Individual differences in visual mental imagery assessed through standard and evolutionary classification images

Mental imagery—the ability to visualize images in the mind’s eye—is associated with many perceptual and cognitive facilities that vary across individuals. Quantifying mental imagery abilities however is challenging and typically relies on subjective and self-report methods, as the world of the imagination is not directly measurable. In contrast to such approaches, here, we propose a method for directly visualising mental images using classification images. Despite their potential, classification images have not been adopted for evaluating individual differences in mental imagery ability due to two primary challenges: the time-consuming nature of traditional reverse correlation that requires many hours of testing, and the uncertainty about how to interpret the reconstructed images. To address these challenges, we first optimized a traditional reverse correlation paradigm to yield recognizable classification images in under 20 minutes, and then developed an additional “evolutionary” paradigm—based on genetic search. We used these methods in an experiment with 20 typical participants who underwent multiple sessions of “standard” and “evolutionary” reverse correlation tasks in which they detected the letter “S” in pure pixel noise images. We fed the generated classification images into deep neural network image classifiers trained at recognizing handwritten letters in noise. We took the networks’ cross-entropy loss as a measure of the quality of the generated classification images, and thus of the mental imagery abilities of each participant. This approach exhibited substantial test-retest reliability within both standard (r=.58, p<.01) and evolutionary (r=.42, p<.05) reverse correlation sessions, as well as across paradigms (r=.55, p<.01), and the reliability of the estimated individual differences improved linearly with increasing number of trials (r=.73, p<.001). These results indicate that both “standard” and “evolutionary” reverse correlation methods consistently measured individual differences in mental imagery. This work thus paves the way for a more nuanced and objective understanding of this complex cognitive function.