Falk Lieder

University of California, Los Angeles | UCLA · Department of Psychology

Many contemporary accounts of human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question of how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metarea...

People’s decisions and judgments are disproportionately swayed by improbable but extreme eventualities, such as terrorism, that come to mind easily. This article explores whether such availability biases can be reconciled with rational information processing by taking into account the fact that decision-makers value their time and have limited cogn...

Marr's levels of analysis-computational, algorithmic, and implementation-have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis...

How do we learn to think better, and what can we do to promote such metacognitive learning? Here, we propose that cog-nitive growth proceeds through metacognitive reinforcement learning. We apply this theory to model how people learn how far to plan ahead and test its predictions about the speed of metacognitive learning in two experiments. In the...

The human mind has an impressive ability to improve itself based on experience, but this potential for cognitive growth is rarely fully realized. Cognitive training programs seek to tap into this unrealized potential but their theoretical foundation is incomplete and the scientific findings on their effectiveness are mixed. Recent work suggests tha...

One explanation for how people can plan efficiently despite limited cognitive resources is that we possess a set of adaptive planning strategies and know when and how to use them. But how are these strategies acquired? While previous research has studied how individuals learn to choose among existing strategies, little is known about the process of...

The challenges of the 21st century, such as climate change, pandemics, and global inequality, necessitate a degree of cooperation that transcends national interests. While national belonging can positively contribute to life satisfaction, it can also reinforce and highlight divisions between people of different nationalities. We set out to document...

How do people know which moral principles to follow in a dilemma? Metacognitive moral learning proposes that they learn to use the decision-making strategy that led to the best outcomes in the past (Maier et al., 2024). Do they do this by using a mental model to reason about outcomes in a new situation, or by assigning value directly to each strate...

Previous research found that people's decision-making is shaped by learning from the consequences of past decisions. We hypothesized that the decision strategy people learn depends on which of the outcomes of their previous decisions they attend to. We ran four pre-registered experiments to test whether manipulating people's attention could be an e...

Moral decision-making is a topic of great interest in the growing field of moral psychology. Yet, to our knowledge, there are no existing self-report scales for measuring the process of decision-making in individual moral dilemmas (as opposed to general moral attitudes or beliefs about moral decision-making in general), nor are there any equivalent...

Previous research found that people's decision-making is shaped by learning from the consequences of past decisions. We hypothesized that the decision strategy people learn depends on which of the outcomes of their previous decisions they attend to. We ran four pre-registered experiments to test whether manipulating people's attention could be an e...

As large language models (LLMs) become more widely used, people increasingly rely on them to make or advise on moral decisions. Some researchers even propose using LLMs as participants in psychology experiments. It is therefore important to understand how well LLMs make moral decisions and how they compare to humans. We investigated this question i...

The decisions of individuals and organizations are often suboptimal because normative decision strategies are too demanding in the real world. Recent work suggests that some errors can be prevented by leveraging artificial intelligence to discover and teach prescriptive decision strategies that take people's constraints into account. So far, this l...

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 us...

Metachangemaking refers to the cultivation of changemakers-people with the motivation and competence to tackle societal issues and promote collective wellbeing. It is not entirely clear how to effectively cultivate changemakers, and relevant research spans many disparate fields. The goals of this article are to introduce the concept of metachangema...

Despite their adaptive moral learning mechanisms, people don't learn optimal decision procedures for moral dilemmas. We resolve this puzzle by experimentally examining the effect of attention on moral learning. Our findings suggest that people learn from their moral mistakes only if they attend to their decisions' most morally significant consequen...

Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation. How can self-regulation be supported or strengthened in ways that are relevant for everyday w...

We designed a new experimental paradigm to investigate moral learning from consequences of previous decisions. Participants (total N=1601) faced a series of realistic moral dilemmas between two conflicting choices: one prescribed by a moral rule (typically 'deontologist') and the other favored by cost-benefit reasoning (CBR; typically 'utilitarian'...

Cushman (2008) explored the puzzling effect of luck on how people are morally judged for accidental harm and proposed that people judge moral wrongness based on intent but consider consequences and intent in judgments of blameworthiness. Three within-subject experiments (total N=716), using varieties of a driving accident scenario, examined how mor...

Background: People often decide against prosocial behavior even when it would benefit their own well-being (Epley et al., 2023). Aims: We investigate the relative importance of two potential bottlenecks to prosocial behavior: a) lack of attention to one's actions' effects on others, and b) considering these effects unimportant. Method: We conducted...

Deep neural networks are increasingly tasked with making complex, real-world decisions that can have morally significant consequences. But it is difficult to predict when a deep neural network will go wrong, and wrong decisions can cause significantly negative outcomes. In contrast, human moral decision-making is often remarkably robust. This is pa...

We explore how people learn to arbitrate between following moral rules vs. cost-benefit reasoning (CBR). In a new experimental paradigm, participants (N=387) saw 13 realistic moral dilemmas with two choices: one prescribed by an intuitive moral rule and one favored by CBR. Before each decision, they saw the outcome of their previous one. In one con...

