Multitasking Research Paper
Neuropsychology
Multitasking
By
Ryan Klette
Student number: 696553
_________________________________________________________________
TABLE OF CONTENTS
_________________________________________________________________
1. ESSAY……………………………………………………………………..…..…………2
2. CONCLUSION…..…….…………………………………………………………..…8
3. REFERENCES ……………………………………………………..……………..9
Multitasking
The purpose of this essay is to contextualise the value of multitasking in today’s high-speed, technology driven world. This paper will focus on cognitive effects of multitasking. Various perspectives will be presented both in favour as well as against multi-tasking. Furthermore, neuroanatomical areas associated with multi-tasking will be discussed. The paper will argue in favour of single task activities especially as this relates to the academic setting. However, it will also show contexts where multi-tasking may be relevant and useful. The paper will be concluded with a summative finding of cases presented and possible ways forward in dealing with what appears to be a challenge inextricably linked to modern day society.
Attention …show more content…
provides and important foundation on which to build arguments relating to multitasking. According to Wood et al. (2012) attention is the ability to focus on a chosen stimulus at the cost of filtering out or ignoring other stimuli. It can be further understood by examining its characteristics, which include alertness, selection of stimuli (according to priority) and its inherent limitation in capacity as a consequence of competing cognitive demands. Furthermore attention is seen to be flexible as individuals have choice about which tasks to focus on (Wood et al., 2012).
Multi-tasking refers to the activity of engaging with more than one task concurrently and can be indirectly examined by the degree of interference it causes. Interference may be seen as the slowing in response of one or both tasks as a result of cognitive bottlenecking, (Wood et al., 2012).
It is important to differentiate between alternatives to multi-tasking such as task switching. Rapid attention switching or task switching refers to attending to multiple stimuli by switching from one task to another rather than both concurrently. According to Dreher, Koechlin, Tierney, & Grafman (2008), multi-tasking activities place greater demand on working memory.
Wood et al. (2012) differentiate between types of interference caused by multi-tasking. General interference is associated with unrelated tasks in contrast to specific interference, which occurs on related tasks. Whilst both types compete for resources, specific interference is seen to have a higher impact on performance because similar cognitive resources are in demand increasing the likelihood of bottlenecking, (Wood et al., 2012).
In this view, performance on a visual task like editing a TV clip might be prone to more interference from another visual task like viewing photos on facebook than an auditory task such as speaking on the phone.
Broadbent’s filter model of attention can be used to further explain how attention works in relation to multitasking, Goldstein (2011). Broadbent proposed that humans process information at a very early stage and with limited capacity. Consequently a filter is needed to select which information to pass for further processing. He believed that all stimuli get evaluated for physical properties such as colour, pitch and tone. Information that passes initial evaluation can then become available to short-term and long-term memory, (Goldstein, 2011).
Broadbent used the dichotic listening experiment to test his theory. In the test participants are presented with different auditory stimuli. Typically headphones are used and each ear receives a different message. Participants are asked to focus on one channel and repeat the message out aloud. The test shows that recall for content in the unattended channel is very low. Even language changes in unattended channel tend to go unnoticed, (Goldstein, 2011).
Broadbent’s findings confirm that attention has a limited capacity and that the choice about what to focus on plays a critical role in what can get encoded into short and long-term memory. This suggests that multitasking is likely to be an ineffective strategy for learning if the objective is memory retention and comprehension.
Sanbonmatsu, Strayer, Medeiros-Ward, & Watson (2013) conducted a study to investigate individual differences in multi-taskers especially in areas of personality traits. Perceived multitasking ability, sensation seeking and impulsivity were measured by the Media Use Questionnaire, Sensation Seeking Scale (SSS) and Barratt Impulsivity Scale (BIS) respectively. In addition, the Operation span task (OSPAN) was used to measure actual multitasking ability and executive control, (Sanbonmatsu, Strayer, Medeiros-Ward, & Watson, 2013)
OSPAN results indicated a negative correlation between ability and multi-tasking activity. Those with better ability tend to multi-task less while those with lower abilities are prone to multi-task more. Further, perceptions about ability were found to be inflated. Those who (inaccurately) judged themselves as above average multi-taskers were also likely to multi-task more, (Sanbonmatsu et al., 2013).
