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  <name xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">Introduction</name>
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      <md:firstname xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">David</md:firstname>
      
      <md:surname xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">Lane</md:surname>
      <md:email xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">lane@rice.edu</md:email>
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      <md:surname xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">Lane</md:surname>
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    <md:keyword xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">introduction</md:keyword>
    <md:keyword xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">neuropsychology</md:keyword>
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  <content xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">
    <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para1">Welcome to this online textbook chapter on
      language. The emphasis will be on a cognitive neuropsychological
      approach, with the data from brain-damaged individuals providing
      insight into how the unimpaired brain processes language. To set
      the scene, we will introduce you to a patient who has suffered a
      stroke affecting various aspects of his language ability. We
      will show you some of the things which are now difficult for
      him, and how surface appearances do not reveal those
      difficulties. Listen to his <link xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" src="http://psych.rice.edu/mmtbn/language/langIntro/MLconv_h.html">conversation</link>
      with Dr Randi Martin, first of all. Notice how he seems to
      unerstand the questions put to him, with no trouble. Notice also
      how his speech production is slow, with his taking much more
      time than most would to express himself, even though the topic
      is something he has talked about many times in the past.
      </para>

  <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para2">One of the things you may wonder about is whether
      he has some speech difficulty which prevented him from speaking
      more fluently than that. We need to look at some other task
      where he has to speak but does not have to contruct sentences in
      order to rule out that possibility. One such task is repeating
      words. <link xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" src="http://psych.rice.edu/mmtbn/language/langIntro/MLrepetition_h.html">Here</link>
      is another clip, showing ML's single-word repetition.
  </para>

  <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para3">You can see that he is able to speak promply,
    without hesitation, when he repears the words. So, it is not the
    case that his converstaion is so hesitant in coversation due to a
    pronunciation difficulty.
    </para>

    <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para4">We hope you noticed how he understood the
    questions put to him by Dr Martin. His comprehension of sentences
    is not as good as it would have been before his stroke,
    however. To give you an idea of this deficit, take a look at the
    <cnxn xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" target="fig1">following graph</cnxn>, showing his
    performance on a comprehension task compared to that of a group of
    eleven controls who were round about his age and education
    level. There were five different sentence types and he was
    appreciably worse than the controls in accuracy on all five
    types. The pattern in his errors is qualitatively similar to the
    controls, however, indicationg that the difficulty of the
    structures (from left to right, difficulty increases) had an
    impact on him, just as it did on the few errors the controls made.
    </para>

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      <name xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/">Comparison of ML's plausibility judgments with those of
      age-matched controls</name>
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    </figure>

    <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para5">Now <link xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" src="http://psych.rice.edu/mmtbn/language/langIntro/MLplausJudge_h.html">watch</link> ML performing this language task (note
    that he uses his index finger for YES and ring finger for NO in
    his judements of plausibility). When he is wrong, you will hear a
    buzzer (the items are from the practice set). When he is right on
    a test item), the time he took to respond will appear on the
    screen. Notice how slow the time on the fifth one is, which is
    hard because of its complex grammatical structure.
    </para>

    <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para6">A stroke generally affects a number of different
    aspects of language. Just to show you more ways in which ML was
    affected by his stroke (on the left side of his brain, toward the
    front), <link xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" src="http://psych.rice.edu/mmtbn/language/langIntro/MLreading_h.html">listen</link>
    to him now reading words and non-words aloud. Non-words are made
    up from real words by substituting one or two letters to make a
    procounceable non-word which does not occur in the English
    language. From being able to read real words fairly well, he now
    shows a lot of difficulty coping with these made-up words. You
    will see the word written on the screen and so will be able to
    judge for yourself whether you could have found a pronunciation
    for each one.
    </para>

    <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para7">This difficulty reading non-words while being
    able to read most words is curious until you understand the
    processes by which most of us read. What has happened with ML is
    that he can no longer make use of the rules he learned as a child
    about how to pronounce letters and letter sequences. He is,
    however, able to refer to his memory for the overall
    pronunciations of words that he has seen before. Because he never
    saw these non-words previously, he has no stored pronunciation of
    them and, without the ability to sound out the letters, struggles
    to pronounce them, often relying on a word he does know that is
    similar in visual appearance.
    </para>

    <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para8">Another curious deficit ML has is with <link xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" src="http://psych.rice.edu/mmtbn/language/langIntro/MLpicnaming_h.html">naming
    pictures</link> of items from the category of body parts. Notice
    how he copes better with naming pictures of other categories, such
    as fruits, vehicles, furniture, kitchen implements, yet struggles
    to find the names of some body parts.
    </para>
  
    <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para9">This curious island of difficulty is not unique
    to ML, though we still do not understand what brings it about. It
    is an illustraion, however, of how it is necessary to zero in on
    quite particular aspects of the skills being tested. It is not
    good enough to have just a mix of pictures to name; they need to
    be selected from different categories known to the patient, across
    the categories the pictures need to be of similar familiarity and
    degree of use in the language, and they need to be of similar
    visual appearance, with no individual category disadvantaged by
    being more complex to illustrate than any other, or more uniform
    amongst its members than the others. Because ML could read real
    words well, and could name the other categories well, it is
    obviously not the case that he has any trouble with perceiving or
    recognizing pictured objects.
    </para>

    <para xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="para10">This little exploration of ML's deficits has
    given you, we hope, a bit of an idea how the cognitive
    neuropsychology approach to testing is carried out. There will be
    many opportunities in this chapter to participate in tasks
    yourself and to get a score similar to what is obtained from
    patients in researching their difficulties. This research helps
    in diagnosis and helps us understand how the intact brain probably
    functions.
    </para>

   <note xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" type="See Also"><link xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" src="http://psych.rice.edu/mmtbn/language/langIntro/MLinterview2_h.html">Conversation
on living with a stroke</link> 
  </note> 
</content> 
</document>