Public policies can have a huge impact on collective well-being. Policymakers often face social dilemmas that require tradeoffs between the effects of a policy on the well-being of different stakeholders across different timescales. Some of these effects are easy to overlook. To investigate which, if any, important effects on well-being decision-m...

Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation . How can self-regulation be supported or strengthened in ways that are relevant for everyday...

BACKGROUND Anxiety disorders are among the most prevalent mental disorders, and stress plays a significant role in their development. Ecological Momentary Interventions (EMIs) hold great potential to help people manage stress and anxiety by training emotion regulation and coping skills in real-life settings. The InsightApp is a gamified EMI and res...

BACKGROUND Anxiety disorders are a common mental health condition, and stress plays a significant role in their development. Ecological Momentary Interventions (EMIs) hold great potential to help people manage stress and anxiety by training emotion regulation and coping skills in real-life settings. The InsightApp is a gamified EMI and research too...

We study human performance in two classical NP-hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem-solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges betwe...

Scientific discovery concerns finding patterns in data and creating insightful hypotheses that explain these patterns. Traditionally, each step of this process required human ingenuity. But the galloping development of computer chips and advances in artificial intelligence (AI) make it increasingly more feasible to automate some parts of scientific...

Short-sighted decisions can have devastating consequences, and teaching people to make their decisions in a more far-sighted way is challenging. Previous research found that reflecting on one’s behavior can boost learning from success and failure. Here, we explore the potential benefits of guiding people to reflect on whether and how they thought a...

Many controversies arise from differences in how people resolve moral dilemmas by following deontological moral rules versus consequentialist cost-benefit reasoning (CBR). This article explores whether and, if so, how these seemingly intractable differences may arise from experience and whether they can be overcome through moral learning. We design...

How is it that humans can solve complex planning tasks so efficiently despite limited cognitive resources? One reason is its ability to know how to use its limited computational resources to make clever choices. We postulate that people learn this ability from trial and error (metacognitive reinforcement learning). Here, we systematize models of th...

How do humans get better at planning? Previous work postulated that the improvement of cognitive strategies occurs through feedback-based metacognitive reinforcement learning (MCRL). However, it is not clear whether and, if so, how people can learn planning strategies without reinforcement. To answer these questions, we experimentally investigated...

People are often confronted with problems whose complexity exceeds their cognitive capacities. To deal with this complexity, individuals and managers can break complex problems down into a series of subgoals. Which subgoals are most effective depends on people's cognitive constraints and the cognitive mechanisms of goal pursuit. This creates an unt...

AI can not only outperform people in many planning tasks, but also teach them how to plan better. All prior work was conducted in fully observable environments, but the real world is only partially observable. To bridge this gap, we developed the first metareasoning algorithm for discovering resource-rational strategies for human planning in partia...

Answering crucial scientific questions could substantially improve our ability to promote human flourishing. However, identifying which research topics are most important for promoting flourishing is very difficult. To overcome this challenge, we are developing a general method for predicting how much research on a given topic might improve the flo...

Answering crucial scientific questions could substantially improve our ability to promote human flourishing. However, identifying which research topics are most important for promoting flourishing is very difficult. To overcome this challenge, we are developing a general method for predicting how much research on a given topic might improve the flo...

BACKGROUND Ecological Momentary interventions (EMIs) open new and exciting possibilities for conducting research and delivering mental health interventions in real-life environments via smartphones. This makes designing psychotherapeutic EMIs a promising step towards cost-effective, scalable digital solutions for improving mental health and underst...

Background Ecological momentary interventions open up new and exciting possibilities for delivering mental health interventions and conducting research in real-life environments via smartphones. This makes designing psychotherapeutic ecological momentary interventions a promising step toward cost-effective and scalable digital solutions for improvi...

BACKGROUND Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission. OBJECTIVE Here, we introduce a principled mathematical method for determining how...

Background Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission. Objective In this study, we introduced a principled mathematical method for deter...

Human decision-making is plagued by many systematic errors. Many of these errors can be avoided by providing decision aids that guide decision-makers to attend to the important information and integrate it according to a rational decision strategy. Designing such decision aids used to be a tedious manual process. Advances in cognitive science might...

People’s intentional pursuit of prosocial goals and values (i.e., well-doing) is critical to the flourishing of humanity in the long run. Understanding and promoting well-doing is a shared goal across many fields inside and outside of social and personality psychology. Several of these fields are (partially) disconnected from each other and could b...

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cog...

Setting the right goals and prioritizing them might be the most crucial and the most challenging type of decisions people make for themselves, their teams, and their organizations. In this article , we explore whether it might be possible to leverage artificial intelligence (AI) to help people set better goals and which potential problems might ari...

Shortsighted decisions can have devastating consequences, and teaching people to make their decisions in a more far-sighted way is challenging. Previous research found that reflecting on one's behavior can boost learning from success and failure. Here, we explore the potential benefits of guiding people to reflect on whether and how they thought ab...

