2022
DOI: 10.1146/annurev-psych-021621-124910
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Computational Psychiatry Needs Time and Context

Abstract: Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, … Show more

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Cited by 76 publications
(61 citation statements)
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“…Finally, note that the positive-feedback loop we are proposing is in the opposite direction as one recently postulated to lead to virtuous cycles of reward surprise and escalating mood caused by serotonergic agonists (Michely et al, 2020) yet at extremes creates oscillatory mood dynamics that could contribute to the symptoms of bipolar disorders (Eldar & Niv, 2015). In short, we propose a positive-feedback loop that predicts real-world dynamics unfolding over time (Hitchcock et al, 2022), which could be tested in future longitudinal research.…”
Section: Discussionmentioning
confidence: 82%
“…Finally, note that the positive-feedback loop we are proposing is in the opposite direction as one recently postulated to lead to virtuous cycles of reward surprise and escalating mood caused by serotonergic agonists (Michely et al, 2020) yet at extremes creates oscillatory mood dynamics that could contribute to the symptoms of bipolar disorders (Eldar & Niv, 2015). In short, we propose a positive-feedback loop that predicts real-world dynamics unfolding over time (Hitchcock et al, 2022), which could be tested in future longitudinal research.…”
Section: Discussionmentioning
confidence: 82%
“…In addition, they can derive mechanistic models that predict disease trajectory and treatment effects. Here, we extend historical discussions on this topic (Adams et al, 2016 ; Paulus et al, 2016 ; Hitchcock et al, 2022 ) by discussing the potential of integrative computational modeling for dementia research. We will discuss the potential translational benefits and how it might account for some of the current limitations in dementia research.…”
Section: Introductionmentioning
confidence: 70%
“…Social media activity and geolocation data has been particularly popular in mental health research, 13 , 15 , 16 18 but other data sources, such as light sensors, voice recordings, accelerometers, and physiological recordings, also hold promise. Bringing together passive and active data sources, for example by collecting eye gazing data during game play, 19 could yield new insights in future studies.…”
Section: Figurementioning
confidence: 99%
“…Because these dynamics cannot be detected when using cross-sectional or temporally sparse assessments, it is important to use repeated longitudinal assessments to assess and exploit these dynamics for modelling mental health. 19 …”
Section: Figurementioning
confidence: 99%