Giacomo Veneri

Influences of data filtering on human-computer interaction by gaze-contingent display and eye-tracking applications

We describe an interactive gaze-contingent display (GCD) applied to clinical applications; the system uses a simple texture hole to inhibit peripheral vision, to test and stress overt mechanisms of visual searching in normal subjects. The correct use of GCD in vision research is affected by tremor of the hole, due to system noise, nystagmus, eye blinking, calibration and subject reactivity. These issues compromise the execution of task. In order to obtain a stable GCD hole, we implemented a predictive gaze-contingent display (PGCD), fitting through dispersion of fixations and modulating a filter. The paper describes the PGCD and compare it with the common technique, providing evidence that humans fit exploration based on the characteristics of the computer system; in particular we found significant difference applying PGCD or a simple finite impulse response filter. We suggest that a correct human-computer interaction applied to neuropsychological context must be developed taking in consideration both technical point of view and human behavior.

Eye fixations identification based on statistical analysis - Case study

Eye movement is the most simple and repetitive movement that enable humans to interact with the environment. The common daily activities, such as watching television or reading a book, involve this natural activity which consists of rapidly shifting our gaze from one region to another. The identification of the main components of eye movement during visual exploration such as fixations and saccades, is the objective of the analysis of eye movements in various contexts ranging from basic neuro sciences and visual sciences to virtual reality interactions and robotics. However, many of the algorithms that detect fixations present a number of problems. In this article, we present a new fixation identification algorithm based on the analysis of variance and F-test. We present the new algorithm and we compare it with the common fixations algorithm based on dispersion. To demonstrate the performance of our approach we tested the algorithm in a group of healthy subjects.

Automatic eye fixations identification based on analysis of variance and covariance

Eye movement is the simplest and repetitive movement that enables humans to interact with the environment. The common daily activities, such as reading a book or watching television, involve this natural activity, which consists of rapidly shifting our gaze from one region to another. In clinical application, the identification of the main components of eye movement during visual exploration, such as fixations and saccades, is the objective of the analysis of eye movements: however, in patients affected by motor control disorder the identification of fixation is not banal. This work presents a new fixation identification algorithm based on the analysis of variance and covariance: the main idea was to use bivariate statistical analysis to compare variance over x and y to identify fixation. We describe the new algorithm, and we compare it with the common fixations algorithm based on dispersion. To demonstrate the performance of our approach, we tested the algorithm in a group of healthy subjects and patients affected by motor control disorder.

Spike removal through multiscale wavelet and entropy analysis of ocular motor noise: a case study in patients with cerebellar disease

Wavelet decomposition of ocular motor signals was investigated with a view to its use for noise analysis and filtering. Ocular motor noise may be physiological, depending on brain activities, or experimental, depending on the eye recording machine, head movements and blinks. Experimental noise, such as spikes, must be removed, preserving noise due to neuro-physiological activities. The proposed method uses wavelet multiscale decomposition to remove spikes and optimizes the procedure by means of the covariance of the eye signals. To measure the noise on eye motor control, we used the wavelet entropy. The method was tested on patients with cerebellar disorders and healthy subjects. A significant difference in wavelet entropy was observed, indicating this quantity as a valuable measure of physiological motor noise.

Evaluating the Influence of Motor Control on Selective Attention through a Stochastic Model: The Paradigm of Motor Control Dysfunction in Cerebellar Patient

Attention allows us to selectively process the vast amount of information with which we are confronted, prioritizing some aspects of information and ignoring others by focusing on a certain location or aspect of the visual scene. Selective attention is guided by two cognitive mechanisms: saliency of the image (bottom up) and endogenous mechanisms (top down). These two mechanisms interact to direct attention and plan eye movements; then, the movement profile is sent to the motor system, which must constantly update the command needed to produce the desired eye movement. A new approach is described here to study how the eye motor control could influence this selection mechanism in clinical behavior: two groups of patients (SCA2 and late onset cerebellar ataxia LOCA) with well-known problems of motor control were studied; patients performed a cognitively demanding task; the results were compared to a stochastic model based on Monte Carlo simulations and a group of healthy subjects. The analytical procedure evaluated some energy functions for understanding the process. The implemented model suggested that patients performed an optimal visual search, reducing intrinsic noise sources. Our findings theorize a strict correlation between the “optimal motor system” and the “optimal stimulus encoders.”

Evaluating gaze control on a multi-target sequencing task

Many high cognitive applications, such as vision processing and representation and understanding of images, often need to analyse in detail how an ongoing visual search was performed in a representative subset of the image, which may be arranged into sequences of loci, called regions of interest (ROIs). We used the Trial Making Test (TMT) in which subjects are asked to fixate a sequence of letters and numbers in a logical alphanumeric order. The main characteristic of TMT is to force the subject to perform a default and well-known path. The comparison of the expected scan-path with the observed scan-path provides a valuable method to investigate how a task force the subject to maintain a top-down internal representation of execution and how bottom-up influences the performance. We developed a mechanism that analyses the scan path using different algorithms, and we compared it with other methods: we found that fixations outside the ROI are direct influence of exploration strategy. The paper discusses the method in healthy subjects.

Spatial ranking strategy and enhanced peripheral vision discrimination optimize performance and efficiency of visual sequential search

Visual sequential search might use a peripheral spatial ranking of the scene to put the next target of the sequence in the correct order. This strategy, indeed, might enhance the discriminative capacity of the human peripheral vision and spare neural resources associated with foveation. However, it is not known how exactly the peripheral vision sustains sequential search and whether the sparing of neural resources has a cost in terms of performance. To elucidate these issues, we compared strategy and performance during an alpha‐numeric sequential task where peripheral vision was modulated in three different conditions: normal, blurred, or obscured. If spatial ranking is applied to increase the peripheral discrimination, its use as a strategy in visual sequencing should differ according to the degree of discriminative information that can be obtained from the periphery. Moreover, if this strategy spares neural resources without impairing the performance, its use should be associated with better performance. We found that spatial ranking was applied when peripheral vision was fully available, reducing the number and time of explorative fixations. When the periphery was obscured, explorative fixations were numerous and sparse; when the periphery was blurred, explorative fixations were longer and often located close to the items. Performance was significantly improved by this strategy. Our results demonstrated that spatial ranking is an efficient strategy adopted by the brain in visual sequencing to highlight peripheral detection and discrimination; it reduces the neural cost by avoiding unnecessary foveations, and promotes sequential search by facilitating the onset of a new saccade.

Evaluating human visual search performance by Monte Carlo methods and heuristic model

Visual search is an everyday activity that enables humans to explore the real world. Given the visual input, during a visual search, its required to select some aspects of the input in order to move to the next location. Exploration is guided by two factors: saliency of image (bottom-up) and endogenous mechanism (top-down). These two mechanisms interact to perform an efficient visual search. We developed a stochastic model, the “break away from fixations” (BAF), to emulate the visual search on a high cognitively demanding task such as a trail making test (TMT). The paper reports a case study providing evidence that human exploration performs an efficient visual search based also on an internal model of regions already explored.