Recent evidence in cognitive neuroscience indicates that the visual system is influenced by the outcome of an early appraisal mechanism that automatically evaluates what is seen as

being harmful or beneficial for the organism. This indicates that there could be valence in perception. But what could it mean for one to see something positively or negatively? Although most theories of emotions accept that valence involves being related to values, the nature of this relation remains highly debated. Some explain valence in terms of evaluative content, others in terms of evaluative attitude. Here I shall argue that an account of affective perception in terms of attitude has more chance to succeed. To do so, I will first highlight the difficulties that a content-based approach faces, considering the many forms it might take. I will conclude that seeing the world positively or negatively involves more than a positive or negative content; it involves a distinctive attitude, but which one? Should it be conceived in imperative or evaluative terms? And what makes this attitude distinct from a proper emotion?

Prof. Frederique de Vignemont
Institut Jean Nicod, CNRS-EHESS-ENS-PSL University

I will present a line of research which aims to create a new conceptual and computational framework for integrating the scientific study of human cognition with the field of Natural Language Processing (NLP). This framework puts forward the idea that NLP systems and machine learning techniques can be highly instrumental for modeling how humans learn and process language. In a complementary vein, it posits that cognitive theory and behavioral data from human language processing can facilitate the development of NLP systems which can process language effectively. In this talk I will focus on work which develops these ideas using eye movements during reading. I will first demonstrate the informativeness of human gaze by using it to infer the linguistic background of readers and shed light on cross-linguistic influence in language comprehension. I will then present an approach for leveraging eye movements during question answering for improving machine reading comprehension. Finally, I will discuss a real-world application for assessment of linguistic proficiency as an automatic byproduct of ordinary reading, as well as a related analysis of the relation between linguistic proficiency and responsiveness to the linguistic characteristics of the text.

Prof. Yevgeni Berzak
The Technion - Israel Institute of Technology