Instrumental Eyes: Enacted and Interactive Perception in Victorian Optical Technologies and Victorian Fiction

This chapter makes a case for the importance of visual interactivity over disengaged spectacularity in the Victorian period by looking at a particular type of optical technology, the persistence of vision device (which would go on to develop into the mechanism behind cinema). They required the continual manipulation of the viewer’s hand to bring their flickering illusion of motion to life: by spinning a drum or turning a handle, the series of sequential illustrations or photographs housed in the device appeared to blend together and display a short animation. In drawing attention to the necessary collaboration between cognition, visual perception, and bodily action when using moving-image technologies, this chapter argues that Victorian optical technologies increased awareness of how crucial physical action was to perception. Vision did not simply take place behind the eyes; the cognitive work of understanding one’s environment as a textured, dimensional space was enhanced by new devices which demonstrated how the hand could help the eye to see, or even to see in newly tactile ways. Beyond being a process of pure opticality, perception was spread through the body, as dependent on the fingers as upon the mind.

A Human-Like Semantic Cognition Network for Aspect-Level Sentiment Classification

In this paper, we propose a novel Human-like Semantic Cognition Network (HSCN) for aspect-level sentiment classification, motivated by the principles of human beings’ reading cognitive process (pre-reading, active reading, post-reading). We first design a word-level interactive perception module to capture the correlation between context words and the given target words, which can be regarded as pre-reading. Second, to mimic the process of active reading, we propose a targetaware semantic distillation module to produce the targetspecific context representation for aspect-level sentiment prediction. Third, we further devise a semantic deviation metric module to measure the semantic deviation between the targetspecific context representation and the given target, which evaluates the degree we understand the target-specific context semantics. The measured semantic deviation is then used to fine-tune the above active reading process in a feedback regulation way. To verify the effectiveness of our approach, we conduct extensive experiments on three widely used datasets. The experiments demonstrate that HSCN achieves impressive results compared to other strong competitors.