Object recognition and Random Image Structure Evolution

We present a technique called Random Image Structure Evolution (RISE) for use in experimental investigations of high-level visual perception. Potential applications of RISE include the quantitative measurement of perceptual hysteresis and priming, the study of the neural substrates of object perception, and the assessment and detection of subtle forms of agnosia. In simple terms, RISE involves the measurement of perceptual and/or neural responses as visual stimuli are systematically transformed—in particular, as recognizable objects evolve from, then dissolve into, randomness. Points along the sequences corresponding to the onset and offset of subjects’ percepts serve as markers for quantitatively and objectively characterizing several perceptual phenomena. Notably, these image sequences are created in a manner that strictly controls a number of important low-level image properties, such as luminance and frequency spectra, thus reducing confounds in the analysis of high-level visual processes. Here we describe the RISE paradigm, report the results of a few basic RISE experiments, and discuss a number of experimental and clinical applications of this approach.