art.pics Database: An Open Access Database for Art Stimuli for Experimental Research

Abstract

While art is omnipresent in human history, the neural mechanisms of how we perceive, value and differentiate art has only begun to be explored. Functional magnetic resonance imaging (fMRI) studies suggested that art acts as secondary reward, involving brain activity in the ventral striatum and prefrontal cortices similar to primary rewards such as food. However, potential similarities or unique characteristics of art-related neuroscience (or neuroesthetics) remain elusive, also because of a lack of adequate experimental tools: the available collections of art stimuli often lack standard image definitions and normative ratings. Therefore, we here provide a large set of well-characterized, novel art images for use as visual stimuli in psychological and neuroimaging research. The stimuli were created using a deep learning algorithm that applied different styles of popular paintings (based on artists such as Klimt or Hundertwasser) on ordinary animal, plant and object images which were drawn from established visual stimuli databases. The novel stimuli represent mundane items with artistic properties with proposed reduced dimensionality and complexity compared to paintings. In total, 2,332 novel stimuli are available open access as “art.pics” database at https://osf.io/BTWNQ/ with standard image characteristics that are comparable to other common visual stimuli material in terms of size, variable color distribution, complexity, intensity and valence, measured by image software analysis and by ratings derived from a human experimental validation study [n = 1,296 (684f), age 30.2 ± 8.8 y.o.]. The experimental validation study further showed that the art.pics elicit a broad and significantly different variation in subjective value ratings (i.e., liking and wanting) as well as in recognizability, arousal and valence across different art styles and categories. Researchers are encouraged to study the perception, processing and valuation of art images based on the art.pics database which also enables real reward remuneration of the rated stimuli (as art prints) and a direct comparison to other rewards from e.g., food or money.Key Messages: We provide an open access, validated and large set of novel stimuli (n = 2,332) of standardized art images including normative rating data to be used for experimental research. Reward remuneration in experimental settings can be easily implemented for the art.pics by e.g., handing out the stimuli to the participants (as print on premium paper or in a digital format), as done in the presented validation task. Experimental validation showed that the art.pics’ images elicit a broad and significantly different variation in subjective value ratings (i.e., liking, wanting) across different art styles and categories, while size, color and complexity characteristics remained comparable to other visual stimuli databases.

Further validating the VAIAK: Defining a psychometric model, configural measurement invariance, reliability, and practical guidelines.

“…Recently, the VAIAK (Specker, Forster, Brinkmann, Boddy, Pelowski, et al, 2020) was introduced as a new way to measure general art interest and general art knowledge 1 . Even though only 3 years have passed since its introduction, the scale has been widely used (Gartus et al, 2020; Grüner et al, 2019; Kim et al, 2019; Reitstätter et al, 2020; Specker, Forster, Brinkmann, Boddy, Immelmann, et al, 2020; Specker, Stamkou, et al, 2020; Steciuch et al, 2019; Thieleking et al, 2020), showing that the VAIAK is a useful tool for researchers interested in empirical aesthetics, creativity, and applied fields 2…”

Investigating the Impact of Microbiome-Changing Interventions on Food Decision-Making: MIFOOD Study Protocol

“…A. Food wanting: We use an event-related design with standardized fMRI task of food (70) and art stimuli (71). Each run contains 160 stimuli (80 food and 80 art).…”

The Vienna Art Picture System (VAPS): A data set of 999 paintings and subjective ratings for art and aesthetics research.

“…Furthermore, two other data sets focus on aesthetic ratings of ordinary objects. The art.pics data set (Thieleking et al, 2020) offers 2,332 of images with everyday objects, animals, and plants, which were drawn from two food-related image data sets (Blechert et al, 2019;Foroni et al, 2013) and modified with a deep learning algorithm that applied eight different artistic styles (Dalí, Hundertwasser, Klimt, Munch, and Picasso) or epochs (Pointillism, Pop-Art, and Azulejos-a Spanish tile pattern). The art.pics stimuli set offers everyday items with artistic properties (e.g., a pillow/snake/ rose with pattern of The Scream painting by Munch).…”