Intersections between emotion and visual mental imagery

Mental imagery is the ability to conjure sensory information in the absence of a corresponding stimulus in the immediate environment. Closely connected to the concept of visual imagination, visual mental imagery (popularly referred to as ‘the mind’s eye’) has been studied from a psychological, neuroscientific, cognitive literary perspective; the ensuing model has been successfully translated to artificial intelligence and machine learning applications. The dissertation at hand follows a multidisciplinary approach in reviewing the existing knowledge surrounding the topic of visual mental imagery, and aims to expand this research in a novel direction. Namely, the dissertation addresses the impact various positive and negative emotions may have on the ease with which mental imagery is conjured while reading fictional text, and the quality of this imagery. As a secondary purpose, this dissertation presents criticism to existing standards of measurements, to the reliance of current testing on the term ‘vividness’, and offers an alternative combination of terms to more accurately measure imagery quality (emotional intensity, detail, saturation, sharpness and latency).
How emotion affects visual mental imagery is a question yet to be explored. Therefore, insight must be gained from interactions between emotion and other cognitive functions which are closely related to mental imagery. Perception has much in common with mental imagery from a phenomenological, structural and functional perspective, and thus could be considered a great predictor. Neuroimaging studies show that cortical areas associated with memory, as well as the default mode network, are active in visual mental imagery exercises. From a cognitive literary perspective, some narratological studies examine what traits in a text cause it to evoke potent imagery. All these explorations coalesce into a within-subjects experiment design whereby participants are presented with four texts, equalised for narrative factors, each evoking one of the following emotions: joy, affection (or empathy), sadness and fear. Multiple ANOVA analyses of the resulting data showed higher saturation scores in positive, but not in negative, emotions. Other variables remained below statistical significance. When data resulting from emotive vs. non-emotive reading was compared, emotion was seen to amplify saturation and sharpness, and greatly amplify intensity. However, it did not affect the level of detail or latency.