GSTF International Journal of Law and Social Sciences (JLSS) Vol.1 No.2, July 2012
Human errors: Role of cognitive factors and
intervention efficacy
Madhavi Latha Maganti., Beena Chintalapuri., and Raghu Venkataraman
errors during mapping operations have been sparse and so the
present study aims to understand the nature of mapping errors
by focusing specifically on error identification, error
prediction, and error reduction.

Abstract-Errors that occur while performing mapping
operations showed that the mean number of errors committed
by thirty participants was 52.90 (SD = 18.55). Participants who
were high on error occurrence (M = .05, SD = .016) made more
number of skill-based performance errors when compared to
participants who were low on error occurrence (M = .03, SD =
.006), F (1, 28) = 31.66, p < .001. Participants who were grouped
as field-dependent perceptual style made more number of skillbased performance errors while impulsive cognitive style
participants made more number of rule-based performance
errors. These factors were identified as risk factors for
predicting error occurrence. Results also indicated that
intervention facilitated error reduction and helped to improve
performance.
A. Error identification
For the purpose of error identification we specifically focus
on information processing paradigm. According to Reason
[2], “planned actions may fail to achieve their desired
outcome because the actions do not go as planned or because
the plan itself was inadequate. In both cases, errors could
occur within the sequence of planning, storage, and execution
of action”.
Information processing is referred to as the degree of
conscious control exercised by the individual over his or her
activities which can result in skill - rule - knowledge based
behaviours [3]. Skill-based behaviour is characterized by
smooth, automated and highly integrated patterns of
behaviour. Rule based behaviour comes from previous
experience and is characterized by composition of a sequence
of subroutines and is consciously controlled by a stored rule
or procedure. Knowledge based behaviour usually occurs in
situations where no rules for control are available from
previous experience. Skill-rule-knowledge-based behaviours
result in skill-rule-knowledge based performance errors due
to information processing constraints [3]. In the present study
the skill-rule-knowledge (SRK) model based on information
processing paradigm was used to distinguish error types
caused due to skill-rule-knowledge based behaviours. The
cognitive processes underlying skill-rule-knowledge levels of
information processing has been outlined in the GEMS model
(Generic Error modelling System) by Reason [2]. Therefore,
we hypothesized that categorizing errors based on SRK
classification will help to understand the causes of errors.
Index Terms - Cognitive factors, Mapping errors, Risk
factors, Skill-rule-knowledge based performance errors.
I. INTRODUCTION
Errors that occur during mapping activity can affect the
accuracy and the quality of maps. Erroneous mapping may
not lead to disastrous outcomes like accidents, but the effort
required for error correction consumes time, increases
workload, and also increases costs. According to Hollnagel
[1], erroneous action fails to provide expected results which
may lead to unwanted consequences. The consequences of
error occurrence during mapping operations can influence the
accuracy of maps resulting in compromised performance
outcomes.
Mapping operations are primarily computer based because
they are structured within the framework of appropriate
software with well specified standards and manuals that serve
as guidelines for performance. These errors are an outcome of
human-computer interaction which requires understanding
technology as well as underlying cognitive processes. For
instance, mapping activities require specific cognitive
processes like perceptual grouping, selective attention,
problem solving etc. Efforts directed towards understanding
B. Error prediction
For the purpose of error prediction, we theorized that skillrule-knowledge based behaviours are performed by human
actions that are embedded in contexts and can only be
described meaningfully in reference to the details of the
context that accompanied and produced them [4]. Context is
defined as any information that can be used to characterize
the situation of a person, place, or object as well as the
dynamic interactions among these entities [5]. In the present
study, the context refers to dynamic interactions between the
mapping operator and the mapping operations he/she
performs primarily on computer to generate ‘error free
maps”. Mapping operations in any given context require
certain cognitive factors, which can help in error prediction.
For the purpose of the present study, we assessed mapping
operators’ perceptual style, cognitive style, and tolerance to
Manuscript submitted for review on June 15, 2012. This work was supported
by a grant from ISRO-RESPOND project awarded to the second author.
A. Madhavi Latha Maganti is a DST-PURSE Post-doctoral fellow with the
Center for Neural and Cognitive Sciences, University of Hyderabad, Hyderabad,
A.P,India(phone: 91-40-23134490; e-mail: [email protected]).
