COMPLEXITY-BASED ANALYSIS OF THE INFLUENCE OF VISUAL STIMULUS COLOR ON HUMAN EYE MOVEMENT

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950002 ◽  
Author(s):  
HEDIEH ALIPOUR ◽  
HAMIDREZA NAMAZI ◽  
HAMED AZARNOUSH ◽  
SAJAD JAFARI

Analysis of eye movement due to different visual stimuli always has been one of the major research areas in vision science. An important category of works belongs to decoding of eye movement due to variations of color of visual stimuli. In this research, for the first time, we employ fractal analysis in order to investigate the variations of complex structure of eye movement time series in response to variations of color of visual stimuli. For this purpose, we applied two different images in three different colors (red, green, blue) to subjects. The result of our analysis showed that eye movement has the greatest complexity in case of green visual stimulus. On the other hand, the lowest complexity of eye movement was observed in case of red stimulus. In addition, the results showed that except for red visual stimulus, applying the visual stimulus with greater complexity causes the lower complexity in eye movements. The employed methodology in this research can be further applied to analyze the influence of other variations of visual stimuli on human eye movement.

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950024 ◽  
Author(s):  
HEDIEH ALIPOUR ◽  
HAMIDREZA NAMAZI ◽  
HAMED AZARNOUSH ◽  
SAJAD JAFARI

Investigating human eye movement is one of the major research topics in vision science. It is known that human eye movement is related to external stimuli. In this way, the analysis of human eye movement due to different types of external stimuli is very important in vision science. Beside all reported analysis, no one has discovered any relation between the complex structure of moving visual stimuli and the complex structure of human eye movement. This study reveals the relationship between the complexity of human eye movement signal and the applied visual stimuli. For this purpose, we employ fractal theory. We demonstrated that the fractal dynamics of human eye movement in both horizontal and vertical directions shifts toward the fractal dynamics of moving visual target as stimulus. The capability observed in this research opens new doors to scientists to study the relation between the human eye movement and the applied stimuli.


2019 ◽  
Vol 18 (03) ◽  
pp. 1950012 ◽  
Author(s):  
Hedieh Alipour ◽  
Farzad Towhidkhah ◽  
Sajad Jafari ◽  
Avinash Menon ◽  
Hamidreza Namazi

Human eye movement is a key concept in the field of vision science. It has already been established that human eye movement responds to external stimuli. Hence, investigating the reaction of the human eye movement to various types of external stimuli is important in this field. There have been many researches on human eye movement that were previously done, but this is the first study to show a relation between the complex structure of human eye movement and the complex structure of static visual stimulus. Fractal theory was implemented and we showed that the fractal dynamics of the human eye movement is related to the fractal structure of visual target as stimulus. The outcome of this research provides new platforms to scientists to further investigate on the relation between eye movement and other applied stimuli.


Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950040 ◽  
Author(s):  
HEDIEH ALIPOUR ◽  
HAMIDREZA NAMAZI ◽  
HAMED AZARNOUSH ◽  
SAJAD JAFARI

An important category of studies in vision science is related to the analysis of the influence of environmental changes on human eye movement. In this way, scientists analyze human eye movement in different conditions using different methods. An important category of works is devoted to the decoding of eye reaction to color tonality. In this research for the first time, we examined the application of fractal theory for decoding of eye reaction to variations in color intensity of visual stimuli. Three green visual stimuli with different color intensities have been applied to subjects and accordingly the fractal dimension of their eye movements has been analyzed. We also tested the eye movement in non-stimulation condition (rest). Based on the obtained results, increasing the color intensity of visual stimuli caused a lower complexity in subject’s eye movement. We also observed that eye movement is less complex in case of non-stimulation compared to different stimulation conditions. The application of fractal theory in analysis of eye movement can be extended to analyze the effect of other stimulation conditions on eye movement to investigate about the decoding behavior of human eye, which is very important in vision science.


