This study presents a dynamic computational phenotyping framework to analyze individual variability in human cognition over time. Using a 12-week longitudinal study with 90 participants, the researchers examined how cognitive parameters, derived from fitting computational models to behavioral data, vary across time. The study included seven cognitive tasks measuring learning, memory, perception, and decision-making, along with weekly surveys tracking mood, habits, and daily activities. The dynamic framework allowed the researchers to disentangle the effects of practice and internal states such as affective valence and arousal on the computational phenotype. The results showed that many computational phenotype dimensions co-vary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. The study highlights the dynamic nature of cognitive variability within an individual, with both practice and affective effects contributing to temporal variability. The computational phenotype was found to be highly stable in some parameters but less so in others, with practice effects being a significant contributor to variability. The study also identified that affective state effects, such as valence and arousal, influence several parameters, particularly those related to choice stochasticity. The study underscores the importance of longitudinal designs in understanding cognitive processes, as they allow for the examination of how cognitive parameters change over time. The findings suggest that the computational phenotype is not merely a reflection of unreliability but rather a dynamic construct influenced by various factors, including practice and affective states. The study also highlights the need for further research to explore additional sources of variability in the computational phenotype, such as neural states, to gain a deeper understanding of the underlying cognitive processes. The results emphasize the importance of considering both systematic and noise-related factors in the interpretation of computational phenotype data.This study presents a dynamic computational phenotyping framework to analyze individual variability in human cognition over time. Using a 12-week longitudinal study with 90 participants, the researchers examined how cognitive parameters, derived from fitting computational models to behavioral data, vary across time. The study included seven cognitive tasks measuring learning, memory, perception, and decision-making, along with weekly surveys tracking mood, habits, and daily activities. The dynamic framework allowed the researchers to disentangle the effects of practice and internal states such as affective valence and arousal on the computational phenotype. The results showed that many computational phenotype dimensions co-vary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. The study highlights the dynamic nature of cognitive variability within an individual, with both practice and affective effects contributing to temporal variability. The computational phenotype was found to be highly stable in some parameters but less so in others, with practice effects being a significant contributor to variability. The study also identified that affective state effects, such as valence and arousal, influence several parameters, particularly those related to choice stochasticity. The study underscores the importance of longitudinal designs in understanding cognitive processes, as they allow for the examination of how cognitive parameters change over time. The findings suggest that the computational phenotype is not merely a reflection of unreliability but rather a dynamic construct influenced by various factors, including practice and affective states. The study also highlights the need for further research to explore additional sources of variability in the computational phenotype, such as neural states, to gain a deeper understanding of the underlying cognitive processes. The results emphasize the importance of considering both systematic and noise-related factors in the interpretation of computational phenotype data.