Catherine A. Hartley

Educational attainment is a major determinant of health, wealth, and relationship quality, making it key for human flourishing. A diverse set of major childhood disruptions are known to predict poorer academic performance. Here we propose that childhood unpredictability constitutes a latent dimension of early-life environments underpinning these effects, suggesting everyday instability, in addition to major disruptions, should determine educational success. To test this hypothesis, we link population-wide Danish administrative records with a validated survey measure to quantify individuals' life disruptions, both major and minor. Among 2,793 respondents, greater unpredictability is associated with lower grade point averages and reduced high school completion, even after accounting for parental socioeconomic status and observed major life disruptions. Our finding that childhood unpredictability alters educational success, suggests a pathway through which disadvantage may be transmitted across generations and highlights everyday unpredictability as a potential target for interventions aimed at improving social mobility.

Most widely studied option evaluation strategies rely on knowledge accumulated across repeated experiences. But how should options be evaluated in unfamiliar environments, in which knowledge is sparse? In these situations, how do decision makers make efficient use of limited past experience to guide their choices? One possible strategy is episodic sampling, in which a decision maker retrieves a small number of past decisions from memory to estimate the value of present options. By virtue of their age, children and adolescents have less experience than adults, making episodic sampling a particularly useful strategy for them. At the same time, the effectiveness of episodic sampling derives from memory’s precision and context sensitivity—properties that continue to develop into adolescence and young adulthood. This tension raises a key question: are developmental differences in episodic memory associated with differences in episodic sampling? To address this question, 106 participants, ages 8 to 25, completed a 2-d choice task that dissociated the influence of a single episodic memory from the influence of multiple episodes sharing a common context. At all ages, single episodes biased choices. But, only adults were sensitive to the broader evoked context. Further clarifying the relationship between episodic memory and decision making, differences in memory precision predicted differences in episodic sampling, even after taking into account age, while episodic sampling, in turn, accounted for individual differences in forward planning. Together, these findings suggest that episodic memory guides decision making throughout development, but the character of its influence evolves as memory becomes more precise and richly structured.

To learn to make good choices, learners must calibrate how quickly they update their beliefs based on the statistics of their environments: When environments change rapidly, people should base their choices on the most recent outcomes they have experienced, whereas when environments are more stable, people should use a longer history of experienced outcomes to guide their decisions. Prior research has revealed that adults dynamically adapt their learning computations in this way, and that such adaptation is a hallmark of healthy psychological functioning. To date, however, it is unclear how this adaptive calibration of reinforcement learning computations changes across adolescence. Here, 105 adolescents and adults (ages 12 - 25) completed a probabilistic learning task in which they learned to avoid aversive outcomes through trial and error, across environments with both stable and volatile reward contingencies. Computational modeling of participant choices revealed that younger participants demonstrated greater adaptation of learning computations across task contexts, placing more weight on recent outcomes and making noisier choices in the volatile versus stable environment. However, concurrent pupil size recordings during the task revealed that participants across age demonstrated similar sensitivity to reward uncertainty and outcome surprise during learning. Together, these findings suggest that adolescence may be characterized by heightened responsivity to environmental volatility, and lay the groundwork for future studies investigating how deviations in trajectories of adaptive learning may relate to the emergence of psychopathology.

Children are adept statistical learners, capable of parsing streams of structured input into meaningful units, but the cognitive processes they engage during learning may differ from those of adults. To date, however, it is unclear how learners of different ages predict upcoming experience when navigating environments with complex structure, as well as how changes in predictive learning mechanisms influence structured knowledge acquisition. To address this question, we tested 106 children, adolescents, and adults, ages 8–22 years, on a predictive learning task, in which they experienced sequences of stimuli with a higher‐order temporal structure. After an initial learning phase, participants’ explicit knowledge of the relations between stimuli was probed via two additional task measures. We used a recently introduced computational model to characterize participants’ response times during learning, and found that all participants relied on simple, recency‐based prediction, anticipating that they would encounter stimuli they recently encountered in the past. With increasing age, however, participants demonstrated greater evidence of additionally relying on a more sophisticated learning mechanism, which captured a predictive representation of the conditional relations between stimuli. Though predictive learning changed with age, we found only weak evidence that these changes related to the acquisition of explicit knowledge of the environment. Our results suggest that the learning mechanisms through which people parse continuous streams of experience change with age, influencing their predictions about upcoming events. Summary: Children, adolescents, and adults all learn to segment continuous streams of structured perceptual input, but they may do so via different learning processes. We examined how the learning mechanisms that enable people to predict upcoming experiences change and relate to structured knowledge acquisition across development. Computational modeling revealed that in a graph‐learning task, younger participants relied on simple, recency‐based prediction, while older participants tracked temporal relations between stimuli. The extent to which participants engaged in this sophisticated form of predictive learning only weakly related to their knowledge of the task's structure.

Anhedonia – the diminished capacity to experience or anticipate pleasure – is among the most common consequences of early-life unpredictability, yet how these co-occurring conditions jointly shape real-world decision-making remains unknown. Here, we use a sequential foraging-under-threat task to probe motivational conflict decisions in 357 individuals varying in early-life unpredictability and anhedonia symptoms. We find that unpredictability and anhedonia exert opposing influences on choice: unpredictability shifts behavior away from the survival-optimal policy in a sex-dependent manner, while anhedonia promotes adherence to it, partly through heightened sensitivity to unexpected threatening outcomes. A mediation analysis reveals that anhedonia partially buffers the deleterious effects of unpredictability on decision quality. These results demonstrate that co-occurring conditions can mask one another’s behavioral signatures and suggest that the heterogeneous expression of transdiagnostic constructs like anhedonia may reflect context-dependent adaptations to distinct underlying etiologies.

