The relationship between anxious traits and learning about changes in stochasticity and volatility

TL;DR

Anxious individuals respond differently to changes in environmental uncertainty, such as noise and volatility, which affects how they learn from rewards and punishments.

Contribution

The study introduces a novel experimental design to separately manipulate and assess the effects of volatility and stochasticity on learning in anxious individuals.

Findings

High volatility increases positive and negative learning rates under low noise conditions.

Anxious traits interact with volatility and noise to influence win-stay and lose-shift behaviors differently.

Results suggest that both noise and volatility should be considered when studying learning under uncertainty.

Abstract

Anxiety is known to alter learning in uncertain environments. Experimental paradigms and computational models addressing these differences have mainly assessed the impact of volatility, with more highly anxious individuals showing a reduced adaptation of learning rate in volatile compared to stable environments. Previous research has not, however, independently assessed the impact of both changes in volatility, i.e., reversals in reward contingency, and changes in stochasticity (noise) in the same individuals. Here, in an original online study (Experiment 1; N = 80) and a pre-registered replication attempt (Experiment 2; N = 160), we use a simple probabilistic reversal learning paradigm to independently manipulate the level of volatility and noise at the experimental level in a fully orthogonal design. We replicate previous studies showing general increases, irrespective of anxiety levels, in positive learning rate (Experiment 1) and negative learning rate (Experiments 1 and 2) for high compared to low volatility, but here only in the context of low noise. Across both experiments, there was an interaction between volatility and noise on behaviour, with more win-stay responses for high compared to low volatility under low noise, but similar or fewer win-stay responses for the same comparison under high noise. The impact of anxious traits presented differently across experiments; in Experiment 1, increases in lose-shift responses in high versus low noise conditions scaled with level of anxious traits, whereas in Experiment 2, there was a full interaction between volatility, noise and anxious traits on win-stay behaviour. These anxiety-related lose-shift or win-stay differences were reflected in their corresponding negative and positive reinforcement learning rate parameters, respectively. Experiment 2 represents a more robust set of results with a larger sample size, balanced gender representation, and extended block order balancing. These findings suggest that changes in both sources of uncertainty - stochasticity and volatility - should be carefully considered when investigating learning and how learning is shaped by anxiety.

Adapting to changes in our environment is a daily endeavour. To do so, humans and animals alike make use of feedback to guide future actions. Uncertainty in the environment can arise from the probabilistic structure inherent in nature (noise) or from an actual change or switch in what is rewarded (volatility). We propose that anxious people respond differently to changes in environmental noise and volatility. Through a combination of computational modelling and behavioural analyses, we demonstrate changes in behaviour that depend on different combinations of noise and volatility, in terms of how much people stick with a previous choice if it was rewarded, or shift to a different option if unrewarded. We also show that anxiety levels play an important role in how people learn under these changeable conditions. Given that anxiety is a common experience for many people in the fast-paced, changing environments of modern society, our modelling approach provides novel insights into the underlying mechanisms at play in the face of different sources of uncertainty, and may help inform interventions aimed at supporting anxious individuals.

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