The Effect of Name Category and Discriminability on the Search Characteristics of Colour Sets

Within (and between) cultures, people tend to agree on which parts of colour space are easiest to name and what the names for these regions are. Therefore it is likely that the manipulation of ease of naming (nameability) of colours should change performance in tasks where categorisation by colour name is important. More specifically, highly ‘nameable’ colour sets should lead to better performance than metrically equivalent but less categorically distinct sets, when the task requires categorisation. This hypothesis was investigated by testing observers on a name-based task, the naming and subsequent identification by name of colour sets with up to sixteen members. These sets were designed to be easy to name (nameable), maximally discriminable, or matched discriminable. The first were derived from previously generated data, the second by a standard algorithm to space colours widely in colour space, and the latter by closely matching their metric characteristics to those of an easy-to-name colour set. This final condition was metrically (but not categorically) equivalent to the nameable set. It was found that sets designed to be nameable did indeed lead to superior performance as measured by response times, confidence ratings, and response accuracy. Perceptual colour similarity, measured by a ΔE metric, did not predict errors. Nameability may thus be a valid, manipulable, aspect of sets of colours, and one which is not otherwise duplicated in the metric characteristics of such sets.

[1]  Robert M. Boynton,et al.  Salience of chromatic basic color terms confirmed by three measures , 1990, Vision Research.

[2]  J. Lammens A computational model of color perception and color naming , 1995 .

[3]  P. Kay,et al.  Basic Color Terms: Their Universality and Evolution , 1973 .

[4]  E. R. Heider Universals in color naming and memory. , 1972, Journal of experimental psychology.

[5]  P. Kay,et al.  Color categories in thought and language: Color naming across languages , 1997 .

[6]  R. M. Boynton,et al.  Locating basic colors in the OSA space , 1987 .

[7]  P. Kay Basic Color Terms: Their Universality and Evolution , 1969 .

[8]  R C Carter,et al.  High-contrast sets of colors. , 1982, Applied optics.

[9]  S. M. Luria,et al.  The Effects of Set Size on Color Matching Using CRT Displays , 1986, Human factors.

[10]  Harvey S. Smallman,et al.  On the usefulness of basic colour coding in an information display , 1993 .

[11]  David L. Post,et al.  Colorimetric Measurement, Calibration, and Characterization of Self-Luminous Displays , 1992 .

[12]  R. E. Christ Review and Analysis of Color Coding Research for Visual Displays , 1975 .

[13]  D. Van Laar,et al.  A name based algorithm for generating maximally discriminable colour sets , 1994 .

[14]  Harvey S. Smallman,et al.  Category effects in color memory , 1989 .

[15]  W De Corte,et al.  Recent developments in the computation of ergonomically optimal contrast sets of CRT colours , 1990 .

[16]  R. M. Boynton Color categories in thought and language: Insights gained from naming the OSA colors , 1997 .

[17]  T. Whitfield Salient features of color space , 1981, Perception & psychophysics.

[18]  Darren Van Laar,et al.  The structure of colour naming space , 2000, Vision Research.

[20]  H. Smallman,et al.  Segregation of basic colors in an information display. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[21]  Julia Sturges,et al.  Locating basic colours in the munsell space , 1995 .

[22]  Gunilla Derefeldt,et al.  Colour concept retrieval by free colour naming. Identification of up to 30 colours without training , 1995 .