ArticleAttention, perception & psychophysics2020
A theoretical analysis of the reward rate optimality of collapsing decision criteria.
Article in Attention, perception & psychophysics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The perils of omitting omissions when modeling evidence accumulation.PLoS computational biology · 2026Article
- Support for the Time-Varying Drift Rate Model of Perceptual Discrimination in Dynamic and Static Noise Using Bayesian Model-Fitting Methodology.Entropy (Basel, Switzerland) · 2024Article
- Article
- Rational inference strategies and the genesis of polarization and extremism.Scientific reports · 2022Article
- Core body temperature speeds up temporal processing and choice behavior under deadlines.Scientific reports · 2019Article
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Authors and funding
5 authors.
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Abstract
A standard assumption of most sequential sampling models is that decision-makers rely on a decision criterion that remains constant throughout the decision process. However, several authors have recently suggested that, in order to maximize reward rates in dynamic environments, decision-makers need to rely on a decision criterion that changes over the course of the decision process. We used dynamic programming and simulation methods to quantify the reward rates obtained by constant and dynamic decision criteria in different environments. We further investigated what influence a decision-maker's uncertainty about the stochastic structure of the environment has on reward rates. Our results show that in most dynamic environments, both types of decision criteria yield similar reward rates, across different levels of uncertainty. This suggests that a static decision criterion might provide a robust default setting.
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