Temporal Dynamics of Figure-Ground Segregation in Human Vision

2007 ◽  
Vol 97 (1) ◽  
pp. 951-957 ◽  
Author(s):  
Peter Neri ◽  
Dennis M. Levi
Keyword(s):  
Visual Stimulus ◽  
Temporal Dynamics ◽  
Human Perception ◽  
Human Vision ◽  
Reverse Correlation ◽  
Single Neurons ◽  
Neural Signals ◽  
Temporal Window ◽  
Electrophysiological Studies ◽  
The Brain

The segregation of figure from ground is arguably one of the most fundamental operations in human vision. Neural signals reflecting this operation appear in cortex as early as 50 ms and as late as 300 ms after presentation of a visual stimulus, but it is not known when these signals are used by the brain to construct the percepts of figure and ground. We used psychophysical reverse correlation to identify the temporal window for figure-ground signals in human perception and found it to lie within the range of 100–160 ms. Figure enhancement within this narrow temporal window was transient rather than sustained as may be expected from measurements in single neurons. These psychophysical results prompt and guide further electrophysiological studies.

Eye Anatomy, Eye Movements and Vision

2012 ◽  
pp. 10-20 ◽  
Author(s):  
Fiona Mulvey
Keyword(s):  
Eye Movements ◽  
Human Computer Interaction ◽  
Eye Movement ◽  
Human Perception ◽  
Human Vision ◽  
Emotional States ◽  
Visual World ◽  
Potential Measurement ◽  
Movement Data ◽  
The Brain

This chapter introduces the basics of eye anatomy, eye movements and vision. It will explain the concepts behind human vision sufficiently for the reader to understand later chapters in the book on human perception and attention, and their relationship to (and potential measurement with) eye movements. We will first describe the path of light from the environment through the structures of the eye and on to the brain, as an introduction to the physiology of vision. We will then describe the image registered by the eye, and the types of movements the eye makes in order to perceive the environment as a cogent whole. This chapter explains how eye movements can be thought of as the interface between the visual world and the brain, and why eye movement data can be analysed not only in terms of the environment, or what is looked at, but also in terms of the brain, or subjective cognitive and emotional states. These two aspects broadly define the scope and applicability of eye movements technology in research and in human computer interaction in later sections of the book.

scholarly journals Harmonics added to a flickering light can upset the balance between ON and OFF pathways to produce illusory colors

2018 ◽  
Vol 115 (17) ◽  
pp. E4081-E4090 ◽  
Author(s):  
Andrew T. Rider ◽  
G. Bruce Henning ◽  
Rhea T. Eskew ◽  
Andrew Stockman
Keyword(s):  
Visual Pathways ◽  
Human Vision ◽  
Perceptual Quality ◽  
Time Varying ◽  
Neural Signals ◽  
Color Processing ◽  
Retinal Photoreceptor ◽  
Half Wave ◽  
On And Off Pathways ◽  
The Brain

The neural signals generated by the light-sensitive photoreceptors in the human eye are substantially processed and recoded in the retina before being transmitted to the brain via the optic nerve. A key aspect of this recoding is the splitting of the signals within the two major cone-driven visual pathways into distinct ON and OFF branches that transmit information about increases and decreases in the neural signal around its mean level. While this separation is clearly important physiologically, its effect on perception is unclear. We have developed a model of the ON and OFF pathways in early color processing. Using this model as a guide, we can produce imbalances in the ON and OFF pathways by changing the shapes of time-varying stimulus waveforms and thus make reliable and predictable alterations to the perceived average color of the stimulus—although the physical mean of the waveforms does not change. The key components in the model are the early half-wave rectifying synapses that split retinal photoreceptor outputs into the ON and OFF pathways and later sigmoidal nonlinearities in each pathway. The ability to systematically vary the waveforms to change a perceptual quality by changing the balance of signals between the ON and OFF visual pathways provides a powerful psychophysical tool for disentangling and investigating the neural workings of human vision.

scholarly journals The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hamidreza Namazi ◽  
Vladimir V. Kulish ◽  
Amin Akrami
Keyword(s):  
Eye Movements ◽  
Visual Stimulus ◽  
Fractal Structure ◽  
Temporal Structure ◽  
Human Vision ◽  
Eeg Signal ◽  
Vision Disorders ◽  
Fixational Eye Movements ◽  
Electroencephalogram Eeg ◽  
The Brain

Abstract One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the ‘complex’ visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders.

scholarly journals Neural dynamics in response to binary taste mixtures

2013 ◽  
Vol 109 (8) ◽  
pp. 2108-2117 ◽  
Author(s):  
Joost X. Maier ◽  
Donald B. Katz
Keyword(s):  
Citric Acid ◽  
Temporal Dynamics ◽  
Neural Dynamics ◽  
Response Patterns ◽  
Single Neurons ◽  
Taste System ◽  
Mixture Suppression ◽  
Access Test ◽  
The Brain ◽  
Related Pattern

