We 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 subtract rather than subtract and add. Third, we show that the distributional semantics of verbs of change, such as to improve and to transform, overlap more with the distributional semantics of add/increase than subtract/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. (shrink)

Affective congruency effects in the evaluation task can be explained by either spreading of activation or response competition. Eliminating effects of response compatibility by using other tasks (semantic categorisation, naming task) typically also eliminates affective congruency effects. However, there is no need for processing the affective information of the stimuli in these tasks either, which could be necessary for an affectively mediated spreading of activation (Spruyt et al., 2007, 2009, 2012). We introduced a new task to further test this hypothesis. (...) The valent/neutral-categorisation task does not confound affective congruency with response compatibility, but still requires a processing of the stimuli's valence. No affective congruency effect was obtained with this task in two experiments, disfavouring a conditional spreading activation account. On the other hand, a significant priming effect was found for associated word pairs in Experiment 1, providing evidence for the sensitivity of the task to detect spreading activation processes. (shrink)

The vast majority of studies on affective processes in reading focus on single words. The most robust finding is a processing advantage for positively valenced words, which has been replicated in the rare studies investigating effects of affective features of words during sentence or story comprehension. Here we were interested in how the different valences of words in a sentence influence its processing and supralexical affective evaluation. Using a sentence verification task we investigated how comprehension of simple declarative sentences containing (...) a noun and an adjective depends on the valences of both words. The results are in line with the assumed general processing advantage for positive words. We also observed a clear interaction effect, as can be expected from the affective priming literature: sentences with emotionally congruent words (e.g., The grandpa is clever) were verified faster than sentences containing emotionally incongruent words (e.g., The grandpa is lonely). The priming effect was most prominent for sentences with positive words suggesting that both, early processing as well as later meaning integration and situation model construction, is modulated by affective processing. In a second rating task we investigated how the emotion potential of supralexical units depends on word valence. The simplest hypothesis predicts that the supralexical affective structure is a linear combination of the valences of the nouns and adjectives (Bestgen, 1994 ). Overall, our results do not support this: The observed clear interaction effect on ratings indicate that especially negative adjectives dominated supralexical evaluation, i.e., a sort of negativity bias in sentence evaluation. Future models of sentence processing thus should take interactive affective effects into account. (shrink)