Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments

Wang, Zhihao; Nan, Tian; Goerlich, Katharina S.; Li, Yiman; Alemán, André; Luo, Yuejia; Xu, Pengfei

doi:10.1101/2022.06.25.497438

Cited by 3 publications

(3 citation statements)

References 83 publications

(185 reference statements)

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“…Our study also revealed a strong positive association between the self-connection of the dACC and high fear-affect in males. The dACC is well-known for its involvement in fear-related emotions (Milad et al, 2007; Sehlmeyer et al, 2011; Wang et al, 2023). It has previously been proposed that the dACC plays a role in the manifestation of fear in humans, potentially paving the way for the development of new treatments for anxiety in the future (Milad et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Sex-specific brain effective connectivity patterns associated with negative emotions

Sultana,

Ijaz,

Khan

et al. 2024

Preprint

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Sex differences in effective brain connectivity in emotional intelligence, emotional regulation, and stimuli-induced negative emotions have been highlighted in previous research. However, to our knowledge, no research has yet investigated the sex-specific effective connectivity related to negative emotions in healthy population during resting-state. The goal of this study is to find the association between sex-specific resting-state effective brain connectivity and basic negative emotions. For this, we have employed the NIH emotion battery of the three self-reported, basic negative emotions: anger-affect, fear-affect, and sadness which we divided into high, moderate, and low emotion scores in each. The dataset comprises 1079 subjects (584 females) from HCP Young Adults. We selected large-scale resting-state brain networks important for emotional processing namely default mode, executive, and salience networks. We employed subject-level analysis using spectral dynamic causal modelling and group-level association analyses using parametric empirical Bayes. We report association of the self-connection of left hippocampus in females in high anger-affect, fear-affect, and sadness, whereas in males we found involvement of dorsal anterior cingulate cortex (dACC) in all three negative emotions - association of right amygdala to dACC in high anger-affect, association of the self-connection of dACC in high fear-affect, and association of dACC to left hippocampus in high sadness. Our findings primarily revealed the effective brain connectivity that is related to the higher levels of negative emotions that may lead to psychiatric disorders if not regulated. Sex-specific therapies and interventions that target psychopathology can be more beneficial when informed by the sex-specific resting-state effective connectivity.

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Section: Discussionmentioning

confidence: 99%

Sex-specific brain effective connectivity patterns associated with negative emotions

Sultana,

Ijaz,

Khan

et al. 2024

Preprint

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“…To best quantify how different events impacted participants' momentary mood during the gambling task, we conducted a stage-wise model construction procedure (36). That is, we added or removed each component to the model progressively, based on the best model from the previous stage.…”

Section: Mood Computational Modelsmentioning

confidence: 99%

Mood computational mechanisms underlying increased risk behavior in suicidal patients

Wang,

Nan,

et al. 2023

Preprint

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Suicidal thoughts and behaviors (STB) are one of leading causes of death worldwide. Although literature has consistently reported increased risk behavior in patients with STB and has proposed mood problems as the core of STB, cognitive and affective computational mechanisms underlying increased risky behavior remain unclear. Here, we asked 83 adolescent inpatients with affective disorders, where 58 patients with STB (S+) and 25 without STB (S-), and 118 gender/age-matched healthy control (HC) to make decisions between certain vs. gamble option with momentary mood ratings. After identifying increased risk behavior in S+than S-and HC, we used approach-avoidance prospect theory model and momentary mood model to quantify patients’ behavior and mood. Choice data analysis showed that heighted approach motivation mediated an increase in gambling choices with STB severity, suggesting impaired Pavlovian approach system in STB resulting in higher gambling in gains. Mood model showed lower mood sensitivity to certain reward in S+than S-and HC, which further explained increased gambling decisions in STB, offering a mood computational account for increased risk behavior in STB. These results remain significant after controlling for demographic and clinical variables and medication factors. Overall, our findings revealed cognitive and affective computational mechanisms underlying increased risk behavior in STB. This work has important implications for prevention and intervention of suicide, especially for clinical populations.

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“…These expectations subsequently guide their successive behaviours. Previous researchers have combined reinforcement learning with studies in other fields (Li et al, 2023;Liu et al, 2023;Hackel et al, 2020;Held et al, 2024;Lockwood & Klein-Flügge, 2021;Wang et al, 2023;Zhang et al, 2023). For instance, Wang et al applied reinforcement learning to emotion processing and elaborated on the cognitive neuroscientific mechanisms of how fear interferes with adaptation to environmental volatility in dynamic environments at the computational level.…”

Section: Introductionmentioning

confidence: 99%

Expectation modulates learning emotional words: Evidence from a hierarchical Bayesian model

Zhang,

Li,

Zhou

et al. 2024

Preprint

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In language acquisition, individuals learn the emotional value of words through external feedback. Previous studies have used emotional words as experimental materials to explore the cognitive mechanism underlying emotional language processing, but have failed to recognize that languages are acquired in a changing environment. To this end, this study aims to combine reinforcement learning with emotional word learning, using a probabilistic reversal learning task to explore how individuals acquire the valence of emotional words in a dynamically changing environment. Our computational modeling on both behavioral and event-related potential (ERP) data revealed that individuals' expectations can modulate the learning speed and temporal processing of emotional words, demonstrating a clear negative bias. Specifically, as the expected value increases, individuals respond faster and exhibit higher amplitudes for negative emotional words. These findings shed light on the neural mechanisms of emotional word learning in a volatile environment, highlighting the crucial role of expectations in this process and the preference for processing negative information.

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Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environments

Cited by 3 publications

References 83 publications

Sex-specific brain effective connectivity patterns associated with negative emotions

Sex-specific brain effective connectivity patterns associated with negative emotions

Mood computational mechanisms underlying increased risk behavior in suicidal patients

Expectation modulates learning emotional words: Evidence from a hierarchical Bayesian model

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