ArticleThe Behavioral and brain sciences2018
Optimality is critical when it comes to testing computation-level hypotheses.
Article in The Behavioral and brain sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Between repulsion and attraction in serial biases: Replication of Chen and Bae (2024).Journal of vision · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We disagree with Rahnev & Denison (R&D) that optimality should be abandoned altogether. Rather, we argue that adopting a normative approach enables researchers to test hypotheses about the brain's computational goals, avoids just-so explanations, and offers insights into function that are simply inaccessible to the alternatives proposed by R&D.
Indexed as
Identifiers
30767804What 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.