Individualized Predictions of Forgetting and Intrusivenessof Traumatic Stimuli from Forgetting Rates

Intrusive memories are one of the core symptoms of Post-Traumatic Stress Disorder (PTSD) and can be conceptualized as involuntary memory retrievals that can vary across individuals. The field of computational psychiatry seeks to develop mechanistic models that can capture this heterogeneity by linking individual-level cognitive parameters to clinically relevant outcomes. In our previously conducted study, we used a two-day image presentation task that captured the spacing effect in memory intrusions. Using the exact timing for each stimuli presentation in the study and the individual-level Seattle-Groningen Memory Assessment (SGMA) index, we computed an ACT-R memory model derived base-level memory activation. Then, we used logistic regression to recover additional parameters of the ACT-R memory model including spreading activation and an emotional intensity component, using the experimental recognition data. The new, comprehensive model of memory was able to predict the frequency of memory intrusions with a better fit to the experimental data than the base model. These analyses provide a modeling framework for predicting long-term retention of emotional memories from spacing intervals and individual cognitive parameters, an example of computational phenotyping, and highlights the potential of individualized predictions in clinical psychology.