More is Better: English Language Statistics are Biased Toward Addition

Abstract

AbstractWe have evolved to become who we are, at least in part, due to our general drive to create new things and ideas. When seeking to improve our creations, ideas, or situations, we systematically overlook opportunities to perform subtractive changes. For example, when tasked with giving feedback on an academic paper, reviewers will tend to suggest additional explanations and analyses rather than delete existing ones. Here, we show that this addition bias is systematically reflected in English language statistics along several distinct dimensions. First, we show that words associated with an increase in quantity or number (e.g.,add, addition, more, most) are more frequent than words associated with a decrease in quantity or number (e.g.,subtract, subtraction, less, least). Second, we show that in binomial expressions, addition‐related words are mentioned first, that is,add and subtractrather thansubtract and add. Third, we show that the distributional semantics of verbs of change, such asto improveandto transform, overlap more with the distributional semantics ofadd/increasethansubtract/decrease, which suggests that change verbs are implicitly biased toward addition. Fourth, addition‐related words have more positive connotations than subtraction‐related words. Fifth, we demonstrate that state‐of‐the‐art large language models, such as the Generative Pre‐trained Transformer (GPT‐3), are also biased toward addition. We discuss the implications of our results for research on cognitive biases and decision‐making.