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Int. J. Human-Computer Studies 69 (2011) 428–439 www.elsevier.com/locate/ijhcs

Attention, polychronicity, and expertise in prospective memory performance: Programmers’ vulnerability to habit intrusion error in multitasking Premjit K. Sanjrama, Azizuddin Khanb,n a

Department of Humanities and Social Sciences, Indian Institute of Technology Bombay, Powai 400 076, Mumbai, Maharashtra, India Psychophysiology Laboratory, Department of Humanities and Social Sciences, Indian Institute of Technology Bombay, Powai 400 076, Mumbai, Maharashtra, India

b

Received 22 June 2009; received in revised form 11 December 2010; accepted 29 January 2011 Communicated by J. Scholtz Available online 19 February 2011

Abstract The paper examines prospective memory (ProM) in programmer multitasking and reports administration of a naturalistic atypical action. The study emphasizes on how attention, time orientation, and expertise affect ProM performance in multitasking among a group of computer science and engineering students (N= 108). The results suggest that attention play a crucial role in multitasking and ProM performance with respect to whether or not a word display requires more attention to be devoted in monitoring and identifying it for an appropriate action. Polychrons exhibit lesser degree of ProM performance failure than monochrons whereas expertise does not have an effect. Finally, results show that out of overall ProM performance failure, habit intrusion errors comprise of 16.22% occurring 1.75 times in every 10 valid click responses of ProM task. Moreover, experts demonstrate a superior performance over novices in programming. & 2011 Elsevier Ltd. All rights reserved. Keywords: Atypical action; HCI; Habit intrusion; ProM error; Programmer multitasking

1. Introduction The rapid progress of technology has led to significant changes in the work environment and the lifestyle of people. One of the most conspicuous manifestations of this progression is in the form of computers and related technologies (Hollnagel and Cacciabue, 1999). Out of the diverse contexts of human–computer interaction (HCI), one of the most important human activities is programming itself. Since programs are necessary for computers to function, development and maintenance of high-quality software systems are crucial (Sonnentag et al., 2006). An important characterization of the profession of programming is that people are usually involved in handling multiple tasks. As a specific example, readers may consider n

Corresponding author. Tel.: +91 9969573299; fax: +91 22 2576 7383. E-mail addresses: [email protected], [email protected] (A. Khan).

1071-5819/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2011.01.006

that a programmer is writing code and at the same time paying attention to multiple windows (e.g., running another program, desktop conferencing, using multiple display systems, replying to instant messages, company mailbox, reminder pop-ups, or even complementary gadgets besides the computer currently used for programming). Under such circumstances, it is important for an individual to keep his/ her current goals activated and also to remember what to do both in the current situation and the future (Johnson and Proctor, 2003, p. 174). In a multitasking environment, one of the essential aspects of human cognition is that of remembering to carry out a delayed intention in fulfilling various task demands (Burgess et al., 2000). Such memory of an action that a person intends to perform in the future has been popularly termed as prospective memory (ProM). One may think of an intended action but may not be able to perform it immediately because of the constraints of the task currently

P.K. Sanjram, A. Khan / Int. J. Human-Computer Studies 69 (2011) 428–439

involved (Einstein et al., 2003). Unforeseen interruptions, sometimes of high priority will occasionally occur, and things will not always go as planned (Burgess, 2000). It also could be just that execution of the intended action is not appropriate at the moment (Vortac et al., 1995). Whatever the circumstances, one is confronted by a demand to perform the ‘right action at the right time’ in fulfilling the desired goal. This implies that the execution of ‘delayed intentions’ requires retaining and retrieving them at an appropriate moment at a later point of time (Ellis and Milne, 1996). What is important for the individuals is to keep track of different types of information, presented at different rates, and to take actions based on what they see, hear, and remember (Pattipati and Kleinman, 1991). Instances of ProM failure are common in the demanding work setting (Stone et al., 2001). As such, ProM errors in the workplace can severely compromise the performance of the individuals leading to negative consequences. This implies the criticality of appropriate action associated with ProM task. Reason (1990) stressed the vulnerability of individuals to ProM errors as among the most common form of human fallibility in everyday life. One of the risks of ProM errors in a demanding environment is that of performing a habitual action instead of the intended task (a critical aspect that existing laboratory research has done little to explore) (Dismukes, 2006; McDaniel and Einstein, 2007, p. 209). ProM errors are not necessarily due to negligence or carelessness of the individuals but they arise from challenges on the human cognitive system (Loukopoulos et al., 2003; McDaniel and Einstein, 2007, p. 194). Human intentions are sometimes antagonistic to routine or long-standing behaviors (McDaniel and Einstein, 2007, p. 115). As a specific real-life experience, one of the authors and colleagues have (several times) experienced the instances of pressing the wrong button of the elevator habitually (of the floor one would usually go to) even though the intended action was to go to another floor of the building. The intended action to be performed breaks the action sequence but people often perform the habitual action. Habits are well-learned simple stimulus– response associations leading to (habitual) responses that are automatically initiated upon the presence of the environmental cues (Danner et al., 2007). William James remarked, ‘‘Habit diminishes the conscious attention with which our acts are performed’’ ((1890/1981), vol. 1, p. 119). With this central issue, the present study makes an attempt to understand the underlying picture of the cognitive processes of ProM in the context of programmer multitasking. The paper presents an experimental paradigm to capture ProM error of habit intrusion. The situation is that a person needs to perform an intended action deviating (occasionally) from the habitual response whenever a target word occurs (in short, hereafter referred to as the target) while programming. Keeping in mind that individuals usually recall what they intended to do, an important issue in performing ProM task is not how people retain the content of their intentions but how they remember to execute the intended

