Odor descriptive ratings can predict some odor-color associations in different color features of hue or lightness

View article
Brain, Cognition and Mental Health

Main article text

 

Introduction

Materials and Methods

Participants

Environment and display settings

Odor stimuli

Experiment

Bayesian estimation

Bayesian multilevel regression analysis

Bayesian within-subject analysis for color response reproducibility

Results

Days-differences of associated colors within participants

Confirmation of the multicollinearity for multilevel regression

Bayesian multilevel regression

Discussion

Conclusions

Supplemental Information

Bayesian multilevel regression models to estimate the sex differences of color responses using odor-level effects.

DOI: 10.7717/peerj.15251/supp-1

Multilevel regression analysis including intercepts from individual errors.

DOI: 10.7717/peerj.15251/supp-2

Sex differences of the odor-color responses in (A) a*-axis, (B) b*-axis, and (C) L*-axis values.

Gray bars indicate the 95% Bayesian confidence interval. Black circles indicate the estimated Bayesian mean values.

DOI: 10.7717/peerj.15251/supp-3

The mean and 95% confidence interval (CI) of the coefficients estimated by the Bayesian multilevel regression model including individual errors for a*-value prediction.

The black vertical line indicates 0. The black dots indicate the Bayesian mean, and bold bars indicate 95% Bayesian CI. The red bars indicate 95% significant coefficients, and the gray bars indicate non-significant coefficients.

DOI: 10.7717/peerj.15251/supp-4

The mean and 95% confidence interval (CI) of the coefficients estimated by the Bayesian multilevel regression model including individual errors for b*-value prediction.

The black vertical line indicates 0. The black dots indicate the Bayesian mean, and bold bars indicate 95% Bayesian CI. The red bars indicate 95% significant coefficients, and the gray bars indicate non-significant coefficients.

DOI: 10.7717/peerj.15251/supp-5

The mean and 95% confidence interval (CI) of the coefficients estimated by the Bayesian multilevel regression model including individual errors for L*-value prediction.

The black vertical line indicates 0. The black dots indicate the Bayesian mean, and bold bars indicate 95% Bayesian CI. The red bars indicate 95% significant coefficients, and the gray bars indicate non-significant coefficients.

DOI: 10.7717/peerj.15251/supp-6

Additional Information and Declarations

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Kaori Tamura conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Tsuyoshi Okamoto conceived and designed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Human Ethics

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

The local ethics committee of the Faculty of Arts and Science, Kyushu University approved the current experiment. All procedures were performed in accordance with the approved guidelines of the local ethics committee of the Faculty of Arts and Science, Kyushu University.

Data Availability

The following information was supplied regarding data availability:

The data and code are available at Zenodo: Kaori Tamura. (2023). Data and codes of “Odor descriptive ratings can predict some odor-color associations in different color features of hue or lightness” (https://gitlab.com/tamurak415/olfqr) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7693843.

Funding

This study was supported by the 2019 QR Program (Qdai-jump Research Program) and Wakaba 405 Challenge (reference number: 01296), and the 2022 Research Support Program for Young Scientists in Fukuoka Institute of Technology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

1 Citation 1,114 Views 56 Downloads

Your institution may have Open Access funds available for qualifying authors. See if you qualify

Publish for free

Comment on Articles or Preprints and we'll waive your author fee
Learn more

Five new journals in Chemistry

Free to publish • Peer-reviewed • From PeerJ
Find out more