close

Вход

Забыли?

вход по аккаунту

?

Do baseline language impairment measures predict anomia treatment outcome?

код для вставкиСкачать
DO BASELINE LANGUAGE IMPAIRMENT MEASURES PREDICT ANOMIA
TREATMENT OUTCOME?
by
Rebekah DeGarmo
Bachelor of Arts
Cedarville University, 2003
___________________________________________________
Submitted in Partial Fulfillment of the Requirements
For the Degree of Master of Speech Pathology in
Speech Pathology
Arnold School of Public Health
University of South Carolina
2010
Accepted by:
Julius Fridriksson, Director of Thesis
Elaine Frank, Reader
Jessica Richardson, Reader
James Buggy, Dean of The Graduate School
UMI Number: 1479440
All rights reserved
INFORMATION TO ALL USERS
The quality of this reproduction is dependent upon the quality of the copy submitted.
In the unlikely event that the author did not send a complete manuscript
and there are missing pages, these will be noted. Also, if material had to be removed,
a note will indicate the deletion.
UMI 1479440
Copyright 2010 by ProQuest LLC.
All rights reserved. This edition of the work is protected against
unauthorized copying under Title 17, United States Code.
ProQuest LLC
789 East Eisenhower Parkway
P.O. Box 1346
Ann Arbor, MI 48106-1346
© Copyright by Rebekah DeGarmo, 2010
All Rights Reserved.
ii
Acknowledgements
I cannot begin to express how grateful I am to each and every person who has
helped me with my thesis. This paper would not have been possible without the help of
my invaluable thesis committee. I owe my deepest gratitude to Dr. Julius Fridriksson, my
thesis advisor, who kept me encouraged and spent countless hours over the last nine
months helping me develop this paper. I am also deeply indebted to Dr. Jessica
Richardson and Dr. Elaine Frank who have provided so many excellent suggestions about
how to strengthen this investigation, structure my paper, and put the finishing touches on
it. Additionally, I would like to thank Astrid Fridriksson, who willingly answered all of
my endless questions about the study and its participants, and Dr. Allen Montgomery,
who was such a valuable resource about statistics. Finally, I must thank my family and
friends who encouraged me and supported so much throughout this process.
iii
Abstract
Most treatment approaches used with people with aphasia include a phonological
or semantic focus; many studies have examined the effectiveness of these approaches.
Some studies have sought to determine if patients’ impairment level is associated with
treatment outcome following administration of a specific treatment approach. Several
studies have produced conflicting and, therefore, inconclusive results concerning whether
treatment specifics can be tailored towards a given patient’s impairment level.
This study proposed that pre-treatment indicators of deficit type may be able to
predict anomia treatment outcome. Pre-treatment indicators included counts of semantic
and phonemic paraphasias; naming ability; and the abilities to make semantic
associations, to repeat words and phrases, to follow directions, and to produce words
fluently. After receiving two weeks of intensive semantic and phonological treatment
through the use of cueing hierarchies, changes in the participants’ correct naming scores
and counts of semantic and phonemic paraphasias post-treatment were documented.
The analysis revealed that few relationships were strongly correlated and
considered significant (p=.01). The strongest relationships revealed that post-treatment
changes to participants’ correct naming scores and paraphasia counts were similar
following both treatment approaches, which suggests that utilizing a specific treatment
approach for specific deficit type is perhaps not as necessary as was previously believed.
iv
Table of Contents
Acknowledgements………………………………………………………………………iii
Abstract…………………………………………………………………………………...iv
List of Tables.……………………………………………………………………………vii
List of Figures.…………………………………………………………………………..viii
Introduction..………………………………………………………………………………1
I. Literature Review..……………………………………………………………………...1
Models of Lexical Access..………………………………………………………..1
Paraphasias..…………………………………………………………………….....6
Semantic and Phonological Treatment Approaches..……………………………10
Cueing Hierarchies..……………………………………………………………...16
Research Questions..……………………………………………………………..17
Purpose……………....…………………………………………………………...18
II. Research Method..…………………………………………………………………….19
Participants..……………………………………………………………………..19
Materials…....……………………………………………………………………20
Procedure…....…………………………………………………………………...23
Data Analysis..…………………………………………………………………...25
III. Results….…....………………………………………………………………….........30
Research Question 1…....………………………………………………………..30
Research Question 2…....………………………………………………………..30
Other Findings…....……………………………………………………..……….30
IV. Discussion.….....……………………………………………………………………..35
v
Research Question 1…....……………………………………………….……….36
Research Question 2…....………………………………………………..………37
Other Results……..…………………………………………………….………...39
Practical Implications……..……………………………………………..………42
Conclusion……..………………………………………………………..………42
References………...……………………………………………………………………..44
Appendix A: Cueing Hierarchy Examples..……………………………………………..48
Appendix B: Stimuli for Phonological Treatment..……………………………………...50
Appendix C: Stimuli for Semantic Treatment..………………………………………….51
Appendix D: Abbreviations..…………………………………………………………….52
Appendix E: Scatter Plots for the Strongest Relationships..……………………………..54
vi
List of Tables
Table 3.1: Relationships between independent and dependent variables..………………32
Table D.1: Abbreviations for independent variables in this study..……………………...53
Table D.2: Abbreviations for dependent variables in this study……..…………………..54
vii
List of Figures
Figure 1.1: Illustrations of a lexical network for the interactive two-step model of
naming...…………………………………………………………………………………...8
Figure 1.2: An outline of the WEAVER++ model..……………………………………..10
Figure 1.3: Schematic representation of the lexical system..…………………………….11
Figure E.1: The relationship between CHG phon par PNT post sem and CHG phon par
PNT post phon..………………………………………………………………………….62
Figure E.2: The relationship between CHG sem par PNT post sem and CHG sem par
PNT post phon..………………………………………………………………………….63
Figure E.3: The relationship between CHG phon par TI post sem and CHG phon par TI
post phon..………………………………………………………………………………..64
Figure E.4: The relationship between CHG cor nam TI post sem and CHG cor nam TI
post phon..………………………………………………………………………………..65
Figure E.5: The relationship between CHG sem par TI post sem and CHG sem par TI
post phon..………………………………………………………………………………..66
Figure E.6: The relationship between CHG cor nam PNT post sem and CHG cor nam
PNT post phon..………………………………………………………………………….67
viii
Chapter 1
Introduction
In the treatment of aphasia, a wide variety of treatment approaches and types of
therapies have been proposed. In general, two common approaches include either a
phonological or semantic focus. Many studies have examined the effectiveness of these
approaches and attempted to determine if one treatment approach is better than another.
Some studies have also sought to determine if patients’ impairment level is associated
with treatment outcome following the administration of a specific treatment approach.
Unfortunately, several studies have produced conflicting results making it difficult to
determine whether treatment elements can be tailored towards a given patient’s
impairment level. In an effort to gain further clarity concerning these issues, the current
study will investigate if a treatment approach with the best prognosis can be determined
based on pre-treatment scores and paraphasia counts.
Literature Review
Models of Lexical Access. Models of lexical access vary somewhat concerning the
method of the lexical access process; nevertheless, two aspects are common among most
models: lemma access and phonological access. The first step, lemma access, occurs after
a concept has been formed. This prompts the selection of a lemma, which is an abstract,
nonphonological representation of the target word. The selection of the lemma prompts
the initiation of the second step, phonological access. During this step, the phonemes
1
within the word are selected, and subsequently the phonological form of the target word
is encoded and prepared for production.
A commonly accepted model of lexical access is the Interactive Spreading
Activation Model, depicted in Figure 1 (Dell, 1986; Dell & O’Seaghdha, 1992; Dell et
al., 1997; Martin, Dell, Saffran, & Schwartz, 1994). In this model, lexical knowledge is
represented in the semantic, word, and phoneme layers, whereas concepts are only
formed in the semantic layer. The formation of the concept prompts the selection and
priming of the target lemma and related lemmas, which may be related semantically or
related semantically and phonologically. This step occurs in the “Words” layer in Figure
1. The target lemma is primed to a greater extent than are related lemmas. The primed
Figure 1.1. “Illustration of a lexical network for the interactive two-step model of
naming. Connections are excitatory and bidirectional. The common semantic features of
cat, dog, and rat are shaded in gray.” © from Dell, G.S., Schwartz, M.F, Martin, N.,
Saffran, E.M., & Gagnon, D.A. (1997). Lexical access in aphasic and nonaphasic
speakers. Psychological Review, 104, 805.
2
lemmas provide feedback to the semantic nodes to confirm that the appropriate nodes
were activated. Simultaneously, phonological nodes that correspond to the activated
lexical nodes are primed. Both the semantic and phonological nodes provide feedback to
the primed lemmas. At this time, the phonological nodes may activate a phonologically
similar, but lexically unrelated lemma. The lemma with the highest level of activation,
which is typically the target word, is selected for phonological encoding and production.
The method of spreading activation that occurs within this model includes “topdown” and “bottom-up” connections, which are locally interactive. This means that as a
node is primed, it provides feedback to the node that activated it, as well as providing
feedforward activation to nodes in the next layer. As a result, a feedback loop is created,
which allows the speaker to self-monitor his/her speech and modify what is said if
necessary.
Another commonly accepted model of lexical access includes the WEAVER++
model (Levelt et al., 1999), which stands for Word-form Encoding by Activation and
VERification, depicted in Figure 2. Within this model, processing occurs at four levels.
At the first level, lexical concepts, including the target concept and semantically-related
concepts, are activated. At the second level, the active concepts prompt the activation of
lemmas. As with the Interactive Spreading Activation Model, the lemma with the highest
level of activation is selected and grammatically encoded. At the third level, the lemma is
then encoded morphologically, phonologically, and prosodically. Then at the final level,
the lemma is encoded phonetically and prepared for production. (See Levelt et al., 1999
for a more detailed explanation of the WEAVER++ model.)
3
Figure 1.2. “An outline of the WEAVER++ model” © from Levelt, W.J.M., Roelofs, A.,
& Meyer, A.S. (1999). A theory of lexical access in speech production. Behavioral and
Brain Sciences, 22, 3.
