ArticleeLife2019
Computational mechanisms of curiosity and goal-directed exploration.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 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.
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Who cites it
83 citing papers in PubMed.
- Active inference and artificial reasoning.Nature communications · 2026Article
- Conative inference as a mechanism of allostatic resilience: wanting to be and being to want.Cognitive, affective & behavioral neuroscience · 2026Review
- Autonomy in learning: Predictability modulates the beneficial effect of choice on memory.Memory & cognition · 2026Article
- An effort recalibration framework for digital media use and cognition.Nature human behaviour · 2026Review
- Maintaining and regaining episodic memory in Alzheimer disease: a circuit-based perspective.Nature reviews. Neurology · 2026Review
- Distinct patterns of social contagion under risk and ambiguity.Communications psychology · 2026Article
- Article
- Cortical Microstructural Variations Correlate With Individual Differences in Gamified Exploration-Exploitation Behaviours.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Embodied neuroaesthetics and the psychotherapeutic relational field.Frontiers in psychology · 2026Review
- Motor and operative intentionality in computational phenomenology: from shallow to deep active inference models.Neuroscience of consciousness · 2026Article
- Arts engagement, active inference, and allostatic regulation: a neurobiological framework for understanding the health effects of aesthetic experience.Frontiers in computational neuroscience · 2026Review
- An Integrative Appraisal Model of Epistemic Curiosity.Affective science · 2025Review
- Born to act: Deferred action and desire as active inference.Cognitive, affective & behavioral neuroscience · 2025Review
- Sophisticated Learning: A novel algorithm for active learning during model-based planning.ArXiv · 2025Article
- Pathfinding: a neurodynamical account of intuition.Communications biology · 2025Review
- SPM-30 years and beyond.Cerebral cortex (New York, N.Y. : 1991) · 2025Review
- Collective predictive coding as model of science: formalizing scientific activities towards generative science.Royal Society open science · 2025Article
- Foraging animals use dynamic Bayesian updating to model meta-uncertainty in environment representations.PLoS computational biology · 2025Article
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- Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. 'Hidden state' exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, 'model parameter' exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of 'Bayes-optimal' behaviour. Our findings provide a computational framework for understanding how distinct levels of uncertainty systematically affect the exploration-exploitation trade-off in decision-making.
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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.