An analysis of the importance to distinguish between different categories of disabled people

Finally, inside each and every neuron is a leaky integrator circuit, composed of a variety of ion channels and continuously fluctuating membrane potentials. One consequence of failing to recognize this difference has been in the field of neuropsychology, where the cognitive performance of brain-damaged patients is examined to determine the computational function of the damaged region.

The brain-computer metaphor obscures this important, though perhaps obvious, difference in raw computational power. Unlike computers, processing and memory are performed by the same components in the brain Computers process information from memory using CPUs, and then write the results of that processing back to memory.

The signals which are propagated along axons are actually electrochemical in nature, meaning that they travel much more slowly than electrical signals in a computer, and that they can be modulated in myriad ways.

Below, I review the most important of these differences and the consequences to cognitive psychology of failing to recognize them: A surprising set of experiments by Jeremy Wolfe has shown that even after being asked hundreds of times which simple geometrical shapes are displayed on a computer screen, human subjects continue to answer those questions by gaze rather than rote memory.

For example, despite your intuitive feeling that you could close your eyes and know the locations of objects around you, a series of experiments in the field of change blindness has shown that our visual memories are actually quite sparse.

For example, a lasting debate in cognitive psychology concerned whether information is lost from memory because of simply decay or because of interference from other information.

This is known as byte-addressable memory. Many are now realizing that this debate represents a false dichotomy.

Any abstract information processing account of cognition will always need to specify how neuronal architecture can implement those processes — otherwise, cognitive modeling is grossly underconstrained.

Processing speed is not fixed in the brain; there is no system clock The speed of neural information processing is subject to a variety of constraints, including the time for electrochemical signals to traverse axons and dendrites, axonal myelination, the diffusion time of neurotransmitters across the synaptic cleft, differences in synaptic efficacy, the coherence of neural firing, the current availability of neurotransmitters, and the prior history of neuronal firing.

Unfortunately, because of the poorly-understood nature of trauma-induced plasticity, the logic cannot be so straightforward.

See here for more on this. In linear networks, any function computed by a 3-layer network can also be computed by a suitably rearranged 2-layer network. The brain is a self-organizing system This point follows naturally from the previous point — experience profoundly and directly shapes the nature of neural information processing in a way that simply does not happen in traditional microprocessors.

Because the brain is nonlinear, and because it is so much larger than all current computers, it seems likely that it functions in a completely different fashion. Although this may seem like a rather minor difference between computers and brains, it has profound effects on neural computation.

In contrast, the computations performed by more realistic i. Brains have bodies This is not as trivial as it might seem: Of course, similar things can be done in computers, mostly by building massive indices of stored data, which then also need to be stored and searched through for the relevant information incidentally, this is pretty much what Google does, with a few twists.

Appreciating these differences may be crucial to understanding the mechanisms of neural information processing, and ultimately for the creation of artificial intelligence. Synapses are far more complex than electrical logic gates Another pernicious feature of the brain-computer metaphor is that it seems to suggest that brains might also operate on the basis of electrical signals action potentials traveling along individual logical gates.

Lichtenberg Although the brain-computer metaphor has served cognitive psychology well, research in cognitive neuroscience has revealed many important differences between brains and computers. This can lead to a variety of interesting changes, including some that seem to unlock unused potential in the brain known as acquired savantismand others that can result in profound cognitive dysfunction as is unfortunately far more typical in traumatic brain injury and developmental disorders.

A wide variety of evidence from other domains suggests that we are only beginning to understand the importance of embodiment in information processing. To use just one example, the cerebellum is often thought to calculate information involving precise timing, as required for delicate motor movements; however, recent evidence suggests that time-keeping in the brain bears more similarity to ripples on a pond than to a standard digital clock.

Similarly, networks of neurons can fire in relative synchrony or in relative disarray; this coherence affects the strength of the signals received by downstream neurons. For example, one of the primary mechanisms of information transmission appears to be the rate at which neurons fire — an essentially continuous variable.

In retrospect, this debate is partially based on the false asssumption that these two possibilities are dissociable, as they can be in computers. As neurons process information they are also modifying their synapses — which are themselves the substrate of memory.

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The brain uses content-addressable memory In computers, information in memory is accessed by polling its precise memory address. The brain is a massively parallel machine; computers are modular and serial An unfortunate legacy of the brain-computer metaphor is the tendency for cognitive psychologists to seek out modularity in the brain.

Unfortunately, this is only half true. In other words, combinations of multiple linear functions can be modeled precisely by just a single linear function. This adds to the complexity of the processing taking place at each synapse — and it is therefore profoundly wrong to think that neurons function merely as transistors.

No such distinction exists in the brain. As a result, retrieval from memory always slightly alters those memories usually making them stronger, but sometimes making them less accurate — see here for more on this.Mar 27,  · Although the brain-computer metaphor has served cognitive psychology well, research in cognitive neuroscience has revealed many important differences between brains.

In a review of the literature, different opinions on the use of concepts, procedures and interpretation in content analysis are presented. However, there are similarities in the way the researchers explain the process: either they do it by using different distinguishing stages, (Burnard,Downe-Wambolt, ), or in running text (Berg,Catanzaro, ).

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An analysis of the importance to distinguish between different categories of disabled people
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