What  is a “normal” (or at least average) reading rate for a score and how does the rate impact upon the amount of sonic detail that is capable of being represented? The table below compares the notional average rate at which the score progresses as the performer reads the work: its “scroll-rate”. The scroll-rate is calculated by dividing the length of the score by its average duration. 
The works are varied: Beethoven Piano Sonata No. 17 in D minor Op. 31 No. 2 (1802) (The Tempest) first movement includes significant changes of tempo in which the performer would be reading at different rates; the Chopin Waltz in D-flat major Op. 64 No. 1 (1847) (Minute Waltz), Ravel Pavane pour une infante défunte  (1899), Debussy Voiles (1909) might be considered examples at the high, low and centre of the scroll-rate speeds. These rates give an indication of what is an acceptable and perhaps even conventional speed to read musical notation. 
The final five works on the table are “scrolling scores” by Cat Hope and Lindsay Vickery, in which the score moves past the performer at a constant rate on an iPad screen. There is, at the least, a psychological distinction between this paradigm, where the performer is forced to view only a portion of the score at any time, and the fixed score where the performer directs their own gaze. 
The scrolling score, although highly useful for synchronising musical events in non-metrical music has particular natural constraints based on the limitations of human visual processing: at scroll rates greater than 3 cm/s the reader struggles to capture information; information dense musical notation may significantly lower this threshold; reading representations of fine metrical structures in a scrolling medium is problematic. The problem may be caused by a conflict between the continuous movement of the score and the relatively slow (in comparison to the ear) fixation rate of the eye or simply a by-product of unfamiliarity with the medium, however the cause is currently unexplained. 

The approach to sonic visualisation used by the Lyrebird Environment Player of also has application to the analysis of electroacoustic music. As Grill and Flexer have indicated, traditional spectrogram “visualizations are highly abstract, lacking a direct relationship to perceptual attributes of sound” (2012). The approach employed by Lyrebird goes someway toward alleviating the problem of “demonstrating coindexation and segmentation due to the difficulty in illustrating differences in timbre” (Adkins 2008) in a spectrogram and provides an (almost) realtime feature analysis of the recording in which contours and timbral shifts are readily recognizable.
The image below shows a representation of Pierre Schaeffer’s Étude aux Chemins de Fer, clearly delineating segments of the work created with varied source materials. The colour scaling in this reading consistently colours sound objects of the same timbre/material. The entire 170 seconds of the work was represented by slowing the scrollrate of the lcd object. The insert shows the whistle that occurs at approximately 112 seconds into the work and illustrates the “Doppler” effect that is heard through a change of both vertical height (pitch) and colour (timbre). By coincidence(?) the Doppler shift is represented by a change from red(dish) to blue(ish) colours. A very small plot of the formal structure is shown below the Lyrebird analysis.

One of the long-crescendo F#s from the clarinet part of Messiaen's Abîme des Oiseaux represented as a spectrogram (using Chris Cannam’s Sonic Visualiser software) and  the Lyrebird Environment Player. Lyrebird represents the pitch of the single strongest detected sinusoidal peak vertical position; amplitude by the size of the rectangle; and brightness, noisiness and bark scale data is used to determine the luminance, hue and saturation of each rectangle.