Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources

2019/02/04 by Falk Lieder, Thomas L. Griffiths · 1 voice · 933 citations
Computer Science · Neuroscience · Psychology · #Artificial intelligence #Cognition #Cognitive Science and Mapping #Cognitive science #Computer science #Ecological rationality #Economics #Epistemology #Irrationality #Management science #Neuroscience #Psychology #Rational analysis #Rationality #Resource (disambiguation)

paper · doi:10.1017/s0140525x1900061x

published in Behavioral and Brain Sciences 43, e1 (Cambridge University Press)

openalex publication_date 2019/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis. The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.

Citations

• Implications of rational inattention
• Subjective probability: A judgment of representativeness
• Availability: A heuristic for judging frequency and probability
• Heuristic Decision Making
• Decision by sampling
• A BEHAVIORAL MODEL OF RATIONAL CHOICE
• A feature-integration theory of attention
• The Tractable Cognition Thesis
• Algorithmic rationality: Game theory with costly computation
• Rate–distortion theory and human perception
• The anchoring bias reflects rational use of cognitive resources
• Prospect Theory: An Analysis of Decision under Risk
• The trouble with overconfidence.
• Building a practically useful theory of goal setting and task motivation: A 35-year odyssey.
• Information Acquisition in a Noisy Rational Expectations Economy
• Six views of embodied cognition
• Advances in prospect theory: Cumulative representation of uncertainty
• Sparse coding with an overcomplete basis set: A strategy employed by V1?
• The misunderstood limits of folk science: an illusion of explanatory depth
• The intrinsic cost of cognitive control
• Judgment under Uncertainty: Heuristics and Biases

Cited by

• Naturalism, tractability and the adaptive toolbox
• Even if suboptimal, novelty drives human exploration
• Imagining and building wise machines: the centrality of AI metacognition
• What Drives Object Selection? The Combined Role of Temporal Costs and Effort During Interactive Search
• Resolving Feynman’s restaurant problem reveals optimal solutions and human strategies
• Conscious active inference II: Quantum orchestrated objective reduction among intraneuronal microtubules naturally accounts for discrete perceptual cycles
• How Fundamental Are Fundamental Inequalities? A Resource-Rational Perspective on Fundamental Inequalities and Interventions to Reduce Them
• Cognitive Design for Artificial Minds
• Conviction Narrative Theory: A theory of choice under radical uncertainty
• Theory Before the Test: How to Build High-Verisimilitude Explanatory Theories in Psychological Science
• A test of attribute normalization via a double decoy effect
• Humans adaptively deploy forward and backward prediction
• Machine culture
• Objectifying the Subjective: Cognitive Biases in Topic Interpretations
• The influence of incidental emotion on novice Pilots’ approach Decision-Making under uncertainty in the context of climate Challenges
• Approximating Bayes? On the role of approximations in Bayesian cognitive science
• Implications of Human Irrationality for Reinforcement Learning
• Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception
• The rhythm of learning: Theta oscillations as an index of active learning in infancy
• Curiosity and the dynamics of optimal exploration
• Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems
• The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective
• Use of theories of human action in recent conservation research

Discussions

• It is published with Falk & Lieder's "Resource-rational analysis", out in Behavioral and Brain Sciences this month: (https://doi.org/10.1017/S0140525X1900061X). I enjoyed reading their piece and think [bsky, 0 points, 1 comments]

Related

• Resource-rationality beyond individual minds: the case of interactive language use
• How Intractability Spans the Cognitive and Evolutionary Levels of Explanation
• Human Decision-Making under Limited Time
• Rationality in current era -- A recent survey
• Unifying Decision-Making: a Review on Evolutionary Theories on Rationality and Cognitive Biases