Aesthetic appraisals of literary style and emotional intensity in narrative engagement are neurally dissociable

Different kinds of simulation during literary reading: Insights from a combined fMRI and eye-tracking study

Mental simulation is an important aspect of narrative reading. In a previous study, we found that gaze durations are differentially impacted by different kinds of mental simulation. Motor simulation, perceptual simulation, and mentalizing as elicited by literary short stories influenced eye movements in distinguishable ways (Mak & Willems, 2019). In the current study, we investigated the existence of a common neural locus for these different kinds of simulation. We additionally investigated whether individual differences during reading, as indexed by the eye movements, are reflected in domain-specific activations in the brain. We found a variety of brain areas activated by simulation-eliciting content, both modality-specific brain areas and a general simulation area. Individual variation in percent signal change in activated areas was related to measures of story appreciation as well as personal characteristics (i.e., transportability, perspective taking). Taken together, these findings suggest that mental simulation is supported by both domain-specific processes grounded in previous experiences, and by the neural mechanisms that underlie higher-order language processing (e.g., situation model building, event indexing, integration).

MA-EM: A neurocognitive model for understanding mixed and ambiguous emotions and morality

Understanding emotions and moral intentions of other people is integral to being human. Humanities scholars have long recognized the complex and ambiguous nature of emotions and morality. People are rarely 'just' happy, or sad. Neither are they 'just' good or bad people. Despite this, most knowledge about the psychological and neural basis of emotions and moral understanding comes from experiments investigating unidimensional and non-ambiguous emotions and morality. The goal of this paper is twofold. First I want to point out why mixed and ambiguous emotions and morality are a promising research topic for cognitive neuroscientists. Observing or experiencing mixed or ambiguous emotions and morality tends to have a strong impact on humans. This impact is clearly visible in narratives and fiction, and I will argue that narratives make an excellent stimulus to study the effects of emotional and moral ambiguity. Second, I will sketch a model to help guide research in this promising corner of human cognition.

Approximate Entropy in Canonical and Non-Canonical Fiction

Computational textual aesthetics aims at studying observable differences between aesthetic categories of text. We use Approximate Entropy to measure the (un)predictability in two aesthetic text categories, i.e., canonical fiction ('classics') and non-canonical fiction (with lower prestige). Approximate Entropy is determined for series derived from sentence-length values and the distribution of part-of-speech-tags in windows of texts. For comparison, we also include a sample of non-fictional texts. Moreover, we use Shannon Entropy to estimate degrees of (un)predictability due to frequency distributions in the entire text. Our results show that the Approximate Entropy values can better differentiate canonical from non-canonical texts compared with Shannon Entropy, which is not true for the classification of fictional vs. expository prose. Canonical and non-canonical texts thus differ in sequential structure, while inter-genre differences are a matter of the overall distribution of local frequencies. We conclude that canonical fictional texts exhibit a higher degree of (sequential) unpredictability compared with non-canonical texts, corresponding to the popular assumption that they are more 'demanding' and 'richer'. In using Approximate Entropy, we propose a new method for text classification in the context of computational textual aesthetics.