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#### List of speakers and abstracts
+ **Arun Chaganty**, Stanford (Computer Science)
*How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions*
How much is 131 million US dollars? While this is a precise number, it is often difficult for people to comprehend the scale of large (or small) absolute values. Rather, studies have shown that providing anchored comparisons, or perspectives, such as “$131 million is about the cost to employ everyone in Texas over a lunch period” significantly improves comprehension when measured in terms of memory retention or outlier detection, despite being gross generalizations. In this talk, we will discuss the role that perspectives can play in the understanding of numbers and present a computational approach to generating said perspectives using concepts of familiarity, numeric proximity and semantic compatibility.
+ **Ellie Chestnut**, Stanford (Psychology)
*When attempts to promote gender equality backfire*
A common, natural way of encouraging girls to enter STEM fields is to say, for example, "Girls are as good as boys at math." In three experiments, we show that statements with this syntactic structure are actually ineffective in communicating gender equality. Instead, by placing girls in the subject position and boys in the complement position, they frame boys as the higher-status standard and perpetuate the stereotype that boys have more raw talent.
+ **Andrei Cimpian**, New York University (Psychology)
*Generics and Stereotypes*
Stereotypes are typically defined as beliefs about groups, but this definition is underspecified. I will ask whether stereotypes are better characterized as generic beliefs about groups or as quantified beliefs. In addition to clarifying the cognitive structure of stereotypes, the answer to this question bears on the longstanding debate about stereotype accuracy: Whether stereotypes can be said to be accurate depends in part on what sorts of beliefs they turn out to be.
+ **Susan Gelman**, University of Michigan (Psychology)
*This time it's personal: What "you" reveals about generic concepts*
Prior psychological work on generics has focused on full NPs with category labels (e.g., "Lions have manes"; "A bird lays eggs"). In the social domain, however, some languages express generics with a 2nd-person pronoun ("You win some, you lose some"; "You eat ice cream with a spoon"; "You never know what might happen"), using you to refer to a broad social group (people in general). In this talk I focus on why a word that is typically characterized by specificity, context-dependence, and focus on the addressee is also used generically to convey broad generalizations that extend beyond time or place. I will discuss a series of experiments with adults and young children (2-10 years of age) suggesting that generic concepts are deeply linked to norms from early in development, and can be used to make meaning out of personal experience.
+ **Bernhard Nickel**, Harvard University (Philosophy)
*The Rich Content of Generics: Implications and Applications*
According to the semantics of generics defended in *Between Logic and the World* (Nickel 2016), generics have rich explanatory information as part of their content.
In this talk, I want to look at the implications of this view for contested and politically charged generics in the social realm.
+ **Rachel Sterken**, University of Oslo (Philosophy)
*On the social, political and epistemically troubling aspects of generic thought and talk*
Recently, psychologists and philosophers have identified several aspects of generic thought and talk that seem socially, politically and epistemically problematic.
In this paper, I aim to identify some further problematic features that arise from thinking about the semantic, communicative and interactional aspects of generic thought and talk.
+ **Michael Henry Tessler**, Stanford University (Psychology)
*Communicating generalizations in computational terms*
Generalizations are the foundation of abstract thought: They allow us to make sense of the world and predict the future.
Probabilistic cognitive models formalize generalizations using the Bayesian probability calculus and have been immensely successful at characterizing human thought in computational terms.
Yet, the *language of generalizations* (e.g., *Birds fly.*) has received comparatively little attention by formal models, despite its ubiquity in everyday discourse and child-directed speech.
*Genericity* is difficult to formalize because it exhibits extreme flexibility in usage (e.g., *not all* birds fly) despite looking so simple.
I formalize the hypothesis that the core meaning of generalizations in language is *simple but underspecified*, and that general communicative principles can be used to establish a more precise meaning in context.
Using a state-of-the-art probabilistic model of pragmatic reasoning, I examine 3 case studies of generalizations in language: generalizations about events (e.g., *John runs*), causes (e.g., *The block makes the machine play music.*), and categories (i.e., *generic language*, e.g., *Birds fly*).
The model is able to predict graded endorsements from the interaction of diverse prior beliefs about properties with general communicative principles, pointing a way towards more complete models of language and cognition.
+ **Nadya Vasilyeva**, UC Berkeley (Psychology)
*Structural interpretation of category properties*
A large body of research has documented pervasive internalist biases in reasoning about social categories: observable features of category members are often thought to stem from deep, stable, inherent properties. We examined one potential alternative to internalist thinking, which we call "structural thinking," drawing upon an emerging literature in philosophy on structural explanation. A hallmark of structural thinking is locating the object of explanation within a larger structure and identifying structural constraints that act on components of the structure to shape the distribution of outcomes for each component. For example, a structural explanation for women's underrepresentation in STEM might appeal to women's socioeconomic role as opposed to properties intrinsic to women. We provide initial data supporting the existence and unique profile of structural thinking in adults and children, and discuss implications of these findings for our understanding of categorical representation.
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