Computational Psychiatry

Rutledge, Robb B.; Adams, Rick A.

doi:10.1002/9781119159193.ch3

Cited by 2 publications

(3 citation statements)

References 76 publications

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“…In this regard, resting-state fMRI in marmosets suggests the existence of such coactive networks involving frontal areas, including the candidate for the default mode network (Hori et al, 2020(Hori et al, , 2021Liu et al, 2019). It is hypothesized that psychotic symptoms of schizophrenia may be caused by the reduced influence of prior beliefs on the sensory data in Bayesian inference (Rutledge and Adams, 2017). Such "influence" could be coded by the coordinated activities of PFC neurons conveyed via the spread-type projections (Figure S7, upper panel, red arrow, "Top-down effects").…”

Section: Functional Implication Of the Population Connectivitymentioning

confidence: 99%

“…[Down state (Disease state)] Due to the low efficacy, the modulatory effects of the diffuse projections would be low or absent, if the source neurons lack coordination. It is hypothesized that psychotic symptoms of schizophrenia might be related to a reduced influence of prior beliefs on sensory data in Bayesian inference 83 . Such an “influence” might be encoded by the coordinated activities of PFC neurons conveyed via the diffuse projections (Upper panel, red arrow, “Top-down effects”).…”

Section: Supplementary Figs and Legendsmentioning

confidence: 99%

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Local and long-distance organization of prefrontal cortex circuits in the marmoset brain

Watakabe

Skibbe

Nakae

et al. 2021

Preprint

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The primate prefrontal cortex (PFC) has greatly expanded to evolve specialized architecture, but its roles in top-down brain control remain enigmatic. Based on connectomics mapping of the marmoset PFC, we characterized two contrasting features of corticocortical and corticostriatal projections. One is the "focalness" of projections, exemplified by multiple columnar axonal terminations in the cortical layers and the other is the "widespreadness" of weaker projections, whose patterns consisted of several common motifs representing the framework of PFC connectivity. We clarified the topographic rules of distribution for these features, which should constrain how PFC neurons can coordinate to control the target regions as populations. These features are observed only primitively in rodents and are considered critical in understanding the roles of the PFC in neuropsychiatric disorders.

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Section: Functional Implication Of the Population Connectivitymentioning

confidence: 99%

Section: Supplementary Figs and Legendsmentioning

confidence: 99%

Local and long-distance organization of prefrontal cortex circuits in the marmoset brain

Watakabe

Skibbe

Nakae

et al. 2021

Preprint

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“…Computational modeling is used increasingly in psychiatry to provide computational accounts of behaviors seen in psychiatric disorders (Addicott, Pearson, Sweitzer, Barack, & Platt, 2017;Rutledge & Adams, 2017;Montague, Dolan, Friston, & Dayan, 2012). In the context of impulsivity, aberrant temporal discounting has been a prevalent explanation for impulsive behavior (Story et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Impulsivity and Active Inference

Mirza

Adams

Parr

et al. 2019

Journal of Cognitive Neuroscience

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This paper characterizes impulsive behavior using a patch-leaving paradigm and active inference—a framework for describing Bayes optimal behavior. This paradigm comprises different environments (patches) with limited resources that decline over time at different rates. The challenge is to decide when to leave the current patch for another to maximize reward. We chose this task because it offers an operational characterization of impulsive behavior, namely, maximizing proximal reward at the expense of future gain. We use a Markov decision process formulation of active inference to simulate behavioral and electrophysiological responses under different models and prior beliefs. Our main finding is that there are at least three distinct causes of impulsive behavior, which we demonstrate by manipulating three different components of the Markov decision process model. These components comprise (i) the depth of planning, (ii) the capacity to maintain and process information, and (iii) the perceived value of immediate (relative to delayed) rewards. We show how these manipulations change beliefs and subsequent choices through variational message passing. Furthermore, we appeal to the process theories associated with this message passing to simulate neuronal correlates. In future work, we will use this scheme to identify the prior beliefs that underlie different sorts of impulsive behavior—and ask whether different causes of impulsivity can be inferred from the electrophysiological correlates of choice behavior.

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Computational Psychiatry

Cited by 2 publications

References 76 publications

Local and long-distance organization of prefrontal cortex circuits in the marmoset brain

Local and long-distance organization of prefrontal cortex circuits in the marmoset brain

Impulsivity and Active Inference

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