Ординатура / Офтальмология / Английские материалы / The Neuropsychology of Vision_Fahle, Greenlee_2003

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Although much remains to be learned about the nature of the perceptual impairments in agnosia, and the current crop of theories may all miss the mark, one important idea has been established. Vision is not a singular entity, but instead it has dissociable components, and different visual tasks used in evaluating agnosia, such as object recognition, copying, and matching, make different demands on these components. Thus, there is nothing patently contradictory about hypothesizing a perceptual impairment in an agnosic patient who performs well in certain perceptual tasks.

Can we reconcile this view of agnosia with evidence for a memory impairment? The apparent contradiction between hypothesizing a perceptual and a memory impairment only arises if we assume that perception and memory are singular and distinct. However, as we will see in the next section, this is true only in certain kinds of computational systems, and there is no reason to assume that it will be true of the human brain.

Where does perception end and memory begin?

Visual recognition requires that memory be searched for a representation that resembles the current stimulus input. In a conventional computer, an input is recognized by comparing a symbolic representation of that input to symbolic representations stored in memory, using an explicit comparison process that is itself part of a stored program in the computer. The process is analogous to taking the title of a book that you have written on a piece of paper, and searching the library shelves to find the same title written on the spine of the book. The slip of paper is the perceptual representation, and the shelved book is the memory representation. In principle, it is possible that visual recognition also works in this way, with a processor comparing a high-level representation of the appearance of the stimulus to stored representations. When a match is found, the associated semantic knowledge of the object is then available, just as the contents of the book become available once the title has been located on the shelf.

However, there is another way of implementing such a search that does not involve distinct perceptual and memory representations. In neural network computation, representations correspond to the activation of certain neuron-like units, which are interconnected. The extent to which the activation of one unit causes an increase or decrease in the activation of a neighbouring unit depends on the ‘weight’ of the connection between them. Positive weights cause units to excite each other and negative weights cause units to inhibit each other. Upon presentation of the input pattern to the input units, all of the units connected with those input units will begin to change their activation under the influence of two kinds of constraints: the activation value of the units to which they are connected and the weights on the connections. These units might in turn connect to others, and influence their activation levels in the same way. In recurrent, or attractor, networks the units downstream will also begin to influence the activation levels of the earlier units. Eventually, these shifting activation levels across the units of the network settle into a stable pattern of activation, which is the representation that corresponds to the recognized object. That pattern is determined

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jointly by the input activation (the stimulus input) and the weights of the network (the system’s knowledge of all objects).

The two ways of implementing search are so different that it is difficult to compare them except at a very abstract level. For instance, one can say that the system’s knowledge in a symbolic implementation of memory search consists of separate stored representations of the stimulus and the comparison procedure, whereas in a neural network it consists just of the connection weights, which store knowledge of object appearance and carry out the search process. In both types of system there is a distinction between the early representations of the stimulus closer to the input level and the high-level object representations that underlie object recognition. However, only the symbolic search involves two tokens of the high-level representation, one ‘perceptual’ (derived from the stimulus) and the other ‘memory’ (previously stored). In a neural network search there is only one token. Distinctions such as ‘perception’ versus ‘memory’, which seem almost logically necessary when one is thinking in terms of symbol manipulation, dissolve when one considers the neural network implementation of memory search.

On this view of visual object recognition, there is a continuum of types of representation, going from early visual areas, in which the representation is determined largely by the innate structure of the visual system, and ending with higher-level areas, in which the representation is determined in large part by learning. When activated, these highlevel visual representations are perceptual, in the sense that their activation comes from retinal input, and they are mnemonic in the sense that the pattern of weights responsible for their derivation is determined by experience (in contrast to the smaller role of experience in setting the weights at earlier stages of visual processing). These same representations can also function in a purely mnemonic role when activated by internal means rather than retinal input, as is the case in mental imagery (Farah 1988, 2000). Thus, one could refer to a more perceptual end and a more mnemonic end of the sequence of representations supporting object recognition, but there is no dividing line where processing goes from perceptual to mnemonic.

Conclusions

Normal visual perception encompasses a series of encodings and re-encodings of the input, through different representations that make different aspects of the stimulus explicit. At the early stages, the nature of the representations is affected only minimally by learning and experience. At progressively later stages, learning plays a more important role in determining what is explicitly represented. The experience of having seen particular objects will shape what the later representations are most useful for representing. For example, having seen many faces of a given race equips these higherlevel representations to represent these faces accurately. In that sense these representations embody memory. Yet they are also perceptual representations, in that a new and unfamiliar face of the same race will be processed by them.