1990 ◽  
Vol 63 (3) ◽  
pp. 523-538 ◽  
Author(s):  
R. Lal ◽  
M. J. Friedlander

1. The nature and time window of interaction between passive phasic eye movement signals and visual stimuli were studied for dorsal lateral geniculate nucleus (LGNd) neurons in the cat. Extracellular recordings were made from single neurons in layer A of the left LGNd of anesthetized paralyzed cats in response to a normalized visual stimulus presented to the right eye at each of several times of movement of the left eye. The left eye was moved passively at a fixed amplitude and velocity while varying the movement onset time with respect to the visual stimulus onset in a randomized and interleaved fashion. Visual stimuli consisted of square-wave modulated circular spots of appropriate contrast, sign, and size to elicit an optimal excitatory response when placed in the neurons' receptive-field (RF) center. 2. Interactions were analyzed for 78 neurons (33 X-neurons, 43 Y-neurons, and 2 physiologically unclassified neurons) on 25-65 trials of identical visual stimuli for each of eight times of eye movement. 3. Sixty percent (47/78) of the neurons tested had a significant eye movement effect (ANOVA, P less than 0.05) on some aspect of their visual response. Of these 47 neurons, 42 (89%) had a significant (P less than 0.05) effect of an appropriately timed eye movement on the number of action potentials, 36 (77%) had a significant effect on the mean peak firing rate, and 31 (66%) were significantly affected as evaluated by both criteria. 4. The eye movement effect on the neurons' visual responses was primarily facilitatory. Facilitation was observed for 37 (79%) of the affected neurons. For 25 of these 37 neurons (68%), the facilitation was significant (P less than 0.05) as evaluated by both criteria (number of action potentials and mean peak firing rate). Ten (21%) of the affected neurons had their visual response significantly inhibited (P less than 0.05). 5. Sixty percent (46/78) of the neurons were tested for the effect of eye movement on both visually elicited activity (visual stimulus contrast = 2 times threshold) and spontaneous activity (contrast = 0). Eye movement significantly affected the visual response of 23 (50%) of these neurons. However, spontaneous activity was significantly affected for only nine (20%) of these neurons. The interaction of the eye movement and visual signals was nonlinear. 6. Nine of 12 neurons (75%) tested had a directionally selective effect of eye movement on the visual response, with most (8/9) preferring the temporal ward direction.(ABSTRACT TRUNCATED AT 400 WORDS)


2019 ◽  
Vol 18 (01) ◽  
pp. 1950010 ◽  
Author(s):  
Hamidreza Namazi ◽  
Asieh Daneshi ◽  
Touraj Shirzadian ◽  
Hamed Azarnoush ◽  
Sajad Jafari

Analysis of the influence of external stimuli on human eye movements is an important challenge in vision research. In this paper, we investigate the influence of applied visual stimuli on variations of eye movements. For this purpose, we employ information theory, which provides us with tools such as Shannon entropy as the indicator of information content of process. This study for the first time reveals the relation between the information content of eye movements and the information content of visual stimuli. Based on the performed analysis, the information content of eye movements time series shifts toward the information content of applied visual stimuli, where the greater variation in Shannon entropy of visual stimuli causes the greater variation in the Shannon entropy of eye movements time series. The observed behavior is explained through nervous system. As a rehabilitation purpose, the employed methodology in this research can be investigated in case of patients with vision problems, with the aim of improving patients’ vision.


2020 ◽  
Vol 19 (04) ◽  
pp. 2050041 ◽  
Author(s):  
Mirra Soundirarajan ◽  
Mohammad Hossein Babini ◽  
Sue Sim ◽  
Visvamba Nathan ◽  
Hamidreza Namazi

In this research, for the first time, we analyze the relationship between facial muscles and brain activities when human receives different dynamic visual stimuli. We present different moving visual stimuli to the subjects and accordingly analyze the complex structure of electromyography (EMG) signal versus the complex structure of electroencephalography (EEG) signal using fractal theory. Based on the obtained results from analysis, presenting the stimulus with greater complexity causes greater change in the complexity of EMG and EEG signals. Statistical analysis also supported the results of analysis and showed that visual stimulus with greater complexity has greater effect on the complexity of EEG and EMG signals. Therefore, we showed the relationship between facial muscles and brain activities in this paper. The method of analysis in this research can be further employed to investigate the relationship between other human organs’ activities and brain activity.


Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5178
Author(s):  
Sangbong Yoo ◽  
Seongmin Jeong ◽  
Seokyeon Kim ◽  
Yun Jang

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.


1992 ◽  
Vol 67 (6) ◽  
pp. 1447-1463 ◽  
Author(s):  
K. Nakamura ◽  
A. Mikami ◽  
K. Kubota

1. The activity of single neurons was recorded extracellularly from the monkey amygdala while monkeys performed a visual discrimination task. The monkeys were trained to remember a visual stimulus during a delay period (0.5-3.0 s), to discriminate a new visual stimulus from the stimulus, and to release a lever when the new stimulus was presented. Colored photographs (human faces, monkeys, foods, and nonfood objects) or computer-generated two-dimensional shapes (a yellow triangle, a red circle, etc.) were used as visual stimuli. 2. The activity of 160 task-related neurons was studied. Of these, 144 (90%) responded to visual stimuli, 13 (8%) showed firing during the delay period, and 9 (6%) responded to the reward. 3. Task-related neurons were categorized according to the way in which various stimuli activated the neurons. First, to evaluate the proportion of all tested stimuli that elicited changes in activity of a neuron, selectivity index 1 (SI1) was employed. Second, to evaluate the ability of a neuron to discriminate a stimulus from another stimulus, SI2 was employed. On the basis of the calculated values of SI1 and SI2, neurons were classified as selective and nonselective. Most visual neurons were categorized as selective (131/144), and a few were characterized as nonselective (13/144). Neurons active during the delay period were also categorized as selective visual and delay neurons (6/13) and as nonselective delay neurons (7/13). 4. Responses of selective visual neurons had various temporal and stimulus-selective properties. Latencies ranged widely from 60 to 300 ms. Response durations also ranged widely from 20 to 870 ms. When the natures of the various effective stimuli were studied for each neuron, one-fourth of the responses of these neurons were considered to reflect some categorical aspect of the stimuli, such as human, monkey, food, or nonfood object. Furthermore, the responses of some neurons apparently reflected a certain behavioral significance of the stimuli that was separate from the task, such as the face of a particular person, smiling human faces, etc. 5. Nonselective visual neurons responded to a visual stimulus, regardless of its nature. They also responded in the absence of a visual stimulus when the monkey anticipated the appearance of the next stimulus. 6. Selective visual and delay neurons fired in response to particular stimuli and throughout the subsequent delay periods. Nonselective delay neurons increased their discharge rates gradually during the delay period, and the discharge rate decreased after the next stimulus was presented. 7. Task-related neurons were identified in six histologically distinct nuclei of the amygdala.(ABSTRACT TRUNCATED AT 400 WORDS)


1995 ◽  
Vol 12 (4) ◽  
pp. 723-741 ◽  
Author(s):  
W. Guido ◽  
S.-M. Lu ◽  
J.W. Vaughan ◽  
Dwayne W. Godwin ◽  
S. Murray Sherman

AbstractRelay cells of the lateral geniculate nucleus respond to visual stimuli in one of two modes: burst and tonic. The burst mode depends on the activation of a voltage-dependent, Ca2+ conductance underlying the low threshold spike. This conductance is inactivated at depolarized membrane potentials, but when activated from hyperpolarized levels, it leads to a large, triangular, nearly all-or-none depolarization. Typically, riding its crest is a high-frequency barrage of action potentials. Low threshold spikes thus provide a nonlinear amplification allowing hyperpolarized relay neurons to respond to depolarizing inputs, including retinal EPSPs. In contrast, the tonic mode is characterized by a steady stream of unitary action potentials that more linearly reflects the visual stimulus. In this study, we tested possible differences in detection between response modes of 103 geniculate neurons by constructing receiver operating characteristic (ROC) curves for responses to visual stimuli (drifting sine-wave gratings and flashing spots). Detectability was determined from the ROC curves by computing the area under each curve, known as the ROC area. Most cells switched between modes during recording, evidently due to small shifts in membrane potential that affected the activation state of the low threshold spike. We found that the more often a cell responded in burst mode, the larger its ROC area. This was true for responses to optimal and nonoptimal visual stimuli, the latter including nonoptimal spatial frequencies and low stimulus contrasts. The larger ROC areas associated with burst mode were due to a reduced spontaneous activity and roughly equivalent level of visually evoked response when compared to tonic mode. We performed a within-cell analysis on a subset of 22 cells that switched modes during recording. Every cell, whether tested with a low contrast or high contrast visual stimulus exhibited a larger ROC area during its burst response mode than during its tonic mode. We conclude that burst responses better support signal detection than do tonic responses. Thus, burst responses, while less linear and perhaps less useful in providing a detailed analysis of visual stimuli, improve target detection. The tonic mode, with its more linear response, seems better suited for signal analysis rather than signal detection.


