This webpage provides an in-depth set of comments and revisions for a sample APA research report written for PSYC 3450 Experimental Psychology. The bottom of the page shows two version of the manuscript, the original version, and a revised and edited version. The main body of this webpage breaks down the original paper into sections, and provides discussion on the editing and comments for each section of the manuscript.
Rapid changes in technology, social interface and work-place cost cutting have accelerated task switch demands on our society. For example, the ubiquity of cell-phone use seems to result in an almost irresistible instinct to respond to constant interruptions at any time and any place. This creates pressure to adapt to frequent interruption of any current task – even while driving or walking. Does this result in a reduction of response time leading to an increasing risk of avoidable accidents? Another example is the implementation of open office configurations (commonly known as the “bull-pen model”) to reduce overhead costs for businesses. This set-up requires a worker to switch quickly from one task to another due to an inability to control interruptions. Multi-tasking has become a desirable skill in the workplace, but do the costs of repeated task switching degenerate productivity and increase error thus decreasing any cost benefits of the open office model? Because of the intensification of modern task-switching, it is becoming increasingly relevant to determine if rapid task-switching negatively impacts task performance.
The good: The first paragraph should grab the reader’s attention and communicate the general topic of the research. This opening paragraph does both by relating task-switching performance costs to real-world situations like using cell-phones or working in an open-office environment.
The bad: The introduction focuses too strongly on the real-world examples, and a reader of the paper might easily be led to think that this research is about multi-tasking in an open-office environment. Some of the claims made in the introduction are not self-evident and should be supported with citations or other evidence. The transition between the examples and the purpose of the present research is not smooth.
Original
Rapid changes in technology, social interface and work-place cost cutting have accelerated task switch demands on our society.
Rewrite:
Everyday situations often require people to juggle the demands of multiple tasks.
It is not self-evident that these factors are accelerating task switch demands, so this kind of statement should be followed by a citation. Terms like “social interface” and “task switch demands” are not well known, and need to be defined in order for the reader to understand their meaning. This sentence is functioning to introduce the idea that people are commonly faced with task-switching demands in everyday life, and this general idea could be expressed in a more direct, simple, and uncontroversial fashion.
Original
For example, the ubiquity of cell-phone use seems to result in an almost irresistible instinct to respond to constant interruptions at any time and any place.
Rewrite:
For example, many people leave their cell-phones on throughout the day allowing incoming calls and messages to interrupt ongoing activities.
Good: The 1st sentence is abstract and needs to be followed by a concrete example, this 2nd sentence fills this role. Bad: The specific meaning of this sentence is not clear. It could mean that cell-phone users allow themselves to be routinely interrupted whenever their phone rings, or it could mean that the act of using cell phones causes users to be susceptible to interruption in general. The current phrasing specifically suggests that the large number of cell phones being used actually causes anybody to be interrupted by anything anywhere (probably not intended by the author). The sentence is too wordy. The use of “seems to result in an almost” is bad for two reasons. First, “seems to” is redundant with “an almost”, both of these express doubt in the claim. Second, by expressing doubt twice in a row, the author is convincing the reader that they should not take their claim very seriously.
Original
This creates pressure to adapt to frequent interruption of any current task – even while driving or walking. Does this result in a reduction of response time leading to an increasing risk of avoidable accidents?
Rewrite:
Cell phone use can be a major source of distraction for other tasks like walking or driving. Indeed, many states have made texting while driving illegal because texting while driving is assumed to seriously impair driving performance.
The word “this” in both sentences is indefinite and it’s referent is unclear. Starting sentences with “this” is generally bad form and should be avoided. The intended meaning of these two sentences is unclear, but appears to center around possible negative consequences of task-switching. These sentiments can be expressed more directly and without posing a question.
Original:
Another example is the implementation of open office configurations (commonly known as the “bull-pen model”) to reduce overhead costs for businesses. This set-up requires a worker to switch quickly from one task to another due to an inability to control interruptions. Multi-tasking has become a desirable skill in the workplace, but do the costs of repeated task switching degenerate productivity and increase error thus decreasing any cost benefits of the open office model?
Rewrite:
Delete
Good: The example is interesting and noteworthy, and establishes that task-switching demands in the workplace may affect productivity. Bad: Given that cell-phones and driving has already been used as a real-world example, the open-office example is not needed. The author elaborates on the open-office example to the point where a reader might become convinced that the research paper is about task-switching in an office context. Another option would be to delete the cell-phone and go with the open-office example. The final sentence poses a question that will not be answered by this research paper, and this would be very misleading to a reader.