4
A primary difference between the WEAVER++ and Dell’s Interactive Spreading
Activation Model is the presence or absence of feedback. Dell’s model involves
feedforward and feedback mechanisms, while WEAVER++ is based on feedforward
activation only, without a specific feedback mechanism. Dell’s model is therefore
considered more interactive (Dell et al., 1997), while the WEAVER++ model is
considered more modular or serial (Roelofs, 2000). Another difference between the two
models is that WEAVER++ was developed primarily based on results from reaction time
experiments (Levelt et al., 1999), while the Interactive Spreading Activation model was
based primarily on speech errors produced by aphasic patients (Dell et al., 1997). More
Visual Input
Auditory Input
Visual Processes
Auditory Processes
Orthographic Input
Lexicon
Phonological Input
Lexicon
Lexical – Semantic
System
Orthographic Output
Lexicon
Phonological Output
Lexicon
Figure 1.3. “Schematic representation of the lexical system” © from Caramazza, A., &
Hillis, A.E. (1990). Where do semantic errors come from? Cortex, 26, 95.
5
specifically, it was based on the presence of mixed phonological and semantic errors and
the preservation of syntactic class in formal errors. It is important to note that although
Dell et al.’s model was not based on results from reaction time experiments, the results
from these studies were considered as the model was developed. Dell et al. (1997)
acknowledged that their model “occupies middle ground between Levelt’s modular
model and other models that are strongly interactive due to the ‘studies of the time course
of lexical access’ ” (p. 829). A third model of lexical processing was developed by
Caramazza and Hillis (1990), depicted in Figure 3. In this model, language is recognized
and comprehended through modality-specific (orthographic or phonological) input
lexicons and the lexical-semantic system. The process of lexical access, similar to the
lemma activation step as described by Dell et al. (1997) and Levelt et al. (1999), occurs
primarily in the lexical-semantic system. Language production occurs through the use of
modality-specific output lexicons. Caramazza (1988) theorized that the input and output
lexicons store information about grammatical form class and morphological structure,
while the lexical-semantic system stores semantic representations for words based on
categorical organization.
Paraphasias. Applications of these models can explain the production of
unintended word or sound substitutions produced by people with aphasia, referred to as
paraphasias. Paraphasias are divided into two main categories: lexical and sublexical
errors (Dell et al., 1997). Lexical errors involve the substitution of one word for another.
Examples of lexical errors include the following: a semantic paraphasia, which is a
substitution of the target word for a word that is related in meaning, such as “dog” for
“cat”; a formal paraphasia, which is a substitution of the target word for a phonologically
6
similar word, such as “hat” for “cat”; mixed paraphasia, which is a substitution of the
target word for a semantically and phonologically related word, such as “rat” for “cat”; or
unrelated error, meaning that there is no relationship between the target word and the
produced word, such as “ocean” for “cat.” Sublexical errors can be defined as words that
have been changed through additions, substitutions, deletions, or transpositions of
phonemes, syllables, or segments within the word (Dell et al., 1997). Sublexical errors
include phonemic paraphasias, which are nonwords that are phonologically similar to the
target word, such as “zat” for “cat”; and neologisms, which are nonwords that do not
appear to relate to the target word, such as /vop/ for “cat.” The exact difference between
phonemic paraphasias and neologisms varies among papers (Dell et al., 1997; Roach,
Schwartz, Martin, Grewal, & Breecher, 1996). A phonemic paraphasia can be defined as
having some type of phonological similarity to the target word, containing half or more
of the target word’s phonemes, or even containing only one similar phonological unit
(Roach et al., 1996).
The occurrence of paraphasias can be explained within the Interactive Spreading
Activation Model. Dell et al. (1997) suggested that sublexical errors, which involve
sounds within words, occur during the phonological access step, and lexical errors, which
involve whole words, occur during the lemma access step. More specifically, both
phonemic paraphasias and neologisms occur as a result of incorrect selection of
phonemes during the process of phonological encoding (Dell et al., 1997), and semantic
paraphasias can be explained by incorrect lemma selection. As the target lemma is
activated during the lemma access step, semantically-related lemmas are activated. An
incorrect lemma may be chosen if its activation somehow exceeds the activation of all
7
other lemmas, including the target lemma (Dell et al., 1997). Formal paraphasias may
result from either phoneme substitution by chance or lexical substitution due to the
spreading activation that occurs within the model (Gagnon et al., 1997), while mixed
errors occur due to concurrent activation of semantic and phonological information (Dell
et al., 1997). “Rat” may be produced erroneously for “cat” because the words are
semantically related and share phonemes, both of which increase the level of activation
for the semantic and phonological nodes that represent “rat.” Unrelated errors, on the
other hand, may occur due to noise in the system and the possibility that some nodes may
be mildly activated because they are distantly related to the target.
Consistent with the WEAVER++ model, Levelt et al. (1999) assume that errors
occur due to noise within the system, which is due to concurrent activation of multiple
nodes simultaneously. The noise can cause an incorrect node to receive higher activation
than the target node, resulting in selection and production of this incorrect node. As with
the Interactive Spreading Activation model, semantic errors arise from incorrect lemma
selection, and phonemic errors arise from incorrect phoneme selection. Mixed
phonological and semantic errors can be explained by the selection of two lemmas at the
same time. Both are encoded, prepared for production, and are blended together as they
are produced. Self-monitoring and lexical bias can cause the combination of the two
lemmas to be produced as a word instead of a nonword (Levelt et al., 1999).
Utilizing their model, Caramazza and Hillis (1990) also described the source of
various speech errors. They suggested that damage to the lexical-semantic system could
result in semantic errors or the production of a word unrelated to the target word, while
damage to the phonological output lexicon could result in formal paraphasias, sublexical
8
errors, or semantic errors. Based thereon, they proposed that semantic errors could occur
as a result of damage to the semantic component or to the phonological output lexicon.
Although it is widely accepted that semantic errors can result from damage to the
semantic component, the claim that semantic errors can result from damage to the
phonological output lexicon is more controversial (Caramazza & Hillis, 1990).
Caramazza and Hillis stated that this hypothesis was based on the assumption that after a
concept is generated, the target word and semantically similar representations are
activated in the lexical-semantic system. This activates phonological representations of
the target word and phonologically similar targets. They suggested that if damage to the
phonological component does not allow access to certain phonological representations,
such as the representation of the target word, a semantically related word may have the
next highest level of activation. If this semantically related word activates a phonological
representation that is intact, then most likely this is the word that will be produced.
Damage to the phonological component, therefore, can result in the production of a
semantically similar word. The behavior of two subjects in the Caramazza and Hillis
study (1990) supported this hypothesis. Testing indicated that they acquired damage to
the phonological output lexicon; however, their errors were primarily semantic in nature.
As this research proposed that error type does not necessarily indicate the level of
the deficit, Caramazza and Hillis suggested that to determine exactly where the damage is
one must consider the type of task in which the errors arise. They proposed that there is
damage to the semantic component if semantic errors are made during production and
comprehension tasks, and there is damage to the phonological output lexicon if semantic
errors occur only during spoken production tasks.
9
Again, this research suggests that the deficit type should not be classified as being
phonological or semantic based only upon the type of error or paraphasia that is most
prevalent in the speech of a person with aphasia. The prevalence of a certain type of
paraphasia may be an indicator of deficit type, although it typically does not stand alone
to define the level of deficit. Other factors should be considered. Wambaugh, Linebaugh,
Doyle, Martinez, Kalinyak-Fliszar, & Spencer (2001), for example, classified their
participants’ deficits by evaluating their ability to categorize, to match auditory or written
words to pictures, their primary type of paraphasia, and their repetition skills. Best,
Herbert, Hickin, Osborne, and Howard (2002) also classified the deficit types of their
participants. To determine if their deficits arose at the semantic level, they evaluated their
written and spoken word-to-picture matching abilities, picture comprehension abilities,
and the prevalence of semantic errors in comparison to the participants’ total errors. To
determine if their deficits were at the phonological level, they evaluated their ability to
repeat and read nonwords and the prevalence of phonological errors in comparison to the
participants’ total errors.
Semantic and Phonological Treatment Approaches. Anomia in aphasia is
commonly treated with semantic or phonological treatment approaches, utilizing various
linguistic tasks, cues, and other methods. The specific tasks that target the semantic
system include, among others, identifying semantic features, generating synonyms and
antonyms, categorizing pictures or objects, and judging if words or concepts are
semantically related. Phonological tasks may be comprised of repeating words, using
initial-phoneme sound cues, judging if words rhyme, and producing rhyming words
(Wambaugh et al., 2001).
10
Several studies have contrasted the treatment approaches to determine which is
the most appropriate to use with individuals with aphasia. Unfortunately, many of these
studies have limited value. Some of the studies compared the treatment approaches
without reference to the deficit type of study participants (Howard et al., 1985b). Others
have contrasted the approaches with consideration to deficit type; however, they only
contain a small number of study participants, and the treatment is very individualized (as
per Wambaugh et al., 2001).
Among the studies that reference the participant deficit type, some revealed that
using model-specific treatment was effective (Miceli, Amitrano, Capasso, & Caramazza,
1996; Nettleton & Lesser, 1991), while others found the opposite to be true (Raymer,
Thompson, Jacobs, & Grand, 1993; Nickels, 2002). Model-specific treatment could be
classified as providing semantic treatment to an individual with a semantic deficit, and
phonological treatment to someone with a phonological deficit. Model-free treatment
could involve providing phonological treatment to a person with a semantic deficit, and
vice versa. These inconclusive results are problematic for clinicians who wish to select
the most appropriate treatment approach for their clients. To further shed light on this
issue, a summary of several pertinent studies and their results follows.
Several studies suggest that model-appropriate treatment is the most effective.
Nettleton and Lesser (1991) conducted a study in which a cognitive neuropsychological
model was applied to determine an appropriate treatment approach. The study included
six participants with aphasia. Two participants had a primary semantic deficit, two
participants had a phonological deficit, and two participants had a deficit at the phoneme
assembly level. The participants with semantic and phonological deficits were given
11
therapy with a treatment approach that was appropriate according the neuropsychological
model. The participants with the phoneme assembly deficit received semantic treatment,
which was considered a model-free approach. Following treatment, three of the four
subjects that received model-specific treatment displayed improved naming ability. The
subjects that received model-free treatment did not demonstrate improved naming ability.
This study, therefore, implied that utilizing a treatment that is impairment specific is
effective, while using a treatment approach not specified by the model would be
ineffective.
Miceli, Amitrano, Capasso, and Caramazza (1996) produced an additional study
that supported the hypothesis that model-specific therapy is effective. This study also
applied a current neurocognitive model of lexical processing to determine deficit type.
