Top 1 Psyarxiv Papers Today in Psychiatry

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#1. Recovering Bistable Systems from Psychological Time Series
Jonas Haslbeck, Oisín Ryan
Conceptualizing mental disorders as complex dynamical systems has become a popular framework to study mental disorders. Especially bistable dynamical systems have received much attention, because their properties map well onto many characteristics of mental disorders. While these models were so far mostly used as stylized toy models, the recent surge in psychological time series data promises the ability to recover such models from data. In this paper we investigate how well popular (e.g., the Vector Autoregressive model) and more advanced (e.g., differential equation estimation) data analytic tools are suited to recover bistable dynamical systems from time series. Using a simulated high-frequency time series (measurement every six seconds) as an ideal case we show that while it is possible to recover global dynamics (e.g., position of fixed points, transition probabilities) it is difficult to recover the microdynamics (i.e., moment to moment interactions) of a bistable system. Repeating all analyses with a sampling frequency...
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EikoFried: New preprint by @jonashaslbeck & Oisín Ryan: "Recovering Bistable Systems from Psychological Time Series". Paper shows that analytic methods are unable to recover underlying #network processes well in case assessment doesn't have right frequency.
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