Metacontrol of decision-making strategies in human aging
- Technische Universität Dresden, Germany
- Harvard University, United States
- Washington University in St. Louis, United States
- University College London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, United Kingdom
- Concordia University, Canada
Figures
Figure 1
Design of the sequential decision-making task.
(A) State transition structure of the task. (B) Trial structure. At the beginning of each trial, a stakes cue signaled the stakes condition of the current trial (low-stakes trial vs. high-stakes …
Figure 2 with 1 supplement
Analysis of complete sample.
(A) Reward per trial (baseline-corrected). Gray dots indicate values for individual participants and bars indicate group means with error bars representing standard error of the mean. (B) Degree of …
Figure 2—figure supplement 1
Degree of model-based control under the assumption of perfect transition learning.
Model-based weights are depicted for the complete sample of younger and older adults as a function of stakes condition and transition variability condition. Gray dots indicate values for individual …
Figure 3
Analysis of performance-matched subsample.
(A) Reward per trial (baseline-corrected) for participants in the performance-matched sample. Gray dots indicate values for individual participants and bars indicate group means. (B) Degree of …
Figure 4
Analysis of second-stage reaction times.
(A) Reaction times at the second stage during variable-transitions blocks for trials in which a change of the task transition structure was observed for the first time (revaluation trials) and all …
Appendix 1—figure 1
Structure of the task-switching task.
https://doi.org/10.7554/eLife.49154.011
Appendix 2—figure 1
Posterior predictive check.
Comparison of simulated vs. empirically observed values for baseline-corrected reward. The diagonal represents the identity line.
Tables
Table 1
Psychometric assessment of complete sample.
https://doi.org/10.7554/eLife.49154.007| Variable | N assessed (younger/older adults) | Mean (SD) younger adults | Mean (SD) older adults | Bayes Factor* |
|---|---|---|---|---|
| Working memory | 62/61 | 18.42 (3.80) | 15.95 (4.22) | 31.88 |
| Verbal intelligence | 60/60 | 69.07% (11.82) | 81.94% (5.71) | >100 |
| Processing speed (reaction times) | 61/60 | 2032.17 s (315.87) | 3319.93 s (516.42) | >100 |
| Processing speed (accuracy) | 61/60 | 94.10% (6.02) | 94.84% (5.48) | 0.24 |
| Need for cognition | 61/59 | 82.18 (13.25) | 81.44 (13.24) | 0.20 |
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*Bayes Factors quantify the evidence in favor of one hypothesis (here: non-equal means) as opposed to a competing hypothesis (here: equal means). Commonly, Bayes Factors between 3 and 10 are considered as representing moderate evidence for the hypothesis and Bayes Factor above 10 as representing strong evidence for the hypothesis. Conversely, Bayes Factors between 1/3 and 1/10 represent moderate evidence for the competing hypothesis and Bayes Factors below 1/10 represent strong evidence for the competing hypothesis (Lee and Wagenmakers, 2013). We calculated Bayes Factors with the BayesFactors package (Morey et al., 2015).
Additional files
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Supplementary file 1
Tables for model parameters, model comparison and statistical results.
- https://doi.org/10.7554/eLife.49154.008
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Transparent reporting form
- https://doi.org/10.7554/eLife.49154.009
