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Related Experiment Video

Updated: Jul 5, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A framework for quantifying individual and collective common sense.

Mark E Whiting^1,2, Duncan J Watts^1,2,3

¹Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104.

Proceedings of the National Academy of Sciences of the United States of America

|January 16, 2024

Summary

This summary is machine-generated.

Common sense knowledge is not as universally shared as believed. This study introduces a framework to measure common sense, finding it rare and influenced by social perceptiveness, not demographics.

Keywords:

AI common sense knowledge

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Area of Science:

Cognitive Science
Social Psychology
Artificial Intelligence

Background:

Common sense is frequently referenced but lacks empirical definition.
Its intrinsic properties and shared nature remain uncharacterized.
Existing notions of common sense may be overly universalistic.

Purpose of the Study:

To introduce an analytical framework for quantifying common sense knowledge.
To empirically measure the commonsensicality of claims and individuals.
To assess the degree to which common sense is shared among people.

Main Methods:

Defined commonsensicality based on agreement propensity and awareness of agreement.
Formalized commonness of common sense using clique detection on a belief graph.
Evaluated the framework on a dataset of human raters and diverse claims.

Main Results:

Commonsensicality correlates with plainly worded, fact-like statements about physical reality.
Social perceptiveness influences individual common sense, but demographics like age and gender do not.
Collective common sense is rare, with limited agreement among large groups.

Conclusions:

Empirical data challenges universalistic beliefs about common sense.
Common sense exhibits variability, impacting both human and artificial intelligence.
Further research is needed to understand the nuances of shared knowledge.