Charley M. Wu

University of Tuebingen | EKU Tübingen · Human and Machine Cognition Lab

How do children and adults differ in their search for rewards? We consider three different hypotheses that attribute developmental differences to either children's increased random sampling, more directed exploration towards uncertain options, or narrower generalization. Using a search task in which noisy rewards are spatially correlated on a grid,...

From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet, how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using var...

How does cooperation arise in an evolutionary context? We approach this problem using a collective search paradigm where interactions are dynamic and there is competition for rewards. Using evolutionary simulations, we find that the unconditional sharing of information can be an evolutionary advantageous strategy without the need for conditional st...

How do people learn functions on structured spaces? And how do they use this knowledge to guide their search for rewards in situations where the number of options is large? We study human behavior on structures with graph-correlated values and propose a Bayesian model of function learning to describe and predict their behavior. Across two experimen...

Learning and generalization in spatial domains is often thought to rely on a “cognitive map”, representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share...

Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from its origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships) to...

There has been much progress in understanding human social learning, including recent studies integrating social information into the reinforcement learning framework. Yet previous studies often assume identical payoffs between observer and demonstrator, overlooking the diversity of social information in real-world interactions. We address this gap...

Humans have developed technologies to adapt to virtually every habitat on Earth. But why do some communities develop thriving technological repertoires while others stagnate? We address this question by analyzing player behavior in One Hour One Life (OHOL), a multiplayer online game where players can build technologically advanced communities from...

A large program of research has aimed to ground large-scale cultural phenomena in processes taking place within individual minds. For example, investigating whether individual agents equipped with the right social learning strategies can enable cumulative cultural evolution given long enough time horizons. However, this approach often omits the cri...

Any new medical innovation must first prove its benefits with reliable evidence from clinical trials. Evidence is commonly expressed using two metrics, summarizing treatment benefits based on either absolute risk reductions (ARRs) or relative risk reductions (RRRs). While both metrics are derived from the same data (e.g., observed frequencies of a...

Collective dynamics emerge from countless individual decisions. Yet, we poorly understand the processes governing dynamically-interacting individuals in human collectives under realistic conditions. We present a naturalistic immersive-reality experiment where groups of participants searched for rewards in different environments, studying how indivi...

What factors influence how much fun people have when engaging in inherently enjoyable tasks? The theory of learning progress predicts that people will have the most fun in environments of intermediate difficulty because these environments offer the most progress in learning about the world. Past studies have frequently focused on simple experimenta...

When watching a negative emotional movie, we differ from person to person in the ease with which we engage and the difficulty with which we disengage throughout a temporally evolving narrative. We investigated neural responses of emotional processing, by considering inter‐individual synchronization in subjective emotional engagement and disengageme...

There has been much progress in understanding human social learning, including recent studies integrating social information into the reinforcement learning framework.Yet previous studies often assume identical payoffs between observer and demonstrator, overlooking the diversity of real-world interactions. We address this gap by introducing a socia...

Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships), to do...

Collective dynamics emerge from countless individual decisions. Yet, we poorly understand the cognitive processes governing dynamically-interacting individuals in human collectives under realistic conditions. We present a naturalistic immersive-reality experiment where groups of participants searched for rewards in different environments, studying...

Human development is often described as a ‘cooling off’ process, analogous to stochastic optimization algorithms that implement a gradual reduction in randomness over time. Yet there is ambiguity in how to interpret this analogy, due to a lack of concrete empirical comparisons. Using data from n = 281 participants ages 5 to 55, we show that cooling...

A large program of research has aimed to ground large-scale cultural phenomena in processes taking place within individual minds. For example, investigating whether individual agents equipped with the right social learning strategies (SLSs) can enable cumulative cultural evolution given long enough time horizons. However, this approach often omits...

Humans are uniquely capable social learners. Our capacity to learn from others across short and long timescales is a driving force behind the success of our species. Yet there are seemingly maladaptive patterns of human social learning, characterized by both overreliance and underreliance on social information. Recent advances in animal research ha...

Previous research shows that variation in coherence (i.e., degrees of respect for axioms of probability calculus), when used as a basis for performance-weighted aggregation, can improve the accuracy of probability judgments. However, many aspects of coherence-weighted aggregation remain a mystery, including both prescriptive issues (e.g., how best...

Humans are remarkably effective social learners, with several recent studies formalizing this capacity using computational models. However, previous research has often been limited to tasks where observer and demonstrator share the same reward function. In contrast, humans can learn from others who have different preferences, skills, or goals. To s...

