# Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking
## 1 Introduction  
Written language is one of the primary sources for knowledge acquisition and communication [1, 2], and reading is a complex series of actions that allows access to this knowledge. \"Reading is a coordinated execution of a series of processes which involve word encoding, lexical access, assigning semantic roles, and relating the information contained in a sentence to earlier sentences in the same text and the reader's prior knowledge\" [2]. While one is reading a text, readability is a crucial factor for successful comprehension [1]. According to [PERSON] and [PERSON] [3], text readability is defined as \"the sum total (including all the interactions) of all those elements within a given piece of printed material that affect the success a group of readers have with it. The success is the extent to which they understand it, read it at optimal speed, and find it interesting.\" These elements include typographical aspects, such as supporting graphics and tables, and readers' interest, which is affected by education level, for example, and writing style (such as text syntax). Another definition of readability by [PERSON], as reported by [4], is \"the ease of understanding or comprehension because of the writing style.\"  
Text readability assessment, which involves developing effective methods for determining readability level, has been a challenging research field since the 1920s [5]. This concern has become more significant in the information age, with the huge amount of written text available in different domains [1]. Although there are suggestions for analyzing a document's comprehension and clarity by observing a sample of its audience or by having professional editors review written documents, these operations are often impractical due to their labor expense, subjectivity, and time consumption [6]. Consequently, the demand has risen for technological tools that help create and edit documents to make them clear and understandable according to how readers comprehend the text [6]. However, because readability depends on many factors that enable readers to process a text, cognitive linguists have pointed out two main features that might influence readers' successful comprehension [2; 6; 7]: readers' background knowledge and the contents of the text itself. Therefore, in addition to the text characteristics, the problem of readability is mainly rooted in the characteristics of the reader, as indicated by [PERSON] and [PERSON] [3], and readability is not just determined by literacy skills, but also by the readers' personal characteristics and backgrounds [5]. Given this, text being considered readable or not and the features used should be built upon the cognitive characteristics and skills of a population. However, although the primary goal of text readability assessment models is to guarantee the predictions that reflect readers' difficulties, the majority of these models are annotated using experts' judgments and not built using the actual audiences' reading performance [8; 9]. Recently, this approach has been criticized in education research. One way to target this limitation is building corpora using humans' physiological data, such as brain electroencephalography (EEG) signals and eye-tracking data to anticipate human processing effort during reading. Collecting this psychometric information has improved many natural language processing (NLP) tasks such as named-entity recognition, sentiment annotation complexity, and sarcasm detection. It allows for a better understanding of a human's cognitive behavior [10; 11; 12]. As indicated by [2], real-time readability assessments from behavioral signals could be a significant sign of text readability. It is one of the potential research directions for the readability assessment task. According to [PERSON] et al. [10], there is a significant correlation between reader eye movement and the cognitive processing of texts.