Teaching Recombinable Motifs Through Simple Examples.

A hallmark of effective teaching is that it grants learners not just a collection of facts about the world, but also a toolkit of abstractions that can be applied to solve new problems. How do humans teach abstractions from examples? Here, we applied Bayesian models of pedagogy to a necklace-building task where teachers create necklaces to teach a learner "motifs" that can be flexibly recombined to create new necklaces. In Experiment 1 (N = 151), we find that human teachers produce necklaces that are simpler (i.

People make suboptimal decisions about existential risks.

Allocating resources to maximize the probability that humanity survives a set of existential risks has a different structure from many decision problems, as the objective is the product of the probabilities of desired outcomes rather than the sum. We derive the optimal solution to this problem and use this solution to evaluate the choices that people make when presented with decisions that have this multiplicative structure. Our participants (total N=2,072) are appropriately sensitive to how responsive a risk is to investment, but are conservative in their decisions and do not allocate enough resources to risks with lower probability of survival.

Predicting human decisions with behavioural theories and machine learning.

Predicting human decisions under risk and uncertainty remains a fundamental challenge across disciplines. Existing models often struggle even in highly stylized tasks like choice between lotteries. Here we introduce BEAST gradient boosting (BEAST-GB), a hybrid model integrating behavioural theory (BEAST) with machine learning.

People evaluate idle collaborators based on their impact on task efficiency.

Humans collaborate to improve productivity, but when is it acceptable for a collaborator to remain idle? Theories from distributed computer systems suggest that, depending on the task structure, division of labor leads to diminishing returns in efficiency as group size increases. We examine whether people are aware of these limitations to collaboration, and how considerations of task efficiency may affect the perceived acceptability of idleness, the withholding of effort during collaborative tasks. Across four experiments (N=1,124), participants saw scenarios where a single collaborator remained idle while other group members washed dishes, prepared a salad, or created flashcards.

Considering What We Know and What We Don't Know: Expectations and Confidence Guide Value Integration in Value-Based Decision-Making.

When making decisions, we often have more information about some options than others. Previous work has shown that people are more likely to choose options that they look at more and those that they are more confident in. But should one always prefer options one knows more about? Intuition suggests not.

Looking deeper into the algorithms underlying human planning.

Humans possess a remarkable ability to form sophisticated multi-step plans even in complex environments. In this review article, we consider efforts that attempt to characterize the mechanisms underlying human planning using a computational framework, primarily focusing on methods that search a tree of possible solutions. These studies range from experimental probes for heuristics that people employ while thinking ahead to normative models for reducing the computational costs of planning.

A foundation model to predict and capture human cognition.

Establishing a unified theory of cognition has been an important goal in psychology. A first step towards such a theory is to create a computational model that can predict human behaviour in a wide range of settings. Here we introduce Centaur, a computational model that can predict and simulate human behaviour in any experiment expressible in natural language.

Rational causal induction from events in time.

A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood.

Capturing the complexity of human strategic decision-making with machine learning.

Strategic decision-making is a crucial component of human interaction. Here we conduct a large-scale study of strategic decision-making in the context of initial play in two-player matrix games, analysing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on this dataset predicts human choices with greater accuracy than leading theories of strategic behaviour, revealing systematic variation unexplained by existing models.

When to keep trying and when to let go: Benchmarking optimal quitting.

Persistence and perseverance, even in the face of great adversity, are admirable qualities. However, knowing pursuing something is as important as exerting effort toward attaining a goal. How do people decide when to persist and when to quit? Here, we design a novel task to study this question, in which people were given a finite number of opportunities to pursue stochastic rewards by selecting among a set of options that provide a reward each trial.

Computation-limited Bayesian updating: A resource-rational analysis of approximate Bayesian inference.

Data and computational capacity are essential resources for any intelligent system that update its beliefs by integrating new information. However, both of these resources are inherently limited. Here, we introduce a new resource-rational analysis of belief updating that formalizes these constraints using information-theoretic principles.

Modeling rapid language learning by distilling Bayesian priors into artificial neural networks.

