ReviewCurrent opinion in neurobiology2019
Optimal models of decision-making in dynamic environments.
Review in Current opinion in neurobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Computational and Neuronal Basis of Visual Confidence.Annual review of vision science · 2025Review
- Magnetic sense-dependent probabilistic decision-making in humans.Frontiers in neuroscience · 2025Article
- Cognitive mechanisms of learning in sequential decision-making under uncertainty: an experimental and theoretical approach.Frontiers in behavioral neuroscience · 2024Article
- Article
- Self-motion perception and sequential decision-making: where are we heading?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Environmental dynamics shape perceptual decision bias.PLoS computational biology · 2023Article
- Article
- Distinct cortico-striatal compartments drive competition between adaptive and automatized behavior.PloS one · 2023Article
- Article
- Stable choice coding in rat frontal orienting fields across model-predicted changes of mind.Nature communications · 2022Article
- A Competition of Critics in Human Decision-Making.Computational psychiatry (Cambridge, Mass.) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects' behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.
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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.