Detecting and recognizing objects in the context

Detecting and recognizing meaningful objects in complex environments is a crucial skill that ensures that we can survive. The recognition process is fast, automatic and is seen as being present standardly. However, it is not constructed as easily as people think. After much research, it has become apparent that the recognition process takes place in the ventral visual system. Broadly speaking, this is done through a number of phases: V1 to V2 to V4 to PIT to AIT (the PIT and AIT together form the IT that you see in other articles). The highest phase of this system is the AIT. It is thought that there are neurons in the AIT that specifically recognize objects.

On a theoretical level there is a growing appreciation for the role of learning in robust, crucial representations of object recognition. The role of learning is approached by studying the visual system. Important points in this regard are the development of the visual system and the way in which the system is used by young people and adults. This paper focuses on experience-related plasticity in the adult visual system. Many adult studies have shown that learning-dependent changes are taking place in distinguishing and recognizing stimuli from tasks. Recent studies have searched for locations in the brain that confirm this.

Objects

The ability to form neuronal representations that are sensitive to pixel combinations of shapes and colors comes from object recognition. This is the quality mark for vision and goes through arithmetic difficulties so that it remains highly sensitive to the identity of image changes. Object recognition requires the visual system to discriminate between different patterns of input. Discrimination is probably done by merging a number of neurons in the early stages of the process. As a result, neurons from higher phases know when to fire at certain patterns, which leads to discrimination and recognition. Arithmetic models have proven that such explicit object recognition can be built using neuronal connections from groups that fire together with similar characteristics of images.

Learning sensitivity

The plasticity of neuronal connections combined with suitable learning rules is a potential mechanism. An example of this is that learning to respond selectively goes hand in hand with learning which things often occur (together) in reality. On a mechanistic level, this occurs when certain neurons respond to the input. An interaction of this strategy per phase of the visual system results in a complex stimulus system, which ensures recognition. Recently evidence has been found for the above theory. The timeframe for learning such recognitions suggests a strong link between neuroplasticity and behavioral improvements.

Learning tolerance

Most studies on visual learning have focused on changes in neuronal or behavioral selectivity. Learning selectivity is not enough for object recognition. Selective object representations must be tolerant of image changes (size, color, angle). Learning from the natural world is the solution. A central idea is that features and objects from the world do not suddenly exist or do not exist, but have (temporary) continuity. If you see an object and you walk on, you still see that object, but always from a different point. This is learning tolerance, because you know that it is still about the same object. That way you recognize it from multiple points of view. Evidence has been found that tolerance is not automatic, but it is not known how it works neuronal.

The visual system must also learn to recognize objects among other objects; called clutter. Therefore, it is suggested that learning has to do with improving correlations between neurons that respond to a characteristic or target against a background of noise. However, you should not always see the noise as noise. This background noise can also ensure that you place something in the right context.

Neuron plasticity

It is often said that the plasticity of perceptual learning is in early visual phases, because this learning is linked to the position of the retina. This means that changes in the receptive field can lead to certain tunings of V1 neurons. Recent image studies have found influence of the V1 when learning object characteristics. However, the evidence remains controversial. One possibility is that V1 learning effects can be found in the average response of a large group of neurons measured with fMRI.

Shape representation can shift from high to low visual areas. This supports fast and automatic searching and discovery with attention control (in cluttered scenes). These findings are consistent with the suggestion that object representation is not only helped by bottom-up processing but also by top-down processing. Learning starts in higher visual areas for easy tasks and continues to lower visual areas if higher resolution is required for more difficult tasks.

One of the biggest advantages of fMRI is that it shows global images of the brain. fMRI is therefore a handy method for studying the brain during visual tasks. It has been found that learning is supported by selective processes with crucial characteristics of objects. Recent neuronal imaging studies have shown that learning is supported by functional interactions between occipital temporal and parietal frontal areas. The findings of these image studies are consistent with the top-down approaches to visual processes. These areas together create a perceptual representation of the world.

In summary, it can be said that current studies indicate that there is no fixed place for brain plasticity in visual learning. At the neuronal level, learning can arise from changes in the feed forward network, especially at higher phases, or through changes in interactions between frontal cortical areas and local connections in the primary visual cortex. Such changes can be adaptive and efficient.

The four conclusions that now can be drawn are:

1. The adult visual system is plastic.

2. There is no place of plasticity that supports object recognition learning.

3. Learning is not always the result of simple, static change at the core of the feedforward network.

4. The relationship between neuronal mechanisms and plasticity remains unknown.