now recognized a key component of schizophrenia. Nonetheless, we are still lacking a mechanistic understanding of effective brain connectivity in schizophrenia during social cognition and how it relates to clinical symptomatology. To address this question, we used fMRI and dynamic causal modelling (DCM) to test for abnormal brain connectivity in twenty-four patients with first-episode schizophrenia (FES) compared to twenty-five matched controls performing the Human Connectome Project (HCP) social cognition paradigm. Patients had not received regular therapeutic antipsychotics, but were not completely drug naïve. Whilst patients were less accurate than controls in judging social stimuli from non-social stimuli, our results revealed an increase in feedforward connectivity from motion-sensitive V5 to posterior superior temporal sulcus (pSTS) in patients compared to matched controls. At the same time, patients with a higher degree of positive symptoms had more disinhibition within pSTS, a region computationally involved in social cognition. We interpret these findings the framework of active inference, where increased feedforward connectivity may encode aberrant prediction errors from V5 to pSTS and local disinhibition within pSTS may reflect aberrant encoding of the precision of cortical representations about social stimuli.