B. Beena Chintalapuri is a Professor with Department of Psychology,
Osmania University and Honorary Director with Indian Council for Social
Science Research, South-Regional Center, Hyderabad, AP, India,(e-mail:
[email protected]).
C. Raghu Venkataraman is General Manager with National Remote Sensing
Center, Hyderabad.
DOI: 10.5176/2251-2853_1.2.31
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GSTF International Journal of Law and Social Sciences (JLSS) Vol.1 No.2, July 2012
ambiguity using standardized assessments. We assumed that
these factors will help to predict error occurrence.
Perceptual style is referred to as individual variation in the
tendency to perceive the global before the local details. The
individual differences in perceptual styles range from fielddependence and field-independence [6]. An individual who is
field-dependent is highly influenced by the context of the
visual scene when processing features, whereas a person
who is field-independent is more able to perceive an
element independently from its context. Empirical studies
have shown that field independent persons have spatial and
visual restructuring ability and tend to see differences
between the stimuli. For efficiency in mapping activity, the
operators should be able to separate the features in the map by
perceiving the local details in the virtual 3D environment.
Therefore, perceptual style is assumed to be an important
cognitive factor.
Cognitive style is referred to as individual variations during
information processing and problem solving behaviour. The
individual differences in cognitive styles yield two categories:
reflective and impulsive styles. Persons with reflective style
tend to be careful and look for details in the internal analysis
of a problem and are most likely to be correct in their
responses. Persons with impulsive cognitive style tend to
respond with the first answer which comes into their mind
and do not take time to consider other possibilities and tend to
omit, substitute, or skip some of the operations in any given
activity. Empirical studies have shown that reflective style
individuals tend to make fewer errors [7] while impulsive
style individuals tend to have low motivation to master tasks
and little attention in monitoring of stimuli [8]. For efficiency
in mapping activity, the operators should be capable of
processing information and solving problems related to
mapping activity. So, cognitive style is another factor
assumed to be important for mapping.
Tolerance to ambiguity is referred to as individual
variations in tolerating ambiguous situations. Persons with
tolerance to ambiguity tend to perceive ambiguous situations
as desirable and do not experience anxiety while persons who
are intolerant to ambiguous situations tend to perceive
ambiguous situations as sources of threat and experience
anxiety and discomfort. Individuals who are high on tolerance
to ambiguity view ambiguity as natural or even desirable and
seek to tolerate and accommodate ambiguity while
individuals with low tolerance to ambiguity fail to be flexible
in transition or mediation, and tend to suffer from anxiety and
discomfort [9]. Intolerance to ambiguity manifests as
resorting to black and white solutions or arriving at
premature closure, often at the neglect of reality [9]. For
mapping activity, tolerance to ambiguity is yet another factor
assumed to be important.
Therefore, we hypothesized that,assessing cognitive factors,
(perceptual style, cognitive style, and tolerance to ambiguity)
can help to identify risk factors which may predispose a
person for error occurrence and thereby facilitate error
prediction. Specifically, we hypothesized that these cognitive
factors can also help to identify skill-rule-knowledge based
performance errors. We argue that error prediction can help
in planning intervention strategies for facilitating error
reduction during mapping activity.
C. Error Reduction
For the purpose of error reduction, intervention can be used
to offer feedback on performance, to provide rationale for
error occurrence caused due to limitations in cognitive
processes, and also to suggest cognitive mechanisms for error
reduction. Intervention is described as “behavioural strategies
planned and delivered to help the individual to attain
understanding and management of problem behaviour” [10].
Therefore, we hypothesized that intervention can help to
improve performance of mapping operators in terms of
improving the quanta of work and also facilitate in error
reduction during mapping activity. More specifically, we also
hypothesized that self awareness about one’s own errors can
foster metacognition. In this situation, intervention may act as
a precondition for insightful learning about cognitive factors
which may underlie error occurrence. Facilitating
metacognition involves self-regulation [11], and evaluation of
one’s progress [12, 13]. These metacognitive activities help
learners to master new tasks [11] and also understand
mechanisms for error causation [14]. In a study by Keith and
Frese [15], metacognitive activities during error management
training helped in mediating self-regulatory processing and
promote performance differences. In the present study, it is
assumed that the process of fostering metacognitive activity,
which formed an integral aspect of intervention, can help to
improve performance of the participants.
