What Are Feature Detectors In Psychology?

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.

Feature detectors are individual neurons—or groups of neurons—in the brain which code for perceptually significant stimuli. 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.

Contents

- 0.0.1 What do feature detectors do?

1 What are feature detectors examples?
- - 1.0.1 What is feature detectors theory in psychology?
2 What are feature detectors in AP Psychology?
- - 2.0.1 Where are feature detectors located in the brain?
3 Do feature detectors detect faces?
4 What are the 3 feature detectors?
5 How are feature detectors activated?
6 What is feature detection in deep learning?
7 What is the main physiological evidence for feature detectors?
8 What are the feature detectors neurons?
- 8.1 What is feature detector vision?
  - 8.1.1 Can everyone see faces in things?
- 8.2 Does facial recognition look at eyes?
9 What do feature detectors in the visual cortex do?
- 9.1 What are feature detectors in CV?
- 9.2 What are feature detectors in image processing?
10 What is the purpose of feature detectors in creating mental representation of objects?

What do feature detectors do?

The ability to detect certain types of stimuli, like movements, shape, and angles, requires specialized cells in the brain called feature detectors. Without these, it would be difficult, if not impossible, to detect a round object, like a baseball, hurdling toward you at 90 miles per hour. Add flashcard Cite Random

What are feature detectors examples?

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 feature detectors theory in psychology?

The theory that all complex stimuli can be broken down into individual parts (features), each of which is analyzed by a specific feature detector.

What are feature detectors in 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.

The optic nerves will then meet at occipital lobe’s visual cortex to deliver the information.
Feature detectors are self-explanatory by their name.
Their job is to detect a scene’s features—edges, lines, angles, and movement.
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 _,

Possible Answers: Explanation : Amplitude is the height of a wave.
The greater the amplitude of a sound wave is, then the greater the activation of hair cells in the ear will be.
Rather than acting singularly, the hair cells attuned for a specific frequency will react with its neighboring hair cells.
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.

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. In other words, they become even more sensitive in the dark. 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 Erika Certified Tutor Furman University, Bachelor in Arts, Psychology. University of North Carolina at Chapel Hill, Doctor of Philosophy, Cognitive. Robert Certified Tutor University of North Carolina at Chapel Hill, Bachelor in Arts, English. North Carolina Central University, Master of Arts, En. Deborah Certified Tutor Southern Connecticut State University, Bachelor in Arts, Social Work. Columbia University in the City of New York, Master of, 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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Where are feature detectors located in the brain?

Feature detectors are nerve cells located in the visual cortex of the occipital lobe that respond to a scene’s edges, lines, angles and movements.

Do feature detectors detect faces?

Feature detector 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 are the 3 feature detectors?

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.

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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.

Two additional and important forces at work in visual perception are perceptual constancy and optical illusions. Perceptual constancy refers to the tendency to experience a stable view of the world in spite of a continuously changing sensory environment. 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).

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.
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.

How are feature detectors activated?

Feature detectors in animal vision Image feature detectors are a common concept between mammalian vision and computer vision. When using them, a raster image is not directly processed to identify complex objects (e.g. a flower, or the digit 2). Instead feature detectors map the distribution of simple figures (such as straight edges) within the image.

Higher layers of the neural network then use these maps for distinguishing objects.
In the mammalian brain’s visual cortex (which is at the back of the head, at the furthest possible point from the eyes) the image on the retina is recreated as a spatially faithful projection of the excitation pattern on the retina.

Overlapping sets of feature detectors use this as input. From eyeball to visual cortex in humans. Note the Ray-Ban-shaped area at the back of the brain where the retinal excitation pattern is projected to with some distortions. (From Frisby: Seeing: the computational approach to biological vision (2010), p 5) How we know about retinotopic projection to the visual cortex: an autoradiography of a macaque brain slice shows in dark the neurons that were most active in result of the animal seeing the image on top left.

From,) A feature detector neuron becomes active when its favourite pattern shows up in the projected visual field – or more exactly in the area within the visual field where each detector is looking.
A typical class of detectors is specific for edges with a specific angle, where one side is dark, and the other side is light.

