Although the stream of information we encounter is continuous, our experiences tend to be discretized into meaningful clusters, altering how we represent our past. Event segmentation theory proposes that clustering ongoing experience in this way is adaptive in that it promotes efficient online processing as well as later reconstruction of relevant information. A growing literature supports this theory by demonstrating its important behavioral consequences. Yet the exact mechanisms of segmentation remain elusive. Here, we provide a brief overview of how event segmentation influences ongoing processing, subsequent memory retrieval, and decision making as well as some proposed underlying mechanisms. We then explore how beliefs, or inferences, about what generates our experience may be the foundation of event cognition. In this inference‐based framework, experiences are grouped together according to what is inferred to have generated them. Segmentation then occurs when the inference changes, creating an event boundary. This offers an alternative to dominant theories of event segmentation, allowing boundaries to occur independent of perceptual change and even when transitions are predictable. We describe how this framework can reconcile seemingly contradictory empirical findings (e.g., memory can be biased toward both extreme episodes and the average of episodes). Finally, we discuss open questions regarding how time is incorporated into the inference process.

The context-dependent memory effect, in which memory for an item is better when the retrieval context matches the original learning context, has proved to be difficult to reproduce in a laboratory setting. In an effort to identify a set of features that generate a robust context-dependent memory effect, we developed a paradigm in virtual reality using two semantically distinct virtual contexts: underwater and Mars environments, each with a separate body of knowledge (schema) associated with it. We show that items are better recalled when retrieved in the same context as the study context; we also show that the size of the effect is larger for items deemed context-relevant at encoding, suggesting that context-dependent memory effects may depend on items being integrated into an active schema.

The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory are themselves inferred from experience with each task. We review this evidence and place it in the context of work that has explicitly characterized representation learning as statistical inference. We discuss how inference can be scaled to real-world decisions by approximating beliefs based on a small number of experiences. Finally, we highlight some implications of this inference process for human decision-making in social environments. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

uring times of crisis, such as wars, natural disasters or pandemics, citizens look to leaders for guidance. Successful crisis management often depends on mobilizing individual citizens to change their behaviours and make personal sacrifices for the public good 1 . Crucial to this endeavour is trust: citizens are more likely to follow official guidance when they trust their leaders 2 . Here, we investigate public trust in leaders in the context of the COVID-19 pandemic, which continues to threaten millions of lives around the globe at the time of writing 3,4 .Because the novel coronavirus is highly transmissible, a critical factor in limiting pandemic spread is compliance with public health recommendations such as social distancing, physical hygiene and mask wearing 5,6 . Trust in leaders is a strong predictor of citizen compliance with a variety of public health policies [7][8][9][10][11][12] . During pandemics, trust in experts issuing public health guidelines is a key predictor of compliance with those guidelines. For example, during the avian influenza pandemic of 2009 (H1N1), self-reported trust in medical organizations predicted self-reported compliance with protective health measures and vaccination rates 13,14 . During the COVID-19 pandemic, data from several countries show that public trust in scientists, doctors and the government is positively associated with self-reported compliance with public health Moral dilemmas and trust in leaders during a global health crisis