The emphasis of the present study is to understand the
process of error identification, examine risk factors for error
prediction, and intervene for error reduction. For facilitating
the process of error identification, we use the SRK model for
classifying mapping errors according to skill-rule-knowledge
based performance errors. For facilitating error prediction, we
assess cognitive factors like perceptual style, cognitive style,
and tolerance to ambiguity for identifying risk factors that
may increase the probability of error occurrence. For
facilitating error reduction, we use intervention strategies that
aim to foster metacognition for enhancing performance.
Therefore, the study aims to identify mapping errors that
occur during mapping, to examine the role of cognitive
factors, and to measure the efficacy of intervention. The
process of error identification, error prediction, and error
reduction are three steps that are interlinked.
II. METHODS
A. Objectives
1) identify the different types of errors during mapping
operations 2) assess cognitive processes related to mapping
operations 3) provide intervention, based on error profile and
psychological profile 4) measure the efficacy of intervention.
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GSTF International Journal of Law and Social Sciences (JLSS) Vol.1 No.2, July 2012
B. Participants:
The sample comprised 30 mapping operators working at
National Remote Sensing Centre (NRSC), Hyderabad. The
participants were recruited by NRSC specifically for mapping
operations. Of the 30 participants, 22 of them participated for
the intervention programme.
and situational factors that will conspire to create these
errors[2].Therefore, categorizing errors helped to
understand the information-processing constraints for error
identification. For the process of error prediction,
standardized tests for assessing cognitive factors were
administered individually in a testing room for each of the
participants on different days. These tests helped to
categorize participants’ perceptual style, cognitive style and
tolerance to ambiguity. Based on these categories the risk
factors that can predispose a person for making errors were
identified by analyzing the error profile of the participants.
C. Materials
The tests used for assessing cognitive factors included:
 Embedded figure test (EFT) by Sinha [16], for
assessing field dependent and field independent
perceptual style.
 Matching Familiar Figure test (MFFT) by Devi, [17],
for assessing reflective and impulsive cognitive
style, and
 Tolerance to ambiguity (TOA) by Budner [18], for
assessing tolerance and intolerance to ambiguity.
For the process of error reduction, intervention
comprising group and individual feedback was used for
creating awareness about errors and also for fostering
metacognition. To foster this process, profiles of the
participants’ scores from the psychological tests as well as
their error profile was prepared and given to each of the
participant. The interpretation of the test scores and the
risk factors for error causation was explained separately for
each of the participant. Group feedback provided scope for
conveying information about error causation, error
identification, and error prediction. Explaining the
cognitive processes for error occurrence helped participants
to exert self-regulatory “control over his or her cognitions”
[11]. Thus error profile helped to create awareness about
errors and psychological profile helped to consciously adopt
self-regulatory measures which formed the basis for
fostering metacognitive strategies.
D. Procedure:
For the process of error identification, the mapping
activity of each mapping operator was recorded during their
work hours which were distributed across 24 hours in terms
of shift A, B, C1. The mapping activity was scanned
carefully for identification of errors and for preparation of
error profile by the quality control experts of digital
mapping department at NRSC. This error profile yielded
pretest measure of error occurrence. The mapping errors
identified by quality control department were categorized as
skill-rule-knowledge based performance errors. Mapping
errors categorized as skill-based performance errors are
closure of polygon, overshoot, undershoot, missing
features, and duplicate tasks because they arise from either
inattention or over attention to the map features or due to
attention to wrong points. Mapping errors categorized as
rule-based performance errors are layer code and layer
check deviations because these errors arise due to
inappropriate matching of environmental signs to the
situational component of well-tried trouble shooting rules.
Mapping errors categorized as knowledge-based
performance errors are interpretation error and layerisation
error because these errors arise due to selecting wrong
features of the problem space, confirmation bias, overconfidence, biased reviewing of plan construction, and
thematic vagabounding. The skill-rule-knowledge based
performance errors are differentiated and analyzed on
several dimensions like type of activity, attention, novice or
expert problem solvers, and influence of situational factors.
Knowledge-based performance errors are related to
behaviors at the knowledge level which come into play in
novel situations for which actions must be planned using
conscious analytical processes and stored knowledge [3].