Taste stimuli encountered in the natural environment are usually combinations of multiple tastants. Although a great deal is known about how neurons in the taste system respond to single taste stimuli in isolation, less is known about how the brain deals with such mixture stimuli. Here, we probe the responses of single neurons in primary gustatory cortex (GC) of awake rats to an array of taste stimuli including 100% citric acid (100 mM), 100% sodium chloride (100 mM), 100% sucrose (100 mM), and a range of binary mixtures (90/10, 70/30, 50/50, 30/70, and 10/90%). We tested for the presence of three different hypothetical response patterns: 1) responses varying monotonically as a function of concentration of sucrose (or acid) in the mixture (the “monotonic” pattern); 2) responses increasing or decreasing as a function of degree of mixture of the stimulus (the “mixture” pattern); and 3) responses that change abruptly from being similar to one pure taste to being similar the other (the “categorical” pattern). Our results demonstrate the presence of both monotonic and mixture patterns within responses of GC neurons. Specifically, further analysis (that included the presentation of 50 mM sucrose and citric acid) made it clear that mixture suppression reliably precedes a palatability-related pattern. The temporal dynamics of the emergence of the palatability-related pattern parallel the temporal dynamics of the emergence of preference behavior for the same mixtures as measured by a brief access test. We saw no evidence of categorical coding.

scholarly journals Stability and flexibility in multisensory sampling: insights from perceptual illusions

2019 ◽  
Vol 121 (5) ◽  
pp. 1588-1590 ◽  
Author(s):  
Luca Casartelli
Keyword(s):  
Temporal Dynamics ◽  
Multisensory Processing ◽  
General Idea ◽  
Perceptual Illusions ◽  
The Brain ◽  
Multisensory Processes

Neural, oscillatory, and computational counterparts of multisensory processing remain a crucial challenge for neuroscientists. Converging evidence underlines a certain efficiency in balancing stability and flexibility of sensory sampling, supporting the general idea that multiple parallel and hierarchically organized processing stages in the brain contribute to our understanding of the (sensory/perceptual) world. Intriguingly, how temporal dynamics impact and modulate multisensory processes in our brain can be investigated benefiting from studies on perceptual illusions.

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

Activity of single neurons in the monkey amygdala during performance of a visual discrimination task

1992 ◽  
Vol 67 (6) ◽  
pp. 1447-1463 ◽  
Author(s):  
K. Nakamura ◽  
A. Mikami ◽  
K. Kubota
Keyword(s):  
Visual Stimulus ◽  
Visual Discrimination ◽  
Discrimination Task ◽  
Discharge Rate ◽  
Visual Stimuli ◽  
Delay Period ◽  
Visual Discrimination Task ◽  
Single Neurons ◽  
Visual Neurons ◽  
Human Faces

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)

Multi-organ coordination of lipoprotein secretion by hormones, nutrients and neural networks

2021 ◽  
Author(s):  
Priska Stahel ◽  
Changing Xiao ◽  
Avital Nahmias ◽  
Lili Tian ◽  
Gary Franklin Lewis
Keyword(s):  
Neural Networks ◽  
Adipose Tissue ◽  
Peptide Yy ◽  
Atherosclerotic Cardiovascular Disease ◽  
Neural Signals ◽  
Nutritional Factors ◽  
Lipoprotein Secretion ◽  
The Central Nervous System ◽  
Regulatory Effects ◽  
The Brain

Abstract Plasma triglyceride-rich lipoproteins (TRL), particularly atherogenic remnant lipoproteins, contribute to atherosclerotic cardiovascular disease (ASCVD). Hypertriglyceridemia may arise in part from hypersecretion of TRLs by the liver and intestine. Here we focus on the complex network of hormonal, nutritional, and neuronal interorgan communication that regulates secretion of TRLs, and provide our perspective on the relative importance of these factors. Hormones and peptides originating from the pancreas (insulin, glucagon), gut (GLP-1, GLP-2, ghrelin, CCK, peptide YY), adipose tissue (leptin, adiponectin) and brain (GLP-1) modulate TRL secretion by receptor-mediated responses and indirectly via neural networks. In addition, the gut microbiome and bile acids influence lipoprotein secretion in humans and animal models. Several nutritional factors modulate hepatic lipoprotein secretion through effects on the central nervous system. Vagal afferent signalling from the gut to the brain and efferent signals from the brain to the liver and gut are modulated by hormonal and nutritional factors to influence TRL secretion. Some of these factors have been extensively studied and shown to have robust regulatory effects whereas others are ‘emerging’ regulators, whose significance remains to be determined. The quantitative importance of these factors relative to one another and relative to the key regulatory role of lipid availability remains largely unknown. Our understanding of the complex interorgan regulation of TRL secretion is rapidly evolving to appreciate the extensive hormonal, nutritional and neural signals emanating not only from gut and liver but also from the brain, pancreas, and adipose tissue.

Neuroendocrine modulation of the “menopause”: insights into the aging brain

1999 ◽  
Vol 277 (6) ◽  
pp. E965-E970 ◽  
Author(s):  
Phyllis M. Wise
Keyword(s):  
Animal Models ◽  
Ovarian Follicles ◽  
Physiological Change ◽  
Menopausal Transition ◽  
Aging Brain ◽  
Neural Signals ◽  
Present Evidence ◽  
The Brain

The menopause marks the permanent end of fertility in women. It was once thought that this dramatic physiological change could be explained simply by the exhaustion of the reservoir of ovarian follicles. New data from studies performed in women and animal models make us reassess this assumption. An increasing body of evidence suggests that there are multiple pacemakers that contribute to the transition to irregular cycles, decreasing fertility, and the timing of the menopause. We will present evidence that lends credence to the possibility that a dampening and desynchronization of the precisely orchestrated neural signals lead to miscommunication between the brain and the pituitary-ovarian axis, and that this constellation of hypothalamic-pituitary-ovarian events leads to the deterioration of regular cyclicity and heralds menopausal transition.

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