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Poster B29 in Poster Session B - Thursday, August 8, 2024, 1:30 – 3:30 pm, Johnson Ice Rink
N3 sleep ameliorates anxiety-induced amplified reward learning in dynamically changing environments
Rakshita Deshmukh1 (), Arjun Ramakrishnan1; 1IIT Kanpur
Coping with dynamic environments has become a modern-day necessity. To do so, one needs to continually adjust their learning rates based on environmental uncertainties due to volatility and stochasticity. Uncertainty assessments are also known to depend on one’s internal states such as anxiety and sleep levels. Anxiety incurs misestimation of volatility and leads to over-learning from negative feedback. Whether this is mediated by stochasticity is not well understood. Anxiety is also related to sleep wherein absence of sleep worsens it, while presence of N3 sleep reduces morning levels of it. Whether this benefit translates to learning rate deficits is also currently unknown. To this end, we used a novel probabilistic reversal learning task in which we simultaneously manipulated stochasticity and volatility. Using computational models, we found that high trait anxious (HTA) individuals misestimate stochasticity for volatility by suboptimally increasing their reward learning rates in stable but highly stochastic environments. In the second experiment, using EEG, we show that N3 sleep alleviates these impairments by downregulating and optimizing reward learning rates. In summary, we observed amplified reward learning among anxious individuals and then we show that N3 sleep helps in regulating it. This highlights N3 sleep’s potential as a non-pharmacological and non-invasive approach for alleviating anxiety-related learning impairments.
Keywords: flexibility reversal learning NREM sleep trait anxiety