A profile of typical qualities associated with multi-tasking emerged from their findings, (Sanbonmatsu et al., 2013). They found that highly impulsive, reward driven; sensation-seeking individuals are more likely to chronically multitask. Furthermore, findings from the OSPAN task show a negative correlation between multitasking and working memory and executive functioning. In other words, individuals with less working memory capacity and executive functioning were found to multi-task more.) Their findings also show that multi-tasking is associated with a lower ability to focus and block out interfering stimulus, (Sanbonmatsu et. al., 2013).
Dreher & Grafman (2003) designed a functional magnetic resonance imaging (fMRI) study to explore the differences in cognitive maps between single and multitasking processing. They found that both processes make use of the prefrontal parietal network. Furthermore, they identified the anterior cingulate and the lateral prefrontal cortex as neuroanatomical areas associated with multi-tasking.
In task switching activities, the left lateral prefrontal cortex and the bilateral intra-parietal sulcus regions were activated in contrast to activation in rostral anterior cingulate cortex under multitasking conditions. Consequently, Dreher & Grafman (2003) proposed that the anterior cingulate is associated with managing conflicting cognitive processes and the lateral prefrontal cortex is responsible for selecting appropriate neural pathways for task switching, (Dreher & Grafman, 2003).
In later research, Dreher, Koechlin, Tierney, & Grafman (2008) hypothesised the frontal polar cortex as a critical area of the brain responsible for multi-tasking. From neuroimaging studies they inferred Broadman’s area 10 to be associated with muti-tasking. By examining people with prefrontal cortical lesions they were able to show a correlation between damage to Broadman’s area 10 and deficits in multitasking capabilities, (Dreher et al.,2008).
Furthermore, their findings suggest that multitasking is lateralised in the left fronto-polar cortex. Participants in their study with damage to this area were seen to have increased multi-tasking deficit, (Dreher et al.,2008). However, these results cannot be seen as conclusive as their research included only one participant with damage to the right fronto-polar cortex and therefore further research is needed.
(Bowman, Levine, Waite, & Gendron, 2010) demonstrated that multitasking increases the duration of the primary task. They conducted an experiment where they exposed two groups to instant messaging (IM) while reading an academic text. One group had access to IM before and the other group during the reading task. A third control group did not have access to IM. Reading time and comprehension was measured after the exercise. Although their results did not indicate differences in comprehension, the group exposed to IM whilst reading took significantly longer to complete the reading task (not taking IM time into account), (Bowman et al., 2010).
Studies were modelled on real life scenarios where participants would not be aware of the timing of messages. Participants also had to respond to messages before returning to the reading task, which prevented negotiated interruption. Bowman et al. (2010) propose that increased reading time may be due to participants re-reading passages when disrupted.
This research implies that whilst students may consider multi-tasking a time saving activity, it seems more likely to lengthen duration of primary activity. Bowman et al. (2010) imply that student’s multi-task for efficiency. However, other reasons like lack of engagement with the material might also account for split in attention.
The Executive-Process/Interactive-Control (EPIC) theory maintains that multi-tasking skills can be developed through repetition and practice, Chiappe, Conger, Liao, Caldwell, & Vu, (2012). According to Chiappe et al. (2012) declarative memory can be converted into non-declarative (procedural) memory through specialised training exercises. Critics argue that applicability is likely to be limited to basic linear tasks and that the theory lacks real world applicability, Wood et al., (2012). However Chiappe et al. (2012) contradict this argument by demonstrating how exposure to more complex activities like action video games can be used to strengthen multi-tasking abilities.
