Cédula de observación (Clave I-1)

5.2.1. Participant HN

HN is a 58 year old right handed woman. She had been a stroke survivor for 2 years when she came to our attention. She attended our lab at the University of Birmingham for a period of 3 months. A basic language assessment was carried out with PALPA (Kay, Lesser

& Coltheart, 1992). Her speech was less fluent than the other participants and had difficulties manipulating her right hand (although not to such an extent as to prevent her driving a car).

138 Due to time constraints, we couldn’t perform as many PALPA tasks as we did with CS.

However, we did manage to capture her overall performance to ensure that errors were not the result of problems in perception, semantics or lexical selection.

Her auditory discrimination was excellent. She correctly identified 97% (35/36) of same pairs and 94% (34/36) of different pairs in nonword minimal pair discrimination (PALPA 1).

In analysing for selection bias we found d’=3.51 and bias=0.15. In word minimal pair discrimination (PALPA 2), she scored 100% (36/36) for same pairs and 92% (33/36) for different pairs. She also scored 96% (69/72) word minimal pairs requiring written selection (PALPA 3) and 98% (39/40) in word minimal pairs requiring picture selection (PALPA 4).

HN scored 100% (40/40) on auditory word-picture matching (PALPA 47) as well as written word-picture matching (PALPA 48). Lexical decision tasks assessing imageability and frequency (PALPA 5) were also good with 100% (20/20). Her Auditory digit span (PALPA 13) was 6.

The above assessments show that HN has little difficulties in processing input from different modalities such as auditory, written and picture naming. Her memory was also good signifying that her errors did not arise from memory issues but higher up in the speech production process. She then participated in the repetition, reading aloud and picture naming tasks with the controlled list presented to CS in chapter 4. Her performance remained stable throughout out data collection period.

5.2.2. Participant JT

JT was a 67 year old right-handed man. He had been a stroke survivor for a year when he started taking part in our study. He attended sessions for 4 months. He also took the same PALPA assessments as HN. He was more fluent than HN or CS and made very few errors in his speech.

139 He had very little difficulty with auditory discrimination scoring 100% (36/36) in both same and different pairs in nonword minimal pair discrimination (PALPA 1). The same was true (72/72) for word minimal pairs (PALPA 2). He scored 97% (70/72) in word minimal pairs requiring written selection (PALPA 3) and 98% (39/40) in word minimal pairs requiring picture selection (PALPA 4).

JT scored 100% (40/40) in auditory word-picture matching (PALPA 47) and 95%

(38/40) in written word-picture matching (PALPA 48). He was good with lexical decision tasks as well, scoring 100% (20/20) in PALPA 5. His auditory digit span (PALPA 13) was 6.

The above assessments show that JT has very good auditory and lexical discrimination.

His memory is also efficient enough to exclude its effects on error production. JT did the same set of controlled stimuli as HN in repetition, reading and picture naming. His performance was stable throughout the sessions.

5.3. Method

The stimuli for the tasks were the same as the controlled list used by CS in chapter 4. As this has been described in detail there, it will not be discussed here. The ethical issues for this study were the same as those discussed in Chapter 4. The ethical approval given by the University of Birmingham’s Ethical Review Board covered our activities with HN and JT as well as CS.

HN and JT were tested in the speech lab at the School of Psychology, University of Birmingham. Each session lasted about two hours. In the repetition tasks, each word was presented by the experimenter and the participants had to repeat it. For reading and picture naming, a PC was used to present the stimuli.

All responses were recorded on a digital recorder to be transcribed and scored. The procedure was consistent with previous studies in Hindi and English: 1) scoring by hand, 2) computational processing to obtain information on the segments involved in various errors,

140 and 3) independent verification by three separate individuals. The computational processing consisted of using a program coded in Java^TM to identify the articulatory place, manner and voicing of the different segments involved in errors as well as changes in syllable structure (based on CV templates). It was also used to retrieve frequency information for each word from the CELEX database as well as calculate word lengths. This made a significant difference in decreasing the time it took to analyse the data.

5.4. General characteristics of errors

The errors were categorised as word and nonword errors. Word errors were further divided into errors that were phonologically related (formal errors), morphological, semantically related or visually related words. These two patients could not take part in all three tasks. HN did the picture naming and Reading aloud while JT did reading aloud and repetition.

Table 40 Word and Nonword Errors of HN and JT

Patient Task Nonword Total Word appears to have a large number of word errors is interesting. It is possible that most of these are by accident as in ‘cube’ /kjuːb//kjuː/ and ‘slit’ /slɪt//slɪk/. But that fact that they form a large proportion of the errors (albeit much smaller in number than HN) would indicate some bias within the system towards producing words rather than nonwords. Picture naming

141 produced fewer formal errors than reading or repetition. In any event, all word errors were removed from further analysis as these do not inform the main hypothesis of our current study.

Nonword errors were classified into individual errors (that involved less than three segments that were not adjacent), sequence errors (that involved the same type of error in two or more adjacent segments) and multiple errors (that involved more than three segments). As with CS multiple errors were removed from further analysis. More detailed descriptions of these error types with examples are discussed in chapter 4.

Table 41 Categories of Errors from HN and JT

Individual Multiple Sequence Total

HN Naming 19 19

Read 219 9 7 235

JT Read 30 3 4 37

Repetition 33 33

The rate of phonetic errors places HN as the least fluent. JT on the other hand is more fluent than HN or CS with only a couple of phonetic errors. The criteria for phonetic errors were the same as that described for CS in chapter 4.

Table 42 Initial Assessment of HN and JT

Phonetic Errors

N Words N Errors Rate

HN 781 132 16.9%

JT 1224 2 0.2%

Due to the small sample size of the errors from these patients, the errors were analysed together rather than according to different tasks. This provided a more statistical power and greater confidence when looking at the difference between various categories (error types, clusters, etc.). Therefore, all the analyses that follow will not be divided according to tasks.

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