One of the most unique and impressive feats of the human mind is its ability to discover and continuously refine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is very difficult because changes in cognitive strategies are not directly observable. One important domain in which strategies and mechanisms ar...

Human cognition is fundamentally goal-directed (Carver & Scheier, 2001), and there are still many open questions about the cognitive mechanisms of goal-setting and how they affect the quality of people’s goals (Kasser & Ryan, 1996). Here, we study in an exploratory way how goals set through deliberate reflection about the future (prospection) diffe...

Teaching people clever heuristics is a promising approach to improve decision-making under uncertainty. The theory of resource rationality makes it possible to leverage machine learning to discover optimal heuristics automatically. One bottleneck of this approach is that the resulting decision strategies are only as good as the model of the decisio...

While making plans, people have to decide how far out into the future they want to plan: days, months, years, or even longer. Overly short-sighted planning can harm people's well-being in important life domains, such as health, finances, and academics. While self-report scales exist to measure people's planning, people's answers to such questions m...

Nowadays, more people can access digital educational resources than ever before. However, access alone is often not sufficient for learners to fulfill their learning goals. To support motivation, learning environments are often gamified, meaning that they offer points for interacting with them. But gamification can add to learners' tendencies to ch...

Many people procrastinate and struggle to prioritize their most important work. To help their users overcome such problems, gamified productivity tools like Habitica use heuristic point systems that can be counterproductive. We recently proposed a more principled way to compute point values that avoids such problems. Although it was promising in th...

To make good decisions in the real world, people need efficient planning strategies because their computational resources are limited. Knowing which planning strategies would work best for people in different situations would be very useful for understanding and improving human decision-making. Our ability to compute those strategies used to be lim...

Significance Many bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intellig...

Human decision-making is plagued by many systematic errors. Many of these errors can be avoided by providing decision aids that guide decision-makers to attend to the important information and integrate it according to a rational decision strategy. Designing such decision aids is a tedious manual process. Advances in cognitive science might make it...

Negative emotions can make maladaptive behavior more likely, especially when people have poor emotion regulation and metacognitive skills (ERMSs). We developed an app to help non-clinical populations train and apply good ERMSs. The app teaches ERMSs with the help of gamified features such as customizable emotion avatars and points for practicing ER...

Teaching people clever heuristics is a promising approach to improve decision-making under uncertainty. The theory of resource-rationality makes it possible to leverage machine learning to discover optimal heuristics automatically. One bottleneck of this approach is that the resulting decision strategies are only as good as the model of the decisio...

For computationally limited agents such as humans, perfectly rational decision-making is almost always out of reach. 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...

One of the most unique and impressive feats of the human mind is its ability to discover and continuously refine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is very difficult because changes in cognitive strategies are not directly observable. One important domain in which strategies and mechanisms ar...

The purpose of the present studies was to identify an effective tool for helping people to select virtuous life goals that promote their own well-being and contribute to the well-being of others (well-doing). Across two studies, we tested four candidate interventions against each other and a control condition. In the first study (N = 218), the inte...

How motivated a person is to pursue a goal may depend on many different properties of the goal, such as how specific it is, how important it is to the person, and how actionable it is. Rigorously measuring all of the relevant goal characteristics is still very difficult. Existing measures are scattered across multiple research fields. Some goal cha...

Human decision-making is plagued by systematic errors that can have devastating consequences. Previous research has found that such errors can be partly prevented by teaching people decision strategies that would allow them to make better choices in specific situations. Three bottlenecks of this approach are our limited knowledge of effective decis...

Highly influential “dual-process” accounts of human cognition postulate the coexistence of a slow accurate system with a fast error-prone system. But why would there be just two systems rather than, say, one or 93? Here, we argue that a dual-process architecture might reflect a rational tradeoff between the cognitive flexibility afforded by multipl...

People's decisions about how to allocate their limited computational resources are essential to human intelligence. An important component of this metacognitive ability is deciding whether to continue thinking about what to do and move on to the next decision. Here, we show that people acquire this ability through learning and reverse-engineer the...

People are able to learn clever cognitive strategies through trial and error from small amounts of experience. This is facilitated by people's ability to reflect on their own thinking which is known as metacognition. To examine the effects of deliberate systematic metacognitive reflection on how people learn how to plan, the experimental group was...

One of the most remarkable aspects of the human mind is its ability to improve itself based on experience. Such learning occurs in a range of domains, from simple stimulus-response mappings, motor skills, and perceptual abilities, to problem-solving, cognitive control, and learning itself. Demonstrations of cognitive and brain plasticity have inspi...

Scientific discovery concerns finding patterns in data and creating insightful hypotheses that explain these patterns. Traditionally, each step of this process required human ingenuity. But the galloping development of computer chips and advances in artificial intelligence (AI) make it increasingly more feasible to automate some parts of scientific...

Most people struggle with prioritizing work. While inexact heuristics have been developed over time, there is still no tractable principled algorithm for deciding which of the many possible tasks one should tackle in any given day, month, week, or year. Additionally, some people suffer from cognitive biases such as the present bias, leading to prio...

When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult prob...