understanding processing speed—its subcomponents and their relationship to characteristics of...
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This article was downloaded by: [Gazi University]On: 16 August 2014, At: 18:22Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Understanding processing speed—itssubcomponents and their relationshipto characteristics of people withschizophreniaMatteo Cellaa & Til Wykesa
a Department of Psychology, Institute of Psychiatry, King'sCollege London, London, UKPublished online: 19 Oct 2012.
To cite this article: Matteo Cella & Til Wykes (2013) Understanding processing speed—itssubcomponents and their relationship to characteristics of people with schizophrenia, CognitiveNeuropsychiatry, 18:5, 437-451, DOI: 10.1080/13546805.2012.730038
To link to this article: http://dx.doi.org/10.1080/13546805.2012.730038
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Understanding processing speed*its subcomponents and
their relationship to characteristics of people with
schizophrenia
Matteo Cella and Til Wykes
Department of Psychology, Institute of Psychiatry, King’s College
London, London, UK
Introduction. Processing speed has been advanced as one of the core cognitivedeficits of schizophrenia. Several methods were developed to assess this domain;however, most tasks, despite indexing several cognitive and motor components,tend to characterise processing speed as a unitary construct. This study explorespotential subcomponents of processing speed in schizophrenia and their relation-ship with demographic, clinical, and neuropsychological characteristics.Methods. One hundred and sixty participants with a diagnosis of schizophreniawere assessed on neuropsychological tasks measuring processing speed, executivefunction, and memory. Demographics and clinical characteristics were alsorecorded. Three independent measures were extracted to account for subcompo-nents of processing speed: behavioural execution, response processing, andaccuracy.Results. The identified components of processing speed were differently predictedby demographic characteristics, clinical characteristics, and overall intelligenceestimates. Age and symptom severity were important predictors for behaviouralexecution; intelligence and social withdrawal predicted response processing; andaccuracy was predicted by illness duration. Correlations showed executive functionand memory to be associated with response processing and accuracy but not withbehavioural execution.Conclusions. Distinct characteristics of schizophrenia seem to predict processingspeed subcomponents. Distinguishing between behavioural, processing, andaccuracy may be a useful way forward to refine our understanding of processingspeed impairment in schizophrenia.
Keywords: Cognition; Cognitive deficit; Neuropsychology; Processing speed;
Psychomotor; Schizophrenia.
Correspondence should be addressed to Matteo Cella, Department of Psychology, Institute
of Psychiatry, King’s College London, De Crespigny Park, London SE5 8AF, UK. Email:
Cognitive Neuropsychiatry, 2013
Vol. 18, No. 5, 437�451, http://dx.doi.org/10.1080/13546805.2012.730038
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INTRODUCTION
A large number of studies have now shown that cognitive impairment is a
reliable feature associated with the diagnosis of schizophrenia. Several
cognitive domains including memory, attention, and executive function
accumulated compelling evidence, supported by meta-analysis, in favour of
their critical role in schizophrenia (Aleman, Hijman, de Haan, & Kahn,
1999; Bokat & Goldberg, 2003; Johnson-Selfridge & Zalewski, 2001). More
recently, research has emphasised the role of processing speed as a possible
critical factor underlying deficits in different cognitive domains and a
significant mediator of functional capacity (Ojeda, Pena, Sanchez,
Elizagarate, & Ezcurra, 2008; Wykes, Reeder, & Corner, 2000). Two large
meta-analyses have characterised the extent of the processing speed deficit in
schizophrenia with effect sizes comprised between �1.3 and �1.5
(Dickinson, Ramsey, & Gold, 2007; Knowles et al., 2010). In the more
recent of these studies, Knowles, David, and Reichenberg (2010) found that
the processing speed deficit was moderated by IQ difference between
comparison subject and schizophrenia patients, study vintage and chlorpro-
mazine equivalent daily dosage.