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action at appropriate moment (Dismukes and Nowinski, 2007). The variance in ProM performance is attributable to the mechanisms by which retrieval is initiated involving cueing and attention (McDaniel and Einstein, 2000; Nowinski et al., 2003). For a cue to be an effective reminder it must not only be highly associated to the specific intention but it must also be salient. That is, the cue needs to have a high likelihood of being noticed at the time that the intention must be performed (Nowinski et al., 2003). When people are faced with two or more tasks concurrently, there is a need of coordinating task strategies. This is often characterized by the difficulty to manage the attentional demands efficiently (Johnson and Proctor, 2003, p. 164). According to James (1890/1981), the essence of attention is focusing of one’s awareness essentially with the withdrawal from several simultaneously possible objects or trains of thought in order to deal effectively with others. Unless intentions are periodically refreshed by attentional checks in the interim, there is a concern that they will become overlaid by other cognitive demands (Bes, 1999). Marsh and Hicks (1998) showed that adding a concurrent task that occupies central executive significantly affects prospective remembering. It is conceivable that the situation will pose a more difficult challenge for the human cognitive system if the intended task itself is atypical in nature and there are tasks competing for attention which may compound possible errors. In this respect an important characterization of attention is the requirement of devoting attention to monitoring to identify a cue namely, contracted attention (CA) and protracted attention (PA). It is important to note that the labels contracted attention and protracted attention used in this research are relative. Researchers can only determine that a situation requires an individual to devote more attention in monitoring than another. Therefore, a situation is characterized by PA if an individual needs to devote more attention in monitoring as compared to another situation. For instance (in the current study), when a word is displayed by fading, an individual needs to devote more attention in monitoring as compared to the display by non-fading of a word (characterized by PA and CA, respectively). In other words, the more an individual is required to devote attention in monitoring to identify a cue the more a situation will be characterized by PA. The point is that if a cue is less likely to capture attention, the more monitoring of its occurrence required (Einstein et al., 1995) and the more an individual directs attention to a cue, the more likely he/she is to recall the associated intention (West and Craik, 2001). Thus, in the current context of programmer multitasking, there is a concern of a detrimental effect on ProM performance if the situation is characterized by PA considering that the intended action must be retrieved at a time when the individual is busy with the current task. This line of thought holds good with the consideration of how ‘executive control processes’ supervise the selection, initiation, execution, and termination of each task in achieving multiple-task performance (Rubinstein et al.,

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2001). The crucial matter of the limited capacity of the executive system is that when people are involved in handling multiple tasks, each requiring the selection of an independent response, a rather stubborn bottleneck could arise (Pashler et al., 2008). An important factor in handling multiple tasks that the current study examines is time orientation of the programmers, as a monochronicity–polychronicity continuum (Zhang et al., 2005). This time related task handling behavior is important in attending to the tasks that a person encounters. Some people are inclined to do one thing at a time whereas others can attend to multiple activities concurrently. Time orientation is potentially an important consideration as it can influence the manner in which individuals respond to the multi-task demands (Zhang et al., 2003). Lindquist and Kaufman-Scarborough (2007) maintained that polychronicity (polychronic time use) is a form of behavior wherein a person engages in two or more activities during the same block of time, while monochronicity (monochronic time use) occurs when a person engages in one activity at a time. Monochronicity and polychronicity are the ends of a continuum thus intermediate preferences exist (Bluedorn et al., 1992; Slocombe and Bluedorn, 1999). Haase et al. (1979) described the characteristics of polychrons as having the ability to cope with stimulus-intense, information-overload environments. This suggests that individuals with polychronic orientation (i.e., polychrons) would be able to switch attention or divide attention in multitasking, while those with monochronic orientation (i.e., monochrons) concentrate all their attention on one thing or on many different aspects of one thing (Zhang et al., 2005). The transitions between the tasks involve paying attention to two or more tasks simultaneously and the more switching among tasks an individual does, the more polychronic his or her behavior is (Slocombe and Bluedorn, 1999). There exists a potential conflict when monochronic workers are assigned to tasks that require polychronic behavior which could compromise with the performance and the wellbeing of the individuals. Zhang et al. (2005) showed that there are strategy and performance differences between monochrons and polychrons in multitasking context of process control operation. Haase et al. (1979) made the following points in this regard: (a) cognitively complex people are more monochronic, especially when the overload is in the form of high volumes of information, (b) the monochronic individual cognitively screens and regulates input to avoid overload, and (c) the cognitively simple person is more polychronic and they allow a good deal of more stimuli from the environment to reach them and warrant attention. The ability of polychrons to handle multiple tasks better than monochrons is thus associated with cognitive processes involved in dealing with information coming from the environment in achieving task goals. The cognitive processes of differentiation, integration, and construction of knowledge are crucial (the first two processes occur primarily in the retrieval system of

