Human brain signals associated with visual perceptual processes have been used for image recognition. This paper presents several insights on the neural correlates of human visual perception by analyzing the neural correlates that result when humans view realistic images using a rapid serial visual presentation (RSVP) image display paradigm. We propose an image information extraction model and examine the relationship between the brain evoked response - using event related potential (ERP) characteristics - and the level of difficulty for humans to detect targets as a function of both visual stimulus complexity and task difficulty. We develop a computational model to quantify subject performance and the difficulty of realistic stimuli. Our results show that: (1) more difficult trials produce less prominent ERP patterns, thus reducing the performance of machine-based ERP detection; (2) on average for the same behavioral performance level, a pair of ERP's extracted from two easy trials are more similar than a pair of ERP's from two hard trials; and (3) both stimulus and task difficulty are correlated with neural activity. Our findings indicate that, for dynamic tasks involved in visual information processing, the brain may allocate additional cognitive resources, such as attention, to a given visual stimulus, as the task and/or stimulus difficulty increases.