Here, the two study participants had acquired damage to the phonological output lexicon.
These researchers utilized a phonological treatment approach with their participants.
Following treatment, both participants demonstrated improved naming ability of treated
items. Generalization to untreated items was not expected, due to the application of the
cognitive model, and did not occur. The authors of this study concluded that phonological
therapy was effective for treating phonological deficits.
Results from other studies, however, have suggested that individuals with
semantic deficits would also benefit from therapy with a phonological focus. In her
review of the literature about impairments of word retrieval, Nickels (2002) suggested
that phonological tasks increase lemma activation, which can result in an increased
probability that the phonological form of the word will be retrieved. Therefore, these
tasks can improve naming ability in individuals with semantic deficits, even though the
12
semantic system is not directly targeted in treatment. Howard (2000) postulated that
semantic improvement may occur during phonological tasks. He stated that if a picture is
present during a phonological task, which is often the case, the picture engages semantic
processing. Wambaugh et al. (2001) also referenced this theory.
Raymer, Thompson, Jacobs, and Le Grand (1993) completed a study that
supported the hypothesis that phonological treatment can be beneficial to individuals with
both types of deficits. Treatment with a phonological focus was administered to four
participants with aphasia, each with different degrees of phonological and semantic
deficits. The results suggested that following phonological treatment, all study
participants experienced greater success with naming trained items. Generalization was
observed in untrained phonologically- and semantically-related items for three of the four
participants. The results of this study suggest that phonological treatment can improve
naming performance of individuals with either type of deficit. To explain this finding, the
authors proposed that the high level of exposure to the target items encouraged repetitive
activation of the target items, thus improving both the semantic and phonological
systems.
In contrast to the Raymer et al. (1993) study, Howard and colleagues (1985b)
found that semantic treatment was more beneficial than phonological treatment. In this
study, semantic and phonological treatment was provided to 12 participants with chronic
aphasia. Half of the participants received four treatment sessions, and the other half
received eight treatment sessions. Both semantic and phonological treatments were
provided to the study participants, and treatment order was counterbalanced. Both types
of treatment resulted in similar improvement in naming ability; however, semantic
13
treatment had a small, significant advantage over phonological treatment [F(1,10)=6.79,
p<.05]. Some generalization to untreated items also occurred. The improvement that
occurred following both treatment approaches was not lasting, as six weeks after therapy
ended, the change was no longer considered significant. Although the treatment did not
provide a lasting treatment effect, this study’s results suggest that semantic treatment is
more efficacious than phonological treatment. Unfortunately, a weakness of this study is
that it did not reference the deficit types of the study participants. Without this
information, it is difficult to determine if semantic treatment would be more beneficial
than phonological treatment for all deficit types.
Howard, Patterson, Franklin, Orchard-Lisle, and Morton (1985a) completed a
secondary study that suggested that semantic treatment is more beneficial than
phonological treatment. Again, no reference was made to the deficit type of study
participants. This study compared how well both types of tasks facilitated improved
naming ability. Semantic tasks involved auditory word-to-picture matching, visual wordto-picture matching, semantic picture associations, and semantic judgments. Phonological
tasks included repetition, rhyme cues, and rhyme judgments. The researchers found that
most of the semantic tasks improved naming abilities for at least 24 hours. These
facilitation techniques were even effective if the facilitating cue did not require that the
participant to name the target item immediately after the cue was provided. Phonological
tasks, however, were only effective for up to 30 minutes following the cues. The outcome
of this study suggests that semantic tasks will result in a treatment effect that persists
longer compared to the results achieved using phonological tasks.
14
The above results were consistent with results found by Patterson, Purell, and
Morton (1983), in which the outcome of word repetition and phonemic cueing were
examined. Semantic tasks were not included in this study. It is important to note that
deficit type was also not mentioned in this article. These researchers found that using
repetition and phonemic cues were both immediately effective for the facilitation of
naming. The effects of the word repetition task disappeared after approximately five
minutes, and benefits from phonemic cues did not last longer than 30 minutes.
Best et al. (2002) conducted a similar experiment, however their results were
somewhat inconsistent with those of Patterson et al. (1983) in the area of long-term
effects of word repetition. In the Best et al. study, a picture was presented. If the
participant was unable to name the picture, then a cue was provided. Cues included the
following: repetition of the target word, a rhyming cue, a phonemic cue, and an
orthographic cue. Following each cue, the participant attempted to name the item again.
An additional naming attempt also occurred at least 10 minutes later. Best et al. found
that the word repetition and phonological and orthographic cues resulted in immediate
effects that were similar to those documented by Patterson et al. (1983) and Howard et al.
(1985). Best et al. found however that the effects of the cues and word repetition lasted
for over 10 minutes, which differed from the findings of Patterson et al.’s study.
Although the studies differed with respect to how long the effects of word repetition
lasted, the differences in the period of elapsed time only varied mildly.
In spite of the inconsistencies regarding the length of the treatment effect of word
repetition and phonemic cues, these three studies described in the previous paragraphs
15
suggest that semantic cues improve naming abilities for a longer period of time than
phonological cues. It appears, however, that the effects of the cues were not long-lasting.
Although many of the studies referenced above produced conflicting results with
regard to the most appropriate therapy approach for deficit type, a hypothesis by Howard
may be able to provide some pertinent insight. Howard (2000) postulated “if we accept
that a spoken or written word will automatically activate both meaning representations
and output phonology, almost all of the treatment techniques are the same, irrespective of
whether, on the surface, they emphasize phonological, lexical, or semantic processing”
(p. 96). Per Nickels (2002), semantic tasks often involve verbal presentation of target
words, which engages phonological processing. Phonological tasks often involve
pictures, which engage semantic processing. As both types of tasks provide semantic and
phonological information, the treatment effects strengthen the connections between the
semantic and phonological forms of the target words. This suggests, therefore, that no
matter the treatment approach, these therapy tasks should result in similar treatment
effects. It should be noted however that according to Nickels (2002), very few studies
have addressed Howard’s theory. This theory, therefore, needs to be investigated further
before it can be validated.
Cueing Hierarchies. A cueing hierarchy is a structure that dictates the order in
which cues will be provided (Linebaugh, Shisler, & Lehner, 2005). Cueing hierarchies
have often been employed in the treatment of aphasia (Abel, Schultz, Radermacher,
Willmes, & Huber, 2005; Hillis, 1989; Linebaugh et al., 2005; Wambaugh et al., 2001).
They have also been used to provide memory treatment to people with amnesia (Riley &
Heaton, 2000) and in the teaching of students with learning disabilities (Worley, Ault, &
16
Doyle, 1992). Cueing hierarchies could include cues generated by the clinician or
personalized cues (Marshall & Freed, 2006).
Cueing hierarchies can also differ based upon whether the cues increase in
strength, also known as errorful learning, or decrease in strength, known as errorless
learning. Abel et al. (2005) compared the use of increasing and decreasing cues to
improve the naming ability of ten participants with aphasia following four weeks of
therapy. The authors concluded that utilizing a cueing hierarchy in therapy sessions was
effective for 80% of their participants. The method appeared to be more helpful with
participants with more severe aphasia, as opposed to those with moderate aphasia.
Concerning the comparison between increasing and decreasing cues, the authors
discovered that both errorful learning and a combination of increasing and decreasing
cues were effective, while errorless learning alone was not effective with any of their
participants.
Research Questions
1. Does pre-treatment naming ability (i.e., correct naming ability, semantic
paraphasia counts or phonemic paraphasia counts) predict anomia treatment
outcome following treatment that utilizes semantic or phonological cueing
hierarchies?
2. Does performance on standardized assessment measures (i.e., scores on the
Pyramids and Palm Trees test or scores on the Repetition, Word Fluency, and
Sequential Commands subtests of the Western Aphasia Battery) predict anomia
treatment outcome following treatment that utilizes semantic and phonological
cueing hierarchies?
17
Purpose
Considering the contradictory results with regard to the short-term and long-term
effects of phonological and semantic treatment and how pertinent deficit type is to
determining the most appropriate treatment approach, this study was designed to further
investigate these issues. It would be of great clinical importance if clinicians could
determine the most appropriate treatment approach based upon their patients’ deficit
types. Therefore, the purpose of this study is to determine if it is possible to utilize pretreatment assessments to predict a change in correct naming scores and paraphasia count
using treatment tasks that assume a semantic or a phonological focus?
The pre-treatment indicators of deficit type that will be analyzed in this study
include the following: 1) naming abilities and the prevalence of semantic and phonemic
paraphasias as recorded during administrations of the Philadelphia Naming Test (PNT;
Roach, Schwartz, Martin, Grewal, & Brecher, 1996) and presentations of the training
items from the study; 2) the scores from the Pictures subtest on the Pyramids and Palm
Trees (PPT) test (Howard & Patterson, 1992), and 3) the scores from the Repetition,
Word Fluency, and Sequential Commands subtests on the Western Aphasia Battery
(WAB; Kertesz, 1982). Each of these tests provides insight concerning the participants’
deficits, specifically concerning the following areas: semantic and phonological abilities,
language comprehension, ability to follow directions, and fluency. It would be ideal if a
strong, significant relationship existed between the pre-treatment assessment factors and
treatment outcome. Such a relationship could enable clinicians to determine the most
appropriate treatment approach based on the prevalence of pre-treatment paraphasias or
results from a specific test.
18
Chapter 2
Research Method
Participants
This retrospective study relies on data collected in an ongoing study at the
Aphasia Laboratory at the University of South Carolina. At present, 25 individuals with
chronic aphasia have completed the study.
For inclusion in the study, participants met the following criteria: 1) ability to
name five pictures during a screening, 2) single, left-hemisphere stroke, 3) mono-lingual
English speakers, 4) passed the vision and hearing screening, and 5) between the ages of
30 and 85. Additionally, participants were excluded from the study if: 1) less than eight
months had passed since their stroke occurred, 2) they had a history of dementia, seizure
disorder, alcohol abuse, psychiatric disorder, or traumatic brain injury, or 3)
contraindications for their use of an MRI examination existed. The final exclusion
criterion was mentioned because all participants received six fMRI examinations during
the course of the Aphasia Laboratory research study. The data acquired from these MRI
examinations will not be used in this research study; therefore, the neuroimaging results
will not be addressed in this project. he 25 participants’ ages ranged from 33 to 81
(M=60.32, SD=12.12). A wide range of aphasia types was represented in this study: 11
were diagnosed with Anomic aphasia, 9 with Broca’s aphasia, 2 with Transcortical Motor
aphasia, 2 with Conduction aphasia, and 1 with Wernicke’s aphasia.