memory while the third further involves the other components of the entire cognitive system) (Tennyson and Breuer, 2002). In line with Schroder (1971) the authors hold the operational term for the retrieval system functions of differentiation and integration as cognitive complexity. In simple terms, cognitive complexity is the extent to which an individual differentiates and integrates an informational event. Differentiation is the aspect of identifying the number of distinctions or separate elements (fragmentation) into which an event is analyzed whereas integration refers to the connections/relationships among these elements. Since monochrons are expected to concentrate their attention on one thing or many different dimensions of one thing (Zhang et al., 2005) polychrons would have advantage in handling multiple tasks. The current framework of the study requires the programmers to write a program while paying attention to what is happening in another window where words will appear. Based on the differences in task handling behavior of the individuals, it is speculative that polychrons are able to handle attentional demands of the multitasking environment accordingly and thus ought to have advantage over the monochrons in performing the ProM task. Several assessment tools of the construct include the Polychronic Index (Haase et al., 1979); Polychronic Attitude Index (PAI) (Kaufman et al., 1991), Monochronic Work Behavior (MWB) (Frei et al., 1999); Polychronic Attitude Index 3 (PAI3) (Kaufman-Scarborough and Lindquist, 1999); Inventory of Polychronic Values (IPV) (Bluedorn et al., 1999); and Modified Polychronic Attitude Index 3 (MPAI3) (Lindquist et al., 2001). Further advancement of the metric is the recent efforts of Lindquist and Kaufman-Scarborough (2007) on ‘Polychronic–Monochronic Tendency Model’ constructed using confirmatory factor analysis towards extension and re-inquiry of the foundational PAI (maintaining comprehensive and rigorous validation process for this extended ‘reflective’ model). This model incorporates measures of an individual’s preferred behavior and feelings about polychronicity/ monochronicity and what they perceive is right for them. The five-item summated scale from this model is named the Polychronic–Monochronic Tendency Scale (PMTS). The focus of the work is on developing a reflective model of individual tendency toward either monochronic or polychronic behavior and the degree of a person’s positive feelings about his or her position on that continuum. It may be specially noted that the scale does not favor or emphasize one end of the continuum over the other: (a) it is general in nature, removing the situation specificity tied to the home or workplace found in other measures and (b) it is not biased toward polychronic behavior. Thus, PMTS shows much promise in evaluating the monochronicity/ polychronicity tendency of individuals. Besides the above issues of time orientation, an important programmer characteristic linked with high performance in the domain of programming is that of expertise. There is a substantial and long history of work on


understanding expertise with respect to human performance (Cross, 2004). Traditionally from a more conceptual perspective, computer programming is comprised of different phases of requirement analysis and moving then to testing and debugging, with design and coding as distinct intermediate phases (processes happening more iteratively) (Bishop-Clark, 1995; Sonnentag et al., 2006). With object-oriented programming approaches, experienced programmers develop complex plans on the basis of the deep structure of the language whereas inexperienced programmers develop plans on the basis of a functional similarity and have more revisions of the plans (De´tienne, 1995). Adequate knowledge representation is crucial in all phases of developing software and as compared to moderate performers; high performers possess more comprehensive representation of the entire task and spend less time in comprehension of the programming problem (Sonnentag et al., 2006). Weiser and Shertz (1983) showed that experienced programmers easily identify more errors quicker than novices. With respect to testing, Teasley et al. (1994) reported a study on positive test strategy (the tendency to test a hypothesis with test cases that confirm rather than disconfirm the hypothesis) in software testing. The study indicated that this limitation was mitigated by higher levels of expertise (although not eliminated). Findings of Grabner et al. (2006) maintained that superior cognitive performance and the underlying cortical activation are linked with the general efficiency of the information processing system. Also, expertise contributes to performance mediated through processing resources (experience can improve processing efficiency, thereby reducing the amount of resources required to produce the same level of performance) (Shiffrin and Schneider, 1977). This suggests that expertise facilitates the effective maintenance of the amount of resources available for performing other activities. Furthermore, superior performance of experts over novice is often attributed to cognitive and perceptual-motor skills and domain-specific physiological and anatomical adaptations (Ericsson and Lehmann, 1996). Considering these points, experts are expected to have an advantage in terms of performance on a ProM task and domain-specific task as compared to novices. In the light of the above mentioned issues of human performance, the main objective of the present study was to understand the nature of ProM errors of habit intrusion. The study emphasized on the effect of attention, time orientation, and expertise on ProM performance failure. The following hypotheses were formulated and investigated in the study. 1. Irrespective of attention condition, time orientation, and expertise, programmers have undifferentiated vulnerability to habit intrusion error. 2. There is a deterioration of ProM performance under PA as compared to CA.

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3. Polychrons have better performance on ProM task as compared to monochrons. 4. Experts have better performance on ProM task and domain-specific task as compared to novices. 2. Method The study presents an experimental approach that allows ProM research to be carried out under a situation in which individuals occasionally face atypical actions. In the atypical action paradigm, whenever a word appears in a small window that remains open on the right side of the computer screen (always on the top of the programming window), the participant has to click the ‘OK’ button. However, if a target appears the participant has to click the ‘Cancel’ button as the target does not occur for a while, the ‘OK’ response represents the habitual response. It is essential to ensure that a target does not occur often. The objective here is to examine whether the participants inappropriately click ‘OK’ (habitually) when they see a target. The occurrence of the target in a window in the corner creates a situation where the person must monitor what is going on in that window for appropriate activation of the intention on hold. The participants are concurrently involved in programming task as well. Thus, in this experiment the participants are involved in a situation of multitasking where they have to program at the same time respond to ProM task by clicking the appropriate button. 2.1. Participants A total of 108 engineering students (M = 21.09 years; SD= 3.24; range = 16–28 years) voluntarily participated in the experiment. There were 33 monochrons, 35 intermediates, and 40 polychrons. 51 of them were beginning students (1st year, B. Tech.) who had just completed basic programming course, i.e., ‘Computer Programming and Utilization’ (course code: CS 101); whereas, 57 of them were advanced students (2nd year, B. Tech. or higher) pursuing higher level computer science and engineering courses at Indian Institute of Technology Bombay. Only 2 female students participated in the study. Each participant was tested individually. The data for 8 participants were discarded due to one of the following reasons: debriefing revealed that the student had not understood the tasks involved, the student withdrew from the experiment before completion, the student was ill, or the computer malfunctioned during the experiment. 2.2. Design The study was a 2 (attention: CA vs. PA) 3 (time orientation: monochrons vs. intermediates vs. polychrons) 2 (expertise: novices vs. experts) mixed factorial design with the first factor as within-group variable. In this experiment CA required less attention to be devoted to monitoring the display of a word (i.e., non-fading display)

