Dutch Golden Age painters could convincingly depict all sorts of materials. How did they do it and how do we perceive them as such, are questions that only recently have started to be addressed by art historians and vision scientists, respectively. This paper aims to discuss how a booklet of pictorial recipes written by the Dutch painter Willem Beurs in 1692 constitutes an index of key image features for material depiction and perception. Beurs' recipes connect different materials according to their shared visual features, and offer the profiles, i.e., the optimal combinations, of these features to render a wide range of materials. By combining representation and perception, the knowledge of painters about the depiction of materials can help to understand the mechanisms of the visual system for material perception, and these in turn can explain the pictorial features that make the pictorial representation of materials so convincing. Keywords

Dutch 17th century painters were masters in depicting materials and their properties in a convincing way. Here, we studied the perception of the material signatures and key image features of different depicted fabrics, like satin and velvet. We also tested whether the perception of fabrics depicted in paintings related to local or global cues, by cropping the stimuli. In Experiment 1, roughness, warmth, softness, heaviness, hairiness, and shininess were rated for the stimuli shown either full figure or cropped. In the full figure, all attributes except shininess were rated higher for velvet, whereas shininess was rated higher for satin. This distinction was less clear in the cropped condition, and some properties were perceived significantly different between the two conditions. In Experiment 2 we tested whether this difference was due to the choice of the cropped area. On the basis of the results of Experiment 1, shininess and softness were rated for multiple crops from each fabric. Most crops from the same fabric differed significantly in shininess, but not in softness perception. Perceived shininess correlated positively with the mean luminance of the crops and the highlights’ coverage. Experiment 1 showed that painted velvet and satin triggered distinct perceptions, indicative of robust material signatures of the two fabrics. The results of Experiment 2 suggest that the presence of local image cues affects the perception of optical properties like shininess, but not mechanical properties such as softness.

Painters mastered replicating the regularities of the visual patterns that we use to infer different materials and their properties, via meticulous observation of the way light reveals the world's textures. The convincing depiction of bunches of grapes is particularly interesting. A convincing portrayal of grapes requires a balanced combination of different material properties, such as glossiness, translucency and bloom, as we learn from the 17th-century pictorial recipe by Willem Beurs. These material properties, together with three-dimensionality and convincingness, were rated in experiment 1 on 17th-century paintings, and in experiment 2 on optical mixtures of layers derived from a reconstruction of one of the 17th-century paintings, made following Beurs's recipe. In experiment 3 only convincingness was rated, using again the 17th-century paintings. With a multiple linear regression, we found glossiness, translucency and bloom not to be good predictors of convincingness of the 17thcentury paintings, but they were for the reconstruction. Overall, convincingness was judged consistently, showing that people agreed on its meaning. However, the agreement was higher when the material properties indicated by Beurs were also rated (experiment 1) than if not (experiment 3), suggesting that these properties are associated with what makes grapes look convincing. The 17thcentury workshop practices showed more variability than standardization of grapes, as different combinations of the material properties could lead to a highly convincing representation. Beurs's recipe provides a list of all the possible optical interactions of grapes, and the economic yet effective image cues to render them.

Citrus fruits are characterized by a juicy and translucent interior, important properties that drive material recognition and food acceptance. Yet, a thorough understanding of their visual perception is still missing. Using citrus fruits depicted in 17th-century paintings as stimuli, we ran three rating experiments. In Experiment 1, participants rated the perceived similarity in translucency or juiciness of the fruits. In Experiment 2, different groups of participants rated one image feature from a list obtained in a preliminary experiment. In Experiment 3, translucency and juiciness were rated. We constructed two-dimensional perceptual spaces for both material properties and fitted the ratings of the image features into the spaces to interpret them. "Highlights," "peeled side," "bumpiness," and "color saturation" fit the juiciness space best and were high for the highly juicy stimuli. "Peeled side," "intensity of light gradient," "highlights," and "color saturation" were the most salient features of the translucency space, being high for the highly translucent stimuli. The same image features were also indicated in a 17th-century painting manual for material depiction (Beurs, 1692; Beurs, in press). Altogether, we disclosed the expertise of painters with regard to material perception by identifying the image features that trigger a visual impression of juiciness and translucency in citrus fruits.

To unveil the mystery of the exquisitely rendered materials in Dutch 17 ^th century paintings, we need to understand the pictorial procedures of this period. We focused on the Dutch master Jan de Heem, known for his highly convincing still-lifes. We reconstructed his systematic multi-layered approach to paint grapes, based on pigment distribution maps, layers stratigraphy, and a 17 ^th century textual source. We digitised the layers reconstruction to access the temporal information of the painting procedure. We combined the layers via optical mixing into a digital tool that can be used to answer “what if” art historical questions about the painting composition, by editing the order, weight and colour of the layers.