Bayesian inference of population prevalence

By Robin A. A. Ince, Angus T. Paton, J. Kay, Philippe G. Schyns. 2020

Subfields: Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. B. Morillon
2. Agnès Trébuchon
3. D. Schön
4. Manuel R Mercier
5. Noémie te Rietmolen

Citations in the corpus

• Speech and music recruit frequency-specific distributed and overlapping cortical networks (2024, background): “…to two more basic types of auditory stimuli: 1) 30-ms–long pure tones, presented binaurally at 500 Hz or 1 kHz (with a linear rise and fall time of 0.3 ms) 110 times each, with an ISI of 1,030 (±200) ms; and 2) /ba/ or /pa/ syllables, pronounced by a French female speaker and presented…”
• Electrophysiological decoding captures the temporal trajectory of face categorization in infants (2024, unknown): “We estimated the population prevalence of above-chance decoding with neural networks using Bayesian inference on population prevalence (Ince et al., 2021, 2022).”