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whereas more attention to monitoring was required in case of PA (i.e., fading display). Essentially, when a word appeared in a corner window, participants were required to identify it and respond by making a mouse click. There was a fixed time interval of 5 min between the disappearance of a word and appearance of the following word. Half of the word-display was CA and the other half was PA. Participants were randomly assigned to the exposure of either CA or PA in the first half of the word list. Time orientation was operationalized having three categories using ‘Polychronic–Monochronic Tendency’ (PMT) score measured by ‘Polychronic–Monochronic Tendency Scale’ (PMTS) (Lindquist and Kaufman-Scarborough, 2007) based on seven-point Likert scale, i.e., (i) monochron (1 r PMT mean score o 4), (ii) intermediate (3 o PMT mean score o 5), and (iii) polychron (5 r PMT mean score r 7) (similar approach used by Zhang et al., 2005). Expertise was operationalized as years of experience. This approach is relative in the sense that less knowledgeable programmers, the ‘novices’ were compared with more knowledgeable programmers the ‘experts’ (Chi, 2006). This approach assumes that expertise develops as a function of time spent within the domain (Sonnentag et al., 2006). Thus, in this experiment, the novice and expert categories of the participants were determined based on the course level (formal academic training of technical skills) in programming. The beginners who had just completed the basic course were compared with advanced students of computer science and engineering. 2.3. Materials The system had Dev-C++ version 4.9.9.2 (open source integrated development environment distributed under the GNU general public license for programming in C/C++) installed. The computer was equipped with a Logitech optical mouse and a Logitech keyboard. Two wide screen, 1900 color monitors (View sonic, LCD, VA 1912 wb) were connected to computer through a VGA (Video Graphics Array) switch (ML202 VGA). A Java based software program was developed specifically for the current experiment to display words and record the click responses. The software also obtained personal data and allowed the experimenter to set the time and nature of the word display, determine the associated file locations, and the storage path. A consent form which provided brief details of the study was used to get written consent from the participants. PMTS was used to determine the time orientation of the participants. Participants also provided the information to determine their level of expertise and their self-rating of their expertise in C++ programming. A sample programming problem was given in the practice trial (without display of targets) and five programming problems were given for the experimental task. Since the novice group of programmers were beginners enrolled for introductory programming course, the problems of the programming

task were set according to the course they had completed. A sheet containing a list of words was provided to the participants for word identification task. The words used for the practice trial, the experimental task, and the word identification task were from the category norms developed by Overschelde et al. (2004). Six categories needed for the study were randomly selected from the total of seventy categories. The category numbers were 49, 10, 55, 43, 69, and 52 for the categories of ‘disease’, ‘color’, ‘state’, ‘vegetable’, ‘gardener’s tool’, and ‘fish’, respectively. Further, from each category, words were randomly selected making three sets of six words (for the practice trial, the experimental task, and the word identification task). The six words for the experimental task according to their sequence of display were: (a) Hepatitis, Tan, and Indiana (for the first half) and (b) Corn, Hose, and Catfish (for the second half). Two targets for the ProM task were randomly chosen, the words being ‘Indiana’ and ‘Catfish’ in the third position of the display sequence irrespective of CA or PA of the half (i.e., these targets appeared during the experimental task , each half having one target). Also, whether the first half would be CA or PA was randomly determined. After the experimental task, the participants were given the word identification task. The displayed words of experimental task were randomly shuffled in the list of the other words included so that the participants would not be able to identify the displayed words if they were simply clicking without reading (i.e., without paying attention to the words). A sheet was provided to each participant to write the subjective report and a food coupon was given to each participant. 2.4. Procedure Prior to the practice trial, the participants filled out a consent form. They responded to PMTS and also provided required information on Vital Information Sheet. A sample programming problem was given in the practice trial. In advance, participants were informed to keep their electronic devices such as, laptop, watch, cell-phone, etc. in a safe place and not to bring any of them to the programming workstation of the experiment. The whole process of each session complied with the following sequence: 1. The participant responded to PMTS. 2. Printed instructions were given. 3. Practice trial began by clicking ‘Start’ (practice version of the software was already kept ready). 4. An opportunity was given to the participant to ask questions about the tasks. 5. The participants typed their personal details and clicked ‘Start’ and the experimental task began.