A number of caveats, however, may be relevant in considering these
results and might warrant a closer look. Processing speed, as originally
defined, refers to the execution speed of a task (Salthouse, 1996). Processing
speed tasks traditionally measure behaviour as their dependent variables;
this is, as for most neuropsychological tasks, the time taken to complete the
mental operation and implement the response behaviour. Most processing
speed tasks therefore assess processing speed by coupling together the
mental processing and the behavioural execution. The widely used digit
symbol coding task, for instance, requires participants to substitute symbols
and digits using a key under time conditions (Weschler, 1997). In this way
the task measures processing speed by assessing both the time in which the
mental operation (i.e., substitution) is performed and the time necessary to
execute the behaviour (i.e., writing the symbol). Both the cognitive and the
behavioural part of the task require time; however, which process is affected
in schizophrenia is still unclear. A task that may be able to disentangle these
two components could characterise effectively what is impaired. Further,
the moderating effect of antipsychotic medication on processing speed
found by Knowles et al. (2010) could, speculatively, be dependent on a
selective effect of the medication on one of these processing speed
components.
A second caveat relates to the construct validity of processing speed. The
concept of processing speed, as mentioned earlier, refers to the velocity with
which a task is accomplished. This definition, however, gives little emphasis
to errors, their potential interference effect, and influence on the cognitive
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system. When assessing processing speed with the digit symbol coding task,
assessors infer processing speed from the number of correct responses given
in a specified time frame. Errors, however, are part of the information
processing accomplished and may interact significantly with performance
(Hester, Madeley, Murphy, & Mattingley, 2009). In the context of
schizophrenia this may signify that the lower processing speed performance
consistently found with the digit symbol coding task could result from
either an excessive number of errors, a lower speed of information
processing, or a combination of these two variables. Research has shown
that schizophrenia is associated with various difficulties in error monitoring
and that these difficulties may mediate performance in a number of
neuropsychological tasks (Huang & Chan, 2007; Mathalon et al., 2002;
Silver & Goodman, 2007). It appears important therefore to investigate
errors in the context of the processing speed deficit observed in
schizophrenia.
Tasks assessing processing speed are often used in studies investigating the
relationship between cognitive characteristics and clinical features of schizo-
phrenia (Fuller & Jahanshahi, 1999; Holthausen, Wiersma, Knegtering, &
Van den Bosch, 1999; Hong et al., 2002; Morrens, Hulstijn, Van Hecke,
Peuskens, & Sabbe, 2006; Van Hoof, Jogems-Kosterman, Sabbe, Zitman, &
Hulstijn, 1998). Further, some longitudinal studies have suggested schizo-
phrenia’s poor prognosis may be associated with reduced processing speed
performance at intake (Gold, Arndt, Nopoulos, O’Leary, & Andreasen, 1999;
Wykes et al., 2000). However, these findings carry a substantial degree of
variability in their results. This variability has been hypothesised to be
dependent on the different tasks used to assess processing speed but also to
the differential relationship that subcomponents of processing speed may
have with schizophrenia clinical features (Morrens, Hulstijn, & Sabbe,
2007).In the current study, we aim to explore three subcomponents of
processing speed, namely: Behavioural Execution (BE), Response Proces-
sing (RP), and Accuracy (AC). A first aim of this study is to assess the
validity of these subcomponents against a traditional measure of processing
speed, the Trail Making test, Part A (Gaudino, Geisler, & Squires, 1995). A
second aim is to investigate how variables with a known effect on
processing speed differentially predict each processing speed subcomponent.
To this end, we explored the role that patients’ characteristics, symptom
severity, medication, and IQ levels may have in predicting different
processing speed components. A third aim of this study is to assess the
association that processing speed components may have with other
impaired neuropsychological domains in schizophrenia: executive function
and memory.
PROCESSING SPEED SUBCOMPONENTS 439
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METHOD
Participants
A total of 160 participants were recruited from local community mental
health teams in the South London and Maudsley National Health ServiceFoundation Trust. Participants were included if they had been in contact
with the services for at least 1 year, were at least 17 years old, had a diagnosis
of schizophrenia based on DSM-IV (American Psychiatric Association,
1994), presented no evidence of organic brain disease, no primary diagnosis
of substance abuse, and have not changed medication in the last month. The
majority of eligible participants were treated as outpatients, but a few were in
residential rehabilitation programmes in psychiatric hospitals. All partici-
pants recruited were medicated with antipsychotic medication. Medicationdosage at assessment is reported in mg of Chlorpromazine equivalent
according to Woods (2003).
