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The aim of this thesis is to improve our understanding of the neural systems involved in schizophrenia by suggesting possible avenues for future computational modelling in an attempt to make sense of the vast number of studies relating to the symptoms and cognitive deficits relating to the disorder. This multidisciplinary research has covered three different levels of analysis: abnormalities in the microscopic brain structure, dopamine dysfunction at a neurochemical level, and interactions between cortical and subcortical brain areas, connected by cortico-basal ganglia circuit loops; and has culminated in the production of five models that provide useful clarification in this difficult field. My thesis comprises three major relevant modelling themes. Firstly, in Chapter 3 I looked at an existing neural network model addressing the Neurodevelopmental Hypothesis of Schizophrenia by Hoffman and McGlashan (1997). However, it soon became clear that such models were overly simplistic and brittle when it came to replication. While they focused on hallucinations and connectivity in the frontal lobes they ignored other symptoms and the evidence of reductions in volume of the temporal lobes in schizophrenia. No mention was made of the considerable evidence of dysfunction of the dopamine system and associated areas, such as the basal ganglia. This led to my second line of reasoning: dopamine dysfunction. Initially I helped create a novel model of dopamine neuron firing based on the Computational Substrate for Incentive Salience by McClure, Daw and Montague (2003), incorporating temporal difference (TD) reward prediction errors (Chapter 5). I adapted this model in Chapter 6 to address the ongoing debate as to whether or not dopamine encodes uncertainty in the delay period between presentation of a conditioned stimulus and receipt of a reward, as demonstrated by sustained activation seen in single dopamine neuron recordings (Fiorillo, Tobler & Schultz 2003). An answer to this question could result in a better understanding of the nature of dopamine signaling, with implications for the psychopathology of cognitive disorders, like schizophrenia, for which dopamine is commonly regarded as having a primary role. Computational modelling enabled me to suggest that while sustained activation is common in single trials, there is the possibility that it increases with increasing probability, in which case dopamine may not be encoding uncertainty in this manner. Importantly, these predictions can be tested and verified by experimental data. My third modelling theme arose as a result of the limitations to using TD alone to account for a reinforcement learning account of action control in the brain. In Chapter 8 I introduce a dual weighted artificial neural network, originally designed by Hinton and Plaut (1987) to address the problem of catastrophic forgetting in multilayer artificial neural networks. I suggest an alternative use for a model with fast and slow weights to address the problem of arbitration between two systems of control. This novel approach is capable of combining the benefits of model free and model based learning in one simple model, without need for a homunculus and may have important implications in addressing how both goal directed and stimulus response learning may coexist. Modelling cortical-subcortical loops offers the potential of incorporating both the symptoms and cognitive deficits associated with schizophrenia by taking into account the interactions between midbrain/striatum and cortical areas.

本研究旨在探討如何將漢語中詞彙的產出，建構在過去的語言生成模型(speech production model)之上。 在過去文獻中，語言生成模型主要有兩大流派：序列模型(serial-ordering model)主張各階段的激活過程只能單向進行，而連接模型(connectionist model)則允許各階段之間有雙向互動。此外，兩大模型闡述綜合性詞誤 (同時在語意及語音方面相關的詞誤)的生成也略有不同：序列模型認為綜合性詞誤起因於功能層次(functional level)與位置層次(positional level)同時發生錯誤，但若綜合性語誤的比例顯著高於機率，則較不支持此說法；連接模型則認為綜合性詞誤來自音系層(phonological stage)和詞條層(lemma stage)間的交互作用，倘若大部分的縱向聚合詞誤(paradigmatic errors)皆具有語音相似度，此說法較能得到驗證。 因此，為估算漢語詞彙選擇中語音的相似度，本研究分析了421個在自然語境中收集的單音節縱向聚合詞誤，結果發現綜合性語誤所佔的比例顯著偏高，此外，所有詞誤在字首、語音特徵、韻母、音節結構和聲調各方面的效應都達到顯著，這說明了語音和語意的確會同時影響詞彙的選擇，這也表示連接模型可能較接近中文的語言生成過程。 另外，語料分析的結果也顯示出漢語詞彙產出的特性。第一、在詞彙提取的過程中，字首比語音特徵和韻母扮演了更重要的角色，這同時也支持音節結構應以字首(C)和韻母劃分(CV/CVC)的假設；第二、聲調效應的顯著顯示聲調應存在於字彙的底層結構，因此聲調的促發應先於詞彙結的激化；第三、目標詞彙(target)的音節結構應在音系層的初期就已指定，因此有相同音節結構的詞彙結容易勝出。綜合以上可知，漢語的生成過程較傾向連接模型的架構，唯聲調和音節結構等語言特色宜納入模型討論。 / This study aims to investigate the process of word production in Mandarin, to see how it can be structured in previous models. Speech production models have two primary sects—the serial-ordering model versus the connectionist model—arguing for uni-directional and bi-directional activation respectively. Besides, between these two models, the generation of both semantically- and phonologically-related lexical errors (mixed errors) is different. On the one hand, the serial-ordering model (Garrett, 1988) interpreted mixed errors as malfunctions occur at both functional and positional level. It may not be favored if mixed errors occur more than chance in the corpus. On the other hand, the connectionist model (Dell, 1986) explained it as the feedback activation from the phonological level back to the lemma level. It would be supported if most paradigmatic substitutions show phonological similarities. Therefore, in order to facilitate the phonological similarities in lexical substitutions, 421 single-syllable paradigmatic (non-contextual) lexical errors collected in natural settings are examined. It is found that the percentage of mixed errors is significantly higher. Moreover, in the corpus the initialness, similarity, rhyme, syllable structure and tone effects are all prominent, which proves the joint effect of semantic and phonology in word selection. In other words, the connectionist network might better account for the processing of Mandarin. In addition, the analysis reveals certain issues of word-production in Mandarin. First, initials are primary activation units in word retrieval, rather than phonetic features or rhymes. It also supports the division of hierarchical syllable structure as an onset (C) and rhyme (VC/CVC). Secondly, the significance of tone effect suggests that tone may be stored in the underlying phonological organization of lexicons and thus is prior in word-activation. Thirdly, the syllable structure of the target is assigned at the beginning of phonological stage and thus lexical nodes with the same syllable structure tend to be selected. In conclusion, the analysis proves that the connectionist model could be applied to Mandarin Chinese if the status of tone and syllable structure is included.