Original:
Because of the intensification of modern task-switching, it is becoming increasingly relevant to determine if rapid task-switching negatively impacts task performance.
Good: The author concludes their first paragraph with a transition sentence linking the real-world examples of task-switching to research about task-switching. Bad: The author suggests that because task-switching demands have increased in the real-world, it becomes important to determine if task-switching negatively impacts task performance. The motivation is partly fine. Task-switching happens in the real world, and provides some motivation for research on the topic. However, it does not motivate the specific need to determine if task-switching negatively impacts performance. This fact has already been determined by research in the literature. So, the motivation for the purpose of the present research report is misplaced. The purpose of the present research was to replicate the task-switching cost in a laboratory procedure. This purpose is very simple and straightforward, and should be motivated in a manner consistent with these simple aims.
Everyday situations often require people to juggle the demands of multiple tasks. For example, many people leave their cell-phones on throughout the day allowing incoming calls of messages to interrupt ongoing activities. Cell phone use can be a major source of distraction for other tasks like walking or driving. Indeed, many states have made texting while driving illegal because texting while driving is assumed to seriously impair driving performance. The notion that switching between tasks can impair performance is a general phenomenon, termed task-switching costs (for a review see, Monsell, 2003). The purpose of the present experiment is to validate a computerized laboratory procedure for measuring task-switching costs.
Laboratory studies have demonstrated that repeated task switching results in increased error and reaction time. It has been proposed that cognitive reconfiguration between tasks may cause Task Switch Costs: a delayed reaction to stimuli and a reduction in accuracy. The cognitive reconfiguration that is necessary to adapt to novel procedure results in delayed and accurate transition from one task to another. (Monsell, 2003; Rogers, et al., 1995).
Rogers and colleagues (1995) investigated if task preparation time may mitigate task set reconfiguration costs. They designed a study model in which switch and non-switch tasks were presented in one trial block, using what is called an “alternating runs paradigm.” They incrementally increased the preparation time between task cue and display of target for each trial and measured any changes in reaction time. Rogers used disparate task cues: a letter for Task 1 and a number for Task 2. They found that increased preparation time reduces time switch costs, but contend this decrease is minimal.
Monsell (2003) reports that a residual shadow of the previous task procedure, or ‘residual cost’ creates interference with execution of the new task. Monsell also used the alternating runs paradigm, reasoning that this model is more useful than an alternate study structure that runs separate trial blocks for task-repeat and task-switch tasks. In this study, Monsell examined whether practice before trial execution would reduce task reconfiguration costs, and reported that practice produced only a nominal reduction in reaction time.
The body of the introduction should elaborate on the background information necessary for understanding the present research. In this example, it would be appropriate to describe how task-switching experiments operationalize/define and measure task-switching costs, and how these costs to performance are explained.
The good: The author briefly describes that prior research has demonstrated that task-switching costs exist, and then briefly describes the task-set reconfiguration hypothesis to account for the switch-costs. The remaining two paragraphs provide informative background information about manipulations that modulate switch-costs and that are relevant to testing predictions of the task-set reconfiguration hypothesis
The bad: The concept of task-switching and task-switching costs should be elaborated more fully with a worked out example. The task-set reconfiguration hypothesis should be explained in more detail. The two paragraphs about other prior work could be placed in the general discussion.
Original
Laboratory studies have demonstrated that repeated task switching results in increased error and reaction time. It has been proposed that cognitive reconfiguration between tasks may cause Task Switch Costs: a delayed reaction to stimuli and a reduction in accuracy. The cognitive reconfiguration that is necessary to adapt to novel procedure results in delayed and accurate transition from one task to another. (Monsell, 2003; Rogers, et al., 1995).
Rewrite:
The influence of task-switching on performance has been a topic of interest for several decades (for early work see, Jersild, 1927). Task-switching effects are commonly investigated by having participants complete at least two different tasks. For example, on each trial a participant could be presented with a single task cue and stimulus. In the procedure used in the present research, the stimulus was a number between 1 and 9 (excluding the number 5). The task cue “odd/even” instructed subjects to determine whether the number was odd or even by pressing one of two response keys. The task cue “magnitude” instructed subjects to determine whether the number was larger or smaller than five by pressing one of two response keys. In this design, the participant completes several trials and the task order is randomized by the computer. On each trial, the participant is instructed to make their response as quickly and accurately as possible.
