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.

Real-world study of the multimorbidity and health service utilisation among individuals with non-diabetic hyperglycemia and type 2 diabetes mellitus in North West London.

Introduction: The prevalence of non-diabetic hyperglycemia (NDH) and type 2 diabetes mellitus (T2DM) is increasing. While T2DM is recognised to be associated with multimorbidity and early mortality, people with NDH are frequently thought to be devoid of such complications, potentially exposing individuals with NDH to suboptimal care. We therefore used the Discover London Secure Data Environment (SDE) dataset to appreciate the relationship of NDH/T2DM with multimorbidity, healthcare usage, and clinical outcomes.

Learning From Aggregated Opinion.

The capacity to leverage information from others' opinions is a hallmark of human cognition. Consequently, past research has investigated how we learn from others' testimony. Yet a distinct form of social information--increasingly guides our judgments and decisions.

Shared functional specialization in transformer-based language models and the human brain.

When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual information across words via structured circuit computations.

Using games to understand the mind.

Board, card or video games have been played by virtually every individual in the world. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal for studying the mind.

Variations in orientation relationships between rutile inclusions and garnet host relate to magmatic growth zoning.

Unlabelled: Rutile inclusions in almandine-spessartine garnet from a peraluminous pegmatoid from the Moldanubian zone (Bohemian Massif, AT) show distinct changes in aspect ratio, shape preferred orientations (SPO) and crystallographic orientation relationships (COR) along the transition between microstructurally different growth zones in the garnet core and rim. For identification of the COR characteristics we pool specific CORs based on their common axial relationship into three COR groups: Group 103/111, Group 001/111 and Group 001/100. The rutile inclusions in the garnet core domains are elongated along the four Grt 111 directions and are dominated by COR Group 103/111.

Identifying resource-rational heuristics for risky choice.

Perfectly rational decision making is almost always out of reach for people because their computational resources are limited. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging.

The universal law of generalization holds for naturalistic stimuli.

Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that the level of perceived similarity between a pair of stimuli should decay as a concave function of their distance when embedded in an appropriate psychological space. While extensively studied, evidence in support of the universal law has relied on low-dimensional stimuli and small stimulus sets that are very different from their real-world counterparts.

Reconciling categorization and memory via environmental statistics.

How people represent categories and how those representations change over time is a basic question about human cognition. Previous research has demonstrated that people categorize objects by comparing them to category prototypes in early stages of learning but consider the individual exemplars within each category in later stages. However, these results do not seem consistent with findings in the memory literature showing that it becomes increasingly easier to access representations of general knowledge than representations of specific items over time.

Replies to commentaries on beyond playing 20 questions with nature.

Commentaries on the target article offer diverse perspectives on integrative experiment design. Our responses engage three themes: (1) Disputes of our characterization of the problem, (2) skepticism toward our proposed solution, and (3) endorsement of the solution, with accompanying discussions of its implementation in existing work and its potential for other domains. Collectively, the commentaries enhance our confidence in the promise and viability of integrative experiment design, while highlighting important considerations about how it is used.

Improving Memory Search Through Model-Based Cue Selection.

We often use cues from our environment when we get stuck searching our memories, but prior research has failed to show benefits of cuing with other, randomly selected list items during memory search. What accounts for this discrepancy? We proposed that cues' content critically determines their effectiveness and sought to select the right cues by building a computational model of how cues affect memory search. Participants ( = 195 young adults from the United States) recalled significantly more items when receiving our model's best (vs.

Machine culture.

The ability of humans to create and disseminate culture is often credited as the single most important factor of our success as a species. In this Perspective, we explore the notion of 'machine culture', culture mediated or generated by machines. We argue that intelligent machines simultaneously transform the cultural evolutionary processes of variation, transmission and selection.

Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures.

People's decisions often deviate from classical notions of rationality, incurring costs to themselves and society. One way to reduce the costs of poor decisions is to redesign the decision problems people face to encourage better choices. While often subtle, these can have dramatic effects on behavior and are increasingly popular in public policy, health care, and marketing.

Rational Simplification and Rigidity in Human Planning.

Planning underpins the impressive flexibility of goal-directed behavior. However, even when planning, people can display surprising rigidity in how they think about problems (e.g.

UVB-emitting LEDs for reptile lighting: Identifying the risks of nonsolar UV spectra.

UVB lamps are used to provide reptiles housed indoors with the UV radiation necessary to synthesize vitamin D in their skin. Since 2019, UVB-LED lamps have been on sale for use in reptile husbandry. We performed spectral analysis and mapped the UV irradiance for 18 of these lamps.