This dissertation contains several projects, each addressing different questions with different techniques. In chapter 1, I argue that they are unified thematically by their goal of 'scaling up psycholinguistics'; they are all aimed at analyzing large datasets using tools that reveal patterns to propose and test mechanism-neutral hypotheses about the brain's language processing architecture. In chapter 2, I investigate the well-known phenomenon that words which are more predictable in context are read faster than words which are not. I suggest that this is best understood as a special case of a phenomenon whereby more predictable events, whether linguistic or not, are in general processed more quickly than unpredictable ones, and propose a general model of why this happens. When combined with the constraints imposed by language's incrementally processed serial structure, this model predicts a logarithmic relationship between word probability and reading time, and I show that this in fact holds in two large data sets. In chapter 3, I turn to the question of how the brain produces fine- grained quantitative predictions; computationally, this seems impossible given the sparsity of the data it has available. This suggests it uses some kind of learning biases to guide generalization (a classic poverty of the stimulus argument), and I propose to study these biases by comparing subjective (cloze) probability with objective corpus probabilities. In Experiment 1, I find a number of candidate biases; in a series of follow-up experiments, I argue that some are probably artifactual, but others may in fact give clues to the brain's mechanisms for generating linguistic expectations from experience. Chapters 4 and 5 develop a methodology for extending ERP analysis to handle continuously varying stimulus attributes, partial confounding, and overlapping brain responses to events occurring in quick succession. These techniques are motivated by the desire to analyze EEG recorded in more naturalistic language paradigms (which contain all of the above challenges), but they are not specific to language studies, and have many potential applications for EEG/MEG research more generally

Individual variation in the addiction liability of amphetamines has a heritable genetic component. We previously identified Hnrnph1 (heterogeneous nuclear ribonucleoprotein H1) as a quantitative trait gene underlying decreased methamphetamine-induced locomotor activity in mice. Here, we showed that mice (both females and males) with a heterozygous mutation in the first coding exon of Hnrnph1 (H1+/-) showed reduced methamphetamine reinforcement and intake and dose-dependent changes in methamphetamine reward as measured via conditioned place preference. Furthermore, H1+/- mice showed a robust decrease in methamphetamine-induced dopamine release in the NAc with no change in baseline extracellular dopamine, striatal whole-tissue dopamine, dopamine transporter protein, dopamine uptake, or striatal methamphetamine and amphetamine metabolite levels. Immunohistochemical and immunoblot staining of midbrain dopaminergic neurons and their forebrain projections for TH did not reveal any major changes in staining intensity, cell number, or forebrain puncta counts. Surprisingly, there was a twofold increase in hnRNP H protein in the striatal synaptosome of H1+/- mice with no change in whole-tissue levels. To gain insight into the mechanisms linking increased synaptic hnRNP H with decreased methamphetamine-induced dopamine release and behaviors, synaptosomal proteomic analysis identified an increased baseline abundance of several mitochondrial complex I and V proteins that rapidly decreased at 30 min after methamphetamine administration in H1+/- mice. In contrast, the much lower level of basal synaptosomal mitochondrial proteins in WT mice showed a rapid increase. We conclude that H1+/- decreases methamphetamine-induced dopamine release, reward, and reinforcement and induces dynamic changes in basal and methamphetamine-induced synaptic mitochondrial function.SIGNIFICANCE STATEMENT Methamphetamine dependence is a significant public health concern with no FDA-approved treatment. We discovered a role for the RNA binding protein hnRNP H in methamphetamine reward and reinforcement. Hnrnph1 mutation also blunted methamphetamine-induced dopamine release in the NAc, a key neurochemical event contributing to methamphetamine addiction liability. Finally, Hnrnph1 mutants showed a marked increase in basal level of synaptosomal hnRNP H and mitochondrial proteins that decreased in response to methamphetamine, whereas WT mice showed a methamphetamine-induced increase in synaptosomal mitochondrial proteins. Thus, we identified a potential role for hnRNP H in basal and dynamic mitochondrial function that informs methamphetamine-induced cellular adaptations associated with reduced addiction liability.