ArticlePLoS computational biology2018
Modeling sensory-motor decisions in natural behavior.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Social wayfinding in virtual reality: Navigational decisions and eye movements in a dynamic environment.Journal of vision · 2026Article
- Computational elements of natural vision.Journal of vision · 2025Review
- Visual search for reach targets in actionable space is influenced by movement costs imposed by obstacles.Journal of vision · 2023Article
- A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning.eLife · 2023Article
- CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning.Frontiers in neuroinformatics · 2023Article
- The Hierarchical Evolution in Human Vision Modeling.Topics in cognitive science · 2021Review
- Intuitive physical reasoning about objects' masses transfers to a visuomotor decision task consistent with Newtonian physics.PLoS computational biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior.
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What OpenQuestion holds
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.