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What Is Feature Detection In Psychology?

• Sabrina Sarro
• 05.05.2023
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From Wikipedia, the free encyclopedia Feature detection is a process by which the nervous system sorts or filters complex natural stimuli in order to extract behaviorally relevant cues that have a high probability of being associated with important objects or organisms in their environment, as opposed to irrelevant background or noise.

1. Feature detectors are individual neurons—or groups of neurons—in the brain which code for perceptually significant stimuli.
2. Early in the sensory pathway feature detectors tend to have simple properties; later they become more and more complex as the features to which they respond become more and more specific.

For example, simple cells in the visual cortex of the domestic cat ( Felis catus ), respond to edges—a feature which is more likely to occur in objects and organisms in the environment. By contrast, the background of a natural visual environment tends to be noisy—emphasizing high spatial frequencies but lacking in extended edges.

What is feature detection in psychology example?

Any of various hypothetical or actual mechanisms within the human information-processing system that respond selectively to specific distinguishing features. For example, the visual system has feature detectors for lines and angles of different orientations as well as for more complex stimuli, such as faces.

What is features detection?

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• Overview: Cross browser testing
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Feature detection involves working out whether a browser supports a certain block of code, and running different code depending on whether it does (or doesn’t), so that the browser can always provide a working experience rather than crashing/erroring in some browsers.

Prerequisites:	Familiarity with the core HTML, CSS, and JavaScript languages; an idea of the high-level principles of cross-browser testing,
Objective:	To understand what the concept of feature detection is, and be able to implement suitable solutions in CSS and JavaScript.

What is a feature detector AP Psychology?

AP Psychology – AP Psychology 121 Which of the following visual receptor cells detects fine detail? Possible Answers: Explanation : Cones, rods, bipolar cells, and ganglion cells are receptor cells found in the outer layer of the retina. Energy is first transmitted to rods and cones, then to bipolar cells, and eventually to ganglion cells.

Rods and cones differ in terms of their locations and purposes. Cones are found clustered around the fovea (area of central focus on the retina). Cones have a direct pathway to the brain. One cone will be connected to one bipolar cell. This ensures that each cone can have its individual message directed to the visual cortex.

The specific connections allow for information to be preserved, also while providing a large input for the visual cortex from the fovea. As a result, cones are better capable of detecting detail. Rods, on the other hand, do not exhibit the same wiring as cones.

Rods will share bipolar cells with other rods; therefore, their input reaches the visual cortex as shared information. Which of the following neurons in the visual cortex receive information from retinal receptor cells? Possible Answers: Correct answer: Feature detectors Explanation : As information is translated into neural impulses in the retina, the information is passed from the rods and cones to bipolar cells, which transfer the impulse to ganglion cells.

Feature detectors are specialized neurons in the visual cortex that receive information from retinal ganglion. In order to receive the information, the impulses must pass through the optic chiasm. This is the “X” created by the two optic nerves crossing below the brain.

1. The optic nerves will then meet at occipital lobe’s visual cortex to deliver the information.
2. Feature detectors are self-explanatory by their name.
3. Their job is to detect a scene’s features—edges, lines, angles, and movement.
4. This information is then passed to cell clusters in other cortical areas that respond to more complex patterns.

Which of the followings statements is true regarding the Young-Helmholtz trichromatic theory? Possible Answers: The retina contains three receptors: one sensitive to blue, one to red, and one to green The retina contains three receptors: one sensitive to red-green, one to yellow-blue, and one to white-black The retina contains two receptors: one sensitive to red-blue-yellow and one sensitive to white-black The retina contains one receptor for each color we sense The retina contains three receptors: one sensitive to red, one to green, and one to yellow Correct answer: The retina contains three receptors: one sensitive to blue, one to red, and one to green Explanation : The Young-Helmholtz trichromatic (three color) theory states that the retina contains three color receptors.

