Neuroscience

Individuals with anxiety and depression use atypical decision strategies in an uncertain world

Zeming Fang
Meihua Zhao
Ting Xu
Yuhang Li
Hanbo Xie
Peng Quan
Haiyang Geng
Ru-Yuan Zhang author has email address

Shanghai Mental Health, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
School of Psychology, Shanghai Jiao Tong University, Shanghai, 200030, China.
School of Psychology, South China Normal University, Guangzhou, 510631, China.
The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China
Centre of Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China
Department of Psychology, University of Arizona, Tucson, Arizona, USA
Research Center for Quality of Life and Applied Psychology, Guangdong Medical University, Dongguan, China.
Tianqiao and Chrissy Chen Institute for Translational Research, Shanghai, 200040, China
Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200241, China.

https://doi.org/10.7554/eLife.93887.2

Open access
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Figures and data

Schematic diagram of the experimental task in Gagne et al. (2020).
A. In each trial, participants were presented with two stimuli associated with their potential feedback magnitude. They were instructed to choose one of the two stimuli to receive feedback, but only one stimulus would result in feedback. Participants were required to complete tasks across four experimental contexts.
B. Each run compromised 90 trials in the stable context and 90 trials in the volatile context. During the stable context, the true environmental probability remains unchanged, while in the volatile context, the probability flips every 20 trials.

Task performance comparison between healthy control participants and patients diagnosed with MDD and GAD. Significance symbols: *: p < 0.05; **: p < 0.01; ***: p < 0.001; n.s.: non-significant. Abbreviations: HC-healthy controls, PAT-patients.
A. Comparison of hit rates for healthy controls and patients in stable and volatile contexts. Error bars represent the standard deviation of the estimated mean across 86 participants.
B. Learning curves for healthy controls and patients throughout the learning process. The dash line represents the exemplar feedback probability sequence. For runs that do not follow this exemplar sequence (e.g., starting with volatile then moving to stable conditions), responses were converted to match the exemplar sequence. The learning curves for both groups were then generated by averaging these converted responses across participants within each group. For better visualization, these curves were then smoothed using a Gaussian kernel with a standard deviation of 2 trials. The blue arrows indicate the apparent deviation between the true feedback probability and the patients’ asymptotic performance.
C. Hit rate differences for healthy controls and patients, and its relationship with participants’ symptom severities. Error bars respectively represent the standard deviation of the estimated mean across 54 healthy controls and 32 patients.

Model’s parameters

Models’ quantitative and qualitative fit to human behavioral data. Significance symbols: *: p < 0.05; **: p < 0.01; ***: p < 0.001; n.s.: non-significant. Abbreviations: HC - healthy controls, PAT - patients.
A. Relative performance of models compared to the MOS6 model, as measured by Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Each dot represents a model’s fit for an individual participant, with error bars showing the standard deviation of the estimated mean across 86 participants.
B. Group-level Bayesian model selection as indicated by Protected Exceedance Probability (PXP).
C-E. Models’ predicted hit rate (C), hit rate differences (D), and learning curves (E) for healthy controls and patients, respectively. Error bars denote the standard deviation of the estimated mean across 54 healthy controls and 32 patients, respectively.

Parameter analyses of the MOS6 model and simulated behaviors for all three strategies. Significance symbol conventions are: *: p < 0.05; **: p < 0.01; ***: p < 0.001; n.s.: non-significant. Abbreviations: HC-healthy controls, PAT-patients.
A. The fitted weighting parameters and learning rate of the MOS6 model. The y-axis means averaged preference over different volatile contexts (volatile/stable) and feedback contexts (reward/aversive). w indicates the averaged weighting parameters for each participant group. Error bars denote the standard deviation of the estimated mean across 54 healthy controls and 32 patients, respectively.
B. Simulated hit rates for the three decision strategies. Error bars represent the standard deviation across 200 simulations. The 200 simulations were evenly divided between groups using parameters similar to the healthy control group and the patient group. The groups differed only in their strategy preference (differences in w_EU, w_MO, w_HA) while all other parameters remained constant. For more simulation details, refer to Method: Simulation details.
C. Averaged simulated learning curve for each strategy across 200 simulations, smoothed with a Gaussian kernel (standard deviation of 2 trials).
D. Simulated hit rate differences between volatile and stable for the three decision strategies. Error bars represent the standard deviation across 200 simulations.
E. Simulated learning curves for the healthy controls and patients, each averaged from 100 simulations within the group and smoothed with a Gaussian kernel (standard deviation of 2 trials).

Predict participants’ symptom severity (g score) using strategy preferences of the MOS6 model. Each dot represents one participant. The shaded areas reflect 95% confidence intervals of the regression prediction.

Reproduction of the two learning rate adaptation effects using the MOS6 model. Significance symbol conventions are: *: p < 0.05; **: p < 0.01; HC represents the healthy-control-like agent; PAT represents the patient-like agent.
A. Simulated learning curves for the healthy controls and patients generated by the MOS6 model, smoothed with a Gaussian kernel (standard deviation of 2 trials).
B. The fitted FLR22 learning rate parameters for the stable context and volatile context. Error bars stand for the standard deviation across 40 synthesized datasets.
C. Learning rate adaptations, calculated by log volatile learning rate - log stable learning rate, for the healthy control-like agent and for the patient-like agent. Error bars stand for the standard deviation across 20 synthesized datasets.

Parameter and model recovery analyses.
A. Parameter recovery for the MOS6 model.
B. Model recovery analysis, showing the performance of models as evaluated by averaged relative AIC and BIC, as well as PXP scores for synthesized data generated from each of the 8 models. Darker tiles indicate better fits to the synthesized data.

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