Measures
Processing speed
Response Processing Task (RPT). The RPT was developed by Wickens(1974) and then tested and adapted to psychiatric settings by Wykes, Katz,
Sturt, and Hemsley (1992). Briefly, the task comprises three conditions where
different types of response are required: an initial simple reaction time
condition, with one light and one button, where participants are asked to
press the button when the light flashes; a second condition, with four lights
and four buttons, where participants are asked to press the button next to the
flashing light; and a final condition, also with four lights and four buttons,
where participants are asked to press the first button clockwise to the lightthat has flashed. Both Conditions 1 and 2 have 40 trials, and Condition 3 has
80 trials. In all the conditions response time is recorded. Condition 1 indexes
a simple motor response (i.e., one button), Condition 2 indexes motor
response with a degree of uncertainty (i.e., four buttons), and Condition 3
indexes the aforementioned processes in addition to the mental time
necessary to adapt an acquired response to contingent information. The
additional time necessary to perform in Condition 3 is therefore indicative of
the ability to inhibit a high-frequency, potent response in favour of thecorrect response. The additional time necessary to perform this operation is
indicative of the speed of inhibition and consequent programming of the
correct response.
Hayling sentence completion test. This task was developed by Burgess
and Shallice (1997) to assess executive function and response speed.
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Specifically, this task is thought to assess both initiation speed and response
suppression (Bielak, Mansueti, Strauss, & Dixon, 2006). It consists of two
parts each comprising 15 sentences missing the last word. Participants are
read each sentence and asked to complete with a word. In Part 1,
participants are asked to complete the sentence with an appropriate word;
in Part 2, they are asked to complete the sentence with an unrelated word.
Response reaction time is recorded for each part of the task and number of
mistakes is recorded for the second part of the task.
Three processing speed components were considered in the analysis:
Behavioural Execution (BE), Response Processing (RP), and Accuracy (AC).
We considered the simple reaction time mean of the Response Processing Task
to Index BE, the difference in mean reaction times between Condition 3 and 2
of the Response Processing Task to Index RP, and the total number of mistakes
in the Hayling Part 2 to Index AC. We preferred to use the Hayling instead of
the Stroop errors because the Stroop errors occurred infrequently in our
sample (i.e., 60% did not have an error on the Stoop against 9% on the
Hayling). In addition to the three subcomponents mentioned we also evaluated
a classical measure of processing speed as a way of measuring the construct
validity of the proposed subcomponents. For this purpose the time to complete
Trail A of the Trail Making test (Gaudino et al., 1995) was chosen.
Neuropsychological measures of memory and executive function
Participants were assessed with widely used neuropsychological tests
assessing memory and executive functions. These were the Digit Span
(Wechsler, 1997), the Stroop Test (Golden, 1978), the Wisconsin Card
Sorting (Heaton, Chelune, Talley, Kay, & Curtiss, 1993), and the Trail
Making test (Gaudino et al., 1995). All tests were administered in computer-
based format. Premorbid intelligence levels were assessed with the National
Adult Reading Test (NART; Russell et al., 2000).
Symptom severity
Symptom severity was measured with the PANSS (Kay, Fizbein, & Opler,
1987) and the BPRS (Overall & Gorham, 1962). The inconsistency between
these measures is due to a modification of the routine symptoms assessment
tools adopted by the recruiting centres, initially using the BPRS and more
recently changed to the PANSS. Due to incomplete overlapping between the
measures, the scales scores were transformed in Clinical Global Impression
(CGI) equivalent levels using Leucht et al. (2006) recommendations. The
Social Behaviour Schedule (Wykes & Sturt, 1986) was used to assess
problematic behaviour and functional levels.
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Analysis
Normality distribution was assessed with examination of skew and kurtosis
values and of P-P plots and inferential tests of normality (i.e., the
Kolmorogov-Smirnov test; Orr, Sackett, & DuBois, 1991). Distributions
with significant skew were normalised with logarithmic transformation.
Hierarchical regression models were used to explore the contribution of
patient’s characteristics and IQ to different components of processing speed.
Predictors shown to have an influence on processing speed were entered in
the first level (e.g., medication), whereas predictors with a less clear
contribution were entered at subsequent stages. The same hierarchical
structure was applied to each subcomponent of processing speed (i.e., BE,
RP, and AC). Pearson product momentum correlation was used to assess the
association between processing speed components, memory, and executive
function. Analysis of variance was used to assess between groups differences.
