Disentangling the systems contributing to changes in learning during adolescence

Master, Sarah L.; Eckstein, Maria K.; Gotlieb, Neta; Dahl, Ronald E.; Wilbrecht, Linda; Collins, Anne

doi:10.1016/j.dcn.2019.100732

Cited by 67 publications

(175 citation statements)

References 68 publications

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“…Rewarddriven choice processes have been extensively modeled using reinforcement learning (RL) algorithms [1]. This formalized account of learning and decision making has contributed significantly to expanding the frontiers of artificial intelligence research [2], our understanding of clinical pathologies [3,4], and research on developmental changes in learning [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…This property of RL suggests that if individuals rely on RL alone, learning should not be affected by the number of rewarding stimulus-response associations they are required to learn in a given task. However, humans learn much less efficiently when the number of associations to be learned in parallel exceeds WM capacity [ 20,6,21], suggesting that RL operates side by side with working memory during learning. Other work has similarly shown that EF-dependent planning contributes to choice alongside core RL computations implemented in the brain [22,23].…”

Section: Introductionmentioning

confidence: 99%

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The role of executive function in shaping reinforcement learning

Rmus

McDougle

Collins

2021

Current Opinion in Behavioral Sciences

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The role of executive function in shaping reinforcement learning

Rmus

McDougle

Collins

2021

Current Opinion in Behavioral Sciences

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“…During development, there may be periods where one form of learning is emphasized over the other. Changes could be gradual and monotonic, but there may also be non-monotonic changes, such as inverted U shapes [3,7,11]. Peaks in inverted U shapes indicate a time when a phenomenon is at its maximum; such peaks for learning processes during adolescence could accommodate the expected increase in uncertainty in the environment with the transition to independence that occurs at that time of life across species [12].…”

Section: Introductionmentioning

confidence: 99%

“…The focus of this paper is on one of these tasks, the Butterfly task [2], where participants needed to learn probabilistic associations that were stable throughout the task. While we mainly present results from the Butterfly task, we also discuss comparisons and relationships with two other tasks in this sequence of four tasks [3,11].…”

Section: Introductionmentioning

confidence: 99%

Modeling Changes in Probabilistic Reinforcement Learning during Adolescence

Xia

Master

Eckstein

et al. 2020

Preprint

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In the real world, many relationships between events are uncertain and probabilistic. Uncertainty is also likely to be a more common feature of daily experience for youth because they have less experience to draw from than adults. Some studies suggests probabilistic learning may be inefficient in youth compared to adults [1], while others suggest it may be more efficient in youth that are in mid adolescence [2, 3]. Here we used a probabilistic reinforcement learning task to test how youth age 8-17 (N = 187) and adults age 18-30 (N = 110) learn about stable probabilistic contingencies. Performance increased with age through early-twenties, then stabilized. Using hierarchical Bayesian methods to fit computational reinforcement learning models, we show that all participants’ performance was better explained by models in which negative outcomes had minimal to no impact on learning. The performance increase over age was driven by 1) an increase in learning rate (i.e. decrease in integration time horizon); 2) a decrease in noisy/exploratory choices. In mid-adolescence age 13-15, salivary testosterone and learning rate were positively related. We discuss our findings in the context of other studies and hypotheses about adolescent brain development.Author summaryAdolescence is a time of great uncertainty. It is also a critical time for brain development, learning, and decision making in social and educational domains. There are currently contradictory findings about learning in adolescence. We sought to better isolate how learning from stable probabilistic contingencies changes during adolescence with a task that previously showed interesting results in adolescents. We collected a relatively large sample size (297 participants) across a wide age range (8-30), to trace the adolescent developmental trajectory of learning under stable but uncertain conditions. We found that age in our sample was positively associated with higher learning rates and lower choice exploration. Within narrow age bins, we found that higher saliva testosterone levels were associated with higher learning rates in participants age 13-15 years. These findings can help us better isolate the trajectory of maturation of core learning and decision making processes during adolescence.

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“…More work needs to be done in order to understand how spine pruning relates to changes in neural computation and cognitive processes during adolescence (Selemon, 2013), but recent studies have made progress, associating changes in spine density onto OFC neurons to alterations in goal-directed behavior (DePoy et al, 2019;Hinton et al, 2019). Data in humans suggests that pubertal status and circulating gonadal hormone levels influence working memory use during learning in both sexes (Master et al, 2019), sensitivity to immediate rewards during intertemporal decision-making in males (Laube et al, 2017), and a variety of social and affective behaviors in both sexes (Vijayakumar et al, 2018;Goddings et al, 2019). Future work will be needed to test whether developmental changes in cSTR structure and function relate to developmental changes in goal-directed learning or other behaviors.…”

Section: Discussionmentioning

confidence: 99%

Sex and pubertal status influence dendritic spine density onto frontal corticostriatal projection neurons

Delevich

Okada

Rahane

et al. 2019

Preprint

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Keywords: spine pruning | corticostriatal | puberty | adolescenceIn humans, nonhuman primates, and rodents, the frontal cortices exhibit grey matter thinning and dendritic spine pruning that extends late into adolescence. This protracted maturation is believed to support higher cognition but may also confer psychiatric vulnerability during adolescence. Currently, little is known about how different cell types in the frontal cortex mature or whether puberty plays a role. Here, we used mice to characterize the spatial topography and adolescent development of cross-corticostriatal (cSTR) neurons that project to the dorsomedial striatum (DMS). We found that apical spine density on cSTR neurons in the medial prefrontal cortex decreased significantly between late juvenile (P29) and young adult time points (P60), with females exhibiting higher spine density than males at both ages. Adult males castrated prior to puberty onset had higher spine density compared to sham controls. Adult females ovariectomized before puberty onset showed greater variance in spine density measures on cSTR cells compared to controls, but their mean spine density did not significantly differ from sham controls. Our findings reveal that these cSTR neurons, a subtype of the broader class of intratelencephalic-type neurons, exhibit significant sex differences and suggest that spine pruning on cSTR neurons is regulated by puberty in males.

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Disentangling the systems contributing to changes in learning during adolescence

Cited by 67 publications

References 68 publications

The role of executive function in shaping reinforcement learning

The role of executive function in shaping reinforcement learning

Modeling Changes in Probabilistic Reinforcement Learning during Adolescence

Sex and pubertal status influence dendritic spine density onto frontal corticostriatal projection neurons

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