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The hypothesis being put forth here is that associative agnosia arises with damage to the later end of this continuum. This is broadly consistent with the reports of perceptual impairment reviewed earlier, although more directed attempts to test this hypothesis would be helpful. It is also consistent with the impairment of imagery, an ostensibly memory-based form of thought, in associative agnosia. Higher-level perceptual representations are not just active during perception, but also play a role in imagery (e.g. Farah 2000). So, in answer to the question ‘Is associative visual agnosia an impairment of perception or memory?’ one might well answer ‘Yes!’

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Part 6

Rehabilitation and recovery

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Chapter 11

Recovery and rehabilitation of cerebral visual disorders

Josef Zihl

Introduction

About 30% of patients with acquired brain injury demonstrate deficits in vision (e.g. Hier et al. 1983a; Uzzell et al. 1988). In the majority of cases, the visual field is affected, but spatial contrast sensitivity and visual acuity, colour vision, visual space perception, visual object and face identification as well as recognition may also be impaired. In addition, specific impairments in visual spatial attention may result in the loss of visual perception in one hemispace, or in both, because of unior bilateral visual inattention.

The posterior brain is a patchwork of probably more than 30 functionally specialized visual areas with flexible networks to subserve complex visual abilities, e.g. visual spatial orientation and visual recognition. Despite this modular organization, selective visual disorders are rare because focal brain injury is only rarely limited to one single visual cortical area (for reviews, see Zeki 1993; Cowey 1994). Since cerebrovascular disorders (stroke, haemorrhage), trauma, and tumours are the most frequent underlying aetiologies, the resulting functional impairment as a rule is not restricted to just one visual deficit. Depending on the site and extent of brain injury, patients typically show a combination of several visual deficits. In accordance with the functional organization of the visual cortex (Ungerleider and Haxby 1998), it can be expected that occipitotemporal injuries affect the processing and thus perception of the ‘what’ properties (e.g. shape, colour) of visual stimuli and thus impair visual identification and recognition. Occipitoparietal injuries, on the other hand, affect the processing and thus perception of the ‘where’ properties (e.g. position, distance, spatial relationships between stimuli), and thus may impair the visual guidance of oculomotor and hand-motor activities as well as visual navigation.

Adequate vision is a crucial prerequisite for performance in many domains of behaviour, e.g. spatial orientation and navigation, learning and memory, and visual guidance of motor activities. Furthermore, rehabilitation outcome and vocational success depend critically on visual capacities (Reding and Potes 1988). For example, hemianopia, the most frequent visual deficit after brain injury, has been found to significantly affect the time and likelihood of achieving a level of functional outcome adequate for effective

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activities of daily living (Patel et al. 2000). Therefore, the diagnosis and treatment of cerebral visual deficits represent important components in neuropsychological rehabilitation settings. In the last few years there has been growing interest in research devoted to the rehabilitation of visual deficits. Concerning their effectiveness, procedures of treatment not only have to fulfil methodological criteria, but also criteria concerning the resulting benefits for the patient, i.e. a reduction of the degree of a patient’s visual handicap (ecological validity) and cost-effectiveness.

This chapter is divided into four parts. The first contains a brief description of visual deficits after brain injury and the second describes observations on spontaneous recovery of vision. In the third and fourth parts the return of visual function using systematic practice and the improvement of visual efficiency by compensation strategies are presented and discussed.

Visual disorders after brain injury

The main visual disorders after acquired brain injury and their frequency of occurrence are summarized in Table 11.1.

Visual field disorders

These undoubtedly represent the largest group of visual disorders. Loss of vision in corresponding parts of both visual fields is called anopia, which signifies total loss of visual perception. Unilateral anopia results from contralateral injury to the retrogeniculate visual pathway including the striate cortex. It can be present in one hemifield (hemianopia), in one quadrant (quadranopia), or in a small part mainly in the paracentral visual field (paracentral scotoma). After bilateral retrogeniculate injury corresponding portions in either visual hemifield are affected. Typical forms are bilateral hemianopia (‘tunnel vision’), bilateral upper or lower quadranopia, and bilateral paracentral scotomata. Loss of vision in the central region of the visual field (central scotoma) results from bilateral injury to the occipital pole, where the central visual field is represented, or to the portion

Table 11.1 Incidence of visual deficits after acquired brain injury (modified from Zihl 1994)

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Table 11.2 Incidence of homonymous visual field defects and of the underlying aetiology (N 636; modified after Zihl 2000)

* Incidence among those with unilateral homonymous visual field defects.

† Incidence among those with bilateral homonymous visual field defects.