Exploring dimensionality in the contamination-relevant semantic network with simulated obsessions and association splitting

Abstract

The semantic network approach to obsessive-compulsive disorder (OCD) represents a new cognitive approach to understanding the condition. With a dimensional perspective on OC symptoms, we attempted to: (1) uncover a contamination-relevant semantic network in an unselected undergraduate sample and (2) validate the purported mechanism of association splitting, a network-based intervention for OC symptoms. Contamination-relevant, negatively valenced, and neutral Deese/Roediger-McDermott (DRM) word lists were presented, accompanied by relational or item-specific semantic processing simulations of obsessions or association splitting, respectively. Good veridical recognition performance across list types was observed with simulated obsessions and association splitting. Substantial false recognition rates across critical lure types followed simulated obsessions; such rates were lower with simulated association splitting. Network-based accounts of the contamination-relevant findings supported the aforementioned research aims. Additionally, enhanced contamination-relevant veridical recognition confidence with simulated association splitting suggests a memory confidence pathway through which the technique might reduce contamination-related symptoms. Lastly, contamination-relevant recognition performance was not related to contamination-related symptom severity across conditions, signaling non-dimensionality in contamination-relevant semantic network intensification. Our findings indicate the need for rigorous research on the semantic network approach to OCD to refine its tenets. Association splitting should also be more extensively researched as a viable technique for treating OCD.