Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comment
. 2020 Nov 13:14:561770.
doi: 10.3389/fnhum.2020.561770. eCollection 2020.

Commentary: Altered learning under uncertainty in unmedicated mood and anxiety disorders

Motofumi Sumiya  1   2 , Kentaro Katahira  1
Affiliations
Comment

Commentary: Altered learning under uncertainty in unmedicated mood and anxiety disorders

Motofumi Sumiya et al. Front Hum Neurosci. .
No abstract available

Keywords: computational modeling; computational psychiatry; hierarchical Bayesian estimation; reinforcement learning (RL); shrinkage.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Estimated parameters of the winning (‘bandit4arm_lapse_decay’) model. (A) Hierarchical Bayesian parameter estimation, (B) maximum likelihood estimation. alphaN, Punishment learning rate; ANX, anxiety/symptomatic/experimental group; HC, healthy control group. Lapse parameter, noisiness of decision-making; decay rate, the propensity to forget the previous values of unchosen options. We found larger distributions for the punishment learning rate in the anxiety group, which is comparable to those in the healthy group (B). These results indicate that a strong shrinkage occurred in the estimates of the punishment learning rate in the anxiety group in this data set (A).
See this image and copyright information in PMC

Comment on

References

    1. Ahn W., Haines N., Zhang L. (2017). Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Comput. Psychiatry 1, 24–57. 10.1162/cpsy_a_00002 - DOI - PMC - PubMed
    1. Ahn W. Y., Vasilev G., Lee S. H., Busemeyer J. R., Kruschke J. K., Bechara A., et al. . (2014). Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users. Front. Psychol. 5:849. 10.3389/fpsyg.2014.00849 - DOI - PMC - PubMed
    1. Aylward J., Valton V., Ahn W. Y., Bond R. L., Dayan P., Roiser J. P., et al. . (2019). Altered learning under uncertainty in unmedicated mood and anxiety disorders. Nat. Hum. Behav. 3, 1116–1123. 10.1038/s41562-019-0628-0 - DOI - PMC - PubMed
    1. Boehm U., Marsman M., Matzke D., Wagenmakers E. J. (2018). On the importance of avoiding shortcuts in applying cognitive models to hierarchical data. Behav. Res. Methods 50, 1614–1631. 10.3758/s13428-018-1054-3 - DOI - PMC - PubMed
    1. Efron B., Morris C. (1977). Stein's paradox in statistics. Sci. Am. 236, 119–127.

LinkOut - more resources