Maartje E. J. Raijmakers

A dynamical model of general intelligence: The positive manifold of intelligence by mutualism

Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.

Evaluating the Negative or Valuing the Positive? Neural Mechanisms Supporting Feedback-Based Learning across Development

How children learn from positive and negative performance feedback lies at the foundation of successful learning and is therefore of great importance for educational practice. In this study, we used functional magnetic resonance imaging (fMRI) to examine the neural developmental changes related to feedback-based learning when performing a rule search and application task. Behavioral results from three age groups (8–9, 11–13, and 18–25 years of age) demonstrated that, compared with adults, 8- to 9-year-old children performed disproportionally more inaccurately after receiving negative feedback relative to positive feedback. Additionally, imaging data pointed toward a qualitative difference in how children and adults use performance feedback. That is, dorsolateral prefrontal cortex and superior parietal cortex were more active after negative feedback for adults, but after positive feedback for children (8–9 years of age). For 11- to 13-year-olds, these regions did not show differential feedback sensitivity, suggesting that the transition occurs around this age. Pre-supplementary motor area/anterior cingulate cortex, in contrast, was more active after negative feedback in both 11- to 13-year-olds and adults, but not 8- to 9-year-olds. Together, the current data show that cognitive control areas are differentially engaged during feedback-based learning across development. Adults engage these regions after signals of response adjustment (i.e., negative feedback). Young children engage these regions after signals of response continuation (i.e., positive feedback). The neural activation patterns found in 11- to 13-year-olds indicate a transition around this age toward an increased influence of negative feedback on performance adjustment. This is the first developmental fMRI study to compare qualitative changes in brain activation during feedback learning across distinct stages of development.

Fitting hidden Markov models to psychological data

Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.

On the Validity of Simulating Stagewise Development by Means of PDP Networks: Application of Catastrophe Analysis and an Experimental Test of Rule‐Like Network Performance

This article addresses the ability of Parallel Distributed Processing (PDP) networks to generate stagewise cognitive development in accordance with Piagets theory of cognitive epigenesis. We carried out a replication study of the simulation experiments by McClelland (1989) and McClelland and Jenkins (1991) in which a PDP network learns to solve balance scale problems. In objective tests motivated from catastrophe theory, a mathematical theory of transitions in epigenetical systems, no evidence for stage transitions in network performance was found. It is concluded that PDP networks lack the ability to recover the positive outcomes of analogous catastrophe analyses of real cognitive developmental data. In an attempt to further characterize the learning behaviour of PDP networks, we carried out a second simulation study using the discrimination-shift paradigm. The results thus obtained indicate that PDP learning is compatible with the learning of stimulus-response relationships, not with the acquisition of mediating rules such as conceived in (neo-)Piagetian theory. In closing, we speculate about the feasibility of simulating stagewise development with alternative network architectures.

Multiple learning modes in the development of performance on a rule-based category-learning task

Behavioral and neuropsychological data suggest that multiple systems are involved in category-learning. In this paper, the existence and the development of multiple modes of learning of a rule-based category structure was examined, and features of different learning processes were identified. Data were obtained in a cross-sectional study by Raijmakers et al. [Raijmakers, M. E. J., Dolan, C. V., & Molenaar, P. C. M. (2001). Finite mixture distribution models of simple discrimination learning. Memory and Cognition, 29, 659-677], in which subjects aged 4-20 years carried out a rule-based category-learning task. Learning models were employed to investigate the development of the learning processes in the sample. The results support the hypothesis of two distinct learning modes, rather than a single general mode of learning with a continuum of appearances. One mode represents sudden rational learning by means of hypothesis testing. In the second, slow learning mode, learning also occurs suddenly as opposed to incrementally. The probability of rational learning increases with age, and seems to be related to dimension preference in the younger age groups. However, the finding of distinct learning modes does not necessarily imply that distinct learning systems are involved. Implications for the interpretation and clinical use of tasks with a category-learning component, such as the Wisconsin Card Sorting Test (WCST [Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, G. G., & Curtis, G. (Eds.). (1993). Wisconsin card sorting test manual: Revised and expanded. Odessa, FL: Psychological Assessment Resources]), are discussed.

The Association Between Autism and Errors in Early Embryogenesis: What Is the Causal Mechanism?

