Behavioral data, though has been an influential index oncognitive processes, is under scrutiny for having poorreliability as a result of noise or lacking replications ofreliable effects. Here, we argue that cognitive modeling canbe used to enhance the test-retest reliability of the behavioralmeasures by recovering individual-level parameters frombehavioral data. We tested this empirically with theProbabilistic Stimulus Selection (PSS) task, which is used tomeasure a participant’s sensitivity to positive or negativereinforcement. An analysis of 400,000 simulations from anAdaptive Control of Thought - Rational (ACT-R) model ofthis task showed that the poor reliability of the task is due tothe instability of the end-estimates: because of the way thetask works, the same participants might sometimes end uphaving apparently opposite scores. To recover the underlyinginterpretable parameters and enhance reliability, we used aBayesian Maximum A Posteriori (MAP) procedure. We wereable to obtain reliable parameters across sessions (IntraclassCorrelation Coefficient ~ 0.5), and showed that this approachcan further be used to provide superior measures in terms ofreliability, and bring greater insights into individualdifferences.

A complete and holistic understanding of human cognitive function using a cognitive model should be able to incorporate both the idiographic biological parameters from behavioral data and interactions between connected brain networks identified by neuroanatomical techniques. Here, we argue that first, computational modeling can be used to extract biological parameters, and second, the biological parameter should be identifiable through the corresponding brain functional networks. We tested this empirically with the long-term memory’s decay rate (rate of forgetting α) measured from a Swahili-English vocabulary learning task, and resting-state fMRI (rs-fMRI) data, both collected from 33 participants. α was estimated based on a rational model of episodic memory. rs-fMRI data from each brain was processed into a 264 x 264 connectivity matrix, and further selected and grouped into memory-related network matrices using group lasso and cross-validation techniques. We were able to (1) predict the observed α using these connectivity matrices, and (2) show that forgetting can be related to specific brain networks.