RESUMEN

BACKGROUND: Children's cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning. The relative dearth of empirical work on variability, historically limited due to a lack of suitable data and quantitative methodology, has left crucial questions unanswered, which the CODEC (COgnitive Dynamics in Early Childhood) study aims to address. METHOD: The CODEC cohort is an accelerated 3-year longitudinal study which encompasses 600 7-to-10-year-old children. Each year includes a 'burst' week (3 times per day, 5 days per week) of cognitive measurements on five cognitive domains (reasoning, working memory, processing speed, vocabulary, exploration), conducted both in classrooms and at home through experience sampling assessments. We also measure academic outcomes and external factors hypothesised to predict cognitive variability, including sleep, mood, motivation and background noise. A subset of 200 children (CODEC-MRI) are invited for two deep phenotyping sessions (in year 1 and year 3 of the study), including structural and functional magnetic resonance imaging, eye-tracking, parental measurements and questionnaire-based demographic and psychosocial measures. We will quantify developmental differences and changes in variability using Dynamic Structural Equation Modelling, allowing us to simultaneously capture variability and the multilevel structure of trials nested in sessions, days, children and classrooms. DISCUSSION: CODEC's unique design allows us to measure variability across a range of different cognitive domains, ages, and temporal resolutions. The deep-phenotyping arm allows us to test hypotheses concerning variability, including the role of mind wandering, strategy exploration, mood, sleep, and brain structure. Due to CODEC's longitudinal nature, we are able to quantify which measures of variability at baseline predict long-term outcomes. In summary, the CODEC study is a unique longitudinal study combining experience sampling, an accelerated longitudinal 'burst' design, deep phenotyping, and cutting-edge statistical methodologies to better understand the nature, causes, and consequences of cognitive variability in children. TRIAL REGISTRATION: ClinicalTrials.gov - NCT06330090.

Asunto(s)

RESUMEN

BACKGROUND: Internet-based cognitive behavioral interventions (iCBTs) are efficacious treatments for depression and anxiety. However, it is unknown whether adding human guidance is feasible and beneficial within a large educational setting. OBJECTIVE: This study aims to potentially demonstrate the superiority of 2 variants of a transdiagnostic iCBT program (human-guided and computer-guided iCBT) over care as usual (CAU) in a large sample of university students and the superiority of human-guided iCBT over computer-guided iCBT. METHODS: A total of 801 students with elevated levels of anxiety, depression, or both from a large university in the Netherlands were recruited as participants and randomized to 1 of 3 conditions: human-guided iCBT, computer-guided iCBT, and CAU. The primary outcome measures were depression (Patient Health Questionnaire) and anxiety (Generalized Anxiety Disorder scale). Secondary outcomes included substance use-related problems (Alcohol Use Disorder Identification Test and Drug Abuse Screening Test-10 items). Linear mixed models were used to estimate the effects of time, treatment group, and their interactions (slopes). The primary research question was whether the 3 conditions differed in improvement over 3 time points (baseline, midtreatment, and after treatment) in terms of depression and anxiety symptoms. Results were analyzed according to the intention-to-treat principle using multiple imputation. Patients were followed exploratively from baseline to 6 and 12 months. RESULTS: In both short-term and long-term analyses, the slopes for the 3 conditions did not differ significantly in terms of depression and anxiety, although both web-based interventions were marginally more efficacious than CAU over 6 months (P values between .02 and .03). All groups showed significant improvement over time (P<.001). For the secondary outcomes, only significant improvements over time (across and not between groups) were found for drug use (P<.001). Significant differences were found in terms of adherence, indicating that participants in the human-guided condition did more sessions than those in the computer-guided condition (P=.002). CONCLUSIONS: The transdiagnostic iCBT program offers a practical, feasible, and efficacious alternative to usual care to tackle mental health problems in a large university setting. There is no indication that human guidance should be preferred over technological guidance. The potential preference of human support also depends on the scale of implementation and cost-effectiveness, which need to be addressed in future trials. TRIAL REGISTRATION: International Clinical Trials Registry Platform NL7328/NTR7544; https://trialsearch.who.int/Trial2.aspx?TrialID=NL-OMON26795.