19
Materials
Standardized Assessment Measures. The following standardized tests were
administered to all participants: the PNT (Roach, et al, 1996), the PPT (Howard &
Patterson, 1992), and the WAB (Kertesz, 1982). In addition, to further characterize the
population, the Boston Naming Test—Second Edition (BNT-2; Kaplan, Goodlgass, &
Weintraub, 2001), the Apraxia Battery for Adults–II (ABA-2; Dabul, 2000), and the
Wechsler Adult Intelligence Scale-III (WAIS-3; Wechsler, 1997) were also administered.
The PNT (Roach et al., 1996) was selected because it provides an in-depth
analysis of naming ability and naming errors of individuals with aphasia. It also provides
an opportunity to assess the generalization of treatment to untrained items. The test is
comprised of 175 picturable nouns that are mid- to high-frequency words according to
Frances and Kucera (1982). Test administration is computer-based and occurred
exclusively during fMRI examinations in this study. Responses are scored as correct or
are classified as one of the following naming errors: semantic paraphasia, phonemic
paraphasia, mixed paraphasia, non-response, neologism, perseveration, or unrelated
response. Scores concerning correct naming and semantic and phonemic paraphasias
from all six administrations of the PNT were used for analysis in this study. Limited
information is available concerning the reliability and validity of this test.
The PPT (Howard & Patterson, 1992) measures how well a person can use
pictures and words to access meaning, therefore assessing the ability to make semantic
associations between words. Essentially, this test can help determine whether a naming
error reflects impairment at the level of semantic or phonological processing. During
administration of this test, an examinee is presented with groupings of three pictures.
20
Two of the pictures are semantically related and the third picture acts as a distracter. The
examinee nonverbally indicates which two of the pictures are semantically related. Fiftytwo triads of pictures are presented during the course of this examination. According to a
recent article about normative data for the PPT (Callahan et al., 2010), only one study has
investigated the instrument’s psychometric properties (Klein & Buchanan, 2009). This
study found that the PPT’s discriminant validity was good, however its test-retest
reliability and convergent validity was poor (Klein & Buchanan, 2009). This study was
based on administrations of the PPT to American college students. As the population for
this study was quite limited, additional studies should further examine the psychometric
properties of this test with a more diverse population. It should be noted that the PPT was
not initially a part of the test battery administered to participants in this study. The PPT
was therefore only administered to 17 of the 25 participants.
The WAB (Kertesz, 1982) was selected primarily to determine each participant’s
aphasia type and severity. The test results provided information about the following
categories: information content, speech fluency, auditory comprehension, repetition,
naming, reading, and writing. Scores from the Repetition, Word Fluency, and Sequential
Commands subtests were used for data analysis of this study. The Repetition subtest
contains 15 items. During administration of this subtest, words, phrases, and sentences of
increasing length are verbally presented to, then repeated by, examinees. Credit is given
for each word that is correctly repeated. In this proposed study, this subtest will be used
as an additional indicator of phonological abilities. The Word Fluency subtest contains
only one task. Examinees must name as many animals as they can in one minute. Credit
is awarded for each unique animal that the examinees name. The Sequential Commands
21
subtest contains 11 commands. During administration of this subtest, single- and
multiple-step commands are verbally presented to then performed by examinees. Credit is
given for each step of the command that is demonstrated correctly. The WAB has high
internal consistency and temporal stability. Intra- and interjudge reliability levels are also
high. Standards for content-validity and construct validity were also met (Shewan &
Kertesz, 1980).
Non-Standardized Assessment Measures. A non-standardized assessment was
also presented to study participants. Eighty words were selected from the experimental
stimuli: 40 words were trained during treatment sessions that utilized semantic cueing
hierarchies, and the other 40 were trained during treatment sessions that utilized
phonological cueing hierarchies. This assessment was administered six times during
fMRI examinations. Each participant’s performance was scored based on correct naming
scores and naming errors. Specifically for this proposed study, only data concerning
correct naming scores and the production of semantic and phonemic paraphasias was
analyzed.
Experimental Stimuli. Using the Frances and Kucera (1982) word list, 160
nouns were selected as treatment items. The target items were divided between the two
treatment approaches (semantic and phonological) based upon typicality, phonological
complexity, semantic category, and word frequency. Test items from the PNT were not
included as target items for treatment sessions. During treatment sessions, the clinician
presented participants with colored pictures that depicted the target words on a computer
screen.
22
Procedure
Assessment. Prior to treatment initiation, all assessment measures were
administered to participants. The PNT and the training items assessment were
administered to participants six times: twice pre-treatment, twice after five days of
semantic treatment, and twice after five days of phonological treatment. These tests were
administered in pairs due to the high variability that characterizes the speech of persons
with aphasia (Freed, Marshall, & Chuhlantseff, 1996). Data from the PNT, the PPT, the
WAB, and presentations of training items were analyzed in this study.
Treatment. Each participant received 10 three-hour sessions for a total of 30
hours of intensive naming treatment which employed the use of cueing hierarchies with
increasing and decreasing cues. Participants received approximately 30 hours of
treatment during their involvement in the study. Treatment sessions were conducted by a
licensed speech-language pathologist or a speech-language pathology graduate student
directly supervised by a licensed SLP.
Five of the treatment sessions had a semantic focus, and the other five had a
phonological focus. Treatment order was counterbalanced and was assigned randomly.
To date, 13 participants have received semantic treatment first, and the other 12
participants received phonological treatment first.
The first five sessions were administered over a five-day period. Following this
first phase of treatment, testing was administered, and then one week passed in which the
participants did not receive treatment or undergo testing. Treatment resumed for five
additional sessions over a five-day period. Additional testing also followed the second
phase of treatment.
23
Cueing Hierarchies. The cueing hierarchy utilized in this study was based on a
cueing system presented by Wambaugh et al. (2001). Their study used increasing and
decreasing cues to compare semantic and phonological treatment. A key difference
between the Wambaugh et al. study and this present study is the inclusion of a
prestimulation period. Prior to asking study participants to name the training stimuli,
Wambaugh et al. prestimulated the semantic or phonological system by presenting
pictures of the training item and three other pictures (two semantically- or
phonologically-related items and one unrelated item). The clinician provided additional
semantic or phonological information, and the participant then attempted to point to the
target item. This present study did not include a prestimulation period.
The number of cues provided to each participant was dependent upon their
naming responses to each item. The participant was first presented with a picture of the
target item and was asked to name the item. If the target item was named incorrectly, or if
no response was given within 10 seconds, then the next cue was provided. Increasing
cues were provided until the participant named the item correctly or the highest level of
cueing was (imitation) reached. Once the picture was correctly named, the same cues
were provided with decreasing strength. Cues continued to decrease until the participant
produced an error or named the target item confrontationally. If the participant was
unable to name the picture as the cues decreased, practice of this target word was
terminated, and a new target word was presented.
The cueing hierarchies for the two phases of treatment differed based upon
whether the cues provided semantic or phonological information. Both hierarchies were
similar as to the number of steps within the cueing hierarchy and the relative level of
24
difficulty of these steps. The cueing hierarchies were also applied in a similar manner for
both treatment approaches. (See Appendix A for examples of both cueing hierarchies.
See Appendices B and C for lists of the stimulus items.)
Data Analysis
Before detailing the data analyses, it is pertinent to operationalize specific terms.
As mentioned earlier, the PNT was administered six times: twice pre-treatment, twice
following semantic treatment, and twice following phonological treatment (the opposite
order was administered to half the participants). The first two administrations of the PNT
will be referred to as PNT B1 and PNT B2. The administrations of the PNT following
semantic treatment will be referred to as PNT S1 and PNT S2, and the administrations of
the PNT following phonological treatment will be referred to as PNT P1 and PNT P2. In
the analysis, the number of semantic paraphasias will not be combined with phonemic
paraphasias during any of these calculations. It is important to remember that treatment
order was counterbalanced among participants to minimize the effect of treatment order.
Independent Variables. First, a total count of pre-treatment paraphasias or items
correctly named was found by adding PNT B1 and PNT B2. (PNT Btotal = PNT B1 +
PNT B2). The resulting number was used as an independent variable in the data analysis.
A similar formula was applied to each participant’s correct naming score and paraphasia
count that occurred during presentations of the training items.
In this analysis, the independent variables included:
•
the total number of pre-treatment semantic paraphasias from the first two
administrations of the PNT (sem par PNT B),
25
•
the total number of pre-treatment phonemic paraphasias from the first two
administrations of the PNT (phon par PNT B),
•
the total number of items named correctly during the first two administrations
of the PNT (cor nam PNT B),
•
the total number of pre-treatment semantic paraphasias from the two initial
presentations of the training items (sem par TI B),
•
the total number of pre-treatment phonemic paraphasias from the two initial
presentations of the training items (phon par TI B),
•
the total number of items named correctly from the two initial presentations of
the training items (cor nam TI B),
•
scores from the Pictures subtest of the PPT (PPT), and
•
scores from the Repetition (Rep WAB), Word Fluency (Flu WAB), and
Sequential Commands (Com WAB) subtests of the WAB.
(See Appendix D for a full list of abbreviations for the independent variables.)
Dependent Variables. Because of the aforementioned variability of performance
by individuals with aphasia, an intra-subject standardized amount of change was
determined for each study participant. To do so, an average amount of change was first
found for each of the PNT pairs (pre-treatment, post-semantic treatment, and postphonemic treatment). This was determined by finding the difference between the
paraphasia count or items correctly named during the first and second PNT
administrations, finding its absolute value, and dividing the number by two.
26
| PNT 2 – PNT 1 |
PNT avg change =
2
An overall variance for each participant was derived by adding the three average
change values and dividing by three.
Overall Variance =
PNT Bavg change + PNT Savg change + PNT Pavg change
3
This value was used in a formula to determine the change in the paraphasia count
or correct naming scores that occurred following treatment (referred to as Treatment
Difference). To find this value, the total count of paraphasias or items correctly named
that occurred pre-treatment (PNT Btotal) was subtracted from the total count of
paraphasias or items correctly named that occurred following either semantic or
phonological treatment (PNT Stotal or PNT Ptotal). This number was then divided by the
participant’s mean amount of change.
