Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches

Letkiewicz, Allison M.; Kottler, Haley C.; Shankman, Stewart A.; Cochran, Amy L.

doi:10.1016/j.neubiorev.2023.105103

Cited by 16 publications

(9 citation statements)

References 99 publications

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“…This finding stresses the importance of paradigm-tailored models and model validation analyses when studying human behavior from a computational perspective (cf. Letkiewicz, Kottler, Shankman, & Cochran, 2023;Wilson & Collins, 2019). Third, due to the focus on introducing a comprehensive agent-based behavioral modeling framework in the context of avoidance learning, our work did not explore inter-individual differences in terms of, for instance, correlations between model-specific parameters (e.g., the learning rate parameter) and clinically relevant variables (e.g., state-and trait-anxiety).…”

Section: Discussionmentioning

confidence: 99%

Agent-Based Behavioral Modeling of Human Associative Learning in a Complex Approach-Avoidance Conflict Task

Usée,

Schmidt,

Pane-Farre

et al. 2024

Preprint

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Despite its key role in the development, maintenance, and treatment of anxiety disorders, the detailed mechanisms of human avoidance learning remain elusive. To contribute to the understanding of avoidance learning, we here report on a novel approach-avoidance conflict task that requires participants to learn associations between complex visual stimuli and combined appetitive and aversive stimuli while actively engaging with the experimental environment. Using an agent-based behavioral modeling approach, we implemented and validated an extensive set of control, heuristic, Rescorla-Wagner learning-based, and hybrid agents. We show that a Rescorla-Wagner learning-based agent with a prior expectation bias parameter best explains the learning behavior of 50 participants. As such, our work complements current research on the computational underpinnings of approach-avoidance behavior by showing paradigm and task instruction dependencies in approach-avoidance-relevant associative learning and contributes to the overall aim of achieving a more fine-grained understanding of the etiology of anxiety disorders.

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Section: Discussionmentioning

confidence: 99%

Agent-Based Behavioral Modeling of Human Associative Learning in a Complex Approach-Avoidance Conflict Task

Usée,

Schmidt,

Pane-Farre

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, infants who displayed little to no freezing at all early in life were at high risk to develop internalizing symptoms up until late adolescence 80,81 . We provide a formal framework that could be used to test transdiagnostically what might drive maladaptive avoidance patterns 6,8,82 . For example, it would be important to test whether avoidance in anxiety and depression are related to altered freezing patterns and if this is driven by changes in the computation of threat value, and reward-threat comparison.…”

Section: Discussionmentioning

confidence: 99%

The neurocomputational link between defensive cardiac states and approach-avoidance arbitration under threat

Klaassen,

de Voogd,

Hulsman

et al. 2023

Preprint

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Fearful avoidance is a hallmark of anxiety disorders and often comes at a cost. It reduces the probability of threat and of obtaining rewards. Theoretical models predict that threat-induced psychophysiological states, such as freezing-related bradycardia, facilitate arbitration of approach-avoidance decisions. We indeed show that bradycardia states are linked to the neurocomputational underpinnings of approach-avoidance arbitration when facing varying reward and threat magnitudes. Bradycardia was not only linked toaversive value-related avoidance decisions but also tovalue comparison(a stronger tendency to approach vs. avoid when expected reward outweighs potential threat). An amygdala-striatal-prefrontal neural circuit supported approach-avoidance arbitration under threat, with specific involvement of the amygdala and dorsal anterior cingulate (dACC) in integrating subjective outcome values and bradycardia states. These findings highlight the role of human freezing states in value-based decision making, relevant for optimal threat coping. They point to a specific role for amygdala/dACC in state-value integration under threat.

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“…Conversely, excessive avoidance can be maladaptive by limiting opportunities to engage in beneficial pursuits. Disorders featuring maladaptive avoidance are observed with both excessive and insufficient avoidance [1][2][3][4] . Thus, it is critical to understand the neurobiological mechanisms that mediate rapid threat avoidance learning.…”

Section: Introductionmentioning

confidence: 99%

Prefrontal dopamine activity is critical for rapid threat avoidance learning

Zeidler,

Gomez,

Gupta

et al. 2024

Preprint

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The medial prefrontal cortex (mPFC) resolves approach-avoidance conflicts and mediates associative processes required for learning to avoid threats. Dopamine (DA) projections from the ventral tegmental area (VTA) to the mPFC carry information about aversive outcomes that may inform prefrontal computations. However, the role of prefrontal DA in learning based on aversive outcomes remains poorly understood. Here, we used platform mediated avoidance (PMA) to study the role of mPFC DA in threat avoidance learning in mice. We show that activity within dopaminergic VTA terminals in the mPFC is required for signaled avoidance learning, but not for escape, conditioned fear, or to recall a previously learned avoidance strategy. Using a Rescorla-Wagner model fit to the behavior of individual mice, we discovered that VTA-mPFC activity drives experience-dependent transitions in the mode of learning. As mice learn PMA, changes in avoidance behavior were initially driven by experiences of shock and later by successful shock avoidance. Inhibition of VTA-mPFC terminals prevented this transition. Taken together, these data indicate that mPFC DA is necessary to rapidly form associations between predictive cues and actions that preempt aversive outcomes.

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Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches

Cited by 16 publications

References 99 publications

Agent-Based Behavioral Modeling of Human Associative Learning in a Complex Approach-Avoidance Conflict Task

Agent-Based Behavioral Modeling of Human Associative Learning in a Complex Approach-Avoidance Conflict Task

The neurocomputational link between defensive cardiac states and approach-avoidance arbitration under threat

Prefrontal dopamine activity is critical for rapid threat avoidance learning

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