Nonretinal vision is a term for visual experience in the absence of visual stimulation (e.g., visual imagery, visual working memory). Many previous studies have found that humans can use nonretinal vision to influence perceptual task performance (e.g., holding the identity of an upcoming target in mind prior to visual search), but different studies have made vastly different conclusions about the extent of this influence. One issue is that individual differences in nonretinal vision are rarely taken into account, but they may greatly impact perception. For example, there is a wide spectrum of visual imagery vividness: on one end, there are people who cannot visualize even concrete objects (aphantasia). On the other end, some people have such strong imagery that it can interfere with visual perception (hyperphantasia). The main goal of this project is to investigate the extent to which individual differences in sensory mental representations influence (and are influenced by) visual perception.
Previous studies have proposed a link between modal imagery vividness and hallucination proneness in pathology (Aleman et al., 2000; Aleman & de Haan, 2004). Exploring the relationship between sensory representation formats and various aspects of anomalous perception in normative samples (in the absence of pathology) will provide much-needed insight about top-down factors that contribute to hallucinatory experience. My research therefore focuses on inducing pseudo-hallucinatory experience in normative samples, using visual noise (pareidolia) and visual flicker (Ganzflicker) paradigms. Currently, I am interested in categorical differences in the likelihood to experience (especially complex) pseudo-hallucinations, in people with different sensory representation abilities.
A Gaussian Mixture Model (GMM) showing two somewhat-overlapping clusters of hypophants and imagers (aphants, who all reported having no imagery, do not fall on the x-axis of vividness ratings from 1-10). The y-axis shows complexity ratings of visual flicker-induced illusions (FII), coded from simple to complex (each level of complexity is illustrated by a gray line). The binary distinction between simple and complex FII is illustrated with background shading (gray shading indicates complex ratings). Blue shading indicates the likely boundaries of each distribution, and darker shades of blue indicate higher density. The distributions that make up hypophants and imagers were estimated by the GMM. This indicates a qualitative difference in FII-complexity between the low and typical ranges of imagery vividness.