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Reply to: Divisive normalization does influence decisions with multiple alternatives

The Original Article was published on 14 September 2020

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Fig. 1: Parameter estimates, model fit and predictions.

Data availability

The data analysed for this work are publicly available on the Open Science Framework (

Code availability

The custom code for the analyses reported here is publicly available on the Open Science Framework (


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Authors and Affiliations



S.G. analysed the data and wrote the manuscript. N.K. and C.L.V. revised the manuscript.

Corresponding author

Correspondence to Sebastian Gluth.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Parameter estimates based on hierarchical Bayesian modeling.

Parameter estimates for ω (based on hierarchical Bayesian modeling) when this parameter is forced to be ≥0 (as assumed by DN; blue) or not (red). Note the shrinkage of individual estimates as compared to the estimates based on maximum likelihood presented in Fig. 1a.

Extended Data Fig. 2 Modeling results of the eye-tracking experiment dataset.

Note that the very large 95% CI for participants with ω > 0 in c is due to the fact that there were only very few participants with a positive estimate of this parameter.

Extended Data Fig. 3 Model comparison of the eye-tracking experiment dataset.

Definitions of the abbreviations are provided in Table 1.

Supplementary information

Supplementary Information

Supplementary methods and references.

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Gluth, S., Kern, N. & Vitali, C.L. Reply to: Divisive normalization does influence decisions with multiple alternatives. Nat Hum Behav 4, 1121–1123 (2020).

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