PNT Stotal - PNT Btotal
Treatment Difference =
PNT Ptotal - PNT Btotal
or
Overall Variance
Overall Variance
Similar formulas were applied to each participant’s naming score and paraphasia
count that occurred during presentations of the training items. Values for average within
pair change, overall variance, and treatment difference were determined for each person
in the study.
27
In this analysis, the independent variables included:
•
the change in semantic paraphasia count on the PNT after semantic treatment
(CHG sem par PNT post sem),
•
the change in semantic paraphasia count on the PNT after phonological
treatment (CHG sem par PNT post phon),
•
the change in phonemic paraphasia count on the PNT after semantic treatment
(CHG phon par PNT post sem),
•
the change in phonemic paraphasia count on the PNT after phonological
treatment (CHG phon par PNT post phon),
•
the change in correct naming scores on the PNT after semantic treatment
(CHG cor nam PNT post sem),
•
the change in correct naming scores on the PNT after phonological treatment
(CHG cor nam PNT post phon),
•
the change in semantic paraphasia count during presentation of training items
after semantic treatment (CHG sem par TI post sem),
•
the change in semantic paraphasia count during presentation of training items
after phonological treatment (CHG sem par TI post phon),
•
the change in phonemic paraphasia count during presentation of training items
after semantic treatment (CHG phon par TI post sem),
•
the change in phonemic paraphasia count during presentation of training items
after phonological treatment (CHG phon par TI post phon),
•
the change in correct naming scores during presentation of training items after
semantic treatment (CHG cor nam TI post sem), and
28
•
the change in correct naming scores during presentation of training items after
phonological treatment (CHG cor nam TI post phon).
(See Appendix D for a full list of abbreviations for the dependent variables in this study.)
Statistical Analysis. The relationships between the study variables were analyzed
by calculating the Pearson’s correlation coefficients between each variable pair. This was
completed so that significant relationships could be identified and to determine the
strength among these relationships. The strongest relationships (as determined by the
magnitude of the correlation coefficients) were also further explored using linear
regression.
In social science, the p-value for determining a statistically significant result is
typically set at p=.05. In this study, p=.05 would be a very liberal criterion to determine
significance as 114 relationships were analyzed. In general terms, that would mean that
out of 114 correlation coefficients, approximately six would, by chance, reach statistical
significance. To reduce the chance of Type I error, this study used a more conservative pvalue threshold of .01. In Table 3, relationships that are significant at p=.05 and p=.01 are
identified.
To interpret the strength of the relationships, the correlation coefficients were
sorted in descending order (based on the absolute value of r), and then split into thirds.
The third with the highest correlation coefficients were determined to be strong
correlations. The middle third was determined to be moderate correlations. The third with
the smallest correlation coefficients were determined to be weak correlations.
29
Chapter 3
Results
Research Question 1
Table E.1 in Appendix E lists the bivariate correlation coefficients. Among the
relationships that were analyzed for this research question, only one relationship (cor nam
TI B – CHG sem par TI post phon) was significant (p=.01).
Research Question 2
Among the relationships that were analyzed for this research question, only one
relationship (Rep WAB – CHG phon par TI post sem) was statistically significant
(p=.01).
Other Findings
The correlation matrix yielded unexpected results. Seven pairs of dependent
variables had strong correlation coefficients and were considered statistically significant
at the p-level of .01. Significant relationships included the following:
•
CHG phon par PNT post sem - CHG phon par PNT post phon
•
CHG sem par PNT post sem - CHG sem par PNT post phon
•
CHG cor nam TI post sem - CHG cor nam TI post phon
•
CHG phon par TI post sem - CHG phon par TI post phon
•
CHG sem par TI post sem - CHG sem par TI post phon
•
CHG cor nam PNT post sem - CHG cor nam PNT post phon
30
•
CHG sem par TI post phon - CHG phon par TI post phon
It should also be noted that the statistical significance of five of these relationships
was below .001. (Scatter plots with regression lines for the strongest relationships can be
found in Appendix F).
31
Table 3.1
Relationships between independent and dependent variables.
Relationship
CHG phon par PNT post sem - CHG phon par PNT post phon**
CHG sem par PNT post sem - CHG sem par PNT post phon**
CHG cor nam TI post sem - CHG cor nam TI post phon**
CHG phon par TI post sem - CHG phon par TI post phon**
CHG sem par TI post sem - CHG sem par TI post phon**
CHG cor nam PNT post sem - CHG cor nam PNT post phon**
cor nam TI B - CHG sem par TI post phon**
CHG sem par TI post phon - CHG phon par TI post phon**
Rep WAB - CHG phon par TI post sem**
Rep WAB - CHG phon par TI post phon**
sem par TI B - CHG sem par TI post phon**
Flu WAB - CHG sem par TI post phon**
Flu WAB - CHG sem par PNT post phon**
sem par TI B - CHG sem par TI post sem**
cor nam TI B - CHG phon par TI post phon**
CHG cor nam TI post phon - CHG sem par TI post phon**
Flu WAB - CHG sem par TI post sem**
Rep WAB - CHG cor nam PNT post phon**
PPT - CHG sem par TI post phon**
cor nam PNT B – CHG cor nam PNT post phon**
cor nam PNT B - CHG sem par PNT post phon**
Com WAB - CHG cor nam PNT post phon**
PPT - CHG sem par PNT post sem**
PPT - CHG cor nam PNT post phon**
CHG sem par TI post phon - CHG phon par TI post sem**
Flu WAB - CHG phon par PNT post phon**
Com WAB - CHG sem par TI post phon**
r
.893
.847
.797
.796
.763
.552
-.547
.520
-.506
-.500
-.493
-.489
-.473
-.472
-.471
-.462
-.459
.457
-.541
.447
-.442
.442
-.525
.525
.430
-.427
-.425
p
.000
.000
.000
.000
.000
.004
.005
.008
.010
.011
.012
.013
.017
.017
.017
.020
.021
.022
.025
.025
.027
.027
.03
.03
.032
.033
.034
Note. Cells highlighted in pink represent relationships with pre-treatment scores from the
PNT and presentation of training items. Cells highlighted in blue represent relationships
with pre-treatment scores on standardized assessment measures. Cells highlighted in
green represent relationships with changes in scores following treatment. Cells
highlighted in gray are significant at p=.05. Cells highlighted in yellow are significant at
p=.01. ** indicates strong correlation, and * indicates moderate correlation. For
relationships with the PPT variable, n=17.For all other relationships, n=25.
32
Relationship
Rep WAB - CHG phon par PNT post phon**
Com WAB - CHG sem par PNT post phon**
Flu WAB - CHG cor nam PNT post phon**
sem par TI B - CHG phon par TI post sem**
Flu WAB - CHG sem par PNT post sem**
Flu WAB - CHG cor nam TI post phon**
cor nam TI B - CHG phon par TI post sem**
Rep WAB - CHG sem par TI post phon**
CHG cor nam TI post phon - CHG phon par TI post phon*
cor nam PNT B - CHG sem par PNT post sem*
PPT - CHG cor nam PNT post sem**
sem par PNT B - CHG cor nam PNT post sem*
Com WAB - CHG sem par TI post sem*
Flu WAB - CHG cor nam PNT post sem*
Rep WAB - CHG sem par PNT post sem*
cor nam PNT B - CHG phon par PNT post phon*
cor nam TI B - CHG sem par TI post sem*
CHG sem par PNT post phon - CHG phon par PNT post phon*
Flu WAB - CHG phon par TI post sem*
cor nam PNT B – CHG cor nam PNT post sem*
PPT - CHG sem par PNT post phon**
cor nam TI B – CHG cor nam TI post phon**
PPT - CHG phon par TI post phon*
sem par TI B - CHG phon par TI post phon*
phon par TI B - CHG sem par TI post sem*
PPT - CHG sem par TI post sem*
Flu WAB - CHG phon par PNT post sem*
CHG sem par PNT post sem - CHG phon par PNT post sem*
PPT - CHG phon par PNT post phon*
CHG sem par PNT post sem - CHG phon par PNT post phon*
Rep WAB - CHG sem par PNT post phon*
Rep WAB - CHG cor nam PNT post sem*
PPT - CHG phon par PNT post sem*
Com WAB - CHG cor nam PNT post sem*
phon par PNT B - CHG phon par PNT post phon*
phon par PNT B - CHG phon par PNT post sem*
Flu WAB - CHG phon par TI post phon*
CHG cor nam TI post phon - CHG phon par TI post sem*
Com WAB - CHG phon par TI post phon*
CHG sem par PNT post phon - CHG phon par PNT post sem*
Rep WAB - CHG phon par PNT post sem*
Com WAB - CHG sem par PNT post sem*
Rep WAB - CHG cor nam TI post phon*
CHG cor nam TI post sem - CHG phon par TI post sem*
33
r
-.423
-.422
.421
-.420
-.414
.411
-.410
-.410
-.407
-.406
.491
-.39
-.386
.384
-.377
-.375
-.375
.364
-.360
.355
-.427
.346
-.418
-.339
-.335
-.396
-.323
.323
-.391
.319
-.318
.318
-.389
.314
-.313
-.31
-.309
-.300
-.298
.293
-.29
-.282
.276
-.274
p
.035
.036
.036
.037
.04
.041
.042
.042
.044
.044
.046
.054
.057
.058
.063
.065
.065
.074
.077
.082
.088
.090
.095
.098
.101
.115
.115
.115
.121
.121
.121
.121
.123
.126
.127
.131
.133
.145
.148
.155
.159
.172
.181
.186
Relationship
Flu WAB - CHG cor nam TI post sem*
Rep WAB - CHG sem par TI post sem*
CHG cor nam TI post phon - CHG sem par TI post sem*
sem par PNT B - CHG sem par PNT post phon*
cor nam PNT B - CHG phon par PNT post sem*
phon par TI B - CHG phon par TI post sem
CHG cor nam PNT post phon - CHG phon par PNT post phon
phon par TI B - CHG sem par TI post phon
sem par TI B - CHG cor nam TI post phon
CHG cor nam TI post sem - CHG phon par TI post phon
CHG cor nam PNT post sem - CHG phon par PNT post sem
Com WAB - CHG cor nam TI post phon
CHG cor nam TI post sem - CHG sem par TI post phon
sem par PNT B - CHG phon par PNT post phon
CHG cor nam PNT post sem - CHG sem par PNT post phon
PPT – CHG cor nam TI post sem
PPT – CHG phon par TI post sem
CHG cor nam PNT post phon - CHG sem par PNT post phon
CHG sem par TI post sem - CHG phon par TI post sem
sem par PNT B - CHG phon par PNT post sem
phon par TI B - CHG cor nam TI post phon
phon par TI B - CHG phon par TI post phon
CHG cor nam TI post sem - CHG sem par TI post sem
phon par PNT B - CHG sem par PNT post phon
phon par TI B - CHG cor nam TI post sem
Com WAB - CHG phon par PNT post phon
cor nam TI B – CHG cor nam TI post sem
Com WAB - CHG cor nam TI post sem
PPT – CHG cor nam TI post phon
phon par PNT B - CHG cor nam PNT post sem
sem par PNT B - CHG sem par PNT post sem
sem par PNT B - CHG cor nam PNT post phon
CHG cor nam PNT post sem - CHG sem par PNT post sem
phon par PNT B - CHG sem par PNT post sem
Com WAB - CHG phon par PNT post sem
Rep WAB - CHG cor nam TI post sem