The association between embryonic errors and the development of autism has been recognized in the literature, but the mechanism underlying this association remains unknown. We propose that pleiotropic effects during a very early and specific stage of embryonic development-early organogenesis-can explain this association. In humans early organogenesis is an embryonic stage, spanning Day 20 to Day 40 after fertilization, which is characterized by intense interactivity among body parts of the embryo. This implies that a single mutation or environmental disturbance affecting development at this stage can have several phenotypic effects (i.e., pleiotropic effects). Disturbances during early organogenesis can lead to many different anomalies, including limb deformities, craniofacial malformations, brain pathology, and anomalies in other organs. We reviewed the literature and found ample evidence for the association between autism and different kinds of physical anomalies, which agrees with the hypothesis that pleiotropic effects are involved in the development of autism. The proposed mechanism integrates findings from a variety of studies on autism, including neurobiological studies and studies on physical anomalies and prenatal influences on neurodevelopmental outcomes. The implication is that the origin of autism can be much earlier in embryologic development than has been frequently reported.

The neural coding of feedback learning across child and adolescent development

The ability to learn from environmental cues is an important contributor to successful performance in a variety of settings, including school. Despite the progress in unraveling the neural correlates of cognitive control in childhood and adolescence, relatively little is known about how these brain regions contribute to learning. In this study, 268 participants aged 8–25 years performed a rule-learning task with performance feedback in a 3T MRI scanner. We examined the development of the frontoparietal network during feedback learning by exploring contributions of age and pubertal development. The pFC showed more activation following negative compared with positive feedback with increasing age. In contrast, our data suggested that the parietal cortex demonstrated a shift from sensitivity to positive feedback in young children to negative feedback in adolescents and adults. These findings were interpreted in terms of separable contributions of the frontoparietal network in childhood to more integrated functions in adulthood. Puberty (testosterone, estradiol, and self-report) did not explain additional variance in neural activation patterns above age, suggesting that development of the frontoparietal network occurs relatively independently from hormonal development. This study presents novel insights into the development of learning, moving beyond a simple frontoparietal immaturity hypothesis.

Nonlinear epigenetic variance: review and simulations

We present a review of empirical evidence that suggests that a substantial portion of phenotypic variance is due to nonlinear (epigenetic) processes during ontogenesis. The role of such processes as a source of phenotypic variance in human behaviour genetic studies is not fully appreciated. In addition to our review, we present simulation studies of nonlinear epigenetic variance using a computational model of neuronal network development. In each simulation study, time series for monozygotic and dizygotic twins were generated and analysed using conventional behaviour genetic modelling. In the results of these analyses, the nonlinear epigenetic variance was subsumed under the non-shared environmental component. As is commonly found in behaviour genetic studies, observed heritabilities and unique environmentabilities increased with time, whereas common environmentabilities decreased. The fact that the phenotypic effects of nonlinear epigenetic processes appear as unsystematic variance in conventional twin analyses complicates the identification and quantification of the ultimate genetic and environmental causes of individual differences. We believe that nonlinear dynamical system theories provide a challenging perspective on the development of individual differences, which may enrich behaviour genetic studies.

Finite mixture distribution models of simple discrimination learning

Through the application of finite mixture distribution models, we investigated the existence of distinct modes of behavior in learning a simple discrimination. The data were obtained in a repeated measures study in which subjects aged 6 to 10 years carried out a simple discrimination learning task. In contrast to distribution models of exclusively rational learners or exclusively incremental learners, a mixture distribution model of rational learners and slow learners was found to fit the data of all measurement occasions and all age groups. Hence, the finite mixture distribution analysis provides strong support for the existence of distinct modes of learning behavior. The results of a second experiment support this conclusion by crossvalidation of the models that fit the data of the first experiment. The effect of verbally labeling the values on the relevant stimulus dimension and the consistency of behavior over measurement occasions are related to the mixture model estimates.

Hidden Markov models for individual time series

This chapter introduces hidden Markov models to study and characterize (individual) time series such as observed in psychological experiments of learning, repeated panel data, repeated observations comprising a developmental trajectory etc. Markov models form a broad and flexible class of models with many possible extensions, while at the same time allowing for relatively easy analysis and straightforward interpretation. Here we focus on hidden Markov models with a discrete underlying state space, and observations at discrete times; however, hidden Markov models are not limited to these situations and some pointers are provided to literature on possible extensions.