Knowledge-based performance errors are less predictable
and at best it is possible to forecast the general cognitive
E. Measuring intervention efficacy
The efficacy of intervention was measured from two
outcome variables. The first outcome variable is the error
occurrence before and after intervention. The second
outcome variable is improvement in the quanta of work.
The error profile for each participant was prepared again,
after the intervention, by quality control experts at NRSC.
The pre-test error profile and post-test error profile of each
participant helped to identify error reduction, if any. The
quantum of work was also analyzed to see the difference in
work output before and after intervention. To further
supplement these measures, the performance of the
participants was evaluated qualitatively on a three point
rating scale by quality control experts at NRSC, Hyderabad.
A behavioral self-contract was also given to the
participants to assess if they made conscious attempt to
work towards error reduction.
III. RESULTS
A. Error identification during mapping activity
The mapping operators made a total number of 1587 errors
during mapping activity across three shifts. The mean
number of errors committed by the thirty participants was
52.90 (SD = 18.55). The number of errors varied across
participants and so the errors were converted to proportions
by dividing number of individual errors by total number of
errors. The mean proportion of error was significant, M = .03,
* Shift timings: 6.00 am to 2.00pm, Shift B timings: 2.00 pm to 8.00pm, Shift
C timings: 8.00 pm to 6.00pm.
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GSTF International Journal of Law and Social Sciences (JLSS) Vol.1 No.2, July 2012
SD = .01, t (29) = 200.71, p < .001, indicating within group
differences in error occurrence.
Further, error occurrence was categorized as high (N =11)
and low (N = 19) based on above mean and below mean error
occurrence. The between group differences in mean
proportion of errors made by participants who were grouped
as high on error occurrence (M= .05, SD = .011) and low on
error occurrence (M= .03, SD = .005), was significant, t (28)
= 6.43, p < .001.
A.1. Error identification based on SRK classification
To test the hypothesis that categorizing errors based on
SRK classification can help to understand the causes of error,
we used a one-way analysis of variance for finding between
group differences in skill-rule-knowledge based performance
errors of participants who were high and low on error
occurrence. The one way analysis of variance showed that the
mean differences were not significant on rule and knowledge
based performance errors for groups who were high and low
on error occurrence, p > .05, as seen from table 1. However,
the mean differences on error proportion for skill-based
performance errors for participants who were high on error
occurrence (M = .05, SD = .016) and participants who were
low on error occurrence (M = .03, SD = .006), varied
significantly, F (1, 28) = 31.66, p < .001. This indicates that
the participants who were high on error occurrence made
more number of skill-based performance errors. Rule and
knowledge based performance errors did not show any
variation in both groups.
field- independent (M = .03, SD = .01). This indicates that
field-dependent perceptual style may be a predisposing factor
for error occurrence.
B.1.1: Error prediction based on perceptual style and SRK
classification
The one way analysis of variance showed that the mean
differences were not significant on rule and knowledge based
performance errors for field- independent and fielddependent participants, p > .05, as seen from table 2. The
mean differences on error proportion for skill-based
performance errors varied significantly because field dependent participants (M = .05, SD = .03) made more
number of skill based performance errors when compared to
field-independent participants (M = .03, SD = .01), F (1, 28)
= 6.69, p = .02. This indicates that the field-dependent
participants made more number of skill-based performance
errors. Rule and knowledge based performance errors were
not affected by perceptual style.
Table 2: Error prediction based on perceptual styles and SRK classification
SRK classification of
errors
Skill-based
performance errors
Rule-based
performance errors
Knowledge- based
performance errors
Skill-based
performance errors
Rule-based
performance errors
Knowledge- based
performance errors
High on error
occurrence
(N = 11)
.05 (.02)*
Low on error
occurrence
(N = 19)
.03 (.006)*
.04 (.03)
.03 (.01)
.03 (.03)
.03 (.03)
Field dependent
perceptual style
(N = 4)
.05 (.03)*
.03 (.02)
.03 (.01)
.03 (.03)
.04 (.02)
* p = .02
B.2: Error prediction based on cognitive style
When assessed on Matching Familiar Figure Test, fifteen
participants were grouped to have reflective style and fifteen
were grouped to have impulsive style based on their scores.
The between group mean differences on error proportion was
not significant, t (28) = .42, p = .68, indicating that reflective
style participants (M = .03, SD = .01) and impulsive style
participants (M = .03, SD = .01), made equal number of
errors. This indicates that both impulsive and reflective
cognitive styles may be predisposing factors for error
occurrence.