In their research, Chiappe et al. (2012) exposed experimental group to a 10-week action video game trial. The control group was not given any training. The reputable Multi-Attribute Task Battery (MATB) was used as a means of correlating findings to real word performance. Findings showed performance gains in secondary tasks without impacting primary task performance in the group that received the video game treatment. The experimental group improved in tasks that involved monitoring activities like response times to instrumentation, (Chiappe et al.,2012)
Video games provide cognitive authenticity as training and real world environments have similar processing demands. Chiappe et al. (2012) show that video game training make it possible for the generation of action schemata that can be applied to real world scenarios outside of simulation. This implies that at least for some scenarios, multi-tasking may be seen as trainable skill and also demonstrates its applicability to certain domains. For example, multi-tasking seems especially relevant for training in tasks like flying.
Wood et al., (2012) argue that within an academic context, multi-tasking tends to play a more disruptive role. They conducted a study to measure the effect of offline multi-tasking on classroom performance. Performance for groups of students using digital technologies was compared to a control group without access to these technologies, (Wood et al., 2012).
Their findings showed that the experimental group with access to digital technologies in the classroom experienced memory and performance related deficits. Furthermore they found a drop in grade average for students using Facebook and IM in lectures. Differences in types of media were also shown to have varying effects on performance. Students using messaging technologies like IM, which place urgency on demand for attention were more likely to engage in multi-tasking, (Wood et al., 2012).
Junco & Cotton (2011) propose that multitasking in an academic setting is likely to limit cognitive processing and dampen depth of learning. Their research reveals a high frequency of students using non-course based Information and communication technology (ICT’s) whilst doing coursework.
They undertook a study to measure effects of multitasking on grade point average. Web survey data from a large number of students was used to extrapolate their findings. The data revealed a negative relationship between using ICT’s whilst engaging in course work and overall GPA. Junco & Cotton (2011) put forward that simultaneously addressing two tasks reduces the ability to pay attention to and process either of them. These findings further support results produced by Wood et al. (2012).
Junco & Cotton (2011) make two important points related to multitasking and learning.
Firstly, that multitasking is likely to limit learning because essential processing is disrupted as ICT’s use cognitive resources that would otherwise be assigned to course work. Secondly, that multi-tasking carries costs on representational holding due to the added tax on working memory. Representation holding is burdened by additional cognitive demands that the alternative task carries, (Junco & Cotton, 2011). This may limit depth of learning, as working memory is necessary for creating meaning and encoding information for deeper learning. For example, texting while in lectures may inhibit learning, as less working memory is available for course related
information.
(Watson & Strayer, 2010) challenge main stream thinking and show that some individuals have extraordinary multitasking ability. They used the OSPAN test to assess ability to engage in driving while simultaneously engaging in an auditory exercise. They predicted performance declines when the driving task was paired with the auditory task, (Watson & Strayer, 2010).
200 hundred students comprising of 90 male and 110 females participated in the study. The group was divided into dual task and single task activities with the primary task being driving (using a simulator). Their results revealed that a very small portion of the group were able to perform both attention-demanding tasks simultaneously without any impact on performance, (Watson & Strayer, 2010).
Research conducted by Medeiros-Ward (2010) indicates that this small segment able to multi-task without cost (referred to as supertaskers) show a lower level of activity in parts of the brain associated with multitasking (including the pre-frontal cortex, dorso lateral prefrontal cortex and anterior cingulate). This suggests more efficient neural networks as lower activity in key multitasking regions imply more efficient processing.
Watson & Strayer (2010) point out that current research approaches this topic as it relates to group norms. Their findings highlight the importance of assessing individual differences as a means of further understanding cognitive processes involved in successful/ non-cost based multi-tasking. They propose that understanding cognitive processes in supertaskers might highlight where ‘normal’ brains fall short, (Watson & Strayer, 2010).
This paper has presented a number of studies that show the negative effect of multitasking. Context is key when assessing relevance and applicability of multitasking, as findings have shown. Multitasking within the context of education should be carefully pursued as majority of research in this area point to deficits outweighing any perceived gains. Other examples presented showed negative effects in other areas such as multitasking while driving.
