ArticlePsychonomic bulletin & review2015
Bayesian inference and "truth": a comment on Hoffman, Singh, and Prakash.
Article in Psychonomic bulletin & review, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Rejoinder: More Limitations of Bayesian Leave-One-Out Cross-Validation.Computational brain & behavior · 2019Article
- Rationality, perception, and the all-seeing eye.Psychonomic bulletin & review · 2017Review
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hoffman, Singh, and Prakash (in press) argue that veridicality is neither required nor achieved by the visual system, and propose a new framework in which the literal truth of perceptual inferences plays no role. In this brief comment, I concur with and advocate their basic position, though I go on to argue that Bayesian inference already embodies a similar epistemological stance.
Indexed as
Identifiers
26384987What 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.