Paranoia is the belief that harm is intended by others. It may arise from selective pressures to infer and avoid social threats, particularly in ambiguous or changing circumstances. We propose that uncertainty may be sufficient to elicit learning differences in paranoid individuals, without social threat. We used reversal learning behavior and computational modeling to estimate belief updating across individuals with and without mental illness, online participants, and rats chronically exposed to methamphetamine, an elicitor of paranoia in humans. Paranoia is associated with a stronger prior on volatility, accompanied by elevated sensitivity to perceived changes in the task environment. Methamphetamine exposure in rats recapitulates this impaired uncertainty-driven belief updating and rigid anticipation of a volatile environment. Our work provides evidence of fundamental, domain-general learning differences in paranoid individuals. This paradigm enables further assessment of the interplay between uncertainty and belief-updating across individuals and species.
Bibliographical noteFunding Information:
This work was supported by the Yale University Department of Psychiatry, the Connecticut Mental Health Center (CMHC) and Connecticut State Department of Mental Health and Addiction Services (DMHAS). It was funded by an IMHRO / Janssen Rising Star Translational Research Award, an Interacting Minds Center (Aarhus) Pilot Project Award, NIMH R01MH12887 (P.R.C.), NIMH R21MH120799-01 (P.R.C. & S.G.) . E.J.R. was supported by the NIH Medical Scientist Training Program Training Grant, GM007205; NINDS Neurobiology of Cortical Systems Grant, T32 NS007224; and a Gustavus and Louise Pfeiffer Research Foundation Fellowship. S.U. received funding from NSF Fellowships DGE1122492 and DGE1752134. S.M.G. and J.R.T. were supported by NIDA DA DA041480. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors thank Dr. James Waltz for providing an earlier version of the reversal-learning e-prime code. The authors acknowledge the help, support, and advice of Dr. Sarah Fineberg, Dr. Albert Powers III, and Dr. Pantelis Leptourgos.
- Computational psychiatry
- Hierarchical gaussian filter
- Unexpected uncertainty