Visual Imagery in Hemianopic Patients

Abstract

In this article we report some findings about visual imagery in patients with stable homonymous hemianopia compared to healthy control subjects. These findings were obtained by analyzing the gaze control through recording of eye movements in different phases of viewing and imagery. We used six different visual stimuli for the consecutive viewing and imagery phases. With infrared oculography, we recorded eye movements during this presentation phase and in three subsequent imagery phases in absence of the stimulus. Analyzing the basic parameters of the gaze sequences (known as "scanpaths"), we discovered distinct characteristics of the "viewing scanpaths" and the "imagery scanpaths" in both groups, which suggests a reduced extent of the image within the cognitive representation. We applied different similarity measures (string/vector string editing, Markov analysis). We found a "progressive consistency of imagery," shown through raising similarity values for the comparison of the late imagery scanpaths. This result suggests a strong top-down component in picture exploration: In both groups, healthy subjects and hemianopic patients, a mental model of the viewed picture must evolve very soon and substantially determine the eye movements. As our hemianopic patients showed analogous results to the normal subjects, we conclude that these patients are well adjusted to their deficit and, despite their perceptual defect, have a preserved cognitive representation, which follows the same top-down vision strategies in the process of visual imagery.

Visual Attention and Saccadic Oculomotor Control in Parkinson's Disease

“…For display, we used the main sequence relationship [31,32,33], that showed in a compressed, double logarithmic plot the parameters: peak velocity, peak positive and peak negative acceleration and duration. Comparison of control subjects' with moderate PD patient's saccades showed characteristic differences of all checked parameters: the patients and the age-matched normal subjects varied in that the PD patients showed lower peak velocities, lower peak accelerations, and prolonged saccadic durations, confirmed in the main sequence results of saccade dynamics.…”

It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach

“…There are ways in which this can be done, and one of the lesser-known methods for scanpath comparison implemented by Gbadamosi and Zangemeister (2001;Gbadamosi, 2000) treats scanpaths as a sequence of vectors, sequence is independent of the stimulus (AOIs are not used), the costs associated with insertions, deletions, and substitutions can be drawn directly from the vector differences (i.e., the Euclidean distance between the endpoints of vector pairs (Fig. 6).…”

A quantitative measure for the uniformity of fixation density: The Voronoi method

“…As an exception, Choi, Mosley, and Stark (1995) developed a quantitative method to compare individual scan paths: the string analysis. This method was also used to compare scan paths made during first-time visual inspection with eye movement trajectories made later during imagery of the same scene (Brandt & Stark, 1997;Gbadamosi & Zangemeister, 2001). The string analysis converts scan paths to strings of characters: Each character represents a particular part of the stimulus that has been fixated.…”

“…Each of these regions is often called a region of interest (ROI) or area of interest (AOI). The subdivision into ROIs (see, e.g., Gbadamosi & Zangemeister, 2001;Laeng & Teodorescu, 2002;Pelz, Hayhoe, & Loeber, 2001;Shinoda, Hayhoe, & Shrivastava, 2001) allows for comparisons of the same feature, for example dwell time. The designation of suitable ROIs usually will not be a problem if the stimulus results in a clustering of fixations, such as with stimuli that consist of a collection of items (Zelinsky et al, 1997).…”