Other neurons recognise more complex patterns, and some also require motion for activation. These detectors together cover the entire visual field, and their excitation pattern is the input to higher layers of processing. We learned about these neurons first by sticking microelectrodes into the visual cortex and measuring electrical activity. A toad’s antiworm detector neuron reacts to a stripe moving across its receptive field. The antiworm may move in any direction, but only crosswise for the neuron to react. The worm detector, for comparison, would react if the stripe moves lengthwise. Toad at the right side with microelectrode, the oscillogram above the screen shows the tapped feature detector neuron’s activity.

Segment from,) A cat visual cortex neuron that is excited only by light spots at the left and right edges along its receptive field.
Light spots at other places are inhibitory: x marks excitation, triangle marks inhibition on the right diagram.
From,) A cat visual cortex neuron that reacts only to a diagonal slit moving up and right.

(From Hubel & Wiesel 1959.) A cat visual cortex neuron that reacts to a vertical edge left or right. Such cells with complex receptive fields do not react to individual spots of light. (From,) In mammals the combined output of many detectors are analysed by higher layers of the visual cortex for complex object recognition.

Sometimes however the raw feature detector output is already good enough for choosing appropriate behaviour. In toads (where feature detectors sit directly in the retina) hunting is driven by a detector for a moving small dark point (a bug detector), and a detector for a lengthy stripe moving longitudinally (a worm detector).

Fleeing is initiated by the big scary moving object detector (moving large rectangle) which overrides the worm and bug detectors ( ). How do feature detectors know what to look for? Are they set up 1) completely before birth without input from experience, or 2) are they refined using input from experience, or 3) are they completely the product of experience? (Disregarding that evolution of development itself is a very slow form of gaining experience.) Different species of animals are probably different in details, but at least for cats and primates 1) is not the case.

If during a critical period of 3-6 months right after birth these animals are completely deprived of visual experience, then they never develop the ability to distinguish even simple shapes such as rectangles from circles. However, once the necessary structures are in place, they are stable. Visual deprivation later in life does not deteriorate abilities with similar finality.

(See and for more detailed descriptions of these experiments.) Each particular feature needs to be seen frequently during the critical period to be well recognised later in life. For example cats only exposed to vertical stripes early on will be later virtually blind to horizontal edges ().

The opposite is true for cats exposed only to horizontal stripes. In electrophysiological measurements there will be a corresponding absence of feature detector neurons for the unexercised direction. Similarly, cats that see a world only illuminated by very short flashes of light with long dark periods in-between, to suppress any sensation of continuous motion, will have difficulty with analysing motion.

Half of the feature detector neurons in these cats respond however to diffuse strobing lights, as the prevalent stimulus during their critical period (). In a different experiment, if the perceived motion during the critical period is always only in one direction, then feature detectors for other directions are less well developed (). Cat exposed to only one orientation in a striped tube. A black collar hides the own body shape. (From,) As a question for a future post, how do neurons choose what they are sensitive for? They might be set up for recognising random figures and shaping this pattern into actually occurring shapes.

There might be also a variety of biases towards shapes, and those cells that find something regularly will survive and refine their target.
For comparison, in a slightly different system, binocular vision (in species where both eyes look at the same image unlike e.g.
Many birds), there are at birth already intertwined anatomical structures that process overlapping visual input from both eyes next to each other (optical dominance columns).

If one eye is deprived of vision during the critical period, then the optical dominance columns for the other eye will enlarge at the cost of the deprived eye’s columns, and the cortex for the deprived eye loses processing abilities. (This becomes a problem for those who are born with strabismus and begin to favour one eye.) Similar competition for input or survival might take place also with feature recognisers: either feature types compete for malleable cells, or relatively fixed cells compete for survival.

What is feature detection in deep learning?

Feature detection is a method to compute abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. Feature detection is a low-level image processing operation.

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What is the basis for feature detection theory?

V Feature Detection – The idea underlying feature detection is that there exist ina sensory pathway neurons which are selectively responsive to some aspect of the stimulus. Ideally, they should be completely indifferent to change in any other aspect of the stimulus, although such a condition is so rare that it usually is not insisted upon.

