Preface viii Chapter 1: Neuronal Circuitry: The Key to Unlocking the Brain 1 Chapter 2: Traditional Views of the Cerebellum 22 Chapter 3: The Cerebellum as a Neuronal Machine 29 Chapter 4: Input and Output Pathways in the Cerebellar Cortex 44 Chapter 5: Inhibitory Interneurons and Glial Cells in the Cerebellar Cortex 51 Chapter 6: Pre- and Post-Cerebellar Neurons 60 Chapter 7: Conjunctive Long-Term Depression (LTD) 69 Chapter 8: Multiplicity and Persistency of Synaptic Plasticity 81 Chapter 9: Network Models 90 Chapter 10: Ocular Reflexes 105 Chapter 11: Somatic and Autonomic Reflexes 121 Chapter 12: Adaptive Control System Models 139 Chapter 13: Voluntary Motor Control 150 Chapter 14: Voluntary Eye Movement 159 Chapter 15: Internal Models for Voluntary Motor Control 167 Chapter 16: Motor Actions and Tool Use 181 Chapter 17: Cognitive Functions 193 Chapter 18: Concluding Thoughts 204 References 213 Index 261

The cerebellum : brain for an implicit self

Consciousness and working memory: Current trends and research perspectives.

Cognitive control is adaptive in the sense that it inhibits automatic processes to optimize goal-directed behavior, but high levels of control may also have detrimental effects in case they suppress beneficial automatisms. Until now, the system neurophysiological mechanisms and functional neuroanatomy underlying these adverse effects of cognitive control have remained elusive. This question was examined by analyzing the automatic exploitation of a beneficial implicit predictive feature under conditions of high versus low cognitive control demands, combining event-related potentials (ERPs) and source localization. It was found that cognitive control prohibits the beneficial automatic exploitation of additional implicit information when task demands are high. Bottom-up perceptual and attentional selection processes (P1 and N1 ERPs) are not modulated by this, but the automatic exploitation of beneficial predictive information in case of low cognitive control demands was associated with larger response-locked P3 amplitudes and stronger activation of the right inferior frontal gyrus (rIFG, BA47). This suggests that the rIFG plays a key role in the detection of relevant task cues, the exploitation of alternative task sets, and the automatic (bottom-up) implementation and reprogramming of action plans. Moreover, N450 amplitudes were larger under high cognitive control demands, which was associated with activity differences in the right medial frontal gyrus (BA9). This most likely reflects a stronger exploitation of explicit task sets which hinders the exploration of the implicit beneficial information in case of high cognitive control demands. Hum Brain Mapp 37:4511-4522, 2016. © 2016 Wiley Periodicals, Inc.

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The system neurophysiological basis of non-adaptive cognitive control: Inhibition of implicit learning mediated by right prefrontal regions.

In this article, we review the literature on the development of visual working memory (VWM). We focus on two major periods of development, infancy and early childhood. First, we discuss the innovative methods that have been devised to understand how the development of selective attention and perception provide the foundation of VWM abilities. We detail the behavioral and neural data associated with the development of VWM during infancy. Next, we discuss various signatures of development in VWM during early childhood in the context of spatial and featural memory processes. We focus on the developmental transition to more adult-like VWM properties. Finally, we discuss computational frameworks that have explained the complex patterns of behavior observed in VWM tasks from infancy to adulthood and attempt to explain links between measures of infant VWM and childhood VWM.

Visual working memory in early development: a developmental cognitive neuroscience perspective.

Advantageous effects of cognitive control are well-known, but cognitive control may also have adverse effects, for example when it suppresses the implicit processing of stimulus-response (S-R) bindings that could benefit task performance. Yet, the neurophysiological and functional neuroanatomical structures associated with adverse effects of cognitive control are poorly understood. We used an extreme group approach to compare individuals who exhibit adverse effects of cognitive control to individuals who do not by combining event-related potentials (ERPs), source localization, time-frequency analysis and network analysis methods. While neurophysiological correlates of cognitive control (i.e. N2, N450, theta power and theta-mediated neuronal network efficiency) and task-set updating (P3) both reflect control demands and implicit information processing, differences in the degree of adverse cognitive control effects are associated with two independent neural mechanisms: Individuals, who show adverse behavioral effects of cognitive control, show reduced small-world properties and thus reduced efficiency in theta-modulated networks when they fail to effectively process implicit information. In contrast to this, individuals who do not display adverse control effects show enhanced task-set updating mechanism when effectively processing implicit information, which is reflected by the P3 ERP component and associated with the temporo-parietal junction (TPJ, BA 40) and medial frontal gyrus (MFG; BA 8). These findings suggest that implicit S-R contingencies, which benefit response selection without cognitive control, are always 'picked up', but may fail to be integrated with task representations to guide response selection. This provides evidence for a neurophysiological and functional neuroanatomical "dual-process" account of adverse cognitive control effects.

Evidence for a neural dual-process account for adverse effects of cognitive control

The constructs of fluid (Gf) and crystalized (Gc) intelligence represent an early attempt to describe the mechanisms of problem solving in the vertebrate brain. Modern neuroscience demonstrates that problem solving involves interplay between the mechanisms of automaticity and anticipatory control, enabling nature's elegant solution to the challenges animals face in their environment. Studies of neural functioning are making clear the primary role of cortical-subcortical interactions in the manifestation of intelligent behavior in humans and other vertebrates. A tridimensional model of intelligent problem solving is explored, wherein the basal ganglia system (BGS) and cerebrocerebellar system (CCS) interact within large scale brain networks. The BGS and CCS work together to enable automaticity to occur. The BGS enables the organism to learn what to do through a powerful instrumental learning system. The BGS also regulates when behavior is released through an inhibitory system which is incredibly sensitive to context. The CCS enables the organism to learn how to perform adaptive behaviors. Internal cerebellar models enable gradual improvements in the quality of behavioral output. The BGS and CCS interact within large scale brain networks, including the dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN) and frontoparietal network (FPN). The interactions of these systems enable vertebrate organisms to develop a vast array of complex adaptive behaviors. The benefits and importance of developing clinical tests to measure the integrity of these systems is considered.

Mechanisms of automaticity and anticipatory control in fluid intelligence

Working memory (WM) impacts a gamut of cognitive abilities, but implicit WM is typically not considered in assessment or treatment, which may explain the variability of results in reviews of WM training. The role of implicit WM in adaptive behavior is reviewed. All we do is action based. Explicit WM plays a major role when we are required to "think"; that is, when we apply previously learned perception-action linkages in new ways to unique situations. Implicit WM is involved in the automation of behavior, which occurs through interaction with cortical and subcortical systems that guide sensory-motor anticipation and the prediction of reward. This article reviews evidence that implicit WM interacts with cortical-cerebellar and cortical-basal ganglia connections to form perception-action linkages. The cerebellum forms an internal model of cortical WM, corrects the content of this internal model, and then projects the improved representation back to the cortex, where it is retained for future use. The basal ganglia also form an anticipatory system, controlling cortical access to WM by allowing or restricting the information that is released based on the probability of reward. This framework is applied to the assessment and treatment of individuals with WM deficits. The ability to automate behavior can be assessed through repeated trials of existing testing instruments, such as the Trails B and Stroop tasks. Application of skill learning emphasizing automation as an end goal offers a model for the development of new types of WM training.

Implicit Working Memory: Implications for Assessment and Treatment

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Mechanisms of automaticity and anticipatory control in fluid intelligence

Implicit Working Memory: Implications for Assessment and Treatment