Friend or Foe: A Review and Synthesis of Computational Models of the Identity Labeling Problem

TL;DR

This paper introduces the identity labeling problem in social cognition, proposes a new Latent Cognitive Social Spaces model incorporating diverse social cues, and demonstrates its superior predictive accuracy over previous models.

Contribution

The paper presents a novel framework for predicting social identity labels using multiple cues, addressing a key theoretical gap in social cognition modeling.

Findings

Model predicts identity labels with 10.9% mean absolute error

Achieves 100% improvement over previous models

Validates model with data from vignette experiments

Abstract

We introduce the identity labeling problem - given an individual in a social situation, can we predict what identity(ies) they will be labeled with by someone else? This problem remains a theoretical gap and methodological challenge, evidenced by the fact that models of social-cognition often sidestep the issue by treating identities as already known. We build on insights from existing models to develop a new framework, entitled Latent Cognitive Social Spaces, that can incorporate multiple social cues including sentiment information, socio-demographic characteristics, and institutional associations to estimate the most culturally expected identity. We apply our model to data collected in two vignette experiments, finding that it predicts identity labeling choices of participants with a mean absolute error of 10.9%, a 100% improvement over previous models based on parallel constraint satisfaction and affect control theory.