Bias exists in how we pick leaders, who we perceive as being influential, and who we interact with, not only in society, but in organizational contexts. Drawing from leadership emergence and social influence theories, we investigate potential interventions that support diverse leaders. Using agent-based simulations, we model a collective search pro...

Previous research shows that variation in coherence (i.e., degrees of respect for axioms of probability calculus), when used as a basis for performance-weighted aggregation, can improve the accuracy of probability judgments. However, many aspects of coherence-weighted aggregation remain a mystery, including both prescriptive issues (e.g., how best...

Many cognitive models provide valuable insights into human behavior. Yet the algorithmic complexity of candidate models can fail to capture how humanreaction times scale with increasing input complexity. In the current work,we want to understand the algorithms underlying human cognitive processes.Computer science characterizes algorithms by their t...

We propose that human social learning is subject to a trade-off between the cost of performing a computation and the flexibility of its outputs. Viewing social learning through this lens sheds light on cases that seem to violate bifocal stance theory (BST) – such as high-fidelity imitation in instrumental action – and provides a mechanism by which...

Humans constantly search for and use information to solve a wide range of problems related to survival, social interactions, and learning. While it is clear that curiosity and the drive for knowledge occupies a central role in defining what being human means to ourselves, where does this desire to know the unknown come from? What is its purpose? An...

Humans constantly search for and use information to solve a wide range of problems related to survival, social interactions, and learning. While it is clear that curiosity and the drive for knowledge occupies a central role in defining what being human means to ourselves, where does this desire to know the unknown come from? What is its purpose? An...

Human reinforcement learning (RL) is characterized by different challenges. Exploration has been studied extensively in multi-armed bandits, while planning has been investigated in multi-step decision tasks. More recent work has added structured rewards to study generalization. However, past studies have often focused on a single one of these aspec...

Analogies to stochastic optimization are common in developmental psychology, describing a gradual reduction in randomness over the lifespan. Yet for lack of concrete empirical comparison, there is ambiguity in how to interpret this analogy. Using data from n=281 participants ages 5 to 55, we show that "cooling off'" does not only apply to the singl...

Humans and other animals are capable of inferring never-experienced relations (for example, A > C) from other relational observations (for example, A > B and B > C). The processes behind such transitive inference are subject to intense research. Here we demonstrate a new aspect of relational learning, building on previous evidence that transitive i...

How does time pressure influence exploration and decision-making? We investigated this question with several four-armed bandit tasks manipulating (within subjects) expected reward, uncertainty, and time pressure (limited vs. unlimited). With limited time, people have less opportunity to perform costly computations, thus shifting the cost-benefit ba...

How does time pressure influence exploration and decision-making? We investigate this question using a within-subject design to manipulate decision time (limited vs. unlimited) and use a range of four-armed bandit tasks, designed to independently manipulate uncertainty and expected reward. With limited time, people have less opportunity to perform...

What makes human social learning so powerful? While past accounts have sometimes prioritized finding the single capacity that makes the largest difference, our social learning abilities span a wide spectrum of capacities from the high-fidelity imitation of behaviors to inferring and learning from hidden mental states. Here, we propose that the powe...

A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimotor information into compact event encodings, which are used to anticipate and interpret environmental dynamics. Here, we introduce a SUrprise-GAted Recurrent neural netw...

Humans and other animals are capable of inferring never-experienced relations (e.g., A>C) from other relational observations (e.g., A>B and B>C). The processes behind such transitive inference are subject to intense research. Here, we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be acc...

Are young children just random explorers who learn serendipitously? Or are even young children guided by uncertainty-directed sampling, seeking to explore in a systematic fashion? We study how children between the ages of 4 and 9 search in an explore-exploit task with spatially-correlated rewards, where exhaustive exploration is infeasible and not...

A key question individuals face in any social learning environment is when to innovate alone and when to imitate others. Previous simulation results have found that the best performing groups exhibit an intermediate balance, yet it is still largely unknown how individuals collectively negotiate this balance. We use an immersive collective foraging...

Sustained attention is a fundamental cognitive process that can be decoupled from distinct external events, and instead emerges from ongoing intrinsic large-scale network interdependencies fluctuating over seconds to minutes. Lapses of sustained attention are commonly associated with the subjective experience of mind wandering and task-unrelated th...

