Literature DB >> 8036116

An interaction model for odor quality and intensity.

M J Olsson1.   

Abstract

The perceived intensity and quality of binary odor mixtures are studied in relation to how their components are perceived when presented separately. Subjects judged the perceived intensity and quality of 6 concentrations of pyridine, 6 concentrations of n-butanol, and their 36 possible combinations. The results show that the perceived intensity of the mixture can be predicted from the perceived intensity of its components presented separately (RA and RB) by the Euclidian arithmetic model. The maximum probability of identifying a mixture as a "mixture" is reached when RA and RB are equally strong. An interaction model for mixtures that relates perceived intensity and quality is presented and tested. The test reveals that both the perceived intensity of a mixture and the probability that it will be identified as the one or the other component can, by a simple interaction model, be successfully predicted from RA and RB.

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Year:  1994        PMID: 8036116     DOI: 10.3758/bf03205294

Source DB:  PubMed          Journal:  Percept Psychophys        ISSN: 0031-5117


  21 in total

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Journal:  Percept Psychophys       Date:  1988-10

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Authors:  B Berglund; M J Olsson
Journal:  J Exp Psychol Hum Percept Perform       Date:  1993-04       Impact factor: 3.332

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Authors:  B Berglund; M J Olsson
Journal:  Percept Psychophys       Date:  1993-05

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Authors:  D G Laing; H Panhuber; M E Willcox; E A Pittman
Journal:  Physiol Behav       Date:  1984-08
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  7 in total

1.  Time and intensity factors in identification of components of odor mixtures.

Authors:  Marion E Frank; Holly F Goyert; Thomas P Hettinger
Journal:  Chem Senses       Date:  2010-08-18       Impact factor: 3.160

2.  Characteristic component odors emerge from mixtures after selective adaptation.

Authors:  Holly F Goyert; Marion E Frank; Janneane F Gent; Thomas P Hettinger
Journal:  Brain Res Bull       Date:  2007-01-16       Impact factor: 4.077

3.  Functional roles of distributed synaptic clusters in the mitral-granule cell network of the olfactory bulb.

Authors:  Michele Migliore; Michael L Hines; Thomas S McTavish; Gordon M Shepherd
Journal:  Front Integr Neurosci       Date:  2010-09-20

4.  Identification and analysis of odor-active compounds from Choerospondias axillaris (Roxb.) Burtt et Hill with different moisture content levels and lacquer treatments.

Authors:  Qifan Wang; Jun Shen; Bin Zeng; Huiyu Wang
Journal:  Sci Rep       Date:  2020-09-09       Impact factor: 4.379

5.  Prediction models for the pleasantness of binary mixtures in olfaction.

Authors:  Hadas Lapid; David Harel; Noam Sobel
Journal:  Chem Senses       Date:  2008-06-04       Impact factor: 3.160

6.  Predicting odor perceptual similarity from odor structure.

Authors:  Kobi Snitz; Adi Yablonka; Tali Weiss; Idan Frumin; Rehan M Khan; Noam Sobel
Journal:  PLoS Comput Biol       Date:  2013-09-12       Impact factor: 4.475

Review 7.  The perception of odor objects in everyday life: a review on the processing of odor mixtures.

Authors:  Thierry Thomas-Danguin; Charlotte Sinding; Sébastien Romagny; Fouzia El Mountassir; Boriana Atanasova; Elodie Le Berre; Anne-Marie Le Bon; Gérard Coureaud
Journal:  Front Psychol       Date:  2014-06-02
  7 in total

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