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Correction to: Do people exploit risk–reward structures to simplify information processing in risky choice?

Published online by Cambridge University Press:  17 January 2025

Christina Leuker Open the ORCID record for Christina Leuker [Opens in a new window] ,
Thorsten Pachur ,
Ralph Hertwig  and
Timothy J. Pleskac
Christina Leuker*
Affiliation:
Max Planck Institute for Human Development, Berlin, Germany
Thorsten Pachur
Affiliation:
Max Planck Institute for Human Development, Berlin, Germany
Ralph Hertwig
Affiliation:
Max Planck Institute for Human Development, Berlin, Germany
Timothy J. Pleskac
Affiliation:
Max Planck Institute for Human Development, Berlin, Germany University of Kansas Max Planck Institute for Human Development, Berlin, Germany
*
e-mail: leuker@mpib-berlin.mpg.de
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Abstract

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Information

Type
Correction
Information
Journal of the Economic Science Association , Volume 5 , Issue 1: Special Issue: Choice-Process Data in Experimental Economics , August 2019 , pp. 95 - 96
Creative Commons
Creative Common License - CCCreative Common License - BY
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
Copyright © The Author(s) 2019

Correction to: Journal of the Economic Science Association https://doi.org/10.1007/s40881-019-00068-y

In the original publication of the article, the author’s correction was missed in Table 1. The original article has been corrected and the correct Table 1 is given below.

Table 1

Overview of regression models for processing and choice. Reference group set for environment: “uncorrelated”. Models included a random effect for “participant.” Coefficients are the mean and the 95% credible intervals of the posterior distributions. Credible differences in bold

Regression

Condition (Within-participant)

Best

Fast

Choice: Higher EV (1)

(Intercept)

0.61 [0.43, 0.79]

0.14 [− 0.01, 0.29]

EV difference

0.13 [0.12, 0.14]

0.06 [0.05, 0.07]

Environment (Negative)

− 0.35 [− 0.60, − 0.11]

− 0.23 [− 0.42, − 0.03]

Environment (Positive)

− 0.30 [− 0.54, − 0.05]

− 0.18 [− 0.38, 0.01]

Processing: RTs (2)

(Intercept)

4.03 [3.50, 4.57]

1.01 [0.95, 1.06]

EV difference

− 0.02 [− 0.03, − 0.14]

0.00 [0.00, 0.00]

Environment (Negative)

− 1.10 [− 1.86, − 0.34]

− 0.04 [− 0.11, 0.03]

Environment (Positive)

− 0.63 [− 1.38, 0.13]

− 0.02 [− 0.10, 0.05]

Processing: AOIs (3)

(Intercept)

3.18 [2.94, 3.41]

2.09 [1.82, 2.36]

EV difference

0.00 [0.00, 0.00]

0.00 [0.00, 0.00]

Environment (Negative)

− 0.20 [− 0.53, 0.13]

− 0.03 [− 0.42, 0.36]

Environment (Positive)

0.07 [− 0.26, 0.39]

0.21 [− 0.18, 0.59]

Processing: Within-gamble transitions (4)

(Intercept)

1.74 [1.38, 2.10]

0.41 [0.31, 0.53]

EV difference

− 0.01 [− 0.02, − 0.01]

0.00 [0.00, 0.00]

Environment (Negative)

− 0.30 [− 0.79, 0.26]

− 0.05 [− 0.21, 0.10]

Environment (Positive)

− 0.09 [− 0.59, 0.41]

0.05 [− 0.11, 0.20]

Processing: Gaze to payoff (5)

(Intercept)

0.51 [0.47, 0.56]

0.57 [0.50, 0.63]

EV difference

0.00 [0.00, 0.00]

0.00 [0.00, 0.00]

Environment (Negative)

0.09 [0.02, 0.16]

0.10 [0.01, 0.19]

Environment (Positive)

0.09 [0.02, 0.15]

0.11 [0.02, 0.21]

Choice: Higher EV pred. from process data (6)

(Intercept)

0.41 [0.11, 0.72]

− 0.51 [− 0.78, − 0.24]

EV difference

0.13 [0.12, 0.15]

0.06 [0.05, 0.07]

Response time

− 0.03 [− 0.06, 0.00]

0.47 [0.25, 0.69]

AOIs inspected

0.13 [0.06, 0.21]

0.08 [0.00, 0.16]

Transitions (within)

0.01 [− 0.05, 0.06]

0.08 [− 0.04, 0.21]

Environment (Negative)

− 0.35 [− 0.60, − 0.11]

− 0.20 [− 0.36, − 0.03]

Environment (Positive)

− 0.30 [− 0.55, − 0.05]

− 0.14 [− 0.31, 0.03]

Footnotes

The original article can be found online at https://doi.org/10.1007/s40881-019-00068-y.

References

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Figure 0

Table 1 Overview of regression models for processing and choice. Reference group set for environment: “uncorrelated”. Models included a random effect for “participant.” Coefficients are the mean and the 95% credible intervals of the posterior distributions. Credible differences in bold