Computational models of motivated frontal function

O’Reilly, Randall C.; Russin, Jacob; Herd, Seth

doi:10.1016/b978-0-12-804281-6.00017-3

Cited by 5 publications

(5 citation statements)

References 145 publications

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“…In complementary work to the PVLV framework, we are currently investigating such mechanisms in the context of broader research on the nature of neocortical learning and the ability of frontal cortical areas to maintain and rapidly update active representations that can provide a dynamic form of contextual modulation for the PVLV model (O’Reilly, Russin & Herd, in press; Pauli et al, 2010; Pauli et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

A systems-neuroscience model of phasic dopamine.

Mollick

Hazy

Krueger

et al. 2020

Psychological Review

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We describe a neurobiologically informed computational model of phasic dopamine signaling to account for a wide range of findings, including many considered inconsistent with the simple reward prediction error (RPE) formalism. The central feature of this PVLV framework is a distinction between a primary value (PV) system for anticipating primary rewards (Unconditioned Stimuli [USs]), and a learned value (LV) system for learning about stimuli associated with such rewards (CSs). The LV system represents the amygdala, which drives phasic bursting in midbrain dopamine areas, while the PV system represents the ventral striatum, which drives shunting inhibition of dopamine for expected USs (via direct inhibitory projections) and phasic pausing for expected USs (via the lateral habenula). Our model accounts for data supporting the separability of these systems, including individual differences in CS-based (sign-tracking) versus US-based learning (goal-tracking). Both systems use competing opponent-processing pathways representing evidence for and against specific USs, which can explain data dissociating the processes involved in acquisition versus extinction conditioning. Further, opponent processing proved critical in accounting for the full range of conditioned inhibition phenomena, and the closely related paradigm of second-order conditioning. Finally, we show how additional separable pathways representing aversive USs, largely mirroring those for appetitive USs, also have important differences from the positive valence case, allowing the model to account for several important phenomena in aversive conditioning. Overall, accounting for all of these phenomena strongly constrains the model, thus providing a well-validated framework for understanding phasic dopamine signaling.

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Section: Discussionmentioning

confidence: 99%

A systems-neuroscience model of phasic dopamine.

Mollick

Hazy

Krueger

et al. 2020

Psychological Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…This approach has been extremely successful in uncovering computations associated with reward learning in coordinated cross-species efforts (e.g., prediction errors coding in the VTA; Dabney et al, 2020;D'Ardenne et al, 2008;Jeong et al, 2022;Schultz et al, 1997) and could be applied to fear and anxiety. Recent efforts have begun to develop novel computational models derived from 1) ethology (Mobbs et al, 2021), 2) the statistics of the environment (Pulcu & Browning, 2019), and 3) the underlying functional neurobiology of brain regions (O'Reilly et al, 2019).…”

Section: Developing New Approaches To Understanding Fear and Anxiety:...mentioning

confidence: 99%

Understanding the heterogeneity of anxiety using a translational neuroscience approach

Drzewiecki,

Fox

2024

Cogn Affect Behav Neurosci

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Anxiety disorders affect millions of people worldwide and present a challenge in neuroscience research because of their substantial heterogeneity in clinical presentation. While a great deal of progress has been made in understanding the neurobiology of fear and anxiety, these insights have not led to effective treatments. Understanding the relationship between phenotypic heterogeneity and the underlying biology is a critical first step in solving this problem. We show translation, reverse translation, and computational modeling can contribute to a refined, cross-species understanding of fear and anxiety as well as anxiety disorders. More specifically, we outline how animal models can be leveraged to develop testable hypotheses in humans by using targeted, cross-species approaches and ethologically informed behavioral paradigms. We discuss reverse translational approaches that can guide and prioritize animal research in nontraditional research species. Finally, we advocate for the use of computational models to harmonize cross-species and cross-methodology research into anxiety. Together, this translational neuroscience approach will help to bridge the widening gap between how we currently conceptualize and diagnose anxiety disorders, as well as aid in the discovery of better treatments for these conditions.

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“…Connectionist models may help us to understand fundamental principles of neural computation but may only sometimes map onto specific neurobiologically realistic mechanisms. However, learning mechanisms that are putatively more biologically realistic have also been incorporated into such models (e.g., O'Reilly et al, 2019).…”

Section: Computational Models Of Proactive Controlmentioning

confidence: 99%

Cognitive Control in Schizophrenia: Advances in Computational Approaches

Barch,

Culbreth,

Sheffield

2023

Curr Dir Psychol Sci

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Psychiatric research is undergoing significant advances in an emerging subspeciality of computational psychiatry, building on cognitive neuroscience research by expanding to neurocomputational modeling. Here, we illustrate some research trends in this domain using work on proactive cognitive control deficits in schizophrenia as an example. We provide a selective review of formal modeling approaches to understanding cognitive control deficits in psychopathology, focusing primarily on biologically plausible connectionist-level models as well as mathematical models that generate parameter estimates of putatively dissociable psychological or neural processes. We illustrate some of the advantages of these models in terms of understanding both cognitive control deficits in schizophrenia and the potential roles of effort and motivation. Further, we highlight critical future directions for this work, including a focus on establishing psychometric properties, additional work modeling psychotic symptoms and their interaction with cognitive control, and the need to expand both behavioral and neural modeling to samples that include individuals with different mental health conditions, allowing for the examination of dissociable neural or psychological substrates for seemingly similar cognitive impairments across disorders.

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Computational models of motivated frontal function

Cited by 5 publications

References 145 publications

A systems-neuroscience model of phasic dopamine.

A systems-neuroscience model of phasic dopamine.

Understanding the heterogeneity of anxiety using a translational neuroscience approach

Cognitive Control in Schizophrenia: Advances in Computational Approaches

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