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Optimal integration of positive and negative outcomes during learning varies depending on an environment's reward statistics. The present study investigated the extent to which children, adolescents, and adults (N = 142 8-25 year-olds, 55% female, 42% White, 31% Asian, 17% mixed race, and 8% Black; data collected in 2021) adapt their weighting of better-than-expected and worse-than-expected outcomes when learning from reinforcement. Participants made choices across two contexts: one in which weighting positive outcomes more heavily than negative outcomes led to better performance, and one in which the reverse was true. Reinforcement learning modeling revealed that across age, participants shifted their valence biases in accordance with environmental structure. Exploratory analyses revealed strengthening of context-dependent flexibility with increasing age.

Previous work has revealed that the ability to strategically en-code high-value information may improve gradually over de-velopment as cognitive control mechanisms mature. However,studies of value-directed memory have relied on explicit cuesof information value, which are rarely present in real-worldcontexts. Here, using a novel fMRI paradigm, we examinedwhether individuals across a wide age range (N = 90; ages 8– 25 years) could learn the value of information from expe-rience and use learned value signals to strategically modulatememory. We found that memory prioritization for high-valueinformation improved across development, and was supportedby increased engagement of the caudate and prefrontal cortexduring both encoding and retrieval of high-value information.Our results suggest that across development, the dynamic ad-justment of memory based on the statistics of the environmentis supported by a wide network of brain regions involved inboth the recognition and use of information value.

Intervening on causal systems can illuminate their underlyingstructures. Past work has shown that, relative to adults, youngchildren often make intervention decisions that confirm sin-gle hypotheses rather than those that discriminate alternativehypotheses. Here, we investigated how the ability to make in-formative intervention decisions changes across development.Ninety participants between the ages of 7 and 25 completed40 different puzzles in which they had to intervene on vari-ous causal systems to determine their underlying structures.We found that the use of discriminatory strategies increasedthrough adolescence and plateaued into adulthood. Our resultsidentify a clear developmental trend in causal reasoning, andhighlight the need to expand research on causal learning mech-anisms in adolescence.

Across development, people tend to demonstrate a preference for contexts in which they have the opportunity to make choices. However, it is not clear how children, adolescents, and adults learn to calibrate this preference based on the costs and benefits of agentic choice. Here, in both a primary, in-person, reinforcement-learning experiment (N = 92; age range = 10–25 years) and a preregistered online replication study (N = 150; age range = 8–25 years), we found that participants overvalued agentic choice but also calibrated their agency decisions to the reward structure of the environment, increasingly selecting agentic choice when choice had greater instrumental value. Regression analyses and computational modeling of participant choices revealed that participants' bias toward agentic choice—reflecting its intrinsic value—remained consistent across age, whereas sensitivity to the instrumental value of agentic choice increased from childhood to early adulthood. [ABSTRACT FROM AUTHOR]

Intervening on causal systems can illuminate their underlying structures. Past work has shown that, relative to adults, young children often make intervention decisions that appear to confirm a single hypothesis rather than those that optimally discriminate alternative hypotheses. Here, we investigated how the ability to make informative causal interventions changes across development. Ninety participants between the ages of 7 and 25 completed 40 different puzzles in which they had to intervene on various causal systems to determine their underlying structures. Each puzzle comprised a three- or four-node computer chip with hidden wires. On each trial, participants viewed two possible arrangements of the chip's hidden wires and had to select a single node to activate. After observing the outcome of their intervention, participants selected a wire configuration and rated their confidence in their selection. We characterized participant choices with a Bayesian measurement model that indexed the extent to which participants selected nodes that would best disambiguate the two possible causal structures versus those that had high causal centrality in one of the two causal hypotheses but did not necessarily discriminate between them. Our model estimates revealed that the use of a discriminatory strategy increased through early adolescence. Further, developmental improvements in intervention strategy were related to changes in the ability to accurately judge the strength of evidence that interventions revealed, as indexed by participants' confidence in their selections. Our results suggest that improvements in causal information-seeking extend into adolescence and may be driven by metacognitive sensitivity to the efficacy of previous interventions in discriminating competing ideas.

Both salient visual events and scene-based memories can influence attention, but it is unclear how they interact in children and adults. In Experiment 1, children (N = 27; ages 7-12) were faster to discriminate targets when they appeared at the same versus different location as they had previously learned or as a salient visual event. In contrast, adults (N = 30; ages 18-31) responded faster only when cued by visual events. While Experiment 2 confirmed that adults (N = 27) can use memories to orient attention, Experiment 3 showed that, even in the absence of visual events, the effects of memories on attention were larger in children (N = 27) versus adults (N = 28). These findings suggest that memories may be a robust source of influence on children's attention. [ABSTRACT FROM AUTHOR]

Television can be a powerful education tool; however, content makers must understand the factors that engage attention and promote learning from screen media. Prior research has suggested that social engagement is critical for learning and that interactivity may enhance the educational quality of children’s media. The present study examined the effects of increasing the social interactivity of television on children’s visual attention and word learning. Three- to 5-year-old (Mage= 4;5,SD= 9 months) children completed a task in which they viewed videos of an actress teaching them the Swahili label for an on-screen image. Each child viewed these video clips in 4 conditions that parametrically manipulated social engagement and interactivity. We then tested whether each child had successfully learned the Swahili labels. Though 5-year-old children were able to learn words in all conditions, we found that there was an optimal level of social engagement that best supported learning for all participants, defined by engaging the child but not distracting from word labeling. Our eye-tracking data indicated that children in this condition spent more time looking at the target image and less time looking at the actress’s face as compared with the most interactive condition. These findings suggest that social interactivity is critical to engaging attention and promoting learning from screen media up until a certain point, after which social stimuli may draw attention away from target images and impair children’s word learning. [ABSTRACT FROM PUBLISHER]

Binz et al. argue that meta-learned models are essential tools for understanding adult cognition. Here, we propose that these models are particularly useful for testing hypotheses about why learning processes change across development. By leveraging their ability to discover optimal algorithms and account for capacity limitations, researchers can use these models to test competing theories of developmental change in learning.