At the brain stem level we could point to neurons which respond to a comparatively narrow band of frequencies with a preferred response centered at some particular frequency (cochlear nucleus) or neurons that vary their response according to the relative time of arrival of a stimulus at the two ears (medial superior olive).

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In a way, both these types of neuron are responding to different aspects of thestimulus and might perhaps be thought of as feature detectors. However, to merit the name “feature detector,” the feature customarily has to be more elaborate than the examples just given.

Nevertheless, it is difficult to say why the color of a line in the visual field should not be regarded as a feature, while its orientation is usually considered to be so.
Because of this difficulty of definition, an empirical approach has been adopted in the cortex.
This approach consists of trying to think what might be features of an auditory stimulus and then using stimuli embodying those features to test the responses of single neurons.

At the simplest end of the scale lie the tone glides of Whitfield and Evans (1965) ; at the most elaborate lie vocalizations of the species being examined ( Funkenstein et al., 1971 ). Because the latter are difficult to interpret, stimuli of intermediate complexity have been used.

Thus Swarbrick and Whitfield (1972) used noise bursts amplitude modulated with a range of triangular envelopes ( Fig.2 ) and found units which gave their best initial response when the envelope was symmetrical.
Feher and Whitfield (1966) found units responding to particular combinations of steady and gliding tones.

In an analogous way, the position of a sound stimulus in space ( Whitfield, 1966 ; Evans, 1968 ) and the precedence effect ( Whitfield, 1974 ) have been shown to give rise to selective responses in cortical units ( Fig.3 ). Fig.2, Preferential response of a unit in the auditory cortex to stimulus shape. Noise bursts were amplitude modulated with envelopes of various shapes and durations (column A). The rise and fall times in milliseconds are shown in column B. There is a strong correlation between the degree of asymmetry and the neural response. Fig.3, Responses to features. (a) A response to a tone glide. (b) A response to a tone pulse followed by an echo delayed 5 msec. L, Left alone; R, right alone; LR, left before right; RL, right before left. (From Whitfield, 1974,) (c) A response to a tone glide accompanied by a steady tone.

(From Whitfield, 1966,) Copyright © 1974 If feature detection is to be functional, the units must fulfill two other criteria: (1) Removal of all such units must abolish the ability of the animal to make a discrimination involving that feature; and (2) it should be possible to detect some anatomical pattern underlying the position in which such units are found, though this pattern may, of course, be very elaborate.

I think it would be true to say that none of these criteria has in fact been fulfilled, though some have been approximated. The most closely approximated will obviously be the physiological response, since this is the point of departure in selecting a feature-detecting unit.

Even here, as has been pointed out, it is virtually unknown to find a unit whose response is a function of the presence or absence of a feature and quite independent of variation in any other parameter.
I do not think it can be argued that this does not matter.
In the medial superior olive we have cells which respond to the relative time of arrival of impulses from the two ears ( Hall, 1964 ), but they cannot themselves be regarded as cells which detect the “feature” of azimuth because they are also susceptible to variations in the overall sound intensity.

To compute azimuth it is necessary to compare the activity of the cells in one nucleus with the activity of those in the nucleus of the opposite side, and so we must postulate a further cell which is a pure ratio detector, and so on. In other words, the concept of feature detection demands the existence of cells “at which the buck stops” and the decision is made.

Such cells have not, strictly, been found. An alternative method is to destroy all those cells thought to mediate the detection of a particular feature and study the effect on behavior—the ablation method. Such an experiment would demand a detailed knowledge of their anatomical organization, and this we do not have.

However, the fact that we are not able to predict, with even a slightly above-chance accuracy, the place where we shall encounter a particular unit, does not of itself show that no such organization exists. There is no a priori reason to think that particular feature detectors are necessarily segregated in particular areas, and, indeed, the reverse might well be expected.

Units responding to even simple features (e.g., location in space) are not common, and the frequency with which they are encountered varies from5 to 0.5% or less according to the feature under consideration.
While 1% of all the units in the auditory cortex is a large number, the absolute number of units encountered experimentally yields, usually, a sample size too small for us to be able to draw any conclusion.

