Towards a Similarity-adjusted Surprisal Theory
This work addresses a problem in psycholinguistics for researchers by providing a complementary measure of comprehension effort, though it is incremental as it builds on existing theories.
The paper tackles the limitation of surprisal theory in overlooking word similarities by introducing similarity-adjusted surprisal, which extends surprisal using a diversity index and aligns with information value; experimental results show it adds predictive power beyond standard surprisal for some datasets.
Surprisal theory posits that the cognitive effort required to comprehend a word is determined by its contextual predictability, quantified as surprisal. Traditionally, surprisal theory treats words as distinct entities, overlooking any potential similarity between them. Giulianelli et al. (2023) address this limitation by introducing information value, a measure of predictability designed to account for similarities between communicative units. Our work leverages Ricotta and Szeidl's (2006) diversity index to extend surprisal into a metric that we term similarity-adjusted surprisal, exposing a mathematical relationship between surprisal and information value. Similarity-adjusted surprisal aligns with information value when considering graded similarities and reduces to standard surprisal when words are treated as distinct. Experimental results with reading time data indicate that similarity-adjusted surprisal adds predictive power beyond standard surprisal for certain datasets, suggesting it serves as a complementary measure of comprehension effort.