Thomas Young and Hermann von Helmholtz understood that any color could be created through combining the primary colors with varying wavelengths. They inferred that each receptor was especially sensitive to one of the primary colors. They deduced that one receptor was sensitive to red, one to green and one to blue.

These receptors were later discovered to be cones. When different combinations of these cones are stimulated, then we are able to see different colors. Which of the following structures is not part of the inner ear? Possible Answers: Explanation : The ear is separated into three parts: the outer ear, the middle ear, and the inner ear.

The outer ear is the visible part of the ear. It acts to funnel the vibrations into the middle ear. The middle ear only contains a piston made up of three tiny bones. These bones are the anvil, hammer, and stirrup. Their purpose is to amplify the vibrations so that they can continue into the inner ear and create the necessary ripples in the basilar membrane to bend hairs.

The bending hairs will initiate neural impulses, sending messages to the brain. It is the inner ear that contains most of the structures required for sensing sounds (i.e. semicircular canals, cochlea, ear drum, and oval window). The amplitude of a sound wave determines _,

1. Possible Answers: Explanation : Amplitude is the height of a wave.
2. The greater the amplitude of a sound wave is, then the greater the activation of hair cells in the ear will be.
3. Rather than acting singularly, the hair cells attuned for a specific frequency will react with its neighboring hair cells.
4. Based on the amount of hair cells affected, the brain can interpret how loud the sound stimulus is; therefore, the greater the amplitude is, then the greater the volume of the sound will be.

When entering the eye, light initially passes through the _, Possible Answers: Explanation : The cornea is the clear thin layer that covers the eye. Aside from the obvious task of protecting the eye from foreign invaders, it is responsible for a majority of the eye’s ability to focus.

Because it is the outermost layer of the eye, light will initially pass through this structure. As the cornea bends the light through the pupil, it will direct it to the lens. The lens then plays the role of refocusing the light and directing it to receptor cells. The iris is responsible for controlling the pupil’s size; therefore, it would be an incorrect answer choice.

The fovea—a structure residing at the back of the eye near the optic nerve—is part of the eye; however, it is not the initial point of central focus. Which of the following is part of the middle ear? Possible Answers: Explanation : The ear is separated into three parts: the outer ear, the middle ear, and the inner ear.

The outer ear is the visible part of the ear. It acts to funnel the vibrations into the middle ear. The middle ear only contains a piston made up of three tiny bones. These bones are the anvil, hammer, and stirrup. Their purpose is to amplify the vibrations so that they can continue into the inner ear and create the necessary ripples in the basilar membrane to bend hairs.

The bending hairs will initiate neural impulses, sending messages to the brain. It is the inner ear that contains most of the structures required for sensing sounds. Which of the following structures of the eye is responsible for creating an image? Possible Answers: Explanation : Light will initially pass through the cornea and bend through the pupil to reach the lens.

• The lens is responsible for taking the refracted light and refocusing it.
• In doing so, the refocused rays will create an inverted image on the retina.
• This is accomplished through a process known as accommodation.
• The retina does not receive a complete image, but instead particles of light energy.
• Its receptor cells will take the light, translate it into neural impulses, and forward them to the brain where they will be reassembled right side up.

When the visual focus point falls in front of the retina it is referred to as which of the following? Possible Answers: Correct answer: Nearsightedness Explanation : The lens of the eye determines where the focus point will be in the rear chamber of the eye.

• When the lens is distorted, it can move the focus point slightly in front of or slightly behind the retina—back of the eye.
• When it is in front of the retina, it’s called nearsightedness (e.g.
• A person can only see clearly when objects are near).
• When the focus point is behind the retina, it’s called farsightedness (e.g.

a person can only see clearly when objects are far away). Color blindness and macular degeneration do not have to do with where the focus point falls on the retina. During “dark adaptation,” the eyes can become more sensitive to light in low illumination.