B.2.1: Error prediction based on cognitive style and SRK
classification
The one way analysis of variance showed that the mean
differences were not significant on skill and knowledge based
performance errors for participants who have impulsive and
reflective cognitive style, p > .05, as seen from table 3. It is
interesting to note that the mean differences on error
proportion for rule-based performance errors varied
significantly because participants grouped as impulsive style
(M = .04, SD = .02) made more number of rule based
performance errors when compared to participants grouped
as reflective cognitive style (M = .03, SD = .02), F (1, 28) =
3.94, p = .05, as seen from table 3. This indicates that
participants who were grouped as impulsive style made more
number of rule-based performance errors. Skill and
knowledge based performance errors were not affected by
cognitive style.
Table 1: Error identification based on SRK classification
SRK classification of
errors
Field independent
perceptual style (N =
26)
.03 (.01)*
* p < .001
B. Error prediction based on Cognitive processes
To test the hypothesis that assessing cognitive processes like
perceptual style, cognitive style and tolerance to ambiguity
can help to identify risk factors and thereby facilitate error
prediction we used an independent samples t-test to find
mean differences on error proportion. Further, we used a oneway analysis of variance to test the hypothesis that these
cognitive processes can help to predict the occurrence of skillrule-knowledge based performance errors.
B.1: Error prediction based on perceptual style
When assessed on Embedded Figures Test (EFT), twentysix participants were grouped as field-independent and only
four were grouped as field-dependent. The between group
mean differences on error proportion was significant, t (28) =
2.01, p = .05, indicating that participants grouped as field dependent perceptual style made more errors (M = .05, SD =
.020 when compared to participants who were grouped as
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GSTF International Journal of Law and Social Sciences (JLSS) Vol.1 No.2, July 2012
C.1: Error reduction and quanta of work before and after
intervention
Quanta of work and total number of errors during mapping
activity at pre-test (before intervention) and post-test (after
intervention) was analyzed using Wilcoxon signed ranks test
for 22 subjects who participated in the intervention. The
quanta of work done ( M = 16.68, SD = 5.45) and total errors
( M = 19.77, SD = 8.51) during mapping activity did not yield
significant differences at pre-test, Z = .99, p = .32, while it
showed significant effects at post-test, Z = 2.82, p = .005,
indicating that the total number of errors(M = 13.23, SD =
6.34) for a given quantum of work, (M = 18.55, SD = 2.57)
decreased significantly after intervention.
Further, intervention efficacy from performance evaluation
by quality control experts showed that 36 % of participants
were rated as above average before intervention, whereas the
rating improved to 59% after intervention.
Table 3: Error prediction based on cognitive style and SRK classification
SRK classification of
errors
Skill-based
performance errors
Rule-based
performance errors
Knowledge- based
performance errors
Reflective
cognitive style
(N = 15)
.03 (.02)
Impulsive cognitive style
(N = 15)
.03 (.01)
.03 (.02)*
.04 (.02)*
.04 (.04)
.03 (.03)
*p=.05
B.3: Error prediction based on tolerance to ambiguity (ToA)
When assessed on tolerance to ambiguity (TOA) test,
twenty–eight participants were grouped as intolerant to
ambiguity and only two were grouped as tolerant to ambiguity
based on their scores. The between group mean differences on
error proportion was not significant, t (28) = .10, p = .33,
indicating that participants who were intolerant to ambiguity
(M = .03, SD = .012) and participants who were tolerant to
ambiguity (M = .03, SD = .007), made equal number of
errors. This indicates that participants who are tolerant to
ambiguity and intolerant to ambiguity are likely to make
errors.
B.3.1: Error prediction based on tolerance to ambiguity and
SRK classification
The one way analysis of variance showed that the mean
differences were not significant on skill-rule-knowledge based
performance errors for participants who were tolerant and
intolerant to ambiguity, p > .05, as seen from table 4. This
indicates that skill, rule and knowledge based performance
errors were not affected by tolerance to ambiguity.
IV. DISCUSSION
A. Error identification
Error identification indicated that the mean number of
errors during mapping activity showed significant differences.
Participants who were grouped as high on error occurrence
made more number of skill-based performance errors. This
may be due to the nature of mapping operations being
predominantly skill based as they involve cognitive processes
like selective attention, perceptual vigilance and at times the
need to divert from activity which has become automatic.