RESULTS
The patients’ general characteristics and neuropsychological measures are
reported in Table 1. Processing speed components did not differ between
patients recruited from different settings (e.g., outpatient units and
residential care settings).
Processing speed
Average processing speed subcomponents with standard deviation are
presented in Table 1. BE significantly correlates with AC, r�.27, p B
.0001, and RP also correlates with AC, r�.24, p B.01. Scores on the Trail
Making Part A positively correlates with BE, r�.41, p B.0001, RP, r�.46,
p B.0001, and AC, r�.25, p B.0001.
Predictors of processing speed
Hierarchical regression models were performed to evaluate the contribution
patients’ characteristics to the three components of processing speed. In the
first level predictors with a known influence on processing speed were
entered. These are age, education, illness length, symptom severity, and
medication. Overall estimate intelligence levels were entered at Level 2.
Model summaries for each of the processing speed components are
presented in the Table 2.
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Processing speed, memory, and executive function
Correlations were performed to explore the relationship between processing
speed, memory, and executive function. BE significantly correlates with Trail
Making A. RP significantly correlates with memory, Stroop number of
words, and Trail Making B � A time. Similarly AC significantly correlates
with memory, Stroop number of words, Trail Making B � A time, but had
also a significant association with the WCST number of preservative errors.
Errors and processing speed
To further explore the relevance that errors may have on processing speed
and its components we divided the entire sample in four quartiles based on
number of errors on Hayling B (i.e., AC score). The bottom quartile
comprises 41 participants with a mean number of errors of 0.93 (SD�0.18),
the top quartile comprises 36 participants with a mean number of errors of
13.06 (SD�3.1); the mean number of errors differed significantly between
TABLE 1Participants’ demographics, clinical characteristics, and neuropsychological mea-
sure scores
Mean (SD)
Age (years) 28 (12.4)
Female n (%) 45 (28.1)
Years of education 11.6 (2)
Illness duration (years) 2.9 (1.1)
Chlorpromazine equivalent (mg) 464.5 (401.3)
CGI equivalent 3.7 (1)
SBS total 12.5 (9.3)
SBS Social Withdrawal 3.2 (2.1)
SBS Thought Disorder 2.4 (2)
IQ (NART) 94.6 (16.2)
BE (RPT simple reaction time; ms) 510.3 (416.6)
RP (RPT processing speed; ms) 513.5 (541.6)
AC (Accuracy) 6 (4.6)
WAIS-II Digit span 14.6 (3.9)
WCST*Preservative errors 47 (34.5)
WCST*Categories achieved 2.3 (2.1)
Stroop n words in 120 s (colour word) 71.9 (25.5)
Stroop time (colour word task � colour task; second) 106.7 (62.7)
Trail Making time A (second) 53.8 (48.2)
Trail Making time B � A (second) 86.8 (99.1)
CGI�Clinical Global Impression; SBS�Social Behaviour Schedule; NART�National Adult
Reading Test.
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the groups, F(1, 79)�654.8, pB.0001. When compared these two groups
showed significant differences in BE, F(1, 79)�6.36, p�.01, and RP,
F(1, 79)�8.1, p�.006, but also on a traditional measure of processing
speed, the Trail Making Part A, F(1, 79)�5.7, p�.02.
Processing speed components and positive and negativesymptoms
As the global CGI score may fail to characterise the clinical complexity of
schizophrenia symptoms, a further analysis was conducted to assess the
association of processing speed subcomponents with positive and negative
symptoms as measured by the PANSS in a subset of patients (n�82) that
completed this assessment instrument (see Table 4).
TABLE 2Stepwise regression
Unstandardised beta Standard error Standardised beta
Behavioural execution
Step 1
Age 0.005 0.001 0.33***
Symptoms severity (CGI) 0.033 0.013 0.22**
Social withdrawal (SBS) 0.01 0.005 0.18*
Step 2
Age 0.005 0.001 0.36***
Symptoms severity (CGI) 0.034 0.012 0.23**
IQ (NART) �0.002 0.001 �0.182*
Response processing
Step 1
Education �0.044 0.017 �0.224**
Social withdrawal (SBS) 0.029 0.011 0.24*
Step 2
Social withdrawal (SBS) 0.025 0.011 0.2*
IQ (NART) 0.009 0.002 �0.33***
Accuracy
Step 1
Duration of illness 0.323 0.116 0.274**
Step 2
Duration of illness 0.376 0.120 0.320**
Variables entered in Step 1: Age, education, SBS total, SBS social withdrawal, SBS thought
disorder, Chlorpromazine equivalent (mg), CGI equivalent score derived from BPRS or PANSS,
illness length. Variables entered in Step 2: NART estimated premorbid IQ. For behavioural
execution, R2�.22*** for Step 1; ^R2�.03*** for Step 2. *pB.05; **pB.01; ***pB.001. For
response processing, R2�.14* for Step 1; ^R2�.1*** for Step 2. *pB.05; **pB.01; ***pB.001.