The experimental task lasted for 30 min, 30 s. Once the experimental task started, it was left to the participants to


handle the tasks at hand. The experimenter sat about 1.5 m away from the participants’ work area. Tasks were presented on a wide screen, 1900 color monitor (View sonic VA 1912 wb) and the responses were recorded by the computer. The experimenter also recorded additional information manually by observing the activities of the participants on another View sonic 1900 color monitor (View sonic VA 1912 wb) which displayed the screen of the programmer’s computer through a VGA (Video Graphics Array) switch (ML202 VGA). The main purpose of this arrangement was to record significant information in an unobtrusive manner as the participants were not aware of the second monitor. For the word display and the recording of the click responses, a Java based software program developed specifically for the current experiment was used. The exposure time of the word display was set according to the reaction time of the words given in the category norms mentioned above (the average being 4.43 s). By taking the successive whole number of the average reaction time, it was set as 5 s (after which a displayed word would disappear). Under the PA condition a word was displayed by fading thus making the fading time as 5 s. However, for the CA, a word would appear on the screen and disappear after 5 s (non-fading). During the practice trial the participants were shown the basic functions of Dev-C++ and their queries were addressed. When the participants were given the instructions concerning the experiment, they were told that the experimenter was interested in their programming performance and their ability to perform multiple tasks at the same time. They were informed that there would be five C++ programming problems and the experimenter encouraged the participants to complete as many programming problems as possible accurately (allowing to perform the programming task in any sequence and having the freedom to attempt an incomplete task later). The participants were however not allowed to open any additional program. They were told not to minimize or close the existing windows on the screen and any attempt to access the internet was prohibited and disabled. Writing materials were not available to the participants during the experimental task. Also, the experimenter disabled in advance any other software which could interrupt the experiment and the clock on the toolbar kept hidden. The experimenter instructed the participants that they had to write the program while performing another task of mouse clicking. They were told to click ‘OK’ (left click of the mouse) whenever a word was displayed on the small window that remained open on the right corner of the screen. They were told to read the word and make the click response before its disappearance from the screen (a click response was considered valid only if it occurred prior to disappearance of the displayed word). Participants were told that the experimenter had an additional interest in the task of ‘executing delayed intention’, i.e., they were asked to click ‘Cancel’ if the word was ‘Indiana’ or ‘Catfish’ (one word from first half and another

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from the second half). The experimenter informed the participants to read each displayed word carefully, identify it and then select their response by clicking the appropriate button. To emphasize this, they were told that certain questions would be asked about the words later. They were told to pay equal attention to the multiple tasks at hand (one should not prioritize either the programming or the mouse clicking as they were equally important). Also, they were instructed to click only once. They were told to stop performing any task when a pop-up of ‘stop message’ appeared on the screen (accompanied by a sound). To make sure that participants had understood the instructions, they were asked to repeat the instructions in their own words to the experimenter. A practice trial followed when it was clear to the experimenter that the participants had fully understood the instructions. After the practice trial, the participants had the opportunity to ask questions about any concerns related the tasks before proceeding to the experimental tasks. Once the participants did not have any further queries, they moved on to the next step, i.e., typing the personal details followed by the experimental task. The experimental task proceeded by displaying first word when ‘Start’ button was clicked. After the task was over, the participants were given a sheet with a list of words and they were instructed to identify the words that they had paid attention to. The list contained the words displayed during the experimental task but randomized the positions along with other words that were never displayed during the experiment. This was mainly to check that participants did not simply make the click responses but paid attention to the words. At the end of the experiment, the participants were debriefed and requested to tell about their experiences regarding the tasks they had performed. Participants were encouraged to talk about any part of the experiment, their views and feelings about the tasks, and any experience that they would like to share with the experimenter. They used subjective report sheets for this purpose thus enabling the experimenter to have written information besides observational records. Every participant got a food coupon as an incentive with a note of thanks for their participation and cooperation in the research. 3. Results An alpha level of .05 was used in all the analyses to infer statistical significance throughout this paper unless otherwise indicated. 3.1. Mouse clicking task An analysis of variance (ANOVA) was carried out for 2 (attention: CA vs. PA) 3 (time orientation: monochrons vs. intermediates vs. polychrons) 2 (expertise: novices vs. experts) mixed factorial design with the first factor as within-group variable on non-click responses, the number of times that the participants failed to make click response


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Table 1 Means and standard deviations of non-click responses.

Mean Cases of Non-click Responses

0.9 0.8 0.7

Time orientation

0.6

Expertise

0.5

CA

0.4 0.3 0.2 0.1 0

N

Attention

CA

Mean

SD

Mean

SD

Monochron

Novice Expert Total

.38 .80 .58

.62 1.08 .89

.75 1.53 1.13

.93 1.25 1.15

16 15 31

Intermediate

Novice Expert Total

1.07 .55 .76

1.14 .76 .96

1.57 .80 1.12

1.16 .89 1.07

14 20 34

Polychron

Novice Expert Total

.40 .15 .26

.74 .37 .56

.47 .70 .60

.64 .86 .77

15 20 35

Total

Novice Expert Total

.60 .47 .53

.89 .79 .83

.91 .96 .94

1.02 1.04 1.02

45 55 100

PA Attention

Fig. 1. Non-click response as a function of attention.

PA

3.2. ProM task

Fig. 2. Non-click response as a function of time orientation.

