Predicting memory color from neural responses to achromatic images of color-diagnostic objects

Some objects that we deal with on a daily basis are associated with an object-specific color [[unable to display character: –]] such as yellow for bananas, red for strawberries, green for lettuce, etc. Such objects are referred to as color-diagnostic and their associated color as their memory color (Hering, 1920). Psychophysical evidence shows that achromatic , i.e. grayscale, images of color-diagnostic objects elicit percepts that are differentially biased towards their memory color (Hansen, Olkkonen, Walter, & Gegenfurtner, 2006; Olkkonen, Hansen, & Gegenfurtner, 2008). This phenomenon suggests some form of learned and automatic association between colors and particular objects. In the present study we tested whether neural responses to color-diagnostic objects convey color-specific information, even when the objects were presented achromatically to subjects who were naïve to the purpose of the study. We first collected fMRI data while participants viewed grayscale images of 8 different color-diagnostic objects (4 colors, 2 per color). We then recorded responses to chromatic stimulation with red, green, blue, and yellow abstract color stimuli that contained no object information. All object and color stimuli were set to equiluminance for each subject individually. To analyze the data, we applied a whole-brain searchlight procedure by training linear support vector machine classifiers to distinguish between local voxel patterns associated with the four colors. They were then tested on patterns elicited by color-diagnostic achromatic objects to predict their correct memory colors. At the group level, we found significant decoding accuracy in a large cluster covering foveal regions of early visual cortex. In some but not all individual subjects, smaller clusters were also evident in the fusiform gyrus. Our results suggest that memory color and color signals evoked by chromatic stimulation share a common neural mechanism in early visual cortex. Retinotopic mapping in combination with classification techniques will be used to clarify the contribution of individual visual areas to this mechanism