A rubric for human-like agents and NeuroAI.

Researchers across cognitive, neuro- and computer sciences increasingly reference 'human-like' artificial intelligence and 'neuroAI'. However, the scope and use of the terms are often inconsistent. Contributed research ranges widely from mimicking , to testing machine learning methods as hypotheses at the cellular or functional levels, or solving problems.

Representations of Temporal Community Structure in Hippocampus and Precuneus Predict Inductive Reasoning Decisions.

Our understanding of the world is shaped by inferences about underlying structure. For example, at the gym, you might notice that the same people tend to arrive around the same time and infer that they are friends that work out together. Consistent with this idea, after participants are presented with a temporal sequence of objects that follows an underlying community structure, they are biased to infer that objects from the same community share the same properties.

Collective minds: social network topology shapes collective cognition.

Human cognition is not solitary, it is shaped by collective learning and memory. Unlike swarms or herds, human social networks have diverse topologies, serving diverse modes of collective cognition and behaviour. Here, we review research that combines network structure with psychological and neural experiments and modelling to understand how the topology of social networks shapes collective cognition.

Predictive Representations in Hippocampal and Prefrontal Hierarchies.

As we navigate the world, we use learned representations of relational structures to explore and to reach goals. Studies of how relational knowledge enables inference and planning are typically conducted in controlled small-scale settings. It remains unclear, however, how people use stored knowledge in continuously unfolding navigation (e.

The Learning Salon: Toward a new participatory science.

The Learning Salon is an online weekly forum for discussing points of contention and common ground in biological and artificial learning. Hosting neuroscientists, computer scientists, AI researchers, and philosophers, the Salon promotes short talks and long discussions, committed to an ethos of participation, horizontality, and inclusion.

Rational use of episodic and working memory: A normative account of prospective memory.

Humans often simultaneously pursue multiple plans at different time scales, a capacity known as prospective memory (PM). The successful realization of non-immediate plans (e.g.

Learning Structures: Predictive Representations, Replay, and Generalization.

Memory and planning rely on learning the structure of relationships among experiences. Compact representations of these structures guide flexible behavior in humans and animals. A century after 'latent learning' experiments summarized by Tolman, the larger puzzle of cognitive maps remains elusive: how does the brain learn and generalize relational structures? This review focuses on a reinforcement learning (RL) approach to learning compact representations of the structure of states.

Anxiety, avoidance, and sequential evaluation.

Anxiety disorders are characterized by a range of aberrations in the processing of and response to threat, but there is little clarity what core pathogenesis might underlie these symptoms. Here we propose that a particular set of unrealistically pessimistic assumptions can distort an agent's behavior and underlie a host of seemingly disparate anxiety symptoms. We formalize this hypothesis in a decision theoretic analysis of maladaptive avoidance and a reinforcement learning model, which shows how a localized bias in beliefs can formally explain a range of phenomena related to anxiety.

Bridge ties bind collective memories.

From families to nations, what binds individuals in social groups is, to a large degree, their shared beliefs, norms, and memories. These emergent outcomes are thought to occur because communication among individuals results in community-wide synchronization. Here, we use experimental manipulations in lab-created networks to investigate how the temporal dynamics of conversations shape the formation of collective memories.

Offline replay supports planning in human reinforcement learning.

Making decisions in sequentially structured tasks requires integrating distally acquired information. The extensive computational cost of such integration challenges planning methods that integrate online, at decision time. Furthermore, it remains unclear whether 'offline' integration during replay supports planning, and if so which memories should be replayed.

Predictive representations can link model-based reinforcement learning to model-free mechanisms.

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning.

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level.

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities.

The Role of the Parietal Cortex in the Representation of Task-Reward Associations.

Rewards obtained from specific behaviors can and do change across time. To adapt to such conditions, humans need to represent and update associations between behaviors and their outcomes. Much previous work focused on how rewards affect the processing of specific tasks.

Encoding of prospective tasks in the human prefrontal cortex under varying task loads.

Successful realization of planned actions requires the brain to encode intentions over delays. Previous research has indicated that several regions in the rostral or anterior prefrontal cortex (PFC) encode delayed intentions. However, different processes may encode the same future task depending on task load during the delay.

Human anterior prefrontal cortex encodes the 'what' and 'when' of future intentions.

On a daily basis we form numerous intentions to perform specific actions. However, we often have to delay the execution of intended actions while engaging in other demanding activities. Previous research has shown that patterns of activity in human prefrontal cortex (PFC) can reveal our current intentions.