Human Representation Learning.

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.

Neural correlates of social perception on response bias.

Accurate person perception is crucial in social decision-making. One of the central elements in successful social perception is the ability to understand another's response bias; this is because the same behavior can represent different inner states depending on whether other people are yea-sayers or naysayers. In the present study, we have tried to investigate how the internal biases of others are perceived. Using a multi-trial learning paradigm, perceivers made predictions about a target's responses to various suggested activities and then received feedback for each prediction trial-by-trial. Our hypotheses were that (1) the internal decision criterion of the targets would be realized through repeated experiences, and (2) due to positive-negative asymmetry, yea-sayers would be recognized more gradually than naysayers through the probabilistic integration of repeated experiences. To find neural evidence that tracks probabilistic integration when forming person knowledge on response biases, we employed a model-based fMRI with a State-Space Model. We discovered that person knowledge about yea-sayers modulated several brain regions, including caudate nucleus, DLPFC, hippocampus, etc. Moreover, when person knowledge was updated with incorrect performance feedback, brain regions including the caudate nucleus, DLPFC, dmPFC, and TPJ were also involved. There were overlapping regions for both processes, caudate nucleus and DLPFC, suggesting that these regions take crucial roles in forming person knowledge with repeated feedback, while reflecting acquired information up to the current prediction.

Biased evaluations emerge from inferring hidden causes.

How do we evaluate a group of people after a few negative experiences with some members but mostly positive experiences otherwise? How do rare experiences influence our overall impression? We show that rare events may be overweighted due to normative inference of the hidden causes that are believed to generate the observed events. We propose a Bayesian inference model that organizes environmental statistics by combining similar events and separating outlying observations. Relying on the model's inferred latent causes for group evaluation overweights rare or variable events. We tested the model's predictions in eight experiments where participants observed a sequence of social or non-social behaviours and estimated their average. As predicted, estimates were biased toward sparse events when estimating after seeing all observations, but not when tracking a summary value as observations accrued. Our results suggest that biases in evaluation may arise from inferring the hidden causes of group members' behaviours.

Structuring Memory Through Inference-Based Event Segmentation.

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.

Context-dependent memory effects in two immersive virtual reality environments: On Mars and underwater.

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.

Moral dilemmas and trust in leaders during a global health crisis.

Trust in leaders is central to citizen compliance with public policies. One potential determinant of trust is how leaders resolve conflicts between utilitarian and non-utilitarian ethical principles in moral dilemmas. Past research suggests that utilitarian responses to dilemmas can both erode and enhance trust in leaders: sacrificing some people to save many others ('instrumental harm') reduces trust, while maximizing the welfare of everyone equally ('impartial beneficence') may increase trust. In a multi-site experiment spanning 22 countries on six continents, participants (N = 23,929) completed self-report (N = 17,591) and behavioural (N = 12,638) measures of trust in leaders who endorsed utilitarian or non-utilitarian principles in dilemmas concerning the COVID-19 pandemic. Across both the self-report and behavioural measures, endorsement of instrumental harm decreased trust, while endorsement of impartial beneficence increased trust. These results show how support for different ethical principles can impact trust in leaders, and inform effective public communication during times of global crisis. PROTOCOL REGISTRATION STATEMENT: The Stage 1 protocol for this Registered Report was accepted in principle on 13 November 2020. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.13247315.v1 .