1. For night vision, which of the following structures are most relied upon? Possible Answers: Explanation : Rods allow us to see in black and white, and adapt much more than cones do when there is low light.
2. In other words, they become even more sensitive in the dark.
3. Cones are used for color vision and seeing in daylight.

The fovea is a tiny spot in the center of the retina, but it contains only cones and allows for sharp visual acuity. The lens does not change depending on the lighting.121 Jenna Certified Tutor University of North Carolina at Charlotte, Bachelor of Science, Psychology. Nathaniel Certified Tutor University of Missouri-Columbia, Bachelor in Arts, History. University of Missouri-Columbia, Master of Arts, History. Erika Certified Tutor Furman University, Bachelor in Arts, Psychology. University of North Carolina at Chapel Hill, Doctor of Philosophy, Cognitive. If you’ve found an issue with this question, please let us know. With the help of the community we can continue to improve our educational resources.

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What is object vs feature detection?

For neural networks that detect objects from an image, the earlier layers arrange low-level features into a many-dimensional space (feature detection), and the later layers classify objects according to where those features are found in that many-dimensional space (object detection).

What is feature detection theory in cognitive psychology?

Abstract – The diagnostic feature-detection theory (DFT) of eyewitness identification is based on facial information that is diagnostic versus non-diagnostic of suspect guilt. It primarily has been tested by discounting non-diagnostic information at retrieval, typically by surrounding a single suspect showup with good fillers to create a lineup.

• We tested additional DFT predictions by manipulating the presence of facial information (i.e., the exterior region of the face) at both encoding and retrieval with a large between-subjects factorial design ( N  = 19,414).
• In support of DFT and in replication of the literature, lineups yielded higher discriminability than showups.

In support of encoding specificity, conditions that matched information between encoding and retrieval were generally superior to mismatch conditions. More importantly, we supported several DFT and encoding specificity predictions not previously tested, including that (a) adding non-diagnostic information will reduce discriminability for showups more so than lineups, and (b) removing diagnostic information will lower discriminability for both showups and lineups.

1. These results have implications for police deciding whether to conduct a showup or a lineup, and when dealing with partially disguised perpetrators (e.g., wearing a hoodie).
2. FormalPara Significance DNA exoneration cases have revealed the prevalence of mistaken eyewitness identifications, and it is critical to develop theory-driven approaches to improving eyewitness identification accuracy.

According to diagnostic feature-detection theory (DFT), eyewitnesses assess suspect guilt by evaluating facial information that matches their memory for the perpetrator but is not also shared by innocent lineup members. We tested several DFT predictions by manipulating the presence of facial information at both encoding (analogous to a perpetrator wearing a hoodie) and retrieval (analogous to police deciding whether to have everyone in a lineup wear a hoodie).

By adding this encoding manipulation, we also tested a popular cognitive theory known as encoding specificity, which predicts that eyewitness performance should be superior when encoding conditions match retrieval conditions (e.g., perpetrator wore a hoodie and everyone in lineup has a hoodie). A nationwide sample of participants viewed either a full face or the internal region only and were later tested with a showup or lineup containing full faces or only internal regions.

We supported DFT by replicating the lineup advantage over showups, and we supported encoding specificity such that match conditions were generally superior to mismatch conditions. We also confirmed DFT predictions that (a) removing diagnostic information will harm performance and (b) adding non-diagnostic information will harm showups more than lineups.

What does feature detection respond to?

Cells in the visual cortex, called feature cells or feature detectors, respond selectively to various components of a visual image, such as orientation of lines, colour, and movement.

What is feature detection vs inference?

What’s the difference between feature detection, feature inference, and using the UA string Feature detection is attempting to determine if a feature exists. For example, if the user’s browser supports LocalStorage or the geolocation APIs. if (navigator.geolocation) Feature inference is assuming that because you’ve detected one feature that you can use other features.