The primary question of interest was whether switching between tasks influences task performance as measured by reaction time and accuracy on each trial. The two important conditions were task-repetition and task-switch trials. Task-repeat trials refer to trial orders where the same task was repeated across trials (e.g., the previous trial involved an odd/even judgment and the current trial involved an odd/even judgment). Task-switch trials refer to trial orders where different tasks were completed across trials (e.g., the previous trial involved an odd/even judgment and the current trial involved a magnitude judgment). Typically, responses are slower and less accurate for task-switch trials than task-repeat trials, and this difference is termed the task-switching cost.
The task-set reconfiguration hypothesis provides one explanation of task-switching costs. According to this view, task performance in general relies on cognitive representations of the task at hand, referred to as task-sets. Task-sets can be likened to a recipe, they provide the instructions and and rules for performing a current task. A major assumption of the task-set reconfiguration hypothesis is that task-sets take time to activate. This assumption explains why task-switching produces a cost to performance in terms of speed and accuracy. For example, performance following a switch is slow because of the time taken to forget the previous task-set and activate the new task set. Performance following a task-repeat is not slowed because the same task-set can be applied. Furthermore, performance could become more error prone after a task switch because the new task-set may not be fully activated, and thus not able to accurately control all aspects of performance.
Original:
The present study is a laboratory experiment. We also utilized the alternating runs model used by Monsell and Rogers to collect data and compare measures of response time during repeated task and task switch trials. Unlike the experiment models used in the studies designed by Rogers and colleagues (1995) and that of Monsell (2003), we chose to use identical targets for both tasks: in this case, a letter for Task 1, and a number for Task 2. We measured the effects of task-switch on reaction time and error rates and chose to limit our analysis to reaction time only. We expected to find data that supports previous findings that task switch trials result in greater costs in reaction time when compared with non-switch trials.
The good: This paragraph is used to transition into the specific aims of the current experiment. The bad: Some of the claims are incorrect. For example the lab experiment did not use the alternating procedure. We did not use a letter for task 1. We measured both RT and errors.
Rewrite:
The purpose of the present experiment was to replicate the task-switching cost in a laboratory procedure. As mentioned previously, on each trial participants received a number between 1 and 9 (excluding 5) and a task cue indicating an odd/even or magnitude judgment. Trial order was randomized. The task-set reconfiguration hypothesis predicts that reaction times will be slower and less accurate for task-switch than repeat trials.
Original
Method
Participants or Subjects
Twenty-nine Brooklyn College undergraduate students participated this study to fulfill a requirement for an Experimental Psychology course. Experimenters also served as subjects in this study.
Apparatus
The task-switching experiment was administered on a desktop computer operating METACARD software. The experiment design utilized the numbers 1,2,3,4,5,6,7,8, and 9 as target stimuli with a task cue of either odd/even or greater than or less than 5. The response keys were as follows: z = odd, x = even and n <5, m > 5.
Procedure
The experiment was a within subject design with task switch and task repeat as the independent variables and response time and error rates as the two dependent variables.We utilized a within subject study model. There were 100 trials per subject. Each trial block consisted of 50 task switches and 50 task repeat conditions. Both conditions were presented in a single trial block utilizing an alternating runs paradigm. METACARD software generated randomized task repeat and task switch conditions within each trial block. The subjects were instructed to respond as quickly as possible using the designated response keys to indicate whether task cue was odd/even or greater/less than 5 based on the instruction of the task cue displayed above the target stimulus. A fixation cross was displayed for 500 milliseconds followed by a simultaneous display of the task cue and one number The task cue and stimulus number remained onscreen until the subject responded. The subject was tasked to respond correctly to the task cue instruction more quickly as possible utilizing the correct response key to report if the number was odd/even, or greater/less than 5. The METACARD program recoded response time and accuracy.
Rewrite
Method
Participants or Subjects
Twenty-nine Brooklyn College undergraduates participated to fulfill a requirement for an Experimental Psychology course.
Apparatus
The task-switching experiment was administered on a desktop computer operating METACARD software. The experiment design utilized the numbers 1,2,3,4,6,7,8, and 9 as target stimuli with a task cue of either odd/even or greater than or less than 5. The response keys were as follows: z = odd, x = even and n <5, m > 5.
Procedure The experiment was a within subject design with task switch and task repeat as the independent variables and response time and error rates as the two dependent variables. There were 100 trials per subject. Each trial block consisted of 50 odd/even trials, and 50 magnitude trials. Trial order was randomized for each subject. As a result, each subject received approximately 50% task-repeat trials, and 50% task-switch trials.
On each trial, a fixation cross was displayed for 500 milliseconds followed by a simultaneous display of the task cue and a target number. The cue and target remained onscreen until the subject responded. The subject was asked to respond as quickly and accurately as possible using the appropriate response buttons. The METACARD program recoded response time and accuracy.