Com WAB - CHG phon par TI post sem
CHG sem par TI post sem - CHG phon par TI post phon
sem par TI B - CHG cor nam TI post sem
CHG cor nam PNT post sem - CHG phon par PNT post phon
phon par PNT B - CHG cor nam PNT post phon
CHG cor nam PNT post phon - CHG phon par PNT post sem
CHG cor nam PNT post phon - CHG sem par PNT post sem
34
r
.264
-.242
-.232
.227
-.217
-.214
-.207
-.207
.201
-.191
.185
.183
-.182
-.181
-.179
-.214
-.208
-.162
.142
-.139
-.128
-.127
-.122
-.12
-.112
-.097
.090
-.083
.101
.081
.079
-.073
-.067
-.043
.041
.041
-.041
.039
-.038
.027
.023
.018
-.006
p
.203
.244
.265
.275
.298
.304
.321
.321
.336
.359
.376
.382
.384
.388
.393
.410
.423
.439
.498
.506
.542
.545
.562
.569
.595
.645
.668
.692
.700
.701
.708
.729
.749
.84
.846
.846
.847
.854
.858
.897
.912
.932
.977
Chapter 4
Discussion
Porch et al. (1980) suggested that there are three main methods of predicting
outcome: examining prognostic variables, classifying participants with specific
behavioral profiles, and statistical prediction. According to de Riesthal and Wertz (2004),
utilizing prognostic variables for prediction is used most frequently. Commonly used
variables for prediction include measures of language impairment or functional
communication. Other predictors include age, years of education, aphasia severity, and
performance during word fluency tasks or conversational tasks (de Riesthal & Wertz,
2004). One study used the Word Fluency subtest of the WAB (Kersetz, 1982) was to
predict the Aphasia Quotient on the WAB (Crary & Rothi, 1989). Functional measures
have also been used as predictors (e.g., Heiss, Kessler, Karbe, Fink, & Pawlik, 1993;
Naseer et al., 1998; Mimura et al., 1998). Unfortunately, this author is unaware of studies
that directly relate test results from the PNT, the PPT test, or the WAB to post-treatment
scores. Therefore it is impossible to specifically compare the current results to those of
other studies.
Although this investigation cannot be directly compared with other studies
concerning the prediction power of its variables, the investigation’s statistical power can
be used as a means of comparison. Of the studies that contrasted semantic and
phonological treatment approaches mentioned in the literature review, most included
35
fewer than 15 participants (Best et al, 2002; Howard et al, 1985b, Miceli et al, 1996;
Nettleton & Lesser, 1991; Patterson et al, 1983; Raymer et al, 1993). The investigation
with the largest number of participants (N=20) was conducted by Howard et al. (1985a).
As the study detailed in this paper included a larger number of participants (N=25), the
statistical power is greater than that of many other studies, therefore a strength of this
study lies in its relatively large sample size.
Research Question 1
Most of the 36 relationships analyzed for this research question were not
significant at p=.01. Some trends in the analyzed relationships can be observed. Three
factors primarily predicted naming change associated with anomia treatment: cor nam TI
B, sem par TI B, and cor nam PNT B, although only one relationship was significant at
p=.01. (It is important to note that pre-treatment phonemic paraphasia counts were not
strongly correlated with any of the variables that represented change following
treatment). Cor nam TI B was the strongest predictor of change, followed by sem par TI
B, then cor nam PNT B. Somewhat unexpectedly, cor nam TI B was a stronger predictor
of changes to paraphasia counts than changes in correct naming scores. In contrast, cor
nam PNT B was a stronger predicator of changes to correct naming scores than
paraphasia count.
Based on a traditional model-based approach, one might postulate that a
participant’s pre-treatment naming ability, especially with a specific profile of semantic
or phonological errors, should change in a predictable manner following a specific
model-based treatment approach. Although the referenced studies (Miceli et al., 1996;
Nettleton & Lesser, 1991) focus on the changes in correct naming scores, it might be
36
reasonable to anticipate that within a model-based approach, phonological treatment
should primarily decrease phonemic paraphasia count instead of semantic paraphasia
count and vice versa. None of the significant relationships (p=.01) supported this
hypothesis.
As previously discussed, an unexpected result of the analysis was that cor nam TI
B was a stronger predictor of changes to paraphasia counts than change in correct naming
scores. Aspects of several other significant relationships were not anticipated. For
example, cor nam PNT B was strongly correlated with CHG sem par PNT post phon, as
was cor nam TI B and CHG phon par TI post sem. These unexpected combinations of
correct naming scores with paraphasia counts lend support to the model-free theory.
Other trends that should be noted include the following: pre-treatment
performance during presentation of training items (six strong relationships) was a
stronger predictor of post-treatment outcome than pre-treatment performance on the PNT
(only two of these were particularly strong relationships). Also, changes in naming ability
following phonological treatment were predicted more reliably than changes following
semantic treatment.
Research Question 2
Only one significant relationship (p=.01) involved performance on standardized
assessment measures (Rep WAB and CHG phon par TI post sem). One aspect of this
relationship was anticipated: that performance during a phonological task might predict a
change in the phonological performance, specifically changes in phonemic paraphasia
counts. The ability to repeat is known to be a reflection of phonological abilities. Both
Wambaugh et al. (2001) and Best et al. (2002) considered repetition abilities when they
37
determined if their participants had deficits at the phonological level. It should be noted
that Rep WAB was a better predictor of change in phonemic paraphasia count following
semantic treatment than changes to the count following phonological treatment, which
was unexpected. As the correlation coefficients and significance values for Rep WAB CHG phon par TI post sem (r = -.506 p = .01) and Rep WAB - CHG phon par TI post
phon (r = -.500, p = .011) were only marginally different, perhaps in a study with
additional participants, the change following phonological treatment might be stronger.
The following discussion is merely an observation of trends in the relationships.
Although many of the relationships discussed were strongly correlated, the vast majority
of the relationships analyzed for this research question were not significant at p=.01.
Rep WAB also predicted changes in correct naming scores and semantic and
phonemic paraphasia counts (CHG cor nam PNT post phon, CHG phon par PNT post
phon, and CHG sem par TI post phon), although none of these relationships were
significant at p=.01. Considering the model-based hypothesis, the most anticipated of
these is the strong relationship with CHG phon par PNT post phon. It is also logical that
these relationships all involve changes that follow phonological treatment.
Scores on the PPT were the best predictors of change in semantic paraphasia
counts, not surprising since the PPT tests one’s ability to make semantic associations.
Performance on the PPT also predicted changes in correct naming scores. None of these
relationships produced a treatment effect since the strength of these correlations were
only marginally different following both treatment approaches.
Flu WAB appeared to be the best predictor for changes in semantic paraphasia
counts on the training items and on the PNT following phonological treatment. It was
38
also a strong predictor for CHG sem par TI post sem, CHG phon par PNT post phon, and
CHG sem par PNT post sem. Flu WAB was also a strong predictor of changes in correct
naming scores on the PNT and presentation of training items following phonological
treatment. This variable also appeared to be a more reliable predictor of changes
following phonological treatment than semantic treatment.
Similarly, Com WAB predicted changes following phonological treatment more
reliably than changes following semantic treatment. There did not appear to be a trend as
to what it predicted best, as it predicted changes in correct naming scores and semantic
paraphasia counts on the PNT and during presentation of training items.
It should be noted that many of these relationships had good predictor value but
lacked specificity. Several pre-treatment measures were able to predict responses to
treatment, however they were unable to specify which treatment approach predicted
changes more reliably.
Other Results
Comparing the results for Research Questions 1 and 2, there were more
relationships with strong correlations involving pre-treatment performance on
standardized assessment measures than relationships with pre-treatment picture naming
ability. The vast majority of these relationships, however, were not significant. Each
research question produced only one strong predictor that was significant at p=.01. Both
of these relationships provide support for the model-free theory.
Additionally, the correlation matrix indicated that the strongest relationships were
not between pre-treatment scores and post-treatment outcome. Instead, the strongest
relationships occurred between changes in the same type of measurement (correct naming
39
scores or paraphasia count) following both semantic and phonological treatment
approaches. This suggests that participants responded to both treatment approaches in a
similar manner.
These findings suggest that although some pre-treatment measures may be able to
predict post-treatment outcome to a certain extent, other factors are more strongly
correlated. It is possible that a different pretreatment score may be a better predictor,
however it seems unlikely that a single test result will be a more accurate predictor of
treatment outcome.
Previous research suggests that correct naming scores, the prevalence of certain
types of paraphasias, and scores on other phonological and semantic tests should be
enough to indicate the severity of damage to the semantic and phonological systems.
Even after classifying participants as having specific types of deficits, previous studies
have produced conflicting results (Miceli et al., 1996; Nettleton & Lesser, 1991; Nickels,
2002; Raymer et al., 1993), which suggest that treatment outcome will probably not be
consistently predicted by language impairment measures. The results of this study
suggest that the measures typically used in clinical settings were not reliable predictors of
change.
