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This thesis considers time and narrative play in dream poems and framed narratives. It begins with a chapter on the history of time perceptions and time-telling, and explores how ideas about time influenced medieval writers. It also surveys some modern views on the history of time-measurement a nd its influences on culture and the collective consciousness. Chapter two, after analysing the treatment of time in the Roman de la Rose, surveys some of the ways in which modern criticism has evaluated and conceived the genre of secular dream literature that developed from the Roman de la Rose. Chapter three examines the innovative use of the convention of beginning a poem with a seasonal opening and theorises that this becomes a `language' open to adaptation and variation. Chapter four looks in detail at Froissart's L`Orloge amoureus and discusses the clock as a new object which, contrary to the views of cultural historians, was embraced by medieval writers, religious and secular, to symbolise a range of virtues, qualities and ideas. I argue that the clock inspired creativity rather than heralding a rationalisation of the mind that would stifle imaginative responses to this new technology. Chapter five explores metafictional and self-reflexive devices in Froissart's Joli Buisson de Jonece and Chaucer's House of Fame. I consider how these texts play with narrative time and sequence by writing the genesis of the text into the poem. Finally, chapter six examines ideas of closure in medieval dream poetry and looks specifically at the reciprocity and inconclusiveness of the Judgement poems of Guillaume de Machaut. Because the second poem reverses the decision of the first poem, it brings into question the authority of the text and the unity of the authorial voice.

Some of the most important discoveries in cognitive neuroscience have come from recent innovations in experimental tools. Computational models that simulate human perception of environmental inputs have revealed the internal processes and features by which those inputs are learned and represented by the brain. We advance this line of work across two separate research studies in which we leveraged these models to both generate experimental task stimuli and make predictions about behavioral and neural responses to those stimuli. Chapter 1 details how nine language models were used to generate controversial sentence pairs for which two of the models disagreed about which sentence is more likely to occur. Human judgments about these sentence pairs were collected and compared to model preferences in order to identify model-specific pitfalls and provide a behavioral performance benchmark for future research. We found that transformer models GPT-2, RoBERTa and ELECTRA were most aligned with human judgments. Chapter 2 utilizes the GloVe model of semantic word vectors to generate a set of schematically structured poems comprising ten different topics whose specific temporal order was learned by a group of participants. The GloVe model was then used to investigate learning-induced changes in the spatial geometry of the representations of the topics across the cortex. A Hidden Markov Model was also used to measure neural event segmentation during poem listening. In both analyses we identified a consistent topography of learning-induced changes in the default mode network, which could be partially explained by the models.