The practical effect of this, when we come to test the effects of ablation, is that we must ablate the whole auditory cortex at least on one side if not on both, in order to be sure of removing all the units selective for a particular feature. In doing so, of course, we inevitably disrupt all mechanisms of that cortex, so that loss of sensitivity to the feature would not be diagnostic should it occur.

On the other hand, failure to lose sensitivity would be an argument against the crucial role of a cortical feature detector, and examples of this certainly occur.
Neff and Diamond (1958) demonstrated that the loss of one auditory cortex had little or no effect on the accuracy with which a cat can localize a simple sound source, and this was so irrespective of whether the ablated cortex was ipsi- or contralateral to the position of the sound.

Yet of the cortical units which have been found to respond selectively to the position of a sound in space, about twice as many are found in the contralateral as in the ipsilateral cortex ( Evans, 1968 ). From the evidence we have available then, the hypothesis that the auditory cortex consists wholly or even largely of a set of feature detectors does not receive much encouragement.

What is the difference between feature detectors 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.

A second stream projects to the temporal lobe and carries both magnocellular (“where”) and parvocellular (“what”) information. 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.

Feature detection is a type of serial processing where increasingly complex aspects of the stimulus are processed in sequence. 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. 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.

• 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.
Key Terms
Superior colliculus : The primary area of the brain where eye movements are coordinated and integrated with auditory information.
Optic chiasma : found at the base of the brain and coordinates information from both eyes.
Suprachiasmatic nucleus : A cluster of cells that is considered to be the body’s internal clock, which controls our circadian (day-long) cycle.
Parallel processing: The use of multiple pathways to convey information about the same stimulus.
Feature detection : A type of serial processing where increasingly complex aspects of the stimulus are processed in sequence.

: Visual Processing – Vision – MCAT Content

What happens if feature detectors are damaged?

Feature Detectors/ Hubel & Wiesel Experiment/ Prosopagnosia – Word(s) of the Day (1) Feature detectors – specialized neurons that respond only to certain sensory information. Hubel and Wiesel Experiment David Hubel and Torsten Wiesel demonstrated that specialized neurons in the occipital lobe’s visual cortex respond to specific features of an image such as angles, lines, curves, & movement. EX. the area behind your right ear enables you to recognize faces. Damage to this area can result in (3) prosopagnosia, ( aka facial blindness ). Someone with prosopagnosia cannot recognize their own face in a mirror. Some conditions may be mistaken for face-blindness, for ex.

What is the main physiological evidence for feature detectors?

What evidence most directly supports the idea of feature detectors? When an experimenter presents a faint light, a particular participant almost always reports seeing it, suggesting great sensitivity to faint lights.

What are the feature detectors neurons?

Feature detectors are neurons that are turned off or on by specific features of visual stimuli like edges and mvmt. Where in the visual system are feature detectors located? occipital cortex.

What is feature detector vision?

Feature detection From wiki.gis.com Feature detection is a process by which specialized nerve cells in the brain respond to specific features of a visual stimulus, such as lines, edges, angle, or movement. The nerve cells fire selectively in response to stimuli that have specific characteristics.

Feature detection was discovered by David Hubel and Torsten Wiesel of Harvard University, an accomplishment which won them the 1981 Nobel Prize.
In the area of computer vision, usually refers to the computation of local image features as intermediate results of making local decisions about the local information contents (image structure) in the image; see also the article on interest point detection.

You might be interested: What Is Role Confusion In Psychology?

In the area of psychology, the feature detectors are neurons in the visual cortex that receive visual information and respond to certain features such as lines, angles, movements, etc. When the visual information changes, the feature detector neurons will quiet down, to be replaced with other more responsive neurons.

Can everyone see faces in things?

Seeing faces in everyday objects is a common experience, but research from The University of Queensland has found people are more likely to see male faces when they see an image on the trunk of a tree or in burnt toast over breakfast. Dr Jessica Taubert from UQ’s School of Psychology said face pareidolia, the illusion of seeing a facial structure in an everyday object, tells us a lot about how our brains detect and recognise social cues.

The aim of our study was to understand whether examples of face pareidolia carry the kinds of social signals that faces normally transmit, such as expression and biological sex,” Dr Taubert said.
Our results showed a striking bias in gender perception, with many more illusory faces perceived as male than female.