Functional accounts of mind, brain, and behavior have profoundly shaped thinking across many disciplines. Despite this common lens, the processes driving adaptive outcomes are often studied in isolation, leading to narrow and localized explanations. This paper brings together perspectives across fields by identifying six dynamics that enable agents to behave adaptively. These dynamics span multiple timescales and systems, including individual-level processes—thinking, learning, and development—and population-level processes—market selection, cultural evolution, and genetic evolution. We examine the reasons behind functional explanations of cognition and behavior, the conditions that foster rationality, and the interactions between adaptive processes. Our goal is to bring the cognitive, social, biological, and computer sciences into closer conversation by exposing researchers to a wide range of perspectives on adaptive decision-making.

Rewards signal information in the environment that is valuable and thus useful to remember. Rewards benefit memory across development, but how reward-associated memories are represented in the brain has not been well characterized. Here we conducted pattern similarity analyses of fMRI data in participants aged 8-25 to elucidate how neural representations in key memory-related brain areas are influenced by reward, and how these relationships change across childhood and adolescence. We found that reward information was reflected in pattern similarity during encoding in ventral temporal cortex and in changes in similarity from encoding to retrieval in anterior hippocampus (aHC). Strikingly, aHC reward-sensitive representations also varied with age such that adults’ memory benefitted from stability of hippocampal representations, whereas younger participants’ memory improvements were associated with greater drift in representations over time. Moreover, across all participants, reward-related univariate activation in the ventral tegmental area was associated with a greater tendency toward representational drift in aHC. Taken together, our findings demonstrate that reward modulates neural memory representations, and that the representational patterns supporting reward-motivated memory shift with age.

Rewards signal information in the environment that is valuable and thus useful to remember. Rewards benefit memory across development, but how reward-associated memories are represented in the brain has not been well characterized. Here we conducted pattern similarity analyses of fMRI data in male and female participants aged 8–25 to elucidate how neural representations in key memory-related brain areas are influenced by reward and how these relationships change across childhood and adolescence. We found that reward information was reflected in pattern similarity during encoding in the ventral temporal cortex and in changes in similarity from encoding to retrieval in anterior hippocampus (aHC). Strikingly, aHC reward-sensitive representations also varied with age such that adults’ memory benefitted from stability of hippocampal representations, whereas younger participants’ memory improvements were associated with greater drift in representations over time. Moreover, across all participants, reward-related univariate activation in the ventral tegmental area was associated with a greater tendency toward representational drift in aHC. Taken together, our findings demonstrate that reward modulates neural memory representations and that the representational patterns supporting reward-motivated memory shift with age.

When adolescents start a new school or arrive at university, they must navigate new campuses, new peers and new routines. Exploration is recognised as a key cognitive process supporting adolescent development and wellbeing but how real-world exploration relates to emotional adjustment during major life transitions remains unclear. To address this question, we tracked adolescents entering high school and university (N=64) over the first three months of transition using continuous smartphone geolocation and ecological momentary assessment (EMA). We quantified daily exploration as the dispersion of time in a day across locations (“roaming entropy”) and the number of newly visited locations (“novelty”) and assessed in-the-moment affect, autonomy and new social connections using EMA. On days when individuals visited more novel locations than usual, they reported higher positive affect, lower negative affect and more new social connections. Novelty was a stronger predictor of these outcomes than roaming entropy. Associations between exploration and positive affect were more pronounced in university students than in high school students, consistent with greater autonomy during this transition. Greater anxiety and depressive symptoms were not associated with lower exploration, but attenuated the positive associations between novelty, affect and perceived autonomy. These findings link theories and laboratory studies of adolescent exploration and mental health to naturalistic behaviour, showing that engagement with novel environments during major transitions is linked to positive emotional and social experiences. Importantly, these associations were weaker when depression and anxiety symptoms were greater. Real-world exploration may therefore represent a behavioural pathway supporting adjustment as young people navigate environmental change.

Children are adept statistical learners, capable of parsing streams of structured input into meaningful units, but the cognitive processes they engage during learning may differ from those of adults. To date, however, it is unclear how learners of different ages predict upcoming experience when navigating environments with complex structure, as well as how changes in predictive learning mechanisms influence structured knowledge acquisition. To address this question, we tested 106 children, adolescents, and adults, ages 8 - 22 years, on a predictive learning task, in which they experienced sequences of stimuli with a higher-order temporal structure. After an initial learning phase, participants’ explicit knowledge of the relations between stimuli was probed via two additional task measures. We used a recently introduced computational model to characterize participants’ response times during learning, and found that all participants relied on simple, recency-based prediction, anticipating that they would encounter stimuli they recently encountered in the past. With increasing age, however, participants demonstrated greater evidence of additionally relying on a more sophisticated learning mechanism, which captured a predictive representation of the conditional relations between stimuli. Though predictive learning changed with age, we found only weak evidence that these changes related to the acquisition of explicit knowledge of the environment. Our results suggest that the learning mechanisms through which people parse continuous streams of experience change with age, influencing their predictions about upcoming events.