Modelling individual variability in cognitive development

Investigating variability in reasoning tasks can provide insights into key issues in the study of cognitive development. These include the mechanisms that underlie developmental transitions, and the distinction between individual differences and developmental disorders. We explored the mechanistic basis of variability in two connectionist models of cognitive development, a model of the Piagetian balance scale task (McClelland, 1989) and a model of the Piagetian conservation task (Shultz, 1998). For the balance scale task, we began with a simple feed-forward connectionist model and training patterns based on McClelland (1989). We investigated computational parameters, problem encodings, and training environments that contributed to variability in development, both across groups and within individuals. We report on the parameters that affect the complexity of reasoning and the nature of ‘rule’ transitions exhibited by networks learning to reason about balance scale problems. For the conservation task, we took the task structure and problem encoding of Shultz (1998) as our base model. We examined the computational parameters, problem encodings, and training environments that contributed to variability in development, in particular examining the parameters that affected the emergence of abstraction. We relate the findings to existing cognitive theories on the causes of individual differences in development

A review of research into the development of radiologic expertise: Implications for computer-based training

Rationale and Objectives. Studies of radiologic error reveal high levels of variation between radiologists. Although it is known that experts outperform novices, we have only limited knowledge about radiologic expertise and how it is acquired.Materials and Methods. This review identifies three areas of research: studies of the impact of experience and related factors on the accuracy of decision-making; studies of the organization of expert knowledge; and studies of radiologists' perceptual processes.Results and Conclusion. Interpreting evidence from these three paradigms in the light of recent research into perceptual learning and studies of the visual pathway has a number of conclusions for the training of radiologists, particularly for the design of computer-based learning programs that are able to illustrate the similarities and differences between diagnoses, to give access to large numbers of cases and to help identify weaknesses in the way trainees build up a global representation from fixated regions

Anatomical and Functional Organization of Domain-General Brain Regions

How does complex brain activity organize thought and behaviour? Theoretical proposals have long emphasized that intelligent behaviour must be supported by a flexible control system. Numerous brain imaging studies identified a domain-general or “multiple-demand” (MD) brain system co-activated accompanying many tasks and is hypothesised to play a central role in cognitive control. However, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding its anatomy and physiology. To address these limitations, the experiments in chapters 2 and 3 capitalize on novel multi-modal magnetic resonance imaging (MRI) methods developed by the Human Connectome Project. Chapter 2 delineated nine cortical MD patches per hemisphere and subdivided them into 10 regions forming a core of most strongly activated and functionally interconnected regions, surrounded by a penumbra of 17 additional regions. MD activations were also identified in specific subcortical and cerebellar regions. Chapter 3 investigated the relation between the newly defined MD regions and previously identified sensory-biased cortical regions. Contrasting auditory and visual low working memory demands revealed the strongest sensory-biases are localized just outside of MD regions. And additional working memory demands revealed MD activations showed no sensory biases. Chapter 4 used human electrophysiological recordings from the lateral frontal cortex to functionally map cognitive control regions during awake neurosurgeries. By contrasting a hard vs easy cognitive demand, spectral analysis revealed localized power increases in the gamma range (>30 Hz) that overlap with a canonical mask of the fronto-parietal control network. These findings contrast with spatially non-specific power decreases in the beta range (12-30 Hz). Thus, using similar task difficulty manipulations, electrophysiology and MRI functional signals converged on localizing lateral frontal regions related to cognitive control and support their clinical potential for intraoperative mapping of cognitive control. All together, the distributed anatomical organization, mosaic functional preferences, and strong functional interconnectivity of MD regions, suggest a skeleton for integrating and organizing the diverse components of cognitive operations. The precise anatomical delineation of MD regions provides the groundwork for refined analyses of their functions

Exploring the Neural Mechanisms of Physics Learning

This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics

Resting state connectivity and cognitive performance in adults with cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy

Cognitive impairment is an inevitable feature of cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), affecting executive function, attention and processing speed from an early stage. Impairment is associated with structural markers such as lacunes, but associations with functional connectivity have not yet been reported. Twenty-two adults with genetically-confirmed CADASIL (11 male; aged 49.8 ± 11.2 years) underwent functional magnetic resonance imaging at rest. Intrinsic attentional/executive networks were identified using group independent components analysis. A linear regression model tested voxel-wise associations between cognitive measures and component spatial maps, and Pearson correlations were performed with mean intra-component connectivity z-scores. Two frontoparietal components were associated with cognitive performance. Voxel-wise analyses showed an association between one component cluster and processing speed (left middle temporal gyrus; peak −48, −18, −14; ZE = 5.65, pFWEcorr = 0.001). Mean connectivity in both components correlated with processing speed (r = 0.45, p = 0.043; r = 0.56, p = 0.008). Mean connectivity in one component correlated with faster Trailmaking B minus A time (r = −0.77, p < 0.001) and better executive performance (r = 0.56, p = 0.011). This preliminary study provides evidence for associations between cognitive performance and attentional network connectivity in CADASIL. Functional connectivity may be a useful biomarker of cognitive performance in this population