Fig. 3. Non-click response as a function of time orientation and expertise.

when a word was displayed. The analysis revealed a significant main effect of attention (CA, M =.53 and PA, M = .94, see Fig. 1), F(1, 94) = 15.399, MSE = .542, p o.0005, Z2p = .141 and time orientation, F(2, 94) = 5.802, MSE = 1.027, p =.004, Z2p =.110. Fisher’s least significant difference (LSD) revealed that the polychrons (M = .86) had significantly fewer non-click responses (i.e., more mouse clicks) than monochrons (M =1.71) and intermediates (M= 1.88), whereas the last two did not differ from one another (Fig. 2). There was no main effect of expertise (novices, M = 1.51, experts, M =1.43), F(1, 94) = .014, p =.908, Z2p = .000). As shown in Fig. 3, There was a significant interaction between time orientation and expertise, F(2, 94) = 6.081, p =.003, Z2p = .115), such that among the monochrons, experts had more non-click responses than the novices but with the polychrons the trend is absent. There were fewer non-click responses for novices as compared to experts among the monochrons. But this was marginally significant, t(29) = 2.011, p =.054, two tailed. Table 1 summarizes the descriptive statistics.

To examine prospective memory performance the cases of habit intrusion error and overall prospective memory performance failure were measured. Habit intrusion error was measured as the number of responses where participants inappropriately clicked the ‘OK’ button instead of clicking the ‘Cancel’ button when a target occurred. The measure for overall ProM performance failure was the number of times participants failed to make a correct click response (irrespective of incorrect click response or failing to click at all) when the target occurred. Therefore, overall ProM performance failure is comprised of habit intrusion errors and other ProM errors. Besides habit intrusion errors and non-click responses, other errors like late responses, and haphazard responses (i.e., invalid click responses) including repetition errors (multiple click responses) form part of the overall ProM performance failure. Initial analysis showed that habit intrusion errors comprised 16.22% of the overall ProM performance failure (analysis excluded click responses after the disappearance of the target event) (see Fig. 4). An ANOVA was performed on the number of habit intrusion errors for 2 (attention: CA vs. PA) 3 (time orientation: monochrons vs. intermediates vs. polychrons) 2 (expertise: novices vs. experts) mixed factorial design with the first factor as within-group variable. The analysis revealed that there were no significant main effects. CA and PA had mean habit intrusion error of .11 and .07, respectively, F(1, 94)= .884, MSE=.078, p=.349, Z2p =.009. Monochrons, intermediate, and polychron had mean habit intrusion error of .19, .24, and .11, respectively, F(2, 94)=.602, MSE=.084, p=.550, Z2p =.013). Novices and experts had mean habit intrusion error of .11 and .24, respectively, F(1, 94)=2.271, p=.135, Z2p =.024. None of the interactions were significant


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0.8 Mean Cases of ProM Perforamnce Failure

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 CA

PA Attention

Fig. 4. Proportion of habit intrusion error in overall ProM performance failure.

Fig. 5. ProM performance failure as a function of attention.

Table 2 Means and standard deviations of habit intrusion errors. Time orientation

Expertise

N

Attention CA

PA

Mean

SD

Mean

SD

Monochron

Novice Expert Total

.188 .07 .13

.40 .26 .34

.00 .13 .06

.00 .35 .25

16 15 31

Intermediate

Novice Expert Total

.07 .25 .18

.27 .44 .39

.00 .10 .06

.00 .31 .24

14 20 34

Novice Expert Total

.00 .05 .03

.00 .22 .17

.07 .10 .09

.26 .31 .28

15 20 35

Novice Expert Total

.09 .13 .11

.29 .34 .31

.02 .11 .07

.15 .34 .26

45 55 100

Polychron

Total

suggesting that the factors did not have a combined effect on habit intrusion error. The descriptive statistics are presented in Table 2. When the proportion of habit intrusion error in valid click responses for ProM target was computed, there were 1.75 cases of habit intrusion error in every 10 valid click responses for ProM target. The PMTS score had a significantly negative correlation with ProM performance failure. On the whole, ProM performance failure was analyzed by performing an ANOVA for 2 (attention: CA vs. PA) 3 (time orientation: monochrons vs. intermediates vs. polychrons) 2 (expertise: novices vs. experts) mixed factorial design with the first factor as within-group variable. The analysis revealed a significant main effect of attention. As shown in Fig. 5, PA (M=.75) had significantly higher ProM performance failure than CA (M=.36), F(1, 94)=50.573, MSE=.152, po.0005, Z2p =.350). Time orientation had a significant main effect, F(2, 94)=3.952, MSE=.263, p=.023, Z2p =.078). Fisher’s LSD revealed that polychrons (M=.83) showed significantly lesser degree of ProM performance failure than monochrons (M=1.23) and intermediates (M=1.29). However, monochrons and intermediates

Fig. 6. ProM performance failure as a function of time orientation.

did not differ from one another. Fig. 6 illustrates the nature of the difference between the means. There was no main effect of expertise (novices, M=1.12, experts, M=1.11), F(1, 94)=.004, p=.951, Z2p o.0005. There was no statistical evidence for any interaction effect of the factors on overall ProM performance failure. Descriptive statistics are presented in Table 3. Furthermore, among the experts, those characterized by monochronicity had a higher level of prospective memory performance failure as compared to those characterized by polychronicity, t(32) = 2.25, p= .032, two tailed. 3.3. Programming performance The performance on programming was measured in terms of the number of programs successfully completed. The data were submitted to an ANOVA which included the between-group variables of time orientation (monochrons vs. intermediates vs. polychrons), and expertise (novices vs. experts). The analysis revealed that the main effect of time orientation (monochron, M= .77, intermediate = .71, polychron= .89) was not significant, F(2, 94) = .548, p = .580, MSE = .715, Z2p = .012). Expertise had a significant main effect, F(1, 94) = 29.026, p o .0005, Z2p = .236). The mean programs successfully completed for novices and experts were M = .29 and 1.20, respectively (see Fig. 7). Time orientation by expertise was not significant, F(2, 94) = .044, p= .957, Z2p =.001). Descriptive statistics are presented in Table 4.