For example if you detect the geolocation API maybe you’d assume your user is on a modern browser and so now LocalStorage is available. It’s usually bad to assume so you’re much better off just using feature detection for each feature you want to take advantage of, and have a fallback strategy in place in the event a feature isn’t available.

Even if a user has a modern browser with geolocation doesn’t mean they’re going to allow your app to use it so plan accordingly. User agent string is just reading the stupid little string that each browser sends along and then you can compare that string with some known browsers you’re targeting.

What do feature detectors help you see?

Feature Detection Cells in the visual cortex, called feature cells or feature detectors, respond selectively to various components of a visual image, such as orientation of lines, colour, and movement.

What is feature detection in psychology quizlet?

Feature detectors. nerve cells in the brain that respond to specific features of the stimulus, such as shape, angle, or movement. parallel processing.

What is feature detection and matching?

CS4670/5670: Project 2: Feature Detection and Matching

• Assigned: Friday, March 16, 2018
• Code Due: Friday, March 30, 2018 (turn in via )
• Teams: This assignment can be done in groups of 2 students or individually.

The goal of feature detection and matching is to identify a pairing between a point in one image and a corresponding point in another image. These correspondences can then be used to stitch multiple images together into a panorama. In this project, you will write code to detect discriminating features (which are reasonably invariant to translation, rotation, and illumination) in an image and find the best matching features in another image.

What is the feature detection algorithm?

1. Definition of a Feature – There is no universal or exact definition of what constitutes a feature, and the exact definition often depends on the problem or the type of application. Nevertheless, a feature is typically defined as an “interesting” part of an image, and features are used as a starting point for many computer vision algorithms.

Since features are used as the starting point and main primitives for subsequent algorithms, the overall algorithm will often only be as good as its feature detector. Consequently, the desirable property for a feature detector is repeatability : whether or not the same feature will be detected in two or more different images of the same scene.

Feature detection is a low-level image processing operation. That is, it is usually performed as the first operation on an image, and examines every pixel to see if there is a feature present at that pixel. If this is part of a larger algorithm, then the algorithm will typically only examine the image in the region of the features.

As a built-in pre-requisite to feature detection, the input image is usually smoothed by a Gaussian kernel in a scale-space representation and one or several feature images are computed, often expressed in terms of local image derivatives operations. Occasionally, when feature detection is computationally expensive and there are time constraints, a higher level algorithm may be used to guide the feature detection stage, so that only certain parts of the image are searched for features.

There are many computer vision algorithms that use feature detection as the initial step, so as a result, a very large number of feature detectors have been developed. These vary widely in the kinds of feature detected, the computational complexity and the repeatability.

What is the difference between feature and object?

Feature – is a ‘feature class’ which is spatial data. Object – is a ‘table’ which is non-spatial data.

What is an example of object detection?

A picture of a dog receives the label ‘dog’. A picture of two dogs, still receives the label ‘dog’. Object detection, on the other hand, draws a box around each dog and labels the box ‘dog’. The model predicts where each object is and what label should be applied.

What are examples of feature theory?

What is an example of feature integration theory? An example of feature integration theory is when an individual is looking for their child during a soccer game. The child is wearing a purple uniform, and the opposing side is wearing yellow uniforms. This is an example of feature searching.

What are the three elements of feature detection?

Intro | Optic Chiasm | Superior Colliculus | Visual Cortex | Lateral Geniculate Nucleus | Optic Nerve | Pulvinar Nucleus | Retina Part 1: Image-Mapped Tutorial Part 2: Matching Self-Test Part 3: Multiple-Choice Self-Test Return to main tutorial page The complexities of Visual Cortex are simplified by understanding that the neurons of this region are distinguished by the stimulus features that each detects.

The three major groups of so-called feature detectors in visual cortex include simple cells, complex cells, and hypercomplex cells. Simple cells are the most specific, responding to lines of particular width, orientation, angle, and position within visual field. Complex cells are similar to simple cells, except that they respond to the proper stimulus in any position within their receptive field.