It can be concluded that drops in performance as a result of multitasking could be applied to many other areas. However, studies have been presented that clearly show exceptions to the rule. Therefore, this paper argues for a focussed single-task approach for contexts where negative performance effect from multi-tasking is obvious. Further, multi-tasking also seems unavoidable in today’s world of information overload and continual advancements in technology. Therefore, this paper proposes that multi-tasking is also an issue about creating awareness and education, which may lead to better decision-making. Finally, our future cognitive potentials are unknown. Developments in understanding genetic and neural markers that set supertaskers aside may set new precedents for this topic. References:
Bowman, L. L., Levine, L. E., Waite, B. M., & Gendron, M. (2010). Can students really multitask? An experimental study of instant messaging while reading. Computers & Education, 54(4), 927-931.
Chiappe, D., Conger, M., Liao, J., Caldwell, J. L., & Vu, K.-P. L. (2012). Improving multi-tasking ability through action videogames. Applied Ergonomics.
Dreher, J.-C., & Grafman, J. (2003). Dissociating the roles of the rostral anterior cingulate and the lateral prefrontal cortices in performing two tasks simultaneously or successively. Cerebral Cortex, 13(4), 329-339.
Dreher, J.-C., Koechlin, E., Tierney, M., & Grafman, J. (2008). Damage to the fronto-polar cortex is associated with impaired multitasking. PLoS One, 3(9), e3227.
Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59(2), 505-514.
Sanbonmatsu, D. M., Strayer, D. L., Medeiros-Ward, N., & Watson, J. M. (2013). Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking. PLoS One, 8(1), e54402.
Shih, S.-I. (2013). A Null Relationship between Media Multitasking and Well-Being. PLoS One, 8(5), e64508.
Watson, J. M., & Strayer, D. L. (2010). Supertaskers: Profiles in extraordinary multitasking ability. Psychonomic Bulletin & Review, 17(4), 479-485.
Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374.
Goldstein, B. E. (2011). Cognitive Psychology (3rd International Edition ed.). Stamford, CT 06902: Wadsworth, Cengage Learning.
Multitasking
By
Ryan Klette
Student number: 696553
_________________________________________________________________
TABLE OF CONTENTS
_________________________________________________________________
1. ESSAY……………………………………………………………………..…..…………2
2. CONCLUSION…..…….…………………………………………………………..…8
3. REFERENCES ……………………………………………………..……………..9
Multitasking
The purpose of this essay is to contextualise the value of multitasking in today’s high-speed, technology driven world. This paper will focus on cognitive effects of multitasking. Various perspectives will be presented both in favour as well as against multi-tasking. Furthermore, neuroanatomical areas associated with multi-tasking will be discussed. The paper will argue in favour of single task activities especially as this relates to the academic setting. However, it will also show contexts where multi-tasking may be relevant and useful. The paper will be concluded with a summative finding of cases presented and possible ways forward in dealing with what appears to be a challenge inextricably linked to modern day society.
Attention …show more content…
provides and important foundation on which to build arguments relating to multitasking. According to Wood et al. (2012) attention is the ability to focus on a chosen stimulus at the cost of filtering out or ignoring other stimuli. It can be further understood by examining its characteristics, which include alertness, selection of stimuli (according to priority) and its inherent limitation in capacity as a consequence of competing cognitive demands. Furthermore attention is seen to be flexible as individuals have choice about which tasks to focus on (Wood et al., 2012).
Multi-tasking refers to the activity of engaging with more than one task concurrently and can be indirectly examined by the degree of interference it causes. Interference may be seen as the slowing in response of one or both tasks as a result of cognitive bottlenecking, (Wood et al., 2012).
It is important to differentiate between alternatives to multi-tasking such as task switching. Rapid attention switching or task switching refers to attending to multiple stimuli by switching from one task to another rather than both concurrently. According to Dreher, Koechlin, Tierney, & Grafman (2008), multi-tasking activities place greater demand on working memory.
