Literature DB >> 35239645

Correction: Bayesian regression explains how human participants handle parameter uncertainty.

Jannes Jegminat, Maya A Jastrzębowska, Matthew V Pachai, Michael H Herzog, Jean-Pascal Pfister.   

Abstract

[This corrects the article DOI: 10.1371/journal.pcbi.1007886.].

Entities:  

Year:  2022        PMID: 35239645      PMCID: PMC8893341          DOI: 10.1371/journal.pcbi.1009932

Source DB:  PubMed          Journal:  PLoS Comput Biol        ISSN: 1553-734X            Impact factor:   4.475


Fig 5 is incorrect. The authors have provided a corrected version here.
Fig 5

Median of each of the bimodal response distribution variance components across all participants and stimuli.

(A) Predicted coefficient of positive mode as a function of the empirical coefficient (across all noise levels). ML-R behaves identically to MAP-R. Thus, the MAP-R curve represents both models. The shaded area shows the 40% and 60% quantiles. (B) Prefactor of bimodal contribution as a function of generative noise. Data jittered for visibility. (C) Unimodal contribution to the variance. Empirical variance computed on mode with majority of responses. (D) Mean dispersion. Only trials with bimodal responses included. As the stimulus becomes more noisy, human responses and B-R variants conform to the prior.

Median of each of the bimodal response distribution variance components across all participants and stimuli.

(A) Predicted coefficient of positive mode as a function of the empirical coefficient (across all noise levels). ML-R behaves identically to MAP-R. Thus, the MAP-R curve represents both models. The shaded area shows the 40% and 60% quantiles. (B) Prefactor of bimodal contribution as a function of generative noise. Data jittered for visibility. (C) Unimodal contribution to the variance. Empirical variance computed on mode with majority of responses. (D) Mean dispersion. Only trials with bimodal responses included. As the stimulus becomes more noisy, human responses and B-R variants conform to the prior.
  1 in total

1.  Bayesian regression explains how human participants handle parameter uncertainty.

Authors:  Jannes Jegminat; Maya A Jastrzębowska; Matthew V Pachai; Michael H Herzog; Jean-Pascal Pfister
Journal:  PLoS Comput Biol       Date:  2020-05-18       Impact factor: 4.475

  1 in total

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