Confirmatory reinforcement learning changes with age during adolescence.

Understanding how learning changes during human development has been one of the long-standing objectives of developmental science. Recently, advances in computational biology have demonstrated that humans display a bias when learning to navigate novel environments through rewards and punishments: they learn more from outcomes that confirm their expectations than from outcomes that disconfirm them. Here, we ask whether confirmatory learning is stable across development, or whether it might be attenuated in developmental stages in which exploration is beneficial, such as in adolescence. In a reinforcement learning (RL) task, 77 participants aged 11-32 years (four men, mean age = 16.26) attempted to maximize monetary rewards by repeatedly sampling different pairs of novel options, which varied in their reward/punishment probabilities. Mixed-effect models showed an age-related increase in accuracy as long as learning contingencies remained stable across trials, but less so when they reversed halfway through the trials. Age was also associated with a greater tendency to stay with an option that had just delivered a reward, more than to switch away from an option that had just delivered a punishment. At the computational level, a confirmation model provided increasingly better fit with age. This model showed that age differences are captured by decreases in noise or exploration, rather than in the magnitude of the confirmation bias. These findings provide new insights into how learning changes during development and could help better tailor learning environments to people of different ages. RESEARCH HIGHLIGHTS: Reinforcement learning shows age-related improvement during adolescence, but more in stable learning environments compared with volatile learning environments. People tend to stay with an option after a win more than they shift from an option after a loss, and this asymmetry increases with age during adolescence. Computationally, these changes are captured by a developing confirmatory learning style, in which people learn more from outcomes that confirm rather than disconfirm their choices. Age-related differences in confirmatory learning are explained by decreases in stochasticity, rather than changes in the magnitude of the confirmation bias.

Variability assessment of (15)N metabolic labeling-based proteomics workflow in mouse plasma and brain.

(15)N metabolic labeling-based quantitative proteomics is used for the identification of disease- and phenotype-related alterations in live organisms. The variability of (15)N metabolic labeling proteomics workflows has been assessed in plants and bacteria. However, no study has addressed this topic in mice. We have investigated the repeatability of a quantitative in vivo(15)N metabolic labeling proteomics workflow in mice by assessing LC variability, peptide and protein profiling characteristics and overall (15)N/(14)N protein quantification accuracy in technical replicates of plasma and brain specimens. We furthermore examined how sample preparation affects these parameters in plasma and brain. We found that specimen type (i.e. plasma or brain) influences the variability of the (15)N metabolic labeling workflow in an LC-independent manner.