Original
Results A paired sample t-test was used to analyze and compare the reaction time means between task repeat versus task switch conditions. The task switch reaction times were significantly higher for task-switch (M = 1447.16 ms; SD = 447.43), than those for the task-repeat condition (M = 1257.01 ms; SD = 288.41), t(28)= -4.326 0.00 ˂.05 The results of this experiment are presented in Figure 1.
The results section is fine. But, reporting of reaction times should be to the nearest millisecond.
Original
The mean reaction time for the task switch condition was greater than that of the task repeat trials. The task-switch condition resulted in a much greater standard deviation than task-repeat, indicating that task-switch reaction times had greater discrepancy between subjects. This may suggest that cognitive reconfiguration times may vary greatly between individuals. The single sample t-test result of 0.00 ˂.05 indicates a significant correlation between task-switch and increased TRC. The overall data analysis provides support for the significant impact of task-switching on reaction time.
The results of our current study are constant with the findings of those conducted by Monsell (2003) and Rogers (1995). Also, we have demonstrated that using the same target stimulus between tasks, and our decision to display the task cue with the target do not significantly reduce response time. Our study supports our prediction that our findings would be consistent with previous studies.
Some problems with our study may have contributed to possible augmentation of our results. Subjects were not alone in the room in which the trials were conducted. Experimenters were engaged in conversation during the subject’s trials. This might have interfered with the subject’s ability to concentrate on the task, thus increasing the difficulty responding quickly to the task-switch condition.
In this study, the experimenters also participated as the subjects. This might have introduced a bias that may have skewed the data.The sample group was small, restricted to 28 subjects. This added another probable confound. A larger sample group would have contributed to the power of this study. Because of changing societal demands and the accelerated evolution in technologies, there is increasing pressure to engage in task-switching. In fact, one could argue that rapid task-switching is becoming more relevant to daily function in society. Future studies should explore how repeated cognitive task reconfiguration impacts functionality in real world situation which can be done in vivo, or by developing simulations of these conditions in the laboratory. Prolonged task switch reconfiguration processing might cause more mental fatigue than concentrating one task at a time. It is relevant to investigate how environmental distractions (noise, peripheral activity) affect task reconfiguration costs. Another interesting study could be requiring a subject to alternate between texting on a cell-phone and attend to walking while negotiating various obstacles. Exploring if and how the effects of repeated task-switch configuration impacts the daily lives of people adapting to increasing demands for cognitive reconfiguration is a meaningful next step.
Edited for grammar and flow. Unnecessary or redundant statements are deleted.
Rewrite
Mean reaction times were slower for task-switch than repeat conditions. As well, reactions times were more variable in the switch than repeat conditions. Thus, the present findings are consistent with prior work (Monsell, 2003; Rogers, 1995).
The following issues may have influenced the results. Experimenters were in the same room as subjects and engaged in conversations that could have been distracting. As well, the experimenters participated as subjects and could have biased the sample. Finally, only a small group of 28 subjects completed the study. Task-switching is increasingly common in everyday life. Future studies should explore how task-switching influences performance in real-world situations. Specifically, environmental distractions (e.g., noise, peripheral activity) may affect task reconfiguration costs. An example of real-world task would be to have subject alternate between texting on a cell-phone and attending to walking while negotiating various obstacles. Exploring the consequences of task-switching in everyday life is a next step for future research.
The above edits do not address the content of the general discussion. The above general discussion is thin on content. The potential problems with the study were generic, and besides the study succeeded in showing task-switching costs, so it was not clear how these issues prevented the aims of the study from being achieved. The suggestion for future research was not motivated or articulated in a clear fashion.
One important aspect of any general discussion is to elaborate on the conceptual ideas behind the research. This includes a discussion of the theory behind the research, and the connections between the present results and the theory. This connection provides a way to think about the limitations of the present study with respect to their ability to test the prevailing theory. For example, the fact that only 28 subjects were used is not a limitation that prevents us from seeing how the results are explained by the task-set reconfiguration hypothesis. Instead, some interesting discussion points could revolve around predictions from the task-set reconfiguration hypothesis that were not tested by the present research, these would be clear limitations of the present study, and would motivate future research. Indeed, the author of this paper mentioned two lines of research in their original introduction that could be expounded upon in the general discussion. Here is a version of the general discussion that edits for content.
Edited for content
The present findings were consistent with previous task-switching studies (Monsell, 2003; Rogers, 1995). Mean reaction times were slower for task-switch than repeat conditions, producing significant task-switching costs.
The pattern of task-switching costs observed here fit well with the predictions of the task-set reconfiguration hypothesis. Task-switch costