materialmodifier: An R package of photo editing effects for material perception research

In this paper, we introduce an R package that performs automated photo editing effects. Specifically, it is an R implementation of an image-processing algorithm proposed by Boyadzhiev et al. (2015). The software allows the user to manipulate the appearance of objects in photographs, such as emphasizing facial blemishes and wrinkles, smoothing the skin, or enhancing the gloss of fruit. It provides a reproducible method to quantitatively control specific surface properties of objects (e.g., gloss and roughness), which is useful for researchers interested in topics related to material perception, from basic mechanisms of perception to the aesthetic evaluation of faces and objects. We describe the functionality, usage, and algorithm of the method, report on the findings of a behavioral evaluation experiment, and discuss its usefulness and limitations for psychological research. The package can be installed via CRAN, and documentation and source code are available at https://github.com/tsuda16k/materialmodifier .

An ecological framework for temporal and individual differences in color preferences

There are well-known and extensive differences in color preferences between individuals, but there are also within-individual differences from one time to another. Despite the seeming independence between these individual and temporal effects, we propose that they have the same underlying cause: people's ecological experiences with color-associated objects and events. Our approach is motivated by the Ecological Valence Theory (EVT; Palmer & Schloss, 2010) which states that preference for a given color is determined by the combined valence (liking/disliking) of all objects and events associated with that color. We define three ecologically-based hypotheses for explaining temporal and individual differences in color preferences concerning: (1) differences in object valences, (2) differences in color-object associations, and (3) differences in object activations in the mind when preferences are measured. We review prior studies that support these hypotheses and raise open research questions about untested predictions. We also extend the computational framework of the EVT by defining a single weighted average equation that captures both individual and temporal differences in color preferences. Finally, we consider other factors that potentially contribute to color preferences, including abstract symbolic associations, color in design, and psychophysical and/or physiological factors.