For accuracy, R2�.14* for Step 1; ^R2�.02** for Step 2. *pB.05; **pB.01; ***pB.001.
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DISCUSSION
A wealth of converging literature suggests that deficits in processing speed
are one of the key cognitive problems associated with schizophrenia
(Bachman et al., 2010; Dickinson et al., 2004; Sanchez et al., 2009).
However, the definition and the traditional assessment of processing speed
encompass a number of subcomponents that may be differentially affected
by psychotic symptoms, medications, demographic characteristics, and
schizophrenia illness course. In this paper, we have attempted to study
TABLE 4Person correlation of procession speed subcomponents and positive and negative
factors from the PANSS
PANSS positive PANSS negative
AC (no. of errors) .13 .19
BE (time in s) �.01 .28**
RP (time in s) .19 .42**
Trail Making A (time in s) �.03 .43**
**p B .0001.
TABLE 3Correlation between processing speed subcomponents, premorbid intelligence,
memory, executive function, and processing speed measures
Behavioural
execution
Response
processing Accuracy
Premorbid intelligence
�.11* �.41** .19*
Memory
WAIS-II Digit span �.19 �.28** �.32**
Executive function
WCST*Preservative errors �.156 .207 �.322***
WCST*Categories achieved �.05 �.26* �.37**
Stroop time colour word task � colour
task
�.16 �.28* .05
Trail Making time B � A .036 .385*** .09
Processing speed
Trail Making time A .414*** .460*** .245**
Stroop n words in 120 s (colour word) �.242 �.647** �.28**
*p B .05; **p B.01; ***p B .001.
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how subcomponents of processing speed may be differentially affected by
patients’ characteristics and to explore their relationship with other known
impaired neuropsychological domains such as memory and executive
function. To do this we identified three subcomponents of processing speed:
BE (indexing the time necessary to execute the response behaviour), RP(indexing the time necessary to mentally compute and plan the response),
and AC (indexing the number of errors made).
We found that global symptom severity and age were the most significant
predictors of BE. This result is in line with the literature on psychomotor
slowing in the general population in regards to age, and also for
schizophrenia where higher symptom severity was found to be associated
with psychomotor slowness (Morrens et al., 2007; Morrens, Hulstijn, &
Sabbe, 2008). In line with previous research, further analysis conducted on asubsample indicated that negative symptoms may be the dimension
prevalently associated with BE problems. However, BE was not associated
with memory and most of the executive function measures employed with
the exception of Trail Making Part A. This association was expected and
indeed supports the validity of BE as the motor-behavioural part of
processing speed tasks. The lack of correlation with most executive function
tests employed may, on the other hand, indicate that the behavioural
component of processing speed could be detached from cognitivecomponents.
Social withdrawal and intelligence were found to be important predictors
of the RP component of processing speed. Social withdrawal, as indexed by
the SBS, measures behaviours considered part of the negative symptoms of
schizophrenia. The relevance of negative symptoms to processing speed is a
consistent finding in the literature (e.g., McDowd, Tang, Tsai, Wang, & Su,
2011); however, our results suggest that the negative symptoms may be more
relevant for the RP rather than affecting equally all processing speedcomponents.
Overall intelligence levels estimates were found to be relevant in predicting
both the BE and the RP. This is perhaps unsurprising as processing speed is
considered one of the core cognitive domains for estimating intelligence. Our
findings, in particular, support the relevance of intelligence for the RP
component. RP represents perhaps the most ‘‘cognitive’’ component of
processing speed and, similarly to computational power for computers, it can
be applied to a number of different tasks but only with the assistance ofother cognitive domains.
