Question: Can you tell whether any of this thesis was written by AI? Recent developments in generative AI have shone a spotlight on high performance synthetic text generation technologies. The wide availability and ease of use of such models highlights the urgent need to provide equally powerful technologies capable of identifying synthetic text. With this in mind, we draw inspiration from psychological studies which suggest that people can be driven by emotion and encode emotion in the text they compose. We hypothesise that pretrained language models (PLMs) have an affective deficit because they lack such an emotional driver when generating text and consequently may generate synthetic text which has affective incoherence i.e. lacking the kind of emotional coherence present in human-authored text. We subsequently develop an emo- tionally aware detector by fine-tuning a PLM on emotion. Experiment results indicate that our emotionally-aware detector achieves improvements across a range of synthetic text generators, various sized models, datasets, and domains. We compare our emotionally-aware synthetic text detector to ChatGPT in the task of identification of its own output and show substantial gains, reinforcing the potential of emotion as a signal to identify synthetic text. These findings support the hypothesis that PLMs may have an affective deficit. Next, we investigate the hypothesis that synthetic text may be affectively incoherent. We create a novel flexible evaluation framework and use it to select an emotion classifier to generate an affective profile for 10k human and synthetic news arti- cles. Our analysis of the human and synthetic affective profiles show that they are similar, but synthetic text is more affectively incoherent and less affectively coherent, than human text. Answer: AI wrote none of this thesis, but how do you know for certain? This lack of certainty motivates the task of synthetic text detection.