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. 2017 Sep 15;82(6):440-446.
doi: 10.1016/j.biopsych.2017.07.007. Epub 2017 Jul 21.

Computational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise

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Free PMC article

Computational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise

He Huang et al. Biol Psychiatry. .
Free PMC article

Abstract

Background: Differentiating whether an action leads to an outcome by chance or by an underlying statistical regularity that signals environmental change profoundly affects adaptive behavior. Previous studies have shown that anxious individuals may not appropriately differentiate between these situations. This investigation aims to precisely quantify the process deficit in anxious individuals and determine the degree to which these process dysfunctions are specific to anxiety.

Methods: One hundred twenty-two subjects recruited as part of an ongoing large clinical population study completed a change point detection task. Reinforcement learning models were used to explicate observed behavioral differences in low anxiety (Overall Anxiety Severity and Impairment Scale score ≤ 8) and high anxiety (Overall Anxiety Severity and Impairment Scale score ≥ 9) groups.

Results: High anxiety individuals used a suboptimal decision strategy characterized by a higher lose-shift rate. Computational models and simulations revealed that this difference was related to a higher base learning rate. These findings are better explained in a context-dependent reinforcement learning model.

Conclusions: Anxious subjects' exaggerated response to uncertainty leads to a suboptimal decision strategy that makes it difficult for these individuals to determine whether an action is associated with an outcome by chance or by some statistical regularity. These findings have important implications for developing new behavioral intervention strategies using learning models.

Keywords: Anxiety; Bayesian models; Change point detection; Computational psychiatry; Decision making; Reinforcement learning.

Conflict of interest statement

Financial Disclosures

Dr. Paulus has received royalties for an article about methamphetamine use disorder from UpToDate.

Dr. Thompson reports no biomedical financial interests or potential conflicts of interest.

Dr. Huang reports no biomedical financial interests or potential conflicts of interest.

Figures

Figure 1
Figure 1
Experimental Paradigm. A. Visual-search task with random-dot motion stimuli. Each trial starts with a central-cross followed by three location cues. Subjects were instructed to use keyboard to select where to search for the target. At any time, they could decide 1) continue searching by switching to another location if they decided the current patch was not the target, or 2) respond by pressing ‘Up’ arrow when they decided the current patch was the target. Feedback with points earned was shown in the end of the trial. B. Volatility condition. There are 3 blocks with 90 trials per block in the experiment. Reward rate at three locations is fixed at 1/13, 3/13, 9/13, but the associated location of the three probability changes within each block based on a Gaussian Distribution (mean = 30, std = 1).
Figure 2
Figure 2
A. Learning in both groups over 3 blocks. It shows both groups had increasing points earned within a run until the change points. B. Win-stay-Lose-shift rate in both groups. No significant difference of Win-stay rate was observed, while HA group had significantly higher lose-shift rate. C. Lose-shit rate over time in 3 blocks. It shows that HA group had increasing lose-shift rate over time while it was not observed in LA group.
Figure 3
Figure 3
A. Simulation of points earned conditioned on lose-shift rate the most likely rewarded location, generated from 500 simulated behavioral sequences with lose-shift rate ranges from 0 to 1 with an increment of 0.1, assuming the same reaction time per location searched. B. Lose-shift rate conditioned on reward probability. It indicates HA group had higher lose-shift rate than low-anxiety group in all choices with different reward-probabilities. C. Lose-shift rate at the most likely rewarded location in 3 blocks. It shows that HA group has increasing lose-shift rate over time while it was not observed in LA group.
Figure 4
Figure 4
A. Group difference in base learning rate from Vmax RL model in Block 3. B. Lose-shift rate as a function of base learning rate.

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