In addition, some complex cells respond particularly to lines or edges moving in a specific direction across the receptive field. Hypercomplex cells are responsive to lines of specific length. It is believed that the information from all feature detectors combine in some way to result in the perception of visual stimulation.

As discussed previously with Figure 14, the encoding of stimulus color begins in the retina, as different wavelengths are transduced by the trichromatically responsive cone cells ( Trichromatic Theory of color vision). Color vision, however, is considerably more complicated than this and includes higher processing along the visual pathway.

Research indicates that the perception of approximately one million colors involves the process of additive color mixing, in which light of varied wavelengths are combined or mixed. The Opponent Process Theory of color perception best explains the contribution made by the LGN and visual cortex to color perception, although this processing also occurs at the retinal level.

• In this process, three types of neurons respond antagonistically to pairs of colors.
• For example, cells respond in opposite ways to blue versus yellow, red versus green, or black versus white.
• This sequential encoding of light wavelength (hue), saturation (purity), and amplitude (brightness) ultimately results in the perception of color.

The ability to perceive form, patterns, and objects not only results from the encoding of stimulus features by visual neurons ( feature analysis ), but is also under the influence of top-down processes. This top-down influence is known as perceptual set, and is the effect of an individual’s unique experiences on her expectations of the world.

The now relatively inactive subfield of Gestalt psychology focuses on processes that affect the perception of “whole”, those that defy an explanation based on a simple combination of the elements that compose the “whole”. These contributions have greatly enhanced the understanding of visual perception.

A phenomenon known as figure and ground reversal is a classic example of the effect of perceptual set on perception. An ambiguous visual stimulus may be perceived in two different ways depending on one’s perceptual set. Both perceptions, however, cannot be seen simultaneously.

Other principles of Gestalt perceptual organization include: 1) Proximity, whereby elements that are close together tend to be grouped together, 2) Closure, whereby missing elements are supplied to complete a familiar object, 3) Simplicity, whereby elements are organized in the simplest way possible, 4) Continuity, whereby elements are seen in a way to produce a smooth continuation, and 5) Similarity, whereby similar elements are grouped together.

Depth or distance perception is provided by a number of cues both monocular (based on an image in either eye alone) and binocular (based on the differing views of each eye). Monocular cues for depth perception include: 1) Linear perspective provided by parallel lines that appear to merge with increased distance, 2) Texture gradient such that a texture is finer for more distant objects, 3) Relative size with closer objects appearing larger than distant objects of the same size, 4) Interposition of closer objects which overlap or mask more distant objects, 5) Light and shadow patterns which create three-dimensional impressions, and 6) Height in plane cues with closer objects appearing lower in the visual field than more distant objects.

1. Two additional and important forces at work in visual perception are perceptual constancy and optical illusions.
2. Perceptual constancy refers to the tendency to experience a stable view of the world in spite of a continuously changing sensory environment.
3. Without this allowance for constant change, our world-view would be chaotic and as confusing as optical illusions, visual stimuli that appear to us quite differently than they occur in reality.

Classic examples of optical illusions include the Muller-Lyer, Ponzo, and Poggendorff. See the world wide web links in the Suggestions for further study section to explore some of these illusions. Visual cortex is divided into 5 separate areas, V1-V5. Primary visual cortex or V1 (often called striate cortex because of its striped appearance under a microscope) receives its information directly from the lateral geniculate nucleus.

Following processing in this region, the visual neuronal impulses are directed to secondary visual cortex or V2. V2 then projects to V3, V4, and V5. Each of these areas is further subdivided and sends information to any of 20 or more other areas of the brain that process visual information. This general arrangement is subdivided into three parallel pathways.

Although each pathway is somewhat distinct in function, there is intercommunication between them. In the first and completely parvocellular pathway, neurons in the interblobs of V1 project to the pale stripes of V2. The pale stripes of V2 project to the inferior temporal cortex.

