How adolescents learn to build social bonds: A developmental computational account of social explore-exploit decision-making

Building social bonds is a critical task of adolescence that affords opportunities for learning, identity formation, and social support. Failing to develop close relationships in adolescence hinders adult interpersonal functioning and contributes to problems such as loneliness and depression. During adolescence, increased reward sensitivity and greater social flexibility both contribute to healthy social development, yet we lack a clear theory of how these processes interact to support social functioning. Here, we propose synthesizing these two literatures using a computational reinforcement learning framework that recasts how adolescents pursue and learn from social rewards as a social explore-exploit problem. To become socially skilled, adolescents must balance both their efforts to form individual bonds within specific groups and manage memberships across multiple groups to maximize access to social resources. We draw on insights from sociological studies on social capital in collective networks and neurocognitive research on foraging and cooperation to describe the social explore-exploit dilemma faced by adolescents navigating a modern world with increasing access to diverse resources and group memberships. Our account provides important new directions for examining the dynamics of adolescent behavior in social groups and understanding how social value computations can support positive relationships into adulthood.

Older adults select different but not simpler strategies than younger adults in risky choice

Younger and older adults often differ in their risky choices. Theoretical frameworks on human aging point to various cognitive and motivational factors that might underlie these differences. Using a novel computational model based on the framework of resource rationality, we find that the two age groups rely on different strategies. Importantly, older adults did not use simpler strategies than younger adults, they did not select among fewer strategies, they did not make more errors, and they did not put more weight on cognitive costs. Instead, older adults selected strategies that had different risk propensities than those selected by younger adults. Our modeling approach suggests that age differences in risky choice are not necessarily a consequence of cognitive decline; instead, they may reflect motivational differences between age groups.

Diminished State Space Theory of Human Aging

Many new technologies, such as smartphones, computers, or public-access systems (like ticket-vending machines), are a challenge for older adults. One feature that these technologies have in common is that they involve underlying, partially observable, structures (state spaces) that determine the actions that are necessary to reach a certain goal (e.g., to move from one menu to another, to change a function, or to activate a new service). In this work we provide a theoretical, neurocomputational account to explain these behavioral difficulties in older adults. Based on recent findings from age-comparative computational- and cognitive-neuroscience studies, we propose that age-related impairments in complex goal-directed behavior result from an underlying deficit in the representation of state spaces of cognitive tasks. Furthermore, we suggest that these age-related deficits in adaptive decision-making are due to impoverished neural representations in the orbitofrontal cortex and hippocampus.

The need to change: Is there a critical role of midlife adaptation in mental health later in life?

Although late-life depression (LLD) is a serious health problem and more common than dementia in people over 60, it is underdiagnosed and undertreated. The cognitive-emotional etiology of LLD is particularly poorly understood. This is in contrast to the now extensive literature from psychology and cognitive neuroscience on the characteristics of emotionally healthy aging. This research consistently shows a change in emotional processing in older adults that is modulated by prefrontal regulation. Lifespan theories explain this change in terms of neurocognitive adaptation to limited opportunities and resources that typically occur in the second half of life. Epidemiological data on an increase in well-being after a low point around age 50 suggest that the majority of people seem quite capable of making this adaptation, even though empirical evidence for a causal modulation of this so called 'paradox of aging' and for the role of the midlife dip is still lacking. Intriguingly, LLD is associated with deficits in emotional, cognitive, and prefrontal functions similar to those shown to be crucial for healthy adaptation. Suspected causes of these deficits, such as white matter lesions or affective instability, become apparent as early as midlife when internal and external changes as well as daily challenges set in. Based on these findings, we propose that some individuals who develop depression at older ages may not have been able to successfully implement self-regulatory adaptation at midlife. Here, we review the current evidence and theories on successful aging, the neurobiology of LLD, and well-being across the lifespan. Drawing on recent advances in lifespan theories, emotion regulation research, and cognitive neuroscience, we propose a model of successful versus unsuccessful adaptation that emphasizes the increasing need for implicit habitual control and resource-based regulatory choice during midlife.

Does prefrontal connectivity during task switching help or hinder children's performance?

The ability to flexibly switch between tasks is key for goal-directed behavior and continues to improve across childhood. Children's task switching difficulties are thought to reflect less efficient engagement of sustained and transient control processes, resulting in lower performance on blocks that intermix tasks (sustained demand) and trials that require a task switch (transient demand). Sustained and transient control processes are associated with frontoparietal regions, which develop throughout childhood and may contribute to task switching development. We examined age differences in the modulation of frontoparietal regions by sustained and transient control demands in children (8-11 years) and adults. Children showed greater performance costs than adults, especially under sustained demand, along with less upregulation of sustained and transient control activation in frontoparietal regions. Compared to adults, children showed increased connectivity between the inferior frontal junction (IFJ) and lateral prefrontal cortex (lPFC) from single to mixed blocks. For children whose sustained activation was less adult-like, increased IFJ-lPFC connectivity was associated with better performance. Children with more adult-like sustained activation showed the inverse effect. These results suggest that individual differences in task switching in later childhood at least partly depend on the recruitment of frontoparietal regions in an adult-like manner.

Valence bias in metacontrol of decision making in adolescents and young adults

The development of metacontrol of decision making and its susceptibility to framing effects were investigated in a sample of 201 adolescents and adults in Germany (12-25 years, 111 female, ethnicity not recorded). In a task that dissociates model-free and model-based decision making, outcome magnitude and outcome valence were manipulated. Both adolescents and adults showed metacontrol and metacontrol tended to increase across adolescence. Furthermore, model-based decision making was more pronounced for loss compared to gain frames but there was no evidence that this framing effect differed with age. Thus, the strategic adaptation of decision making continues to develop into young adulthood and for both adolescents and adults, losses increase the motivation to invest cognitive resources into an effortful decision-making strategy.

Meta-control: From psychology to computational neuroscience

Research in the past decades shed light on the different mechanisms that underlie our capacity for cognitive control. However, the meta-level processes that regulate cognitive control itself remain poorly understood. Following the terminology from artificial intelligence, meta-control can be defined as a collection of mechanisms that (a) monitor the progress of controlled processing and (b) regulate the underlying control parameters in the service of current task goals and in response to internal or external constraints. From a psychological perspective, meta-control is an important concept because it may help explain and predict how and when human agents select different types of behavioral strategies. From a cognitive neuroscience viewpoint, meta-control is a useful concept for understanding the complex networks in the prefrontal cortex that guide higher-level behavior as well as their interactions with neuromodulatory systems (such as the dopamine or norepinephrine system). The purpose of the special issue is to integrate hitherto segregated strands of research across three different perspectives: 1) a psychological perspective that specifies meta-control processes on a functional level and aims to operationalize them in experimental tasks; 2) a computational perspective that builds on ideas from artificial intelligence to formalize normative solutions to meta-control problems; and 3) a cognitive neuroscience perspective that identifies neural correlates of and mechanisms underlying meta-control.