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When evaluating sensory stimuli, people tend to prefer those with not too little or not too much complexity. A recent theoretical proposal for this phenomenon is that preference has a direct link to the Observed Fisher Information that a stimulus carries about the environment. To make this theory complete, one must specify the model that the brain has about complexities in the world. Here, we develop this model by first obtaining the distributions of three indices of complexity measured as normalized Shannon Entropy in real-world images from seven environments. We then search for a parametric model that accounts for these distributions. Finally, we measure the Observed Fisher Information that each image has about the parameters of this model. The results show that with few exceptions, the distributions of image complexities are unimodal, have negative skewness, and are leptokurtotic. Moreover, the sign and magnitude of the skewness varies systematically with the location of the mode. After investigating tens of models for these distributions, we show that the Logit-Losev function, a generalization of the hyperbolic-secant distribution, fits them well. The Observed Fisher Information for this model shows the inverted-U-shape behavior of complexity preference. Finally, we discuss ways to test our Fisher-Information theory. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) has been developed as a direct, extractionless, nonhydrolysis tool to detect lake pigments and colorants of various classes used in a variety of artist materials. Presented first is the SERS analysis of the natural colorant turmeric (Curcuma longa L.), main component curcumin, as present in dry lake pigment grains, dyed textile yarns, and reference paint layers containing the lake pigment bound in animal glue painted on glass. This experiment demonstrated that it is possible to detect the chromophore in various matrixes of increasing complexity, allowing its unambiguous identification in a wide range of artists' materials, even at very low concentration and in the presence of binders such as glue. In addition, removal of the colorant from the complex with the inorganic substrate or mordanted yarn was not necessary for identification. This proof-of-concept study was then extended to include analysis of several pastel sticks from a historical pastel box and two samples from a pastel artwork, both attributed to American painter Mary Cassatt (1844-1926). This study represents the first extractionless, nonhydrolysis direct SERS study of multiple artist materials, including identification of natural and synthetic colorants and organic pigments contained in historic artists' pastels spanning a broad range of chemical classes: polyphenols, rhodamines, azo pigments, and anthraquinones. Successful identification is demonstrated on samples as small as a single grain of pigment.

Tailored ad-hoc methods must be developed for successful identification of minute amounts of natural dyes on works of art using Surface-Enhanced Raman Spectroscopy (SERS). This article details two of these successful approaches using silver film over nanosphere (AgFON) substrates and silica gel coupled with citrate-reduced Ag colloids. The latter substrate functions as the test system for the coupling of thin-layer chromatography and SERS (TLC-SERS), which has been used in the current research to separate and characterize a mixture of several artists' dyes. The poor limit of detection of TLC is overcome by coupling with SERS, and dyes which co-elute to nearly the same spot can be distinguished from each other. In addition, in situ extractionless non-hydrolysis SERS was used to analyze dyed reference fibers, as well as historical textile fibers. Colorants such as alizarin, purpurin, carminic acid, lac dye, crocin, and Cape jasmine were thus successfully identified.