Asunto(s)

RESUMEN

(e.g., characters or fractals) and concrete stimuli (e.g., pictures of everyday objects) are used interchangeably in the reinforcement-learning literature. Yet, it is unclear whether the same learning processes underlie learning from these different stimulus types. In two preregistered experiments (N = 50 each), we assessed whether abstract and concrete stimuli yield different reinforcement-learning performance and whether this difference can be explained by verbalization. We argued that concrete stimuli are easier to verbalize than abstract ones, and that people therefore can appeal to the phonological loop, a subcomponent of the working-memory system responsible for storing and rehearsing verbal information, while learning. To test whether this verbalization aids reinforcement-learning performance, we administered a reinforcement-learning task in which participants learned either abstract or concrete stimuli while verbalization was hindered or not. In the first experiment, results showed a more pronounced detrimental effect of hindered verbalization for concrete than abstract stimuli on response times, but not on accuracy. In the second experiment, in which we reduced the response window, results showed the differential effect of hindered verbalization between stimulus types on accuracy, not on response times. These results imply that verbalization aids learning for concrete, but not abstract, stimuli and therefore that different processes underlie learning from these types of stimuli. This emphasizes the importance of carefully considering stimulus types. We discuss these findings in light of generalizability and validity of reinforcement-learning research.

RESUMEN

It has recently been suggested that parameter estimates of computational models can be used to understand individual differences at the process level. One area of research in which this approach, called computational phenotyping, has taken hold is computational psychiatry. One requirement for successful computational phenotyping is that behavior and parameters are stable over time. Surprisingly, the test-retest reliability of behavior and model parameters remains unknown for most experimental tasks and models. The present study seeks to close this gap by investigating the test-retest reliability of canonical reinforcement learning models in the context of two often-used learning paradigms: a two-armed bandit and a reversal learning task. We tested independent cohorts for the two tasks (N = 69 and N = 47) via an online testing platform with a between-test interval of five weeks. Whereas reliability was high for personality and cognitive measures (with ICCs ranging from .67 to .93), it was generally poor for the parameter estimates of the reinforcement learning models (with ICCs ranging from .02 to .52 for the bandit task and from .01 to .71 for the reversal learning task). Given that simulations indicated that our procedures could detect high test-retest reliability, this suggests that a significant proportion of the variability must be ascribed to the participants themselves. In support of that hypothesis, we show that mood (stress and happiness) can partly explain within-participant variability. Taken together, these results are critical for current practices in computational phenotyping and suggest that individual variability should be taken into account in the future development of the field.

RESUMEN

Adolescence is characterized by a surge in maladaptive risk-taking behaviors, but whether and how this relates to developmental changes in experience-based learning is largely unknown. In this preregistered study, we addressed this issue using a novel task that allowed us to separate the learning-driven optimization of risky choice behavior over time from overall risk-taking tendencies. Adolescents (12-17 years old) learned to dissociate advantageous from disadvantageous risky choices less well than adults (20-35 years old), and this impairment was stronger in early than mid-late adolescents. Computational modeling revealed that adolescents' suboptimal performance was largely due to an inefficiency in core learning and choice processes. Specifically, adolescents used a simpler, suboptimal, expectation-updating process and a more stochastic choice policy. In addition, the modeling results suggested that adolescents, but not adults, overvalued the highest rewards. Finally, an exploratory latent-mixture model analysis indicated that a substantial proportion of the participants in each age group did not engage in experience-based learning but used a gambler's fallacy strategy, stressing the importance of analyzing individual differences. Our results help understand why adolescents tend to make more, and more persistent, maladaptive risky decisions than adults when the values of these decisions have to be learned from experience.