Wood et al. (2012) differentiate between types of interference caused by multi-tasking. General interference is associated with unrelated tasks in contrast to specific interference, which occurs on related tasks. Whilst both types compete for resources, specific interference is seen to have a higher impact on performance because similar cognitive resources are in demand increasing the likelihood of bottlenecking, (Wood et al., 2012).
In this view, performance on a visual task like editing a TV clip might be prone to more interference from another visual task like viewing photos on facebook than an auditory task such as speaking on the phone.
Broadbent’s filter model of attention can be used to further explain how attention works in relation to multitasking, Goldstein (2011). Broadbent proposed that humans process information at a very early stage and with limited capacity. Consequently a filter is needed to select which information to pass for further processing. He believed that all stimuli get evaluated for physical properties such as colour, pitch and tone. Information that passes initial evaluation can then become available to short-term and long-term memory, (Goldstein, 2011).
Broadbent used the dichotic listening experiment to test his theory. In the test participants are presented with different auditory stimuli. Typically headphones are used and each ear receives a different message. Participants are asked to focus on one channel and repeat the message out aloud. The test shows that recall for content in the unattended channel is very low. Even language changes in unattended channel tend to go unnoticed, (Goldstein, 2011).
Broadbent’s findings confirm that attention has a limited capacity and that the choice about what to focus on plays a critical role in what can get encoded into short and long-term memory. This suggests that multitasking is likely to be an ineffective strategy for learning if the objective is memory retention and comprehension.
Sanbonmatsu, Strayer, Medeiros-Ward, & Watson (2013) conducted a study to investigate individual differences in multi-taskers especially in areas of personality traits. Perceived multitasking ability, sensation seeking and impulsivity were measured by the Media Use Questionnaire, Sensation Seeking Scale (SSS) and Barratt Impulsivity Scale (BIS) respectively. In addition, the Operation span task (OSPAN) was used to measure actual multitasking ability and executive control, (Sanbonmatsu, Strayer, Medeiros-Ward, & Watson, 2013)
OSPAN results indicated a negative correlation between ability and multi-tasking activity. Those with better ability tend to multi-task less while those with lower abilities are prone to multi-task more. Further, perceptions about ability were found to be inflated. Those who (inaccurately) judged themselves as above average multi-taskers were also likely to multi-task more, (Sanbonmatsu et al., 2013).
A profile of typical qualities associated with multi-tasking emerged from their findings, (Sanbonmatsu et al., 2013). They found that highly impulsive, reward driven; sensation-seeking individuals are more likely to chronically multitask. Furthermore, findings from the OSPAN task show a negative correlation between multitasking and working memory and executive functioning. In other words, individuals with less working memory capacity and executive functioning were found to multi-task more.) Their findings also show that multi-tasking is associated with a lower ability to focus and block out interfering stimulus, (Sanbonmatsu et. al., 2013).
Dreher & Grafman (2003) designed a functional magnetic resonance imaging (fMRI) study to explore the differences in cognitive maps between single and multitasking processing. They found that both processes make use of the prefrontal parietal network. Furthermore, they identified the anterior cingulate and the lateral prefrontal cortex as neuroanatomical areas associated with multi-tasking.
In task switching activities, the left lateral prefrontal cortex and the bilateral intra-parietal sulcus regions were activated in contrast to activation in rostral anterior cingulate cortex under multitasking conditions. Consequently, Dreher & Grafman (2003) proposed that the anterior cingulate is associated with managing conflicting cognitive processes and the lateral prefrontal cortex is responsible for selecting appropriate neural pathways for task switching, (Dreher & Grafman, 2003).
In later research, Dreher, Koechlin, Tierney, & Grafman (2008) hypothesised the frontal polar cortex as a critical area of the brain responsible for multi-tasking. From neuroimaging studies they inferred Broadman’s area 10 to be associated with muti-tasking. By examining people with prefrontal cortical lesions they were able to show a correlation between damage to Broadman’s area 10 and deficits in multitasking capabilities, (Dreher et al.,2008).