Humans can learn languages from remarkably little experience. Developing computational models that explain this ability has been a major challenge in cognitive science. Existing approaches have been successful at explaining how humans generalize rapidly in controlled settings but are usually too restrictive to tractably handle naturalistic data.

Binary climate data visuals amplify perceived impact of climate change.

For much of the global population, climate change appears as a slow, gradual shift in daily weather. This leads many to perceive its impacts as minor and results in apathy (the 'boiling frog' effect). How can we convey the urgency of the crisis when its impacts appear so subtle? Here, through a series of large-scale cognitive experiments (N = 799), we find that presenting people with binary climate data (for example, lake freeze history) significantly increases the perceived impact of climate change (Cohen's d = 0.

Can children and adults balance majority size with information quality in learning from preferences?

We investigate how 3- to 5-year-old U.S. and Canadian children ( = 189) and U.

Explicitly unbiased large language models still form biased associations.

Large language models (LLMs) can pass explicit social bias tests but still harbor implicit biases, similar to humans who endorse egalitarian beliefs yet exhibit subtle biases. Measuring such implicit biases can be a challenge: As LLMs become increasingly proprietary, it may not be possible to access their embeddings and apply existing bias measures; furthermore, implicit biases are primarily a concern if they affect the actual decisions that these systems make. We address both challenges by introducing two measures: LLM Word Association Test, a prompt-based method for revealing implicit bias; and LLM Relative Decision Test, a strategy to detect subtle discrimination in contextual decisions.

Automating the practice of science: Opportunities, challenges, and implications.

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches.

Exploring the hierarchical structure of human plans via program generation.

Human behavior is often assumed to be hierarchically structured, made up of abstract actions that can be decomposed into concrete actions. However, behavior is typically measured as a sequence of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically structured plans, using an experimental paradigm with observable hierarchical representations: participants create programs that produce sequences of actions in a language with explicit hierarchical structure.

Costly exploration produces stereotypes with dimensions of warmth and competence.

Traditional explanations for stereotypes assume that they result from deficits in humans (ingroup-favoring motives, cognitive biases) or their environments (majority advantages, real group differences). An alternative explanation recently proposed that stereotypes can emerge when exploration is costly. Even optimal decision makers in an ideal environment can inadvertently form incorrect impressions from arbitrary encounters.

Inverting Cognitive Models With Neural Networks to Infer Preferences From Fixations.

Inferring an individual's preferences from their observable behavior is a key step in the development of assistive decision-making technology. Although machine learning models such as neural networks could in principle be deployed toward this inference, a large amount of data is required to train such models. Here, we present an approach in which a cognitive model generates simulated data to augment limited human data.

Building machines that learn and think with people.

What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations.

Environmental complexity and regularity shape the evolution of cognition.

The environmental complexity hypothesis suggests that cognition evolves to allow animals to negotiate a complex and changing environment. By contrast, signal detection theory suggests cognition exploits environmental regularities by containing biases (e.g.

Embers of autoregression show how large language models are shaped by the problem they are trained to solve.

The widespread adoption of large language models (LLMs) makes it important to recognize their strengths and limitations. We argue that to develop a holistic understanding of these systems, we must consider the problem that they were trained to solve: next-word prediction over Internet text. By recognizing the pressures that this task exerts, we can make predictions about the strategies that LLMs will adopt, allowing us to reason about when they will succeed or fail.

Meta-learning as a bridge between neural networks and symbolic Bayesian models.

Meta-learning is even more broadly relevant to the study of inductive biases than Binz et al. suggest: Its implications go beyond the extensions to rational analysis that they discuss. One noteworthy example is that meta-learning can act as a bridge between the vector representations of neural networks and the symbolic hypothesis spaces used in many Bayesian models.

Large language models predict human sensory judgments across six modalities.

Determining the extent to which the perceptual world can be recovered from language is a longstanding problem in philosophy and cognitive science. We show that state-of-the-art large language models can unlock new insights into this problem by providing a lower bound on the amount of perceptual information that can be extracted from language. Specifically, we elicit pairwise similarity judgments from GPT models across six psychophysical datasets.