ArticleJournal of mathematical psychology2019
Optimizing sequential decisions in the drift-diffusion model.
Article in Journal of mathematical psychology, 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
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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
11 citing papers in PubMed.
- Quantifying uncertainty in drift diffusion models of decision making under temporal dependence and parameter variability.bioRxiv : the preprint server for biology · 2026Article
- Human decision-makers terminate evidence accumulation using flexible decision rules.bioRxiv : the preprint server for biology · 2026Article
- Altered System-Level Integration Between Working Memory and Gaze Dynamics in Children Treated for Posterior Fossa Tumors.Psychology in Russia : state of the art · 2026Article
- Trial-history biases in evidence accumulation can give rise to apparent lapses in decision-making.Nature communications · 2024Article
- Trial-history biases in evidence accumulation can give rise to apparent lapses.bioRxiv : the preprint server for biology · 2023Article
- Informational Entropy Threshold as a Physical Mechanism for Explaining Tree-like Decision Making in Humans.Entropy (Basel, Switzerland) · 2022Article
- Suboptimal human inference can invert the bias-variance trade-off for decisions with asymmetric evidence.PLoS computational biology · 2022Article
- Is value-based choice repetition susceptible to medial frontal transcranial direct current stimulation (tDCS)? A preregistered study.Cognitive, affective & behavioral neuroscience · 2021Article
- Article
- Optimal models of decision-making in dynamic environments.Current opinion in neurobiology · 2019Review
- Drift-diffusion models for multiple-alternative forced-choice decision making.Journal of mathematical neuroscience · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
To make decisions organisms often accumulate information across multiple timescales. However, most experimental and modeling studies of decision-making focus on sequences of independent trials. On the other hand, natural environments are characterized by long temporal correlations, and evidence used to make a present choice is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, thus providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.
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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.