Related Experiment Video
Updated: Jun 28, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
Predicting attitudes toward ambiguity using natural language processing on free descriptions for open-ended question
Jimpei Hitsuwari1,2, Hirohito Okano1, Michio Nomura3
1Graduate School of Education, Kyoto University, Kyoto, Japan.
Abstract:
Individual traits and reactions to ambiguity differ and are conceptualized in terms of an individual's attitudes toward ambiguity or ambiguity tolerance. The development of natural language processing technology has made it possible to measure mental states and reactions through open-ended questions, rather than predefined numerical rating scales, which have traditionally been the dominant method in psychological research. This study presented three ambiguity-related situations and responses collected online from 591 participants in an open-ended format. After the analysis with bidirectional encoder representations from transformers, correlations were calculated using scores from the numerical evaluation by conventional questionnaire, and a significant moderate positive correlation was found. Therefore, this study found that attitudes toward ambiguity can be measured using an open-ended response method of reporting everyday life states. It is a novel methodology that can be expanded to other scales in psychology and can potentially be used in educational and clinical situations where participants can be asked to respond with minimal burden.
More Related Videos
05:22Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
Published on: May 9, 2019
09:27Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
Published on: October 13, 2018
Related Concept Videos
Surveys
Attitudes
Naturalistic Observations
Stereotype Threat and Self-fulfilling Prophecies
Stereotype Content Model
Hindsight Biases