ReviewTrends in cognitive sciences2024
Naturalistic reinforcement learning.
Review in Trends in cognitive sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Towards naturalistic social neuroscience: A multi-level framework integrating real-world phenotyping, neurobiology, and computational mechanisms.Cognitive, affective & behavioral neuroscience · 2026Review
- Domain-specific contexts promote model-based decision making for basketball players.Scientific reports · 2026Article
- Neighborhood disorder impacts cognitive processing during navigation on a novel virtual reality paradigm.Scientific reports · 2026Article
- AI-driven fungicide design: From target identification to field application.Plant communications · 2026Review
- Cognitive modeling of real-world behavior for understanding mental health.Trends in cognitive sciences · 2026Review
- Rethinking category-selectivity in human visual cortex.Cognitive neuroscience · 2026Review
- Review
- Transdiagnostic mental health symptom dimensions predict use of flexible model-based inference in complex environments.Translational psychiatry · 2026Article
- Prosocial decisions in naturalistic helping scenarios are predicted by cost-benefit tradeoffs and individual disposition.Communications psychology · 2025Article
- Signatures of reinforcement learning in natural behavior.Current directions in psychological science · 2025Article
- An Information-Theoretic Framework for Understanding Learning and Choice Under Uncertainty.Entropy (Basel, Switzerland) · 2025Article
- Time Spent Thinking in Online Chess Reflects the Value of Computation.Cognitive science · 2025Article
- Feature-based reward learning shapes human social learning strategies.Nature human behaviour · 2025Article
- Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety.PLoS computational biology · 2025Article
- Approach-avoidance conflict recruits lateral frontoparietal and cinguloinsular networks in a predator-prey game setting.Cognitive, affective & behavioral neuroscience · 2025Article
- Computational markers show specific deficits for dyslexia and ADHD in complex learning settings.NPJ science of learning · 2025Article
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Rewards transiently and automatically enhance sustained attention.Journal of experimental psychology. General · 2025Article
- Schemas, reinforcement learning and the medial prefrontal cortex.Nature reviews. Neuroscience · 2025Review
- Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments.Advances in neural information processing systems · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.