One may postulate, therefore, that when utilizing this specific type of treatment
(cueing hierarchies with increasing and decreasing cues), pre-treatment performance on
assessment measures and even the selection of a specific treatment approach are likely
not the most important factors in predicting treatment outcome. The participants’
consistent performance following both approaches, which was the strongest predictor of
post-treatment outcome, suggests that other factors (such as the intensity of treatment or
40
brain fitness) may be more important than treatment approach. Future research studies
should further investigate what these factors are specifically.
It should be noted that this paper was not designed to definitively answer the
question of whether the model-based theory or model-free theory is more valid. The
participants within this study represent a clinical population, meaning that there was a
heterogeneous blend of participants with unprofiled deficits. The main characteristic that
they have in common is that they all have chronic aphasia. None of the participants were
classified as primarily having phonological or semantic deficits. Pre-treatment testing
was not in-depth enough to make these classifications. Also, some of the variables used
as predictors were low in sensitivity and specificity. All references to model-based versus
model-free treatment approaches should therefore be viewed in a clinical context, as this
study was unable to compare populations that purely have semantic or phonological
deficits. Perhaps if a more detailed picture of each participant’s semantic and
phonological abilities were generated using more sensitive measures and in-depth testing,
our findings would be different.
Overall, general trends in significant relationships within this study appeared to
provide more support for the model-free as opposed to the model-based approach,
however this by no means is a conclusive result that the model-free approach is more
valid and should be applied to all types of treatment. Additional research is needed to
verify these results. What can be concluded is that the treatment used in this study
(treatment with response contingent interaction) produced similar changes following both
treatment approaches.
41
Practical Implications
The results of the current study primarily suggest that an individual with aphasia
is more likely to respond in a consistent manner to semantic and phonological treatment
than to have a better response to one treatment approach over another. Additionally, some
pre-treatment scores were strongly correlated with post-treatment outcome, but the
strength of these relationships was overshadowed by the stronger relationships between
the dependent variables in this study. This finding, if confirmed by future investigations,
suggests that clinicians do not necessarily need to spend exorbitant amounts of time
determining if their patients’ deficits are primarily semantically- or phonologically-based
as patients will most likely respond to both treatment approaches in a similar manner.
This finding appears to support Howard’s hypothesis (2000) that semantic and
phonological treatment are similar. Both types of tasks strengthen the connections
between the semantic and phonological systems, resulting in similar treatment effects.
Without question, these findings need to be authenticated further in an
investigation with a larger number of participants. Additional research could also be
conducted in the following areas: Does performance remain consistent following a
different form of semantic and phonological treatment? How does a combination of
semantic and phonological treatment compare to treatment that is purely one approach or
the other? Is another measure, such as a functional measure or intensity of treatment, a
better predictor of outcome following treatment?
Conclusion
The strongest relationships existed between changes in the same type of
measurement (correct naming scores or paraphasia count) following both semantic and
42
phonological treatment approaches. This suggested that participants responded to both
treatment approaches in a similar manner, which lends support to the model-free theory.
The only significant relationships (p=.01) that answered the research questions
put forth in this investigation, if pre-treatment measures could predict post-treatment
outcome, were cor nam TI B – CHG sem par TI post phon and Rep WAB – CHG phon
par TI post sem. The reason for the strong correlations between these specific variables
was unclear, but these correlations provided support for the model-free theory. As only
two relationships for research questions one and two were statistically significant, this
suggests that pre-treatment measures of language impairment were not accurate
predictors of anomia treatment outcome.
Few relationships with strong correlation supported the model-based hypothesis
that a specific type of paraphasia count would be affected in a more predictable manner
following a treatment approach that is model-based as opposed to a model-free. The only
strong relationships that supported this theory included PPT – CHG sem par PNT post
sem (r=-.541), Rep WAB – CHG phon par TI post phon (r=-.5), sem par TI B – CHG
sem par TI post sem (r=-.493), and Rep WAB – CHG phon par PNT post phon (r=-.423).
Although these relationships were strongly correlated, they were not significant at p=.01.
Conversely, many unexpected relationships (according to model-based theory) were also
strongly correlated. Several of these relationships were statistically significant.
Overall, the finding of this study appeared to provide strong support for modelfree treatment. Further research must be completed to determine if these findings are
consistent other methods of treatment (other than treatments that utilize cueing
hierarchies or seek to improve naming).
43
References
Abel, S., Schultz, A., Radermacher, I., Willmes, K., & Huber, W. (2005). Decreasing and
increasing cues in naming therapy for aphasia. Aphasiology, 19, 831-848.
Best, W., Herbert, R., Hickin, J., Osborne, F., & Howard, D. Phonological and
orthographic facilitation of word-retrieval in aphasia: Immediate and delayed
effects. Aphasiology, 16, 151-168.
Callahan, B. L., Macoir, J., Hudon., C., Bier, N., Chouinard N., Cossette-Harvey, M., . . .
Potvin, O. (2002). Normative data for the Pyramids and Palm Trees Test in the
Quebec-French population. Archives of Clinical Neuropsychology, 25, 212-217.
Caramazza, A. (1988). Some aspects of language processing revealed through the
analysis of acquired aphasia: The lexical system. Annual Review of Neuroscience,
11, 395-421.
Caramazza, A., & Hillis, A. E. (1990). Where do semantic errors come from? Cortex, 26,
95-122.
Cohen, J. (1988). Statistical power analysis for social sciences. Hillsdale, NJ: Erlbaum.
Crary, M.A., & Rothi, L.J.G. (1989). Predicting the Western Aphasia Battery Aphasia
Quotient. Journal of Speech and Hearing Disorders, 54, 163-166.
Dabul, B. (2000). Apraxia Battery for Adults. Tigard, OR: C.C. Publications, Inc.
Davis, G. A. (2007). Aphasiology: Disorders and clinical practice (2nd ed.). Boston:
Pearson Education.
de Riesthal, M, & Wertz, R.T. (2004). Prognosis for aphasia: Relationships between
selected biographical and behavioural variables and outcome and improvement.
Aphasiology, 18, 899-915.
Dell, G. S. (1986). A spreading-activation theory of retrieval in sentence production.
Psychological Review, 93, 283-321.
Dell, G. S., & O’Seaghdha, P. G. (1992). Stages of lexical access in language production.
Cognition, 42, 287-314.
Dell, G. S., Schwartz, M. F., Martin, N., Saffran, E. M., & Gagnon, D. A. (1997). Lexical
access in aphasic and nonaphasic speakers. Psychological Review, 104, 801-838.
Frances, H. and Kucera, W. N. (1982). Frequency Analysis of English Usage. Boston:
Houghton Mifflin.
44
Freed, D. B., Marshall, R. C., & Chuhlantseff, E. A. (1996). Picture naming variability: A
methodological consideration of inconsistent naming responses in fluent and
nonfluent aphasia. Clinical Aphasiology, 24, 193-205.
Gagnon, D. A., Schwartz, M. F., Martin, N., Dell, G. S., & Saffran E. M. (1997). The
origins of formal paraphasias in aphasics’ picture naming. Brain and Language, 59,
450-472.
Heiss, W.D., Kessler, J., Karbe, H., Fink, G.R., & Pawlik, G. (1993). Cerebral glucose
metabolism as a predictor of recovery from aphasia in ischemic stroke. Archives of
Neurology, 50, 958-964.
Hillis, A. E. (1989). Efficacy and generalization of treatment for aphasic naming errors.
Archives of Physical Medicine and Rehabilitation, 70, 632-636.
Howard, D. (2000). Cognitive neuropsychology and aphasia therapy: The case of word
retrieval. In I. Papathansiou (Ed.), Acquired neurogenic communication disorders:
A clinical perspective. (76-99). London: Whurr.
Howard, D. & Patterson, K. (1992). The Pyramids and Palm Trees Test: A test of
semantic access from words and pictures. London: Harcourt Assessment.
Howard, D., Patterson, K., Franklin, S., Orchard-Lisle, V., & Morton, J. (1985a). The
facilitation of picture naming in aphasia. Cognitive Neuropsychology, 2, 49-80.
Howard, D., Patterson, K., Franklin, S., Orchard-Lisle, V., & Morton, J. (1985b).
Treatment of word retrieval deficits, in aphasia: A comparison of two therapy
methods. Brain, 108, 817-829.
Kaplan, E., Goodglass, H., & Weintraub, S. (2001). Boston Naming Test—Second
Edition. Philadelphia: Lippincot, Williams, and Wilkins.
Kertesz, A. (1982) The Western Aphasia Battery. New York: Grune and Stratton.
Klein, L. A., & Buchanan, J. A. (2009). Psychometric properties of the Pyramids and
Palm Trees Test. Journal of Clinical and Experimental Neuropsychology, 31, 803808.
Levelt, W. J. M., Roelofs, A., & Meyer, A. S. (1999). A theory of lexical access in
speech production. Behavioral and Brain Sciences, 22, 1-75.
Linebaugh, C. W., Shisler, R. J., & Lehner, L. (2005). Cueing hierarchies and word
retrieval: A therapy program. Aphasiology, 19, 77-92.
Marshall, R. C., Freed, D. B. (2006). The personalized cueing method: From the
laboratory to the clinic. American Journal of Speech-Language Pathology, 15, 103111.
45
Martin, N., Dell, G. S., Saffran, E. M., & Schwartz, M. F. (1994). Origins of paraphasias
in deep dysphasia: Testing the consequences of a decay impairment to an
interactive spreading activation model of lexical retrieval. Brain and Language, 47,
609-660.
Miceli, G., Amitrano, A., Capasso, R., & Caramazza, A. (1996). The treatment of anomia
resulting from output lexical damage: Analysis of two cases. Brain and Language,
52, 150-174.
Mimura, M., Kato, M., Kato, M., Sano, Y., Kojima, T., Naeser, M., & Kashima, H.
(1998). Prospective and retrospective studies of recovery in aphasia: Changes in
cerebral blood flow and language functions. Brain, 121, 2083-2094.
Naeser, M.A., Baker, E.H., Palumbo, C.L., Nicholas, M., Alexander, M.P., Samaraweera,
R., …Weissman, T. (1998). Lesion site patterns in severe, nonverbal aphasia to
predict outcome with a computer-assisted treatment program. Archives of
Neurology, 55, 1438-1448.
Nettleton, J., & Lesser, R. (1991). Therapy for naming difficulties in aphasia: Application
of a cognitive neuropsychological model. Journal of Neurolinguistics, 6, 139-157.
Nickels, L. (2002). Therapy for naming disorders: Revisiting, revising, and reviewing.
Aphasiology, 16, 935-979.