B. Error Prediction
The three variables that were used for error prediction
helped to identify risk factors for error occurrence. Error
prediction based on perceptual style showed that participants
who were field- dependent made more number of errors when
compared to field- independent individuals. Further, persons
who were field-dependent also made more number of skillbased performance errors. This clearly shows that field dependent individuals may be predisposed for error
occurrence. The cause of error occurrence may be due to
difficulty in discriminating the visual stimuli and perceiving
it distinctly within a given field. Mapping activities require
attention to navigate through concepts and therefore persons
whose perceptual style is field-dependent tend to have
difficulty.
Error prediction based on cognitive style showed that
participants who were reflective and impulsive made equal
number of errors. This indicates that both categories of
participants may be prone for error occurrence. Further,
persons who were grouped as impulsive on cognitive style
made more number of rule based errors when compared to
skill and knowledge based errors. This indicates that persons
with impulsive style may be predisposed for making rulebased performance errors. The cause of error occurrence may
be due to the tendency of persons with impulsive cognitive
Table 4: Error prediction based on tolerance to ambiguity and SRK classification
SRK classification of
errors
Intolerance to
ambiguity
(N = 28)
Tolerance to ambiguity
(N = 2)
Skill-based
performance errors
.03 (.02)
.02 (.00)
Rule-based
performance errors
.03 (.02)
.03 (.01)
Knowledge- based
performance errors
.03 (.03)
.05 (.06)
C. Error reduction based on intervention efficacy
To test the hypothesis that intervention can help to
improve performance of mapping operators in terms of
improving the quanta of work and reduction of errors during
mapping activity, we used Wilcoxon signed ranks, a nonparametric test, for the 22 subjects who participated in the
intervention. Error proportions were not used because when
errors are proportioned to one, the pre-post error differences
are equated and so they do not provide a robust measure to
assess the efficacy of intervention.
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GSTF International Journal of Law and Social Sciences (JLSS) Vol.1 No.2, July 2012
style to respond hastily. Rule based performance errors arise
due to misapplication of good rules. In the context of
mapping, impulsive style participants may have difficulty to
predict and apply appropriate rules for successful outcomes.
Error prediction based on tolerance to ambiguity
showed that both tolerant and intolerance to ambiguity leads
to error occurrence. Further, both the categories of
participants did not differ on skill, rule, or knowledge based
performance errors. This clearly indicates that probability of
error occurrence is equal for both categories of individuals. In
the context of mapping, being tolerant or intolerant to
ambiguity does not affect error occurrence as mapping
operations are structured activities and require a given set of
heuristics that are specified in the manuals.
Error prediction showed that field dependence perceptual
style and impulsive cognitive style may be considered as risk
factors for error occurrence during mapping activity. In the
context of mapping activity, we can see how the cognitive
processes of the mapping operator can act as risk factors
during mapping operations, resulting in error occurrence.
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C. Error Reduction
Providing intervention helped to reduce the
occurrence of errors while performing mapping operations.
The focus of the intervention was on self-regulatory measures
to reduce errors. Reduction of errors after intervention could
have resulted in bringing about metacognitive awareness
leading to reduction in error occurrence. Studies related to
fostering metacognitive activities as part of error management
training have shown to be effective [14].
V.
CONCLUSION
The study showed that the process of error identification
involved categorizing errors using SRK model that helped to
understand the causes of errors. The process of error
prediction helped to identify field- dependent perceptual style
and impulsive cognitive style as risk factors that may increase
the probability of error occurrence. The process of error
reduction showed that metacognitive strategies that were
integral to intervention helped to improve performance.
Error research related to mapping operations have been
sparse and therefore further research is needed to address
these questions using controlled experimental designs which
can help to formulate a working model for effective digital
mapping.
ACKNOWLEDGMENT
The research study was funded by Indian Space Research Organization under
the ISRO-RESPOND project given to the first author. The authors wish to thank
the mapping operators working at National Remote Sensing Centre for their
cooperation and participation in this study. We thank Mr.G.Subhakar, Research
Associate, ISRO-RESPOND project, and Ms. Dimple Kala, M.A student,
Department of Psychology, Osmania University for their assistance with data
collection and data entry.
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© 2012 GSTF