Furthermore, their findings suggest that multitasking is lateralised in the left fronto-polar cortex. Participants in their study with damage to this area were seen to have increased multi-tasking deficit, (Dreher et al.,2008). However, these results cannot be seen as conclusive as their research included only one participant with damage to the right fronto-polar cortex and therefore further research is needed.
(Bowman, Levine, Waite, & Gendron, 2010) demonstrated that multitasking increases the duration of the primary task. They conducted an experiment where they exposed two groups to instant messaging (IM) while reading an academic text. One group had access to IM before and the other group during the reading task. A third control group did not have access to IM. Reading time and comprehension was measured after the exercise. Although their results did not indicate differences in comprehension, the group exposed to IM whilst reading took significantly longer to complete the reading task (not taking IM time into account), (Bowman et al., 2010).
Studies were modelled on real life scenarios where participants would not be aware of the timing of messages. Participants also had to respond to messages before returning to the reading task, which prevented negotiated interruption. Bowman et al. (2010) propose that increased reading time may be due to participants re-reading passages when disrupted.
This research implies that whilst students may consider multi-tasking a time saving activity, it seems more likely to lengthen duration of primary activity. Bowman et al. (2010) imply that student’s multi-task for efficiency. However, other reasons like lack of engagement with the material might also account for split in attention.
The Executive-Process/Interactive-Control (EPIC) theory maintains that multi-tasking skills can be developed through repetition and practice, Chiappe, Conger, Liao, Caldwell, & Vu, (2012). According to Chiappe et al. (2012) declarative memory can be converted into non-declarative (procedural) memory through specialised training exercises. Critics argue that applicability is likely to be limited to basic linear tasks and that the theory lacks real world applicability, Wood et al., (2012). However Chiappe et al. (2012) contradict this argument by demonstrating how exposure to more complex activities like action video games can be used to strengthen multi-tasking abilities.
In their research, Chiappe et al. (2012) exposed experimental group to a 10-week action video game trial. The control group was not given any training. The reputable Multi-Attribute Task Battery (MATB) was used as a means of correlating findings to real word performance. Findings showed performance gains in secondary tasks without impacting primary task performance in the group that received the video game treatment. The experimental group improved in tasks that involved monitoring activities like response times to instrumentation, (Chiappe et al.,2012)
Video games provide cognitive authenticity as training and real world environments have similar processing demands. Chiappe et al. (2012) show that video game training make it possible for the generation of action schemata that can be applied to real world scenarios outside of simulation. This implies that at least for some scenarios, multi-tasking may be seen as trainable skill and also demonstrates its applicability to certain domains. For example, multi-tasking seems especially relevant for training in tasks like flying.
Wood et al., (2012) argue that within an academic context, multi-tasking tends to play a more disruptive role. They conducted a study to measure the effect of offline multi-tasking on classroom performance. Performance for groups of students using digital technologies was compared to a control group without access to these technologies, (Wood et al., 2012).
Their findings showed that the experimental group with access to digital technologies in the classroom experienced memory and performance related deficits. Furthermore they found a drop in grade average for students using Facebook and IM in lectures. Differences in types of media were also shown to have varying effects on performance. Students using messaging technologies like IM, which place urgency on demand for attention were more likely to engage in multi-tasking, (Wood et al., 2012).
Junco & Cotton (2011) propose that multitasking in an academic setting is likely to limit cognitive processing and dampen depth of learning. Their research reveals a high frequency of students using non-course based Information and communication technology (ICT’s) whilst doing coursework.
They undertook a study to measure effects of multitasking on grade point average. Web survey data from a large number of students was used to extrapolate their findings. The data revealed a negative relationship between using ICT’s whilst engaging in course work and overall GPA. Junco & Cotton (2011) put forward that simultaneously addressing two tasks reduces the ability to pay attention to and process either of them. These findings further support results produced by Wood et al. (2012).
Junco & Cotton (2011) make two important points related to multitasking and learning.