We found RP to correlate with various executive function tests. A
particularly strong association was found between RP and the number of
correct coloured words in the Stroop test. This association suggests that the
response processing indexed by the response processing task may draw
considerably on response inhibition. Recent work conducted in the general
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population suggests that response inhibition is an important, although
distinct, component of processing speed (McAuley & White, 2011). In our
view the task used here to index RP features clear elements of response
inhibition that may be difficult to disentangle. Indeed if RP can be defined as
the time necessary to complete the mental operations required to accomplisha task, this time is clearly dependent on the amount and relevance of the
information that the cognitive system requires to use but also inhibit.
The result of the regression analysis suggested illness duration as a
significant predictor of AC. This is an interesting and in part unexpected
finding. Some research has speculatively suggested that error monitoring and
insensitivity to errors may be a characteristic associated with various features
of schizophrenia including symptoms severity, illness chronicity, and
prognosis (Huang & Chan, 2007; Silver & Goodman, 2007; Silver et al.,2006). Our results partially support this claim and advance the role of illness
length as a better predictor compared to previously suggested features such
as global symptom severity. Research on factors affecting error monitoring
and accuracy in schizophrenia suggest a general decline associated with the
illness course; however, additional research is required to reach more
conclusive findings (Johnson-Selfridge & Zalewski, 2001).
AC was also found to inversely correlate with memory and the WCST.
The association with the WCST number of category achieved suggests thatAC may require substantial support from the attentional shifting component
traditionally indexed by this measure and consistently found impaired in
schizophrenia (Koren et al., 1998). Tasks indexing processing speed through
the exclusive measure of correct behaviour (e.g., digit coding) do not require
attentional shift to monitor errors. Our findings suggest, in line with
previous research, that attentional shifting may be important for error
monitoring, has a general impact on executive function, and may have a
specific relevance for some components of processing speed (Kim et al.,2006).
Although a less conventional component of processing speed, AC
influenced traditional components such as BE and RP. The results of the
sensitivity analysis showed that participants with higher number of errors on
the Hayling B also had higher behavioural and processing response time as
well as higher overall processing speed time on the Trail Making Part A. This
suggests that processing speed can be influenced by errors made during the
task.A second sensitivity analysis conducted on a subset of participants
assessed with the PANSS confirmed the association of processing speed
components with negative symptoms with the exception of AC. The lack of
association between errors and negative symptoms may be due to the
oversensitivity to negative feedback observed in negative affect (Cella,
Dymond, & Cooper, 2010; Martin-Soelch, 2009).
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None of the processing speed components examined was predicted by
chlorpromazine equivalent dose. This finding adds to the controversial
literature on the effects of antipsychotic medication on processing speed.
Early studies suggested that neuroleptic medications were strongly associated
with, if not the cause of, slow psychomotor behaviour observed in
schizophrenia (King, 1990). More recent studies, however, suggest that
atypical antipsychotic treatment is associated with improvement in psycho-
motor performance (Bilder et al., 2002; Purdon et al., 2000). The incon-
clusive findings warrant further research focusing on the role of different
medication on processing speed.The current paper also reintroduces the use of the Response Processing
Task to measure processing speed (Wykes at al., 1992). This task carries
considerable advantages compared to the traditional digit symbol coding
task. A first advantage is the possibility to disentangle behavioural execution
from information processing. The second advantage is that the response
required is simple (i.e., press a button) and does not involve more advanced
motor planning such as copying symbols. The task also carries the limitation
of not recording errors and therefore biasing participants towards correct
rather than faster performance, although the task can be adapted.
A further limitation to our findings is the absence of a healthy control
group with which compare the findings of our clinical group. In the absence
of a control group, it is difficult to say with certainty whether the pattern of
association evidenced from the current data might differ in a group of
individuals not affected by schizophrenia.
Although processing speed deficit in schizophrenia is widely reported in
the literature very little research has tried to explore in details what might
contribute to the behavioural response considered by processing speed tasks.
In this study we have attempted to identify relevant subcomponents of
processing speed and assess how neuropsychological and clinical features
could influence this process. Our results support the notion that the
behavioural response to tasks indexing processing speed consists of different
subcomponents differently affected by schizophrenia characteristics.
Manuscript received 11 January 2012
Revised manuscript received 5 July 2012
First published online 19 October 2012
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