Citation

Letkiewicz AM, Cochran AL, Cisler JM. J. Psychiatr. Res. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.jpsychires.2020.10.046

PMID

33199053

Abstract

Trauma and trauma-related disorders are characterized by impaired learning processes, including reinforcement learning (RL). Identifying which aspects of learning are altered by trauma is critical endeavor, as this may reveal key mechanisms of impairment and potential intervention targets. There are at least two types of RL that have been delineated using computational modeling: model-free and model-based RL. Although these RL processes differentially predict decision-making behavior, most research has examined the impact of trauma on model-free RL. Currently unclear whether model-based RL, which involves building abstract and nuanced representations of stimulus-outcome relationships, is impaired among individuals with a history of trauma. The present study sought to test the hypothesis of impaired model-based RL among adolescent females exposed to assaultive trauma. Participants (n = 60; 29 without a history of assault and 31 with a history of assault with and without PTSD) completed a three-arm bandit task during fMRI acquisition. Two computational models compared the degree to which participants' task behavior fit the use of a model-free versus model-based RL strategy. Although a history of assaultive trauma did not predict poorer model-based RL, greater sexual abuse severity predicted less use of model-based compared to model-free RL. Additionally, severe sexual abuse predicted less left frontoparietal network encoding of model-based RL updates. Altered model-based RL, which supports goal-directed behavior, may be an important route through which clinical impairment emerges among individuals with a history of severe sexual abuse and should be examined further in future studies.

Language: en

Keywords

Trauma; Sexual abuse; Computational modeling; Frontoparietal network; Model-based reinforcement learning; Model-free reinforcement learning