Experiential diversity promotes well-being in animal models. Here, using geolocation tracking, experience sampling, and neuroimaging, we found that daily variability in physical location was associated with increased positive affect in humans. This effect was stronger for individuals who exhibited greater functional coupling of the hippocampus and striatum. These results link diversity in real-world daily experiences to fluctuations in positive affect and identify a hippocampal-striatal circuit associated with this bidirectional relation.
The ability to anticipate and respond appropriately to the challenges and opportunities present in our environments is critical for adaptive behavior. Recent methodological innovations have led to substantial advances in our understanding of the neurocircuitry supporting such motivated behavior in adulthood. However, the neural circuits and cognitive processes that enable threat-and rewardmotivated behavior undergo substantive changes over the course of development, and these changes are less well understood. In this article, we highlight recent research in human and animal models demonstrating how developmental changes in prefrontal-subcortical neural circuits give rise to corresponding changes in the processing of threats and rewards from infancy to adulthood. We discuss how these developmental trajectories are altered by experiential factors, such as early-life stress, and highlight the relevance of this research for understanding the developmental onset and treatment of psychiatric disorders characterized by dysregulation of motivated behavior.
Epidemiological data suggest that risk taking in the real world increases from childhood into adolescence and declines into adulthood. However, developmental patterns of behaviour in laboratory assays of risk taking and impulsive choice are inconsistent. In this article, we review a growing literature using behavioural economic approaches to understand developmental changes in risk taking and impulsivity. We present findings that have begun to elucidate both the cognitive and neural processes that contribute to risky and impulsive choice, as well as how age-related changes in these neurocognitive processes give rise to shifts in choice behaviour. We highlight how variability in task parameters can be used to identify specific aspects of decision contexts that may differentially influence risky and impulsive choice behaviour across development.
This article is part of the theme issue ‘Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications’.
The model-free algorithms of "reinforcement learning" (RL) have gained clout across disciplines, but so too have model-based alternatives. The present study emphasizes other dimensions of this model space in consideration of associative or discriminative generalization across states and actions. This "generalized reinforcement learning" (GRL) model, a frugal extension of RL, parsimoniously retains the single rewardprediction error (RPE), but the scope of learning goes beyond the experienced state and action. Instead, the generalized RPE is efficiently relayed for bidirectional counterfactual updating of value estimates for other representations. Aided by structural information but as an implicit rather than explicit cognitive map, GRL provided the most precise account of human behavior and individual differences in a reversallearning task with hierarchical structure that encouraged inverse generalization across both states and actions. Reflecting inference that could be true, false (i.e., overgeneralization), or absent (i.e., undergeneralization), state generalization distinguished those who learned well more so than action generalization. With highresolution high-field fMRI targeting the dopaminergic midbrain, the GRL model's RPE signals (alongside value and decision signals) were localized within not only the striatum but also the substantia nigra and the ventral tegmental area, including specific effects of generalization that also extend to the hippocampus. Factoring in generalization as a multidimensional process in value-based learning, these findings shed light on complexities that, while challenging classic RL, can still be resolved within the bounds of its core computations.
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