Firstly, that multitasking is likely to limit learning because essential processing is disrupted as ICT’s use cognitive resources that would otherwise be assigned to course work. Secondly, that multi-tasking carries costs on representational holding due to the added tax on working memory. Representation holding is burdened by additional cognitive demands that the alternative task carries, (Junco & Cotton, 2011). This may limit depth of learning, as working memory is necessary for creating meaning and encoding information for deeper learning. For example, texting while in lectures may inhibit learning, as less working memory is available for course related
information.
(Watson & Strayer, 2010) challenge main stream thinking and show that some individuals have extraordinary multitasking ability. They used the OSPAN test to assess ability to engage in driving while simultaneously engaging in an auditory exercise. They predicted performance declines when the driving task was paired with the auditory task, (Watson & Strayer, 2010).
200 hundred students comprising of 90 male and 110 females participated in the study. The group was divided into dual task and single task activities with the primary task being driving (using a simulator). Their results revealed that a very small portion of the group were able to perform both attention-demanding tasks simultaneously without any impact on performance, (Watson & Strayer, 2010).
Research conducted by Medeiros-Ward (2010) indicates that this small segment able to multi-task without cost (referred to as supertaskers) show a lower level of activity in parts of the brain associated with multitasking (including the pre-frontal cortex, dorso lateral prefrontal cortex and anterior cingulate). This suggests more efficient neural networks as lower activity in key multitasking regions imply more efficient processing.
Watson & Strayer (2010) point out that current research approaches this topic as it relates to group norms. Their findings highlight the importance of assessing individual differences as a means of further understanding cognitive processes involved in successful/ non-cost based multi-tasking. They propose that understanding cognitive processes in supertaskers might highlight where ‘normal’ brains fall short, (Watson & Strayer, 2010).
This paper has presented a number of studies that show the negative effect of multitasking. Context is key when assessing relevance and applicability of multitasking, as findings have shown. Multitasking within the context of education should be carefully pursued as majority of research in this area point to deficits outweighing any perceived gains. Other examples presented showed negative effects in other areas such as multitasking while driving.
It can be concluded that drops in performance as a result of multitasking could be applied to many other areas. However, studies have been presented that clearly show exceptions to the rule. Therefore, this paper argues for a focussed single-task approach for contexts where negative performance effect from multi-tasking is obvious. Further, multi-tasking also seems unavoidable in today’s world of information overload and continual advancements in technology. Therefore, this paper proposes that multi-tasking is also an issue about creating awareness and education, which may lead to better decision-making. Finally, our future cognitive potentials are unknown. Developments in understanding genetic and neural markers that set supertaskers aside may set new precedents for this topic. References:
Bowman, L. L., Levine, L. E., Waite, B. M., & Gendron, M. (2010). Can students really multitask? An experimental study of instant messaging while reading. Computers & Education, 54(4), 927-931.
Chiappe, D., Conger, M., Liao, J., Caldwell, J. L., & Vu, K.-P. L. (2012). Improving multi-tasking ability through action videogames. Applied Ergonomics.
Dreher, J.-C., & Grafman, J. (2003). Dissociating the roles of the rostral anterior cingulate and the lateral prefrontal cortices in performing two tasks simultaneously or successively. Cerebral Cortex, 13(4), 329-339.
Dreher, J.-C., Koechlin, E., Tierney, M., & Grafman, J. (2008). Damage to the fronto-polar cortex is associated with impaired multitasking. PLoS One, 3(9), e3227.
Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59(2), 505-514.
Sanbonmatsu, D. M., Strayer, D. L., Medeiros-Ward, N., & Watson, J. M. (2013). Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking. PLoS One, 8(1), e54402.
Shih, S.-I. (2013). A Null Relationship between Media Multitasking and Well-Being. PLoS One, 8(5), e64508.
Watson, J. M., & Strayer, D. L. (2010). Supertaskers: Profiles in extraordinary multitasking ability. Psychonomic Bulletin & Review, 17(4), 479-485.
Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374.
Goldstein, B. E. (2011). Cognitive Psychology (3rd International Edition ed.). Stamford, CT 06902: Wadsworth, Cengage Learning.