What are we curious about? Dubey and Griffiths propose a rational theory of curiosity that unifies previously contradictory novelty-based and complexity accounts. It also paves the way for future investigations, such as studying approximate models of curiosity as well as what causes abnormal levels of exploration.

What are we curious about? Dubey and Griffiths propose a rational theory of curiosity that unifies previously contradictory novelty-based and complexity accounts. It also paves the way for future investigations, such as studying approximate models of curiosity as well as what causes abnormal levels of exploration.

Are young children just random explorers who learn serendipitously? Or are even young children guided by uncertainty-directed sampling, seeking to explore in a systematic fashion? We study how children between the ages of 4 and 9 search in an explore-exploit task with spatially-correlated rewards, where exhaustive exploration is infeasible and not...

How do children and adults differ in their search for rewards? We considered three different hypotheses that attribute developmental differences to (a) children’s increased random sampling, (b) more directed exploration toward uncertain options, or (c) narrower generalization. Using a search task in which noisy rewards were spatially correlated on...

The Max Planck Society represents a unique place for principal investigators, but its benefits are not necessarily reaped by the students, argue the Max Planck PhDnet Survey Group. Policy changes, however, could alleviate publication and other pressures for students.

How do people navigate the vastness of real-world environments where it is not feasible to explore all possibilities and the exact same situation is rarely encountered twice? The study of human learning has made rapid progress in the past decades, from discovering the neural substrate of reward prediction errors, to using similar principles to buil...

How does time pressure influence attitudes towards uncertainty? When time is limited, do people engage in different exploration strategies? We study human exploration in a range of four-armed bandit tasks with different reward distributions and manipulate the available time for each decision (limited vs. unlimited). Through multiple behavioral and...

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We...

Accurate forecasts are critical for predicting and responding to future events. While there has been much research on developing methods for improving forecast accuracy––using information presentation formats, recalibration of judgments, and aggregation––relatively little research has examined how the various approaches work in tandem. We address t...

There is a longstanding interest in methods to improve posterior probability judgments through selection of information presentation, recalibrating judgments to improve coherence (or “coherentizing”), and aggregation. Little research has examined how these approaches work together. Reanalyzing a large dataset from Wu et al. (2017), we found that th...

How does time pressure influence attitudes towards uncertainty? When time is limited, do people engage in different exploration strategies? We study human exploration in a range of four-armed bandit tasks with different reward distributions and manipulate the available time for each decision (limited vs. unlimited). Through multiple behavioral and...

From social networks to public transportation, graph structures are a ubiquitous feature of life. Yet little is known about how humans learn functions on graphs, where relationships are defined by the connectivity structure. We adapt a Bayesian framework for function learning to graph structures, and propose that people perform generalization by di...

From foraging for food to learning complex games, many aspects of human behaviour can be framed as a search problem with a vast space of possible actions. Under finite search horizons, optimal solutions are generally unobtainable. Yet how do humans navigate vast problem spaces, which require intelligent exploration of unobserved actions? Using a va...

Information sharing in competitive environments may seem counterintuitive, yet it is widely observed in humans and other animals. For instance, the open-source software movement has led to new and valuable technologies being released publicly to facilitate broader collaboration and further innovation. What drives this behavior and under which condi...

The idea of a "cognitive map" was originally developed to explain planning and generalization in spatial domains through a representation of inferred relationships between experiences. Recently, new research has suggested similar principles may also govern the representation of more abstract, conceptual knowledge in the brain. We test whether the s...

How do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how...

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working towards it. How should peo- ple allocate time between such make-or-break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance?...

Humans regularly invest time towards activities characterized by dramatic success or failure outcomes, where critically, the outcome is uncertain ex-ante. How should people allocate time between such make-or-break activities and other safe alternatives , where rewards are more predictable (e.g., linear) functions of time? We present a formal framew...

We introduce the spatially correlated multi-armed bandit as a task coupling function learning with the exploration-exploitation trade-off. Participants interacted with bi-variate reward functions on a two-dimensional grid, with the goal of either gaining the largest average score or finding the largest payoff. By providing an opportunity to learn t...

While the influence of presentation formats have been widely studied in Bayesian reasoning tasks, we present the first systematic investigation of how presentation formats influence information search decisions. Four experiments were conducted across different probabilistic environments, where subjects (N = 2,858) chose between 2 possible search qu...

In groups and organizations, agents use both individual and social learning to solve problems. The balance between these two activities can lead collectives to very different levels of performance. We model collective search as a combination of simple learning strategies to conduct the first large-scale comparative study, across fifteen challenging...