This study explores the relationship between predictability, as measured by surprisal, and processing difficulty in code comprehension. We investigate whether similar mechanisms govern the processing of programming and natural languages. Previous research suggests that programmers prefer and produce more predictable code, akin to natural language patterns. We utilize eye-tracking data from the Eye Movements in Programming (EMIP) dataset to examine the impact of surprisal on various eye movement measures. Contrary to expectations based on natural language processing, our results reveal that surprisal does not significantly influence fixation metrics. Additionally, regressions in code reading show an unexpected inverse relationship with surprisal, suggesting that readers have different reasons for making regressions while reading code versus natural text. These findings contribute insights into the unique dynamics of code comprehension and opens avenues for further research in this domain.

In English binomial expressions, “bread and butter” is preferred over “butter and bread”. Morgan & Levy (2015) show that for these types of expressions, frequently used phrases tend to have stronger, more extreme preferences. In contrast, there is roughly an equal preference for “bishops and rooks” versus “rooks and bishops”, a much less common pairing. This paper extends this research to the concept of Mandarin elastic word length, a phenomenon in which most Mandarin words have long and short forms. We find evidence for frequency-dependent regularization in the elastic length of Noun-Noun compounds in Chinese, demonstrating that frequency-dependent regularization extends to structures with more than two alternations and to languages other than English.