Join us for short talks from our newest MindCORE fellows.
Tyler Knowlton – “Each and every meaning explained”
How do linguistic meanings make contact with the rest of cognition? I’ll consider the universal quantifiers “each” and “every” as a case study because they’re definable and because the potentially related non-linguistic cognitive systems are well-studied. I’ll argue that “each” has an individual-based meaning that serves as an instruction to our mind’s system for representing object-files, whereas “every” has a group-based meaning that serves as an instruction to our mind’s system for representing ensembles. This proposed difference explains a variety of linguistic and psycholinguistic data. First, when deciding whether a sentence like “each circle is green” is true, participants encode and recall individual properties (a particular circle’s hue), whereas when deciding whether a sentence like “every circle is green” is true, they encode and recall group properties (the number of circles). Second, “each” is less amenable to generic interpretations; whereas “each martini needs an olive” sounds like a claim about particular martinis that lack garnishes, “every martini needs an olive” sounds like a component of a drink recipe. Third, in child-directed speech, parents use “each” to talk about small numbers of physically-present things, but they use “every” to make broader generalizations. This case study shows that meanings of logical words offer surprisingly precise instructions to cognition.
Linda Chang – “Quantification Myopia”
In the information age, much of what was once intangible is now represented numerically: individuals and organizations use metrics, ratings, and rankings to make decisions and form judgments. However, we often have to evaluate a mix of quantitative and qualitative information to make decisions (e.g., we combine SAT scores with recommendation letters and essays to evaluate college applicants). We investigate how presenting the same information quantitatively rather than qualitatively influences people’s choices and demonstrate a phenomenon we call quantification myopia: people prioritize the same information more when it is described numerically rather than qualitatively. Across four pre-registered experiments (N = 4,600), we show consistent preference reversals demonstrating quantification myopia. Specifically, we present people with choices that differ on multiple dimensions (e.g., the priciness of a restaurant and the travel time to reach it), and we randomize which dimension is described numerically. We find that quantification skews choices such that people are more likely to choose the option that is more attractive on the quantified dimension. We show that this happens even when the quantified information is provided in the form of a meaningless numerical scale (e.g., a car condition score). Our findings have implications for how decisions are made in a wide range of consequential contexts where people consider quantitative and qualitative information side-by-side. They suggest that when we count, we also change what counts.