Intersectional Male-Centric and White-Centric Biases in Collective Concepts.

In principle, the fundamental concepts person, woman, and man should apply equally to people of different genders and races/ethnicities. In reality, these concepts might prioritize certain groups over others. Based on interdisciplinary theories of androcentrism, we hypothesized that (a) person is more associated with men than women (person = man) and (b) woman is more associated with women than man is with men (i.e., women are more gendered: gender = woman). We applied natural language processing tools (specifically, word embeddings) to the linguistic output of millions of individuals (specifically, the Common Crawl corpus). We found the hypothesized person = man / gender = woman bias. This bias was stronger about Hispanic and White (vs. Asian) women and men. We also uncovered parallel biases favoring White individuals in the concepts person, woman, and man. Western society prioritizes men and White individuals as people and "others" women as people with gender, with implications for equity across policy- and decision-making contexts.

Based on billions of words on the internet, people = men.

Recent advances have made it possible to precisely measure the extent to which any two words are used in similar contexts. In turn, this measure of similarity in linguistic context also captures the extent to which the concepts being denoted are similar. When extracted from massive corpora of text written by millions of individuals, this measure of linguistic similarity can provide insight into the collective concepts of a linguistic community, concepts that both reflect and reinforce widespread ways of thinking. Using this approach, we investigated the collective concept person/people, which forms the basis for nearly all societal decision- and policy-making. In three studies and three preregistered replications with similarity metrics extracted from a corpus of over 630 billion English words, we found that the collective concept person/people is not gender-neutral but rather prioritizes men over women-a fundamental bias in our species' collective view of itself.

Early sensitivity of left perisylvian cortex to relationality in nouns and verbs.

The ability to track the relationality of concepts, i.e., their capacity to encode a relationship between entities, is one of the core semantic abilities humans possess. In language processing, we systematically leverage this ability when computing verbal argument structure, in order to link participants to the events they participate in. Previous work has converged on a large region of left posterior perisylvian cortex as a locus for such processing, but the wide range of experimental stimuli and manipulations has yielded an unclear picture of the region's exact role(s). Importantly, there is a tendency for effects of relationality in single-word studies to localize to posterior temporo-parietal cortex, while argument structure effects in sentences appear in left superior temporal cortex. To characterize these sensitivities, we designed two MEG experiments that cross the factors relationality and eventivity. The first used minimal noun phrases and tested for an effect of semantic composition, while the second employed full sentences and a manipulation of grammatical category. The former identified a region of the left inferior parietal lobe sensitive to relationality, but not eventivity or combination, beginning at 170ms. The latter revealed a similarly-timed effect of relationality in left mid-superior temporal cortex, independent of eventivity and category. The results suggest that i) multiple sub-regions of perisylvian cortex are sensitive to the relationality carried by concepts even in the absence of arguments, ii) linguistic context modulates the locus of this sensitivity, consistent with prior studies, and iii) relationality information is accessed early - before 200ms - regardless of the concept's event status or syntactic category.