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Table 3 Means and standard deviations of ProM performance failures (overall cases). Time orientation

Expertise

N

Attention CA

PA

Mean

SD

Mean

SD

Monochron

Novice Expert Total

.44 .40 .42

.51 .51 .50

.75 .87 .81

.45 .35 .40

16 15 31

Intermediate

Novice Expert Total

.43 .55 .50

.51 .51 .51

.86 .75 .79

.36 .44 .41

14 20 34

Polychron

Novice Expert Total

.20 .15 .17

.41 .37 .38

.67 .65 .66

.49 .49 .48

15 20 35

Total

Novice Expert Total

.36 .36 .36

.48 .49 .48

.76 .75 .75

.43 .44 .44

45 55 100

Mean Cases of Successfully Completed Programs

1.4 1.2 1

scale, novices had a mean self-rating of 3.89 and experts had a mean self-rating of 4.71, with SD of 1.57 and 1.50, respectively. A t-test revealed a significant difference between the two, t(98) = 2.67, p= .009, two tailed. With respect to job experience, none of the novices had any kind of job experience at the time of participating in the study whereas experts had a mean industrial on the job experience (industrial internship or earlier employment) of 4.25 months. Also, experts had a mean completed C++ projects (academic as well as on the job projects) of 3.02 as compared to a mean of 1.2 classroom assignment projects for the novices, Levene’s test was significant, t(75.996) = 2.265, p = .026, two tailed. When asked, participants indicated that they had understood the instructions of the ProM task. This was achieved through the word identification task where participants were encouraged to identify the words only if he/she was sure of the target and self-reports of the participants. The check on whether the participants knew that they had to make atypical response for the ‘targets’, revealed that performance failures were not a result of problems associated with understanding the task or remembering the targets (indicating that the performance issue is not of retrospective memory problem). All the participants except five easily identified the targets. These five were only able to identify one of the two targets.

0.8 0.6

4. Discussion

0.4 0.2 0 Novices

Experts Expertise

Fig. 7. Programming performance as a function of expertise.

Table 4 Means and standard deviations of successfully completed programs. Time orientation

Expertise

Mean

SD

N

Monochron

Novice Experts Total

.31 1.27 .77

.87 1.03 1.06

16 15 31

Intermediate


.14 1.10 .71

.36 .79 .80

14 20 34

Polychron


.40 1.25 .89

.63 1.07 .999

15 20 35

Total


.29 1.20 .79

.66 .95 .95

45 55 100

3.4. Other measures The self-rating of expertise in C++ programming reported before the commencement of the experiment was examined for novices and experts. On a 7 point Likert

The results of the experiment demonstrated that people are vulnerable to habit intrusion errors. Programmers had undifferentiated vulnerability to habit intrusion errors regardless of attention condition, time orientation, and expertise. The findings provided a primary understanding of the proportion of habit intrusion error in overall ProM performance failure. Interestingly, the occurrence of habit intrusion error was 1.62 times in every 10 cases of ProM performance failure. This proportion of habit intrusion error underscores the considerable vulnerability of individuals when a ProM task involves an atypical action. This figure is impressive considering the earlier analysis of the database of selfreported ProM errors of airline pilots by Nowinski et al. (2003). They reported that 19% of the errors involved instances in which the pilot performed a habitual task instead of the intended task. The results of the present study affirms that due to the environmental cues that facilitate activation of the habitual action, maintaining intentions in memory could suffer at the time of performing the right action is crucial. It implies that by making the response of clicking ‘OK’ button in a habitual manner, the intention of making an atypical response of clicking ‘Cancel’ button is feeble in the action schema. On the other hand, as a result of frequent performance of the same behavior, environmental cue triggers habitual response which is a rigid behavioral pattern that does not need top-down-driven processes (Danner et al., 2007). Without any explicit contemporaneous determination of a person, the stimulus is capable of directly setting off the habitual response once a stimulus–response combination is


learned (Danner, 2007). The observation of the participants’ behavior and self-reports during debriefing supported the interpretation. The experimenter observed that some of the participants expressed frustrating experiences when they made habit responses instead of the intended action (such as uttering ‘Oh God!’, ‘Oho’, etc.). During the debriefing, participants who had habit intrusion habit intrusion errors reported that their response were not intentional and realized the inappropriate action as soon as they made the response. Also, a check on whether the participants knew that they had to make an atypical response for the ‘targets’, gave a fair idea about their understanding of the tasks. The finding of high ProM performance failure under PA as compared to that of CA indicates that when a person is in the midst of the multiple tasks and occupied with a current task ProM processes suffer unless the cue is easily noticeable and the appropriate action is identified. In the context of programmer multitasking, the study created a situation where the person must monitor what was going on in a corner window. The participants busy with programming, had a situation where events occurred with low frequency. Therefore, a more salient cue would naturally facilitate performance in the ProM task. Such a demanding situation of handling multiple tasks interferes with ProM having difficulty to maintain the intention in working memory. Also, intention is rapidly forgotten when one is busily engaged in the current activities, making it difficult for the cognitive systems to maintain the intention in the focal awareness (Einstein et al., 2003). Moreover, holding an atypical intention to react at the right moment is not as effortless as in the case of a habitual activity. In other words, when engrossed in programming the programmers’ failure to realize a delayed intention is compounded by the nature of the target, i.e., whether or not the target requires more attention to be devoted in monitoring and identifying it for ProM action. This is consistent with results yielded by the analysis of non-click responses on the whole. There were more non-click responses for the under PA as compared to CA. That is why polychronic behavior has an important concern in this context since polychrons have the tendency to engage in multiple tasks concurrently. The finding of polychrons’ superior performance on ProM task over the monochrons as expected is in convergence with the finding that monochrons had more nonclicking responses. Besides, non-click responses had a significantly positive correlation with ProM performance failure. Having scores on the higher side of PMTS is linked with positive feeling about multitasking behavior (Lindquist and Kaufman-Scarborough, 2007) thus polychrons in general would be comfortable in monitoring what was happening in the other small window and consequently noticing the targets for ProM task. Surprisingly, novices and experts had undifferentiated ProM performance failure. Investigators had expected that experts would have an advantage over novices. Analysis of non-click responses and the significant interaction between time orientation and expertise provides

