State anxiety biases estimates of uncertainty and impairs reward learning in volatile environments

Hein, Thomas; Fockert, Jan de; Ruiz, María Herrojo

doi:10.1016/j.neuroimage.2020.117424

Cited by 71 publications

(215 citation statements)

References 106 publications

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“…This meant, as detailed in Hein et al (2021), that StA participants were told just before TB1 that they would need to present a piece of abstract art for 5 minutes to a panel of academic experts after completing the reward-learning task, with 3 minutes preparation time. By contrast, the Cont group were informed that they would need to give a mental description of the piece of abstract artwork for the same time privately (rather than to a panel of experts, see Hein et al, 2021). Importantly, the state anxiety manipulation was then revoked in the StA group directly after completing the second reward-learning block (TB2) and before the second resting state block (R2).…”

Section: Manipulation and Assessment Of State Anxietymentioning

confidence: 99%

“…The behavioural data in our paradigm were analysed in Hein et al (2021) using the Hierarchical Gaussian Filter (HGF, Mathys et al, 2011Mathys et al, , 2014. This model describes hierarchically structured learning across various levels, corresponding to hidden states of the environment x1, x2,..., xn and defined as coupled Gaussian random walks.…”

Section: Behavioural Analysis and Modellingmentioning

confidence: 99%

“…To model learning about the tendency towards reward for blue/orange stimuli and the rate of change in that tendency (volatility), we used three alternative HGF models, and two reinforcement learning models (Hein et al, 2021). The input to the models was the series of 400 outcomes and the participant's responses.…”

Section: Behavioural Analysis and Modellingmentioning

confidence: 99%

“…See Mathys et al (2011Mathys et al ( , 2014 for detailed mathematical expressions for the HGF. Details on the free parameters of the HGF model that were estimated, including prior values, can be found in Hein et al (2021), and are briefly mentioned in Figure 1A. Hein et al (2021).…”

Section: Behavioural Analysis and Modellingmentioning

confidence: 99%

“…Details on the free parameters of the HGF model that were estimated, including prior values, can be found in Hein et al (2021), and are briefly mentioned in Figure 1A. Hein et al (2021). The free perceptual model parameters ω2, ω3 and the response parameter were estimated by fitting the HGF to observed inputs (ui (k) ) and individual responses (y (k) ).…”

Section: Behavioural Analysis and Modellingmentioning

confidence: 99%

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State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward learning

Hein

2021

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Anxiety influences how the brain estimates and responds to uncertainty. These behavioural effects have been described within predictive coding and Bayesian inference frameworks, yet the associated neural correlates remain unclear. Recent work suggests that predictions in generative models of perception are represented in alpha-beta oscillations (8-30 Hz), while updates to predictions are driven by prediction errors weighted by precision (inverse variance; pwPE) and encoded in gamma oscillations (>30 Hz). We tested whether state anxiety alters the neural oscillatory activity associated with predictions and pwPE during learning. Healthy human participants performed a probabilistic reward-learning task in a volatile environment. In our previous work, we described learning behaviour in this task using a hierarchical Bayesian model, revealing more precise (biased) beliefs about the reward tendency in state anxiety, consistent with reduced learning in this group. The model provided trajectories of predictions and pwPEs for the current study, allowing us to assess their parametric effects on the time-frequency representations of EEG data. Using convolution modelling for oscillatory responses, we found that, relative to a control group, state anxiety increased alpha-beta activity in frontal and sensorimotor regions during processing pwPE, and in fronto-parietal regions during encoding predictions. No effects of state anxiety on gamma modulation were found. Our findings expand prior evidence on the oscillatory representations of predictions and pwPEs into the reward-learning domain. The results suggest that state anxiety modulates oscillatory correlates of pwPE and predictions in generative models, providing insights into a potential mechanism explaining biased belief updating and poorer reward learning.

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Section: Manipulation and Assessment Of State Anxietymentioning

confidence: 99%

Section: Behavioural Analysis and Modellingmentioning

confidence: 99%

Section: Behavioural Analysis and Modellingmentioning

confidence: 99%

Section: Behavioural Analysis and Modellingmentioning

confidence: 99%

Section: Behavioural Analysis and Modellingmentioning

confidence: 99%

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State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward learning

Hein

2021

Preprint

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Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

Vidaurre

Nikulin²,

Ruiz³

2022

Neural Comput & Applic

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Anxiety affects approximately 5–10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4–7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8–12 Hz) or beta (13–30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.

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Cool and hot executive function problems in young children: linking self-regulation processes to emerging clinical symptoms

Anning,

Langley,

Hobson

et al. 2024

Eur Child Adolesc Psychiatry

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Self-regulation (SR) difficulties are implicated in a wide range of disorders which develop in childhood, including attention deficit hyperactivity disorder (ADHD), oppositional defiance disorder (ODD), anxiety and depression. However, the integration of the existing research evidence is challenging because of varying terminology and the wide range of tasks used, as well as the heterogeneity and comorbidity within and across diagnostic categories. The current study used the Research Domain Criteria (RDoC) framework to guide the examination of different SR processes in young children showing a wide range of symptomatology. Children (aged 4–8) referred by teachers for moderate-to-high conduct, hyperactivity and/or emotional problems at school (assessed using the Strengths and Difficulties Questionnaire (SDQ) subscales; n = 212), and children in SDQ typical ranges (n = 30) completed computerised cognitive control and decision-making tasks. Parents completed questionnaires to assess ADHD, ODD, anxiety and depression symptoms (n = 191). Compared to children with no teacher-reported difficulties, those with moderate-to-high problems showed poorer visuomotor control and decision-making. A factor analysis revealed that task variables adhered to RDoC dimensions and predicted variance in specific disorders: difficulties in cognitive control predicted ADHD symptoms, low reward-seeking was associated with depression and high reward-seeking was associated with ODD. This study highlights how the assessment of cognitive processes positioned within the RDoC framework can inform our understanding of disorder-specific and transdiagnostic difficulties in SR which are associated with diverse clinical symptoms in children.

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State anxiety biases estimates of uncertainty and impairs reward learning in volatile environments

Cited by 71 publications

References 106 publications

State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward learning

State anxiety alters the neural oscillatory correlates of predictions and prediction errors during reward learning

Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety

Cool and hot executive function problems in young children: linking self-regulation processes to emerging clinical symptoms

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