This is the pathway composed of feature detectors (simple, complex and hypercomplex cells) as described in the basic information section. Parvocellular neurons show a low sensitivity to contrast, high spatial resolution, and low temporal resolution or sustained responses to visual stimuli. These cellular characteristics make the parvocellular division of the visual system especially suited for the analysis of detail in the visual world.

Neurons found in the inferior temporal cortex respond to very complex stimulus features of a specific nature regardless of size or position on the retina. Some neurons in this region respond selectively to faces of particular overall feature characteristics.

It is not surprising, therefore, to learn that this region is intimately involved in visual memory. Damage to the parvocellualar pathway will induce disorders of object recognition. Common examples of such disorders include visual agnosia, or the inability to identify objects in the visual realm, and prosopagnosia, a subtype of visual agnosia that affects specifically the recognition of once familiar faces.

This division of the visual system tells us to identify what we see. In the second visual cortical pathway, the neurons in lamina (layer) 4B of V1 project to the thick stripes of V2. Area V2 then projects to V3, V5 (or MT, middle-temporal cortex), and MST (medial superior temporal cortex).

• This pathway is an extension of the magnocellular pathway from the retina and LGN, and continues the processing of visual detail leading to the perception of shape in area V3 and movement or motion in areas V5 and MST.
• Cells in V5 are particularly sensitive to small moving objects or the moving edge of large objects.

Cells in dorsal MST respond to the movement (rotation) of large scenes such as is caused with head movements, whereas cells in ventral MST respond to the movement of small objects against their background. Magnocellular neurons show a high sensitivity to contrast, low spatial resolution, and high temporal resolution or fast transient responses to visual stimuli.

These cellular characteristics make the magnocellular division of the visual system especially able to quickly detect novel or moving stimuli, the abilities that allow us to respond quickly and adaptively to possible threatening stimuli. Perhaps this is why this division was the first to evolve. Finally in the third and mixed visual cortical pathway, neurons in the “blobs” of V1 project to the thin stripes of V2.

The thin stripes of V2 then project to V4. Area V4 receives input from both the parvo- and magnocellular pathways of the retina and LGN. The parvocellular portion of (V4) is particularly important for the perception of color and maintenance of color perception regardless of lighting (color constancy).

1. The V4 neurons associated with the (non-color) magnocellular pathway appear to be involved somehow in the control of visual attention to less noticeable, subtle stimuli in the environment.
2. The question of how these different areas work together to result in our final perception of the visual world is often referred to as the “perceptual binding problem”.

This issue is discussed in considerable detail at one of the links provided below.

What is the difference between feature detection and parallel processing?

Visual Processing – Vision – MCAT Content Visual processing is the interpretation of otherwise raw sensory data to produce visual perception. The myelinated axons of ganglion cells make up the optic nerves. Within the nerves, different axons carry different parts of the visual signal.

Some axons constitute the magnocellular (big cell) pathway, which carries information about form, movement, depth, and differences in brightness. Other axons constitute the parvocellular (small cell) pathway, which carries information on color and fine detail. Some visual information projects directly back into the brain, while other information crosses to the opposite side of the brain.

This crossing of optical pathways produces the distinctive optic chiasma (Greek, for “crossing”) found at the base of the brain and allows us to coordinate information from both eyes. Once in the brain, visual information is processed in several places.

• Its routes reflect the complexity and importance of visual information to humans and other animals.
• One route takes the signals to the thalamus, which serves as the routing station for all incoming sensory impulses except smell.
• In the thalamus, the magnocellular and parvocellular distinctions remain intact; there are different layers of the thalamus dedicated to each.

When visual signals leave the thalamus, they travel to the primary visual cortex at the rear of the brain. From the visual cortex, the visual signals travel in two directions. One stream that projects to the parietal lobe, in the side of the brain, carries magnocellular (“where”) information.

