On Semantic Cognition, Inductive Generalization, and Language Models

Bayesian Models of Inductive Generalization

We argue that human inductive generalization is best explained in a Bayesian framework, rather than by traditional models based on similarity computations. We go beyond previous work on Bayesian concept learning by introducing an unsupervised method for constructing flexible hypothesis spaces, and we propose a version of the Bayesian Occam’s razor that trades off priors and likelihoods to preve...

A Note on Inductive Generalization

Language Networks as Models of Cognition: Understanding Cognition through Language

Language is inherently cognitive and distinctly human. Separating languageas-object from the human mind that processes and creates language fails to capture the full language system. Linguistics traditionally has focused on the study of language as a static representation, removed from the human mind. Network analysis has traditionally been focused on the properties and structure that emerge fr...

Social Cognition of Gender and Language

The question, Do men and women use language differently played a central part in the emergence of feminist socio linguistics more than two decades ago, and it casts a long shadow. This paper focuses on the literature that has contributed to the understanding of the major research questions underlying two major strands, language and gender, concentrating on the development of the literature from...

Probabilistic models of cognition: exploring representations and inductive biases.

Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive problems. Mental processes are then modeled using algorithms for approximating these solutions, and neural processes are viewed as mechanisms for implementing these ...