Abstract
Ordinary language users group colours into categories that they refer to by a name e.g. pale green. Data on the colour categories of English speakers was collected using online crowd sourcing – 1,000 subjects produced 20,000 unconstrained names for 600 colour stimuli. From this data, using the framework of Information Geometry, a Riemannian metric was computed throughout the RGB cube. This is the first colour metric to have been computed from colour categorization data. In this categorical metric the distance between two close colours is determined by the difference in the distribution of names that the subject population applied to them. This contrasts with previous colour metrics which have been driven by stimulus discriminability, or acceptability of a colour match. The categorical metric is analysed and shown to be clearly different from discriminability-based metrics. Natural units of categorical length, area and volume are derived. These allow a count to be made of the number of categorically-distinct regions of categorically-similar colours that fit within colour space. Our analysis estimates that 27 such regions fit within the RGB cube, which agrees well with a previous estimate of 30 colours that can be identified by name by untrained subjects.
Highlights
Colour is a perceptual quality sitting midway in a causal chain linking the physical world to the cultural
English has colour names (e.g. ‘red’, ‘peach’, ‘warm brown’) that refer to a region of colour space known as a colour category; the category forms part of the concept related to the name, plus other information such as what objects typically have that colour
Information Geometry is concerned with defining a metric on statistical manifolds, which are continuous multi-dimensional families of probability distribution functions
Summary
Colour is a perceptual quality sitting midway in a causal chain linking the physical world (light sources, surfaces, sensory mechanisms) to the cultural (categories, concepts, language). In this paper we use colour categories to define a geometry on the manifold of surface colours. Colours are close in this geometry if they are categorized . In the introduction we review previous work on colour geometry, on colour categories, and on the relation between colour categories and geometry. In 5 we use the dataset to constrain a continuous model of naming across colour space.
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