1. A second stream projects to the temporal lobe and carries both magnocellular (“where”) and parvocellular (“what”) information.
2. Another important visual route is a pathway from the retina to the superior colliculus in the midbrain, where eye movements are coordinated and integrated with auditory information.

Finally, there is the pathway from the retina to the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN, a cluster of cells, is considered to be the body’s internal clock, which controls our circadian (day-long) cycle. The SCN sends information to the pineal gland, which is important in sleep/wake patterns and annual cycles. There are two types of bottom-up processing that take place in visual processing: feature detection and parallel processing. Parallel processing is the use of multiple pathways to convey information about the same stimulus. It starts at the level of the bipolar and ganglion cells in the eye, allowing information from different areas of the visual field to be processed in parallel.

Through two types of ganglion cells, visual information is split into two pathways: one that detects and processes information about motion and one that is concerned with the form of stimuli (like shape and color). The motion and form pathways project to separate areas of the lateral geniculate nucleus (LGN) and visual cortex.

Once visual information reaches the visual cortex via parallel pathways, it is analyzed by feature detection, There are cells in the visual cortex of the brain that optimally respond to particular aspects of visual stimuli. These cells provide information concerning the most basic features of objects, which are integrated to produce a perception of the object as a whole.

1. Feature detection is a type of serial processing where increasingly complex aspects of the stimulus are processed in sequence.
2. In perception of light by the eye, the proximal stimulus refers to physical stimulation that is available to be measured by an observer’s sensory apparatus.
3. In the case of the eye the diistal Stimulus is any physical object or event in the external world that reflects light.

This light or energy, called the proximal stimulus, is what excites the receptors on our eyes, leading to visual perception.

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• Key Points
• • The magnocellular pathway carries information about form, movement, depth, and differences in brightness; the parvocellular pathway carries information on color and fine detail.
• • The optic chiasma allows us to coordinate information between both eyes and is produced by crossing optical information across the brain.
• • Visual signals move from the visual cortex to either the parietal lobe or the temporal lobe.
• • Some signals move to the thalamus, which sends the visual signals to the primary cortex.
• • Visual signals can also travel from the retina to the superior colliculus, where eye movements are coordinated with auditory information.
• • Visual signals can move from the retina to the suprachiasmatic nucleus (SCN), the body’s internal clock, which is involved in sleep/wake patterns and annual cycles.
• • There are two types of bottom-up processing that take place in visual processing: feature detection and parallel processing.

• Through two types of ganglion cells, visual information is split into two pathways: one that detects and processes information about motion and one that is concerned with the form of stimuli (like shape and color). The motion and form pathways project to separate areas of the lateral geniculate nucleus (LGN) and visual cortex.

1. • In light processing, the distal Stimulus is any physical object or event in the external world that reflects light and the light itself is the proximal stimulus, is what excites the receptors on our eyes, leading to visual perception.
2. Key Terms
3. Superior colliculus : The primary area of the brain where eye movements are coordinated and integrated with auditory information.
4. Optic chiasma : found at the base of the brain and coordinates information from both eyes.
5. Suprachiasmatic nucleus : A cluster of cells that is considered to be the body’s internal clock, which controls our circadian (day-long) cycle.
6. Parallel processing: The use of multiple pathways to convey information about the same stimulus.
7. Feature detection : A type of serial processing where increasingly complex aspects of the stimulus are processed in sequence.

: Visual Processing – Vision – MCAT Content

What is feature theory and what findings does it explain?

