Abstract:While it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce developmental outcomes. Here we test an interactive model of learning and development where certain learning-related changes are constrained by developmental changes in the brain against an alternative development-as-practice model where outcomes are determined primarily by the accumulation of experience regardless of age. Par… Show more
“…3 ; Table 2 ). In a real data application ( McCormick et al, 2021 ), we observed that an unmodeled effect of repeated practice artificially inflated the effects of age on learning performance (compare estimates in Table 1 , Table 3 ) exactly as these results would suggest. Fig.…”
Section: Resultssupporting
confidence: 63%
“…and age-related changes in outcomes of interest is a persistent issue, the utility of the MLMGM generalizes to many modeling contexts. Experience effects are often treated as a nuisance variable (( Ferrer et al, 2004 ; Rabbitt et al, 2001 ) but see ( McCormick et al, 2021 )) that contaminates the developmental effect. As such, MLMGMs can be used to partial out variance associated with repeated assessment.…”
Section: Discussionmentioning
confidence: 99%
“…As I have hopefully demonstrated with a small subset of potential scenarios, these models can be applied to understand a wide array of substantive theoretical questions. In addition to being useful for testing novel questions, MLMGMs could be used to re-visit previous data where simultaneous growth processes were not considered (i.e., age-only models; see ( McCormick et al, 2021 ) for an example). Additionally, the findings here highlight the clear importance of sampling design for fitting models appropriately in longitudinal data.…”
Section: Discussionmentioning
confidence: 99%
“…presents a threat to the internal validity of developmental inferences made from cohort longitudinal data. The effects of repeated exposure (e.g., practice effects) to observation could artificially enhance (or entirely account for) developmental effects estimated in longitudinal models ( Ferrer et al, 2004 ; McArdle et al, 1997 ; McCormick et al, 2021 ; Rabbitt et al, 2001 ), or conversely interfere and mask the effects of maturation (e.g., habituation). Fortunately, an alternative, the accelerated longitudinal design ( Fig.…”
Longitudinal models have become increasingly popular in recent years because of their power to test theoretically derived hypotheses by modeling within-person processes with repeated measures. Growth models constitute a flexible framework for modeling a range of complex trajectories across time in outcomes of interest, including non-linearities and time-varying covariates. However, these models can be expanded to include the effects of multiple growth processes at once on a single outcome. Here, I outline such an extension, showing how multiple growth processes can be modeled as a specific case of the general ability to include time-varying covariates in growth models. I show that this extension of growth models cannot be accomplished by statistical models alone, and that study design plays a crucial role in allowing for proper parameter recovery. I demonstrate these principles through simulations to mimic important theoretical conditions where modeling the effects of multiple growth processes can address developmental theory including, disaggregating the effects of age and practice or treatment in repeated assessments and modeling age- and puberty-related effects during adolescence. I compare how these models behave in two common longitudinal designs, cohort and accelerated, and how planned missingness in observations is key to parameter recovery. I conclude with directions for future substantive research using the method outlined here.
“…3 ; Table 2 ). In a real data application ( McCormick et al, 2021 ), we observed that an unmodeled effect of repeated practice artificially inflated the effects of age on learning performance (compare estimates in Table 1 , Table 3 ) exactly as these results would suggest. Fig.…”
Section: Resultssupporting
confidence: 63%
“…and age-related changes in outcomes of interest is a persistent issue, the utility of the MLMGM generalizes to many modeling contexts. Experience effects are often treated as a nuisance variable (( Ferrer et al, 2004 ; Rabbitt et al, 2001 ) but see ( McCormick et al, 2021 )) that contaminates the developmental effect. As such, MLMGMs can be used to partial out variance associated with repeated assessment.…”
Section: Discussionmentioning
confidence: 99%
“…As I have hopefully demonstrated with a small subset of potential scenarios, these models can be applied to understand a wide array of substantive theoretical questions. In addition to being useful for testing novel questions, MLMGMs could be used to re-visit previous data where simultaneous growth processes were not considered (i.e., age-only models; see ( McCormick et al, 2021 ) for an example). Additionally, the findings here highlight the clear importance of sampling design for fitting models appropriately in longitudinal data.…”
Section: Discussionmentioning
confidence: 99%
“…presents a threat to the internal validity of developmental inferences made from cohort longitudinal data. The effects of repeated exposure (e.g., practice effects) to observation could artificially enhance (or entirely account for) developmental effects estimated in longitudinal models ( Ferrer et al, 2004 ; McArdle et al, 1997 ; McCormick et al, 2021 ; Rabbitt et al, 2001 ), or conversely interfere and mask the effects of maturation (e.g., habituation). Fortunately, an alternative, the accelerated longitudinal design ( Fig.…”
Longitudinal models have become increasingly popular in recent years because of their power to test theoretically derived hypotheses by modeling within-person processes with repeated measures. Growth models constitute a flexible framework for modeling a range of complex trajectories across time in outcomes of interest, including non-linearities and time-varying covariates. However, these models can be expanded to include the effects of multiple growth processes at once on a single outcome. Here, I outline such an extension, showing how multiple growth processes can be modeled as a specific case of the general ability to include time-varying covariates in growth models. I show that this extension of growth models cannot be accomplished by statistical models alone, and that study design plays a crucial role in allowing for proper parameter recovery. I demonstrate these principles through simulations to mimic important theoretical conditions where modeling the effects of multiple growth processes can address developmental theory including, disaggregating the effects of age and practice or treatment in repeated assessments and modeling age- and puberty-related effects during adolescence. I compare how these models behave in two common longitudinal designs, cohort and accelerated, and how planned missingness in observations is key to parameter recovery. I conclude with directions for future substantive research using the method outlined here.
“…It produces statistical inference maps and probability maps, "which display the likelihood of a given term being used in a study if activation is observed at a particular voxel." More detailed information about the methods used to generate the images is available in the platform's methods paper (Yarkoni et al, 2011) Braams, Raijmakers, Peters, Van Duijvenvoorde, Koolschijn, & Crone, 2016), the results demonstrate (1) that complex learned behaviors involve the integrated and dynamic coordination of distributed brain circuits (Gerraty et al, 2018), and (2) adolescence may be a unique life phase for increased feedback-learning performance, particularly when it interacts with prior skill or experience (McCormick et al, 2021).…”
Section: Reinforcement Learning and Decision-makingmentioning
Adolescence is a developmental period characterized by substantial psychological, biological, and neurobiological changes. This review discusses the past decade of research on the adolescent brain, as based on the overarching framework that development is a dynamic process both within the individual and between the individual and external inputs. As such, this review focuses on research showing that the development of the brain is influenced by multiple ongoing and dynamic elements. It highlights the implications this body of work on behavioral development and offers areas of opportunity for future research in the coming decade.
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