437

some insights. Due to a lack of expertise in the programming domain of C++ monochronic novices were at least intermittently able to do mouse clicking better than monochromic experts, whereas polychrons (irrespective of novices or experts) were able to perform mouse clicking concurrently while programming. However, unlike monochronic experts, it also means particularly that novice programmers who are monochrons will not be so engrossed in the programming task as such if the programming problems are difficult for them (since monochrons tend to focus on one thing, in this case mouse clicking). Also, the data indicate a possible contribution of the proportion of habit intrusion errors in overall prospective memory performance failure. The mean habit intrusion error for experts was more than the double of mean habit intrusion error for novices (even though both the groups were vulnerable to habit intrusion error). Essentially, what is more important is to understand the context and the underlying cognitive processes. It is clear that monochrons have a tendency to focus on one task at a time which in fact implies less attention devoted in monitoring. It is more likely for a person to recall the associated intention if a target is seen. Nevertheless the matter is not the case that monochrons forget the content of the ProM action or ignored a cue even if it was noticed. So, the finding does not imply higher cases of forgetting of the content of the ProM task among the monochrons. In post-experiment debriefing and word identification task, participants successfully identified the targets and knew what they had to do. There was no indication that monochrons and polychrons differed in identifying the targets. The results do not imply to assert that polychrons would have better performance on any ProM task in general. Further research in other contexts will be required to gain better understanding of the phenomenon since the experiment was pursued in a specific context of programmer multitasking. There is, however, a possible concern that monochrons had more cognitive workload under the multitasking environment than polychrons. Moreover, if we look at some of the distinct characteristics of experts as compared to novices, there is reasonably rich information about their performance differences. The findings of the study further substantiate the superior performance of experts over novices in programming (regardless of time orientation). In this study, experts showed significantly higher self-rating score on their expertise in C++ programming, indicating that they were more confident than novices in the programming task. One major concern is that novices lack experiences in real, domain-relevant situations from which experts gain knowledge (Kolodner, 1983). None of the novices had any kind of job experience whereas experts on an average had job experience of 4.25 months. Additionally, experts had completed more C++ projects (even though there was no equality of variance in this aspect). Schenk et al. (1998) noted the following points: (a) novices employ fewer cognitive processes and their knowledge structures are smaller and less detailed than those of experts, (b) experts are able to access

438


information quickly whereas novices require great effort, (c) in addition, because of differences in novice and expert knowledge organization, experts are more effective at recognizing appropriate problem-solving strategies for domain-specific issues than are novices. Novices have fewer heuristics (rules of thumb) that allow simplification of knowledge structure so as to reduce information demands (Schenk et al., 1998). Experts are also ascribed for having judgments which are more accurate and reliable, whose performance shows consummate skill and economy of effort, and who can deal effectively with rare or ‘tough’ cases (with special skills or knowledge derived from experience with sub-domains) (Hoffman et al., 1995). In summary, the study highlights the issues of attention, time orientation, and expertise having practical implications on how efficiently one performs programmer multitasking. It is implicative that further studies will need to identify in what way the difficulty level of the programming task would have affected the novices and experts in concurrently performing the mouse clicking task (having ProM task embedded). Also, recording multiple clicks could have provided additional information on errors committed by the participants. It appears from the data of non-click responses and the debriefing that novices found the programming problems difficult and it caused them to occasionally focus on mouse clicking (particularly, among monochrons). The investigators have the view that better understanding of habit intrusion errors in complex real-world task of programming could be achieved with converging evidences from different types of research strategies such as ethnographic studies, personal reports, and experimentations. The results of the study provide insights into the ways programmers deal with multitasking and related issues of ProM error having important implications not only in the context of HCI but also in most of the work situations that involve interaction with technological systems. It may be noted that human work has irreversibly become work with technology and the nature of work has changed to make the role of human cognition more important (Hollnagel and Cacciabue, 1999). Technological innovation and growing demands for computer mediated automation have in fact transformed the nature of work by putting greater emphasis on human cognitive systems and reducing physical functions. In general, the study will have practical significance with respect to user-interface design, usability, safety, system display designs, human and system performance evaluation, selection, training, crew resource management, work design, etc.

Acknowledgments We thank Mr. Naorem Khogendro for writing the Java based software, named IITB-Response Recorder (IITB-RR). We acknowledge the generous inputs of two anonymous reviewers in terms of meticulously providing constructive and helpful comments on our manuscript.

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