Patterson, K. E., Purell, C., & Morton, J. (1983). Facilitation of word retrieval in aphasia.
In C. Code and D. J. Muller (Eds.), Aphasia Therapy. (76-87). London: Arnold.
Porch, B.E., Collins, M., Wertz, R.T., Friden, T.P. (1980). Statistical prediction of change
in aphasia. Journal of Speech and Hearing Research, 23, 312-321.
Raymer, Thompson, Jacobs, and Le Grand. (1993). Phonological treatment of naming
deficits in aphasia: Model-based generalization analysis. Aphasiology, 7, 27-53.
Riley, G. A., & Heaton, S. (2000). Guidelines for the selection of a method of fading
cues. Neuropsychological Rehabilitation, 10, 133-149.
Roach, A., Schwartz, M. F., Martin, N., Grewal, R. S., & Brecher, A. (1996). The
Philadelphia Naming Test: Scoring and rationale. Clinical Aphasiology, 24, 121133.
Roelofs, A. (2000). WEAVER++ and other computational models of lemma retrieval and
word-form encoding. In L. Wheeldon (Ed.), Aspects of language production (pp.
71-114). Philadelphia, PA: Psychology Press.
Shewan, C.M., & Kertesz, A. (1980). Reliability and validity characteristics of the
Western Aphasia Battery (WAB). Journal of Speech and Hearing Disorders, 45,
308-324.
46
Wambaugh, J. L., Linebaugh, C. W., Doyle, P. J., Martinez, A. L., Kalinyak-Fliszar, M.,
& Spencer, K. A. (2001). Effects of two cueing treatments on lexical retrieval in
aphasic speakers with different levels of deficit. Aphasiology, 15, 933-950.
Wechsler, D. (1997). Wechsler Adult Intelligence Scale—III. San Antonio, TX: The
Psychological Corporation.
Wolery, M., Ault, M. J., & Doyle. P. M. (1992). Teaching students with moderate to
severe disabilities: Use of response prompting strategies. New York: Longman.
47
Appendix A: Cueing Hierarchy Examples
Phonological Cueing Hierarchy
1. The picture of shorts is presented. The clinician asks the participant to name the
picture by saying, “What is this?”
2. The clinician provides a non-word rhyming cue by saying, “It rhymes with jorts.”
3. The clinician provides a phonemic cue by saying, “It starts with ‘sh’.”
4. The clinician provides a combined non-word rhyming and phonemic cue by
saying, “Its name rhymes with jorts. Its ‘sh’…”
5. The clinician models the target word by saying, “Repeat after me: shorts.” The
participant repeats the target word.
6. All of the above cues are provided with decreasing strength until the participant
produces an error or can name the stimulus item confrontationally.
Semantic Cueing Hierarchy
1. The picture of a cradle is presented. The clinician asks, “What is this?”
2. The clinician provides a verbal description of the target or its function by saying,
“A small bed for infants that rocks.”
3. The clinician provides a sentence completion cue by saying, “The baby slept in
the…”
4. The clinician provides a sentence completion cue with a semantically-loaded
sentence by saying, “In the nursery, the baby slept in the…”
5. The clinician models the target word by saying, “Repeat after me: cradle.” The
participant repeats the target word.
48
6. All of the above cues are provided with decreasing strength until the participant
produces an error or can name the stimulus item confrontationally.
49
Appendix B: Stimuli for Phonological Treatment
1. Shelf
2. Hockey
3. Oak
4. Eagle
5. Hood
6. Sheep
7. Shirt
8. Telephone
9. Ice skater
10. Farmer
11. Rollerskates
12. Puzzle
13. Computer
14. Rocking chair
15. Elevator
16. Spaghetti
17. Fence
18. Teddy bear
19. Cardinal
20. Dryer
21. Skirt
22. Blimp
23. Gutter
24. Motorcycle
25. Painter
26. Dolphin
27. Ham
28. Truck
29. Billboard
30. Crayons
31. Fender
32. Screwdriver
33. Shorts
34. Seatbelt
35. Tie
36. Plow
37. Boat
38. Coffee
39. Potatoes
40. Cookies
41. Brush
42. Driver
43. Hair dryer
44. Needle
45. Marbles
46. Carpenter
47. Vacuum cleaner
48. Pilot
49. Diver
50. Phonebook
51. Chef
52. Doorbell
53. Golf
54. Volleyball
55. Card
56. Harm
57. Squid
58. Chisel
59. Roof
60. Paint brush
61. Lizard
62. Softball
63. Balcony
64. Engine
65. Puppet
66. Chicken
67. Stool
68. Hay bale
69. Barn
70. Dresser
71. Maple
72. Rose
73. Skateboard
74. Raincoat
75. Catfish
76. Weed
77. Scroll
78. Chest
79. Grass
80. Bible
50
Appendix C: Stimuli for Semantic Treatment
1. Cradle
2. Bacon
3. Helmet
4. Basketball
5. Meat
6. Airplane
7. Copier
8. Drill
9. Pants
10. Soccer
11. Brochure
12. Porch
13. Submarine
14. Policeman
15. Television
16. Pine
17. Couch
18. Bookshelf
19. Tricycle
20. Windshield
21. Doctor
22. Underwear
23. Tape measure
24. Skunk
25. Radio
26. Ceiling
27. Bathing suit
28. Bicycle
29. Manual
30. Shark
31. Transmission
32. Ferry
33. Wheel
34. Steps
35. Vice
36. Sandal
37. Stamps
38. Daisy
39. Chandelier
40. Lily
41. Cabinet
42. Kangaroo
43. Mechanic
44. Rocking horse
45. Sugar
46. Baseball
47. Tennis
48. Skylight
49. Blocks
50. Hoe
51. Priest
52. Lemon
53. Shovel
54. Palm trees
55. Farm
56. Tulip
57. Trunk
58. Magazine
59. Doll
60. Rice
61. Scooter
62. Drawer
63. Numbers
64. Rat
65. Mixer
66. Tire
67. Pizza
68. Gymnastics
69. Cotton
70. Mouse
71. Wave
72. Yoyo
73. Hay
74. Counter
75. Refrigerator
76. Menu
77. Bear
78. Seaweed
79. Subway
80. Mailman
51
Appendix D: Abbreviations
Table D.1
Abbreviations for independent variables in this study
Independent Variables
total number of pre-treatment semantic
paraphasias from the first two
administrations of the PNT
the total number of pre-treatment phonemic
paraphasias from the first two
administrations of the PNT
the total number of items named correctly
during the first two administrations of the
PNT
the total number of pre-treatment semantic
paraphasias from the two initial
presentations of training items
the total number of pre-treatment phonemic
paraphasias from the two initial
presentations of training items
the total number of items named correctly
from the two initial presentations of
training items
scores on the Pictures subtest from the PPT
test
scores from the Repetition subtest of the
WAB
scores from the Word Fluency subtest of
the WAB
scores from the Sequential Commands
subtest of the WAB
Abbreviation
sem par PNT B
phon par PNT B
cor nam PNT B
sem par TI B
phon par TI B
cor nam TI B
PPT
Rep WAB
Flu WAB
Com WAB
52
Table D.2
Abbreviations for dependent variables in this study
Dependent Variables
the change in semantic paraphasia count on
the PNT after semantic treatment
the change in phonemic paraphasia count
on the PNT after semantic treatment
the change in semantic paraphasia count on
the PNT after phonological treatment
the change in phonemic paraphasia count
on the PNT after phonological treatment
the change in correct naming scores on the
PNT after semantic treatment
the change in correct naming scores on the
PNT after phonological treatment
the change in semantic paraphasia count
during presentation of training items after
semantic treatment
the change in phonemic paraphasia count
during presentation of training items after
semantic treatment
the change in semantic paraphasia count
during presentation of training items after
phonological treatment
the change in phonemic paraphasia count
during presentation of training items after
phonological treatment
the change in correct naming scores during
presentation of training items after
semantic treatment
the change in correct naming scores during
presentation of training items after
phonological treatment
Abbreviation
CHG sem par PNT post sem
CHG sem par PNT post phon
CHG phon par PNT post sem
CHG phon par PNT post phon
CHG cor nam PNT post sem
CHG cor nam PNT post phon
CHG sem par TI post sem
CHG sem par TI post phon
CHG phon par TI post sem
CHG phon par TI post phon
CHG cor nam TI post sem
CHG cor nam TI post phon
53
CHG phon par PNT post sem
Appendix E: Scatter plots for the strongest relationships
CHG phon par PNT post phon
Figure E.1. The relationship between the change in phonemic paraphasia count on
the PNT after semantic treatment (CHG phon par PNT post sem) and the change in
phonemic paraphasia count on the PNT after phonological treatment (CHG phon par
PNT post phon).
54
CHG sem par PNT post sem
CHG sem par PNT post phon
Figure E.2. The relationship between the change in semantic paraphasia count on the
PNT after semantic treatment (CHG sem par PNT post sem) and the change in semantic
paraphasia count on the PNT after phonological treatment (CHG sem par PNT post
phon).
55
CHG phon par TI post sem
CHG phon par TI post phon
Figure E.3. The relationship between the change in phonemic paraphasia count during
presentation of training items after semantic treatment (CHG phon par TI post sem) and
the change in phonemic paraphasia count during presentation of training items after
phonological treatment (CHG phon par TI post phon).
56
CHG cor nam TI post sem
CHG cor nam TI post phon
Figure E.4. The relationship between the change in correct naming scores during
presentation of training items after semantic treatment (CHG cor nam TI post sem) and
the change in correct naming scores during presentation of training items after
phonological treatment (CHG cor nam TI post phon).
57
CHG sem par TI post sem
CHG sem par TI post phon
Figure E.5. The relationship between the change in semantic paraphasia count during
presentation of training items after semantic treatment (CHG sem par TI post sem) and
the change in semantic paraphasia count during presentation of training items after
phonological treatment (CHG sem par TI post phon).
58
CHG cor nam PNT post sem
CHG cor nam PNT post phon
Figure E.6. The relationship between the change in correct naming scores on the PNT
after semantic treatment (CHG cor nam PNT post sem) and the change in correct
naming scores on the PNT after phonological treatment (CHG cor nam PNT post phon).
59
Документ
Категория
Без категории
Просмотров
0
Размер файла
606 Кб
Теги
sdewsdweddes
1/--страниц
Пожаловаться на содержимое документа