1996 ◽  
Vol 76 (3) ◽  
pp. 1439-1456 ◽  
Author(s):  
P. Mazzoni ◽  
R. M. Bracewell ◽  
S. Barash ◽  
R. A. Andersen

1. The lateral intraparietal area (area LIP) of the monkey's posterior parietal cortex (PPC) contains neurons that are active during saccadic eye movements. These neurons' activity includes visual and saccade-related components. These responses are spatially tuned and the location of a neuron's visual receptive field (RF) relative to the fovea generally overlaps its preferred saccade amplitude and direction (i.e., its motor field, MF). When a delay is imposed between the presentation of a visual stimulus and a saccade made to its location (memory saccade task), many LIP neurons maintain elevated activity during the delay (memory activity, M), which appears to encode the metrics of the next intended saccadic eye movements. Recent studies have alternatively suggested that LIP neurons encode the locations of visual stimuli regardless of where the animal intends to look. We examined whether the M activity of LIP neurons specifically encodes movement intention or the locations of recent visual stimuli, or a combination of both. In the accompanying study, we investigated whether the intended-movement activity reflects changes in motor plan. 2. We trained monkeys (Macaca mulatta) to memorize the locations of two visual stimuli and plan a sequence of two saccades, one to each remembered target, as we recorded the activity of single LIP neurons. Two targets were flashed briefly while the monkey maintained fixation; after a delay the fixation point was extinguished, and the monkey made two saccades in sequence to each target's remembered location, in the order in which the targets were presented. This "delayed double saccade" (DDS) paradigm allowed us to dissociate the location of visual stimulation from the direction of the planned saccade and thus distinguish neuronal activity related to the target's location from activity related to the saccade plan. By imposing a delay, we eliminated the confounding effect of any phasic responses coincident with the appearance of the stimulus and with the saccade. 3. We arranged the two visual stimuli so that in one set of conditions at least the first one was in the neuron's visual RF, and thus the first saccade was in the neuron's motor field (MF). M activity should be high in these conditions according to both the sensory memory and motor plan hypotheses. In another set of conditions, the second stimulus appeared in the RF but the first one was presented outside the RF, instructing the monkey to plan the first saccade away from the neuron's MF. If the M activity encodes the motor plan, it should be low in these conditions, reflecting the plan for the first saccade (away from the MF). If it is a sensory trace of the stimulus' location, it should be high, reflecting stimulation of the RF by the second target. 4. We tested 49 LIP neurons (in 3 hemispheres of 2 monkeys) with M activity on the DDS task. Of these, 38 (77%) had M activity related to the next intended saccade. They were active in the delay period, as expected, if the first saccade was in their preferred direction. They were less active or silent if the next saccade was not in their preferred direction, even when the second stimulus appeared in their RF. 5. The M activity of 8 (16%) of the remaining neurons specifically encoded the location of the most recent visual stimulus. Their firing rate during the delay reflected stimulation of the RF independently of the saccade being planned. The remaining 3 neurons had M activity that did not consistently encode either the next saccade or the stimulus' location. 6. We also recorded the activity of a subset of neurons (n = 38) in a condition in which no stimulus appeared in a neuron's RF, but the second saccade was in the neuron's MF. In this case the majority of neurons tested (23/38, 60%) became active in the period between the first and second saccade, even if neither stimulus had appeared in their RF. Moreover, this activity appeared only after the first saccade had started in all but two of


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