The study explores the dynamic nature of the computational phenotype, which is a set of interpretable parameters derived from computational models fitting behavioral data. To understand the temporal variability of these parameters, a 12-week longitudinal study was conducted using seven tasks that measure learning, memory, perception, and decision-making. Participants also reported their mood and daily activities weekly. The results show that many phenotype dimensions covary with practice and affective factors, indicating that apparent unreliability may reflect underlying structure. The study supports a dynamic understanding of cognitive variability within individuals, highlighting the importance of considering both practice and affective effects. The findings suggest that the computational phenotype is influenced by multiple sources of variability, including random noise, practice, and state effects, and that a more nuanced understanding of cognitive processes can be gained through longitudinal designs.The study explores the dynamic nature of the computational phenotype, which is a set of interpretable parameters derived from computational models fitting behavioral data. To understand the temporal variability of these parameters, a 12-week longitudinal study was conducted using seven tasks that measure learning, memory, perception, and decision-making. Participants also reported their mood and daily activities weekly. The results show that many phenotype dimensions covary with practice and affective factors, indicating that apparent unreliability may reflect underlying structure. The study supports a dynamic understanding of cognitive variability within individuals, highlighting the importance of considering both practice and affective effects. The findings suggest that the computational phenotype is influenced by multiple sources of variability, including random noise, practice, and state effects, and that a more nuanced understanding of cognitive processes can be gained through longitudinal designs.