Asunto(s)

RESUMEN

Attention-Deficit/Hyperactivity Disorder (ADHD) in children is associated with several adverse family characteristics, such as higher parenting stress, more conflicted parent-child relationships, lower parental competence, and higher levels of parental psychopathology. Hence, children with ADHD more often grow up under suboptimal circumstances, which may impact the development of their attachment representations. Here, we investigated whether children with ADHD have more insecure and disorganized attachment representations than their typically developing peers, and which factors could explain this association. We included 104 children between 4 and 11 years old, 74 with ADHD (without Conduct Disorder) and 30 typically developing control children. Children completed a state-of-the-art story stem task to assess their attachment representation, and we measured parents' expressed emotion (as an index of parent-child relationship quality), parents' perceived sense of competence, parental education levels, and parent-rated ODD symptoms of the child. We found that, after controlling for multiple comparisons, children with ADHD had less secure and more ambivalent and disorganized attachment representations relative to their typically developing peers. These group differences were independent of comorbid ODD and parental education levels. There were no group differences on avoidant attachment representations. Explorative analyses within the ADHD group showed that attachment representations were not related to parent-child relationship quality, perceived parenting competence, parental education levels, and comorbid ODD symptoms. We conclude that children with ADHD disproportionately often have attachment problems. Although this conclusion is important, treatment implications of this co-occurrence are yet unclear as research on ADHD and attachment is still in its infancy.

RESUMEN

Recent studies that compared effects of pre-learning advice on experience-based learning in adolescents and adults have yielded mixed results. Previous studies on this topic used choice tasks in which age-related differences in advice-related learning bias and exploratory choice behavior are difficult to dissociate. Moreover, these studies did not examine whether effects of advice depend on working memory load. In this preregistered study (in adolescents [13-15 years old] and adults [18-31 years old]), we addressed these issues by factorially combining advice and working memory load manipulations in an estimation task that does not require choices and hence eliminates the influence of known age-related differences in exploration. We found that advice guided participants' initial estimates in both age groups. When advice was correct, this improved estimation performance, especially in adolescents when working memory load was high. When advice was incorrect, it had a longer-lasting effect on adolescents' performance than on adults' performance. In contrast to previous findings in choice tasks, we found no evidence that advice biased learning in either age group. Taken together, our results suggest that learning in an estimation task improves between adolescence and adulthood but that the effects of advice on learning do not differ substantially between adolescents and adults.

Asunto(s)

RESUMEN

Healthy adults flexibly adapt their learning strategies to ongoing changes in uncertainty, a key feature of adaptive behaviour. However, the developmental trajectory of this ability is yet unknown, as developmental studies have not incorporated trial-to-trial variation in uncertainty in their analyses or models. To address this issue, we compared adolescents' and adults' trial-to-trial dynamics of uncertainty, learning rate, and exploration in two tasks that assess learning in noisy but otherwise stable environments. In an estimation task-which provides direct indices of trial-specific learning rate-both age groups reduced their learning rate over time, as self-reported uncertainty decreased. Accordingly, the estimation data in both groups was better explained by a Bayesian model with dynamic learning rate (Kalman filter) than by conventional reinforcement-learning models. Furthermore, adolescents' learning rates asymptoted at a higher level, reflecting an over-weighting of the most recent outcome, and the estimated Kalman-filter parameters suggested that this was due to an overestimation of environmental volatility. In a choice task, both age groups became more likely to choose the higher-valued option over time, but this increase in choice accuracy was smaller in the adolescents. In contrast to the estimation task, we found no evidence for a Bayesian expectation-updating process in the choice task, suggesting that estimation and choice tasks engage different learning processes. However, our modeling results of the choice task suggested that both age groups reduced their degree of exploration over time, and that the adolescents explored overall more than the adults. Finally, age-related differences in exploration parameters from fits to the choice data were mediated by participants' volatility parameter from fits to the estimation data. Together, these results suggest that adolescents overestimate the rate of environmental change, resulting in elevated learning rates and increased exploration, which may help understand developmental changes in learning and decision-making.

Asunto(s)