Changes in resting-state functionally connected parieto-frontal networks after videogame practice

Neuroimaging studies provide evidence for organized intrinsic activity under task-free conditions. This activity serves functionally relevant brain systems supporting cognition. Here, we analyze changes in resting-state functional connectivity after videogame practice applying a test–retest design. Twenty young females were selected from a group of 100 participants tested on four standardized cognitive ability tests. The practice and control groups were carefully matched on their ability scores. The practice group played during two sessions per week across 4 weeks (16 h total) under strict supervision in the laboratory, showing systematic performance improvements in the game. A group independent component analysis (GICA) applying multisession temporal concatenation on test–retest resting-state fMRI, jointly with a dual-regression approach, was computed. Supporting the main hypothesis, the key finding reveals an increased correlated activity during rest in certain predefined resting state networks (albeit using uncorrected statistics) attributable to practice with the cognitively demanding tasks of the videogame. Observed changes were mainly concentrated on parietofrontal networks involved in heterogeneous cognitive functions

Beta Oscillations and Hippocampal Place Cell Learning during Exploration of Novel Environments

Berke et al. (2008) reported that beta oscillations occur during the learning of hippocampal place cell receptive fields in novel environments. Place cell selectivity can develop within seconds to minutes, and can remain stable for months. Paradoxically, beta power was very low during the first lap of exploration, grew to full strength as a mouse traversed a lap for the second and third times, and became and remained low again after the first two minutes of exploration. Beta oscillation power also correlated with the rate at which place cells became spatially selective, and not with theta oscillations. We explain such beta oscillations as a consequence of how place cell receptive fields may be learned as spatially selective categories due to feedback interactions between entorhinal cortex and hippocampus. Top-down attentive feedback helps to ensure rapid learning and stable memory of place cells. Beta oscillations are generated when top-down feedback mismatches bottom-up data as place cell receptive fields are refined. Beta oscillations do not occur on the first trial because adaptive weights in feedback pathways are all sufficiently large then to match any input pattern. On subsequent trials, adaptive weights become pruned as they learn to match the sharpening receptive fields of the place cell categories, thereby causing mismatches until place cell receptive fields stabilize.National Science Foundation (SBE-0354378

Subitizing with Variational Autoencoders

Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience

Biobehavioral Predictors Of Cannabis Use In Adolescence

Cannabis use initiated during adolescence may precipitate lasting consequences on the brain and behavioral health of the individual. However, research on the risk factors for cannabis use during adolescence has been largely cross-sectional in design. Despite the few prospective studies, even less is known about the neurobiological predictors. This dissertation improves on the extant literature by leveraging a large longitudinal study to uncover the predictors of cannabis use in adolescent samples collected prior to exposure. All data were drawn from the IMAGEN study and contained a large sample of adolescents studied at age 14 (N=2,224), and followed up at age 16 and 19. Participants were richly characterized using psychosocial questionnaires, structural and functional MRI, and genetic measurements. Two hypothesis-driven studies focused on amygdala reactivity and two data-driven studies across the feature domains were completed to characterize cannabis use in adolescence. The first study was cross-sectional and identified bilateral amygdala hyperactivity to angry faces in a sample reporting cannabis use by age 14 (n=70). The second study determined this amygdala effect was predictive of cannabis use by studying a sample of cannabis-naïve participants at age 14 who then used cannabis by age 19 (n=525). A dose-response relationship was observed such that heavy cannabis users exhibited higher amygdala reactivity. Exploratory analyses suggested amygdala reactivity decreased from age 14 to 19 within the cannabis sample, although statistical significance was not found. In the third study, data-driven machine learning analyses predicted cannabis initiation by age 16 separately for males (n=207) and females (n=158) using data from all feature domains. These analyses identified a sparse set of shared psychosocial predictors, whereas the identified brain predictors exhibited sex- and drug-specificity. Additional analyses predicted initiation by age 19 and identified a sparse set of psychosocial predictors for females only (n=145). The final study improved on drug-specificity by performing differential prediction analyses between matched samples of participants who initiated cannabis+binge drinking vs. binge drinking only by age 16 (males n=178; females n=148). A sparse subset of psychosocial predictors identified in the third study was reproduced, and novel brain predictors were identified. Those analyses were unique as they compared two machine learning algorithms, namely regularized logistic regression and random forest analyses. These studies substantiated the use of both hypothesis- and data-driven prediction analyses applied to large longitudinal datasets. They also addressed common issues related to human addiction research by examining sex-differences and drug-specificity. Critically, these studies uncovered predictors of use in samples collected prior to cannabis-exposure. The identified predictors are therefore disentangled from consequences of use. Results from all studies inform etiological mechanisms influencing cannabis use in adolescence. These findings can also be used to stratify risk in vulnerable adolescents and inform targets for interventions designed to curb use