Representing and Reasoning about the Categories of Social Life

Verantwortlich

Dr. Vaughn Becker vaughn.becker[uk]asu.edu

Weiter verantwortlich

Zeitraum

01.04.2018-31.12.2020

Förderung

Beschreibung

Explaining how people form social categories (e.g., “Us” and “Them”) and associate these categories with attributes (e.g., “Us” = “good”) is one of the theoretically most important and fundamental questions within social psychological research. It has implications for intergroup conflict, cooperation, stereotypes and prejudice. The influx of refugee populations lends particular urgency to the need for new insights into how social categories and associations develop, as well as the errors and biases that compromise accurate appraisals. The present project will advance the understanding about how people represent and reason about social categories (e.g., incoming refugees) using two methods: Multi-Dimensional Scaling (MDS; specifically, 3-mode MDS) and Multi-Cue Contingency-Learning (MCCL). We have experience with these methods, but new developments are necessary, and the two separate methods can be integrated. The results will reveal both the structural landscape of people’s mental representations as well as the ways in which new information about novel categories build, transform and even distort such representational landscapes.

Description

Explaining how people form social categories (e.g., “Us” and “Them”) and associate these categories with attributes (e.g., “Us” = “good”) is one of the theoretically most important and fundamental questions within social psychological research. It has implications for intergroup conflict, cooperation, stereotypes and prejudice. The influx of refugee populations lends particular urgency to the need for new insights into how social categories and associations develop, as well as the errors and biases that compromise accurate appraisals. The present project will advance the understanding about how people represent and reason about social categories (e.g., incoming refugees) using two methods: Multi-Dimensional Scaling (MDS; specifically, 3-mode MDS) and Multi-Cue Contingency-Learning (MCCL). We have experience with these methods, but new developments are necessary, and the two separate methods can be integrated. The results will reveal both the structural landscape of people’s mental representations as well as the ways in which new information about novel categories build, transform and even distort such representational landscapes.