The study conducted by Bishop and Gagne (2018) focused on the topic of depression and anxiety and the difficulties they cause in the decision-making process. The authors reviewed the pieces of evidence that show which of the computations that support decision-making are altered in cases of anxiety and depression. The authors were expecting to find consistent patterns in previous studies and statistics that could be utilized in determining which of the computations that support decision making are altered in anxiety and depression. However, the study showed that anxiety and depression could be linked to ‘increased estimates of future probability’ (Bishop & Gagne, 2018, p. 384). The authors also admitted that depression could potentially be linked to ‘lower estimates of future probability’ (Bishop & Gagne, 2018, p. 384). The authors acknowledge that the study was limited and lacked aspects of brain mechanisms and processes. The study’s findings highlighted areas in which the knowledge is lacking and provides a valuable source of literature review with evidence of aversive outcomes in cases of anxiety and depression.
On the other hand, an article by Zhang and Ru (2018), provides detailed information on the brain mechanisms in sequential decision-making and how it is affected by anxiety. In the study, the authors investigated neural processes behind behavioral preferences and made recorded each step of the decision-making with an electroencephalograph (EEG). The authors were expecting to find evidence for the role of emotions in decision-making. The study’s findings showed that the tendency to gamble in the decision-making process could be related to the individual’s level of anxiety (Zhang & Ru, 2018). The technical limitations of EEG limited the study, and the authors admit that the reliability of their conclusions could be affected by the low accuracy of EEG. The authors also suggest that further studies could use an alternative task design. The study’s findings provide a valuable perspective on sequential decision-making, which is frequently overlooked in the literature, showing that anxious people tend to make impulsive decisions more.
In a similar study, Soshi et al. (2019) used the Iowa Gambling Test (IGT) to investigate how anxiety influences decision-making. However, in their study, the decision-making performance was evaluated in conditions that featured temporal pressures. The authors were expecting to find information that could be used to ‘successfully predict decision-making behaviors’ (Soshi et al., 2019, p.1). Similar to Zhang and Ru’s findings, the study conducted by Soshi et al. (2019) showed that higher anxiety trait levels predicted riskier choices. The article states that in global decision-making, there were more risky choices in conditions without temporal pressure, but in local decision-making, anxiety predicted risk-taking in forced-paced conditions (Soshi et al., 2019). As for the study’s limitations, the authors point out that the time frame for assessing the state of anxiety and IGT might have been too short, which possibly influenced the study results. However, the study’s findings provide a decent amount of IGT data and different perspectives on the global decision-making aspect and local decision-making.
As cases of anxiety are more frequent in the population of people affected by chronic kidney disease, the article by Berezza et al. (2018) provides a perspective from pre-dialysis CKD patients. The study features analysis results from stage 4-5 CKD patients and compares the analysis based on anxiety, depression, and stress levels. The authors were expecting to find whether anxiety, stress, and depression impact the choice of dialysis modality. The study’s limitation mainly refers to the small number of participants. Even though the study’s findings indicated that anxiety level is not related to the choice of dialysis therapy, the study also indicated that the level of anxiety significantly decreases after the initiation of dialysis (Berezza et al., 2018).
Overall, the gaps in the body of research indicate the lack of information on the actual decision-making steps. It is important to continue collecting the evidence to establish connections between levels of anxiety and computations that support decision-making. The study will contribute to the gaps in the body of research and analyze each step of the decision-making process and how it could potentially be influenced by anxiety.
Bezerra, C. I. L., Silva, B. C., & Elias, R. M. (2018). Decision-making process in the pre-dialysis CKD patients: do anxiety, stress and depression matter? BMC Nephrology, 19(1), 1- 6. doi:10.1186/s12882-018-0896-3
Bishop, S. J., & Gagne, C. (2018). Anxiety, depression, and decision making: A computational perspective. Annual Review of Neuroscience, 41(1), 371–388. doi:10.1146/annurev-neuro-080317-062007
Soshi, T., Nagamine, M., Fukuda, E., & Takeuchi, A. (2019). Pre-specified anxiety predicts future decision-making performances under different temporally constrained conditions. Frontiers in Psychology, 10, 1-17. doi:10.3389/fpsyg.2019.01544
Zhang, D., & Gu, R. (2018). Behavioral preference in sequential decision-making and its association with anxiety. Human Brain Mapping, 39(6), 2482–2499. doi:10.1002/hbm.24016