“As illusory faces do not have a biological sex, this bias is significant in revealing an asymmetry in our face evaluation system when given minimal information. “The results demonstrate visual features required for face detection are not generally sufficient for the perception of female faces.” More than 3800 participants were shown numerous examples of face pareidolia and inanimate objects with no facial structure and they were asked to indicate whether each example had a distinct emotional expression, age, and biological sex, or not.

We know when we see faces in objects, this illusion is processed by parts of the human brain that are dedicated to processing real faces, so in theory, face pareidolia ‘fools the brain’,” Dr Taubert said.
The participants could recognise the emotional expressions conveyed by these peculiar objects and attribute a specific age and gender to them.

“Now we have evidence these illusory stimuli are being processed by the brain by areas involved in social perception and cognition, so we can use face pareidolia to identify those specific areas. “We can compare how our brains recognise emotion, age, and biological sex, to the performance of computers trained to recognize these cues.

Further we can use these interesting stimuli to test for abnormal patterns of behaviour.” The UQ research team wants to gather more examples of face pareidolia and is encouraging people to email any illusions they come across to [email protected], The study is published in Proceedings of the National Academy of Sciences,

Media: Dr Jessica Taubert, [email protected], +61 (0)7 3365 7181 ; Kirsten O’Leary, UQ Communications, [email protected], +61 (0)412 307 594, @UQHealth.

Does facial recognition look at eyes?

Our brain extracts important information for face recognition principally from the eyes, and secondly from the mouth and nose, according to a new study from a researcher at the University of Barcelona. This result was obtained by analyzing several hundred face images in a way similar to that of the brain.

Imagine a photograph showing your friend’s face.
Although you might think that every single detail in his face matters to recognize him, numerous experiments have shown that the brain prefers a rather coarse resolution instead, irrespective of the distance at which a face is seen.
Until now, the reason for this was unclear.

By analyzing 868 male and 868 female face images, the new study may explain why. The results indicate that the most useful information is obtained from the images if their size is around 30 x 30 pixels. Moreover, images of eyes give the least “noisy” result (meaning that they convey more reliable information to the brain compared to images of the mouth and nose), suggesting that face recognition mechanisms in the brain are specialized to the eyes.

What do feature detectors in the visual cortex do?

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.

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.

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 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.

Two additional and important forces at work in visual perception are perceptual constancy and optical illusions. Perceptual constancy refers to the tendency to experience a stable view of the world in spite of a continuously changing sensory environment. 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.

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.

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.

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 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.

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 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.
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 are feature detectors in CV?

Scale-Invariant Feature Transform (SIFT) – When we rotate an image or change its size, how can we make sure the features don’t change? The methods I’ve used above aren’t good at handling this scenario. For example, consider these three images below of the Statue of Liberty in New York City. OpenCV has an algorithm called SIFT that is able to detect features in an image regardless of changes to its size or orientation. This property of SIFT gives it an advantage over other feature detection algorithms which fail when you make transformations to an image.

Here is an example of code that uses SIFT: # Code source: https://docs.opencv.org/master/da/df5/tutorial_py_sift_intro.html import numpy as np import cv2 as cv # Read the image img = cv.imread(‘chessboard.jpg’) # Convert to grayscale gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY) # Find the features (i.e.

keypoints) and feature descriptors in the image sift = cv.SIFT_create() kp, des = sift.detectAndCompute(gray,None) # Draw circles to indicate the location of features and the feature’s orientation img=cv.drawKeypoints(gray,kp,img,flags=cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # Save the image cv.imwrite(‘sift_with_features_chessboard.jpg’,img) Here is the before: Here is the after. Each of those circles indicates the size of that feature. The line inside the circle indicates the orientation of the feature:

What are feature detectors in image processing?

What is the purpose of feature detectors in creating mental representation of objects?

Feature detectors respond to certain characteristics of objects, such as the direction its moving or the length of the object. The combination of inputs from multiple feature detectors tells the brain a number of characteristics about an object, which in turn are put together in to a mental representation.

Sabrina Sarro