Modeling Negative Symptoms in Schizophrenia

Matthew A. Albrecht, James A. Waltz, Michael J. Frank, James M. Gold

Research output: Chapter in Book/Conference paperChapterpeer-review

1 Citation (Scopus)


Here we review a series of reinforcement learning (RL) experiments carried out in our laboratory that attempt to more closely examine the computational processes involved in the formation of negative symptoms in schizophrenia. Analysis of the behavior suggested reward-learning specific deficits, but spared punishment learning especially in patients with a greater negative symptom burden. Modeling of the behavior suggested that higher order processes were likely to be responsible for the associations between RL performance and negative symptoms. Specifically, reductions in two parameters indicating greater relative contributions of the frontal cortex over the basal ganglia and the likelihood of adjusting strategic responding during uncertainty were related with negative symptoms. The application of computational modeling to negative symptoms has identified novel mechanisms that may not be explicitly identifiable given a simple analysis of behavior.

Original languageEnglish
Title of host publicationComputational Psychiatry
Subtitle of host publicationMathematical Modeling of Mental Illness
EditorsAlan Anticevic, John D. Murray
PublisherElsevier- Hanley and Belfus Inc.
Number of pages28
ISBN (Electronic)9780128098264
ISBN (Print)9780128098257
Publication statusPublished - 2018
Externally publishedYes


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