References –

• Anne Treisman and Garry Gelade (1980). “A feature-integration theory of attention.” Cognitive Psychology, 12 (1), pp.97–136.
• Anne Treisman and Hilary Schmidt (1982). “Illusory conjunctions in the perception of objects.” Cognitive Psychology, 14, pp.107–141.
• Anne Treisman and Janet Souther (1986). “Illusory words: The roles of attention and of top–down constraints in conjoining letters to form words.” Journal of Experimental Psychology: Human Perception and Performance, 12 (1), pp.3–17
• Anne Treisman (1988). “Features and objects: the fourteenth Bartlett Memorial Lecture.” Quarterly Journal of Experimental Psychology, 40A, pp.201–236.
• Anne Treisman and Nancy Kanwisher (1998). “Perceiving visually presented objects: recognition, awareness, and modularity.” Current Opinion in Neurobiology, 8, pp.218–226.
• J.M. Wolfe (1994). “Guided Search 2.0: A revised model of visual search.” Psychonomic Bulletin & Review, 1, pp.202–238

What is an example of feature theory?

What is an example of feature integration theory? An example of feature integration theory is when an individual is looking for their child during a soccer game. The child is wearing a purple uniform, and the opposing side is wearing yellow uniforms. This is an example of feature searching.

What is detection with example?

Other forms: detections Detection is the act of noticing or discovering something. At the airport, you might see German Shepherds trained in the detection of drug smuggling or explosives in luggage. Detection, detect, detective, detector — all are about noticing and discovering.

A detective looks for clues that lead to the detection of the person who committed a crime. A metal detector is a machine created for the detection of coins people have left behind on the beach. Some teachers seem to have a third eye they use primarily for the detection of kids passing notes, or checking their cell phones during class.

Definitions of detection

noun the perception that something has occurred or some state exists “early detection can often lead to a cure” synonyms: sensing noun the act of detecting something; catching sight of something noun the detection that a signal is being received noun a police investigation to determine the perpetrator ” detection is hard on the feet” synonyms: detecting, detective work, sleuthing

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What is the example of the features in image processing?

What are features? – Features are parts or patterns of an object in an image that help to identify it. For example — a square has 4 corners and 4 edges, they can be called features of the square, and they help us humans identify it’s a square. Features include properties like corners, edges, regions of interest points, ridges, etc. (src:https://commons.wikimedia.org/wiki/File:Writing_Desk_with_Harris_Detector.png )

What is an example of feature matching theory of perception?

Feature-matching theories – Feature-matching theories propose that we decompose visual patterns into a set of critical features, which we then try to match against features stored in memory. For example, in memory I have stored the information that the letter “Z” comprises two horizontal lines, one oblique line, and two acute angles, whereas the letter “Y” has one vertical line, two oblique lines, and one acute angle.

I have similar stored knowledge about other letters of the alphabet. When I am presented with a letter of the alphabet, the process of recognition involves identifying the types of lines and angles and comparing these to stored information about all letters of the alphabet. If presented with a “Z”, as long as I can identify the features then I should recognise it as a “Z”, because no other letter of the alphabet shares this combination of features.

The best known model of this kind is Oliver Selfridge’s Pandemonium, One source of evidence for feature matching comes from Hubel and Wiesel’s research, which found that the visual cortex of cats contains neurons that only respond to specific features (e.g.

• One type of neuron might fire when a vertical line is presented, another type of neuron might fire if a horizontal line moving in a particular direction is shown).
• Some authors have distinguished between local features and global features.
• In a paper titled Forest before trees David Navon suggested that “global” features are processed before “local” ones.

He showed participants large letter “H”s or “S”s that were made up of smaller letters, either small Hs or small Ss. People were faster to identify the larger letter than the smaller ones, and the response time was the same regardless of whether the smaller letters (the local features) were Hs or Ss.

However, when required to identify the smaller letters people responded more quickly when the large letter was of the same type as the smaller letters. One difficulty for feature-matching theory comes from the fact that we are normally able to read slanted handwriting that does not seem to conform to the feature description given above.

For example, if I write a letter “L” in a slanted fashion, I cannot match this to a stored description that states that L must have a vertical line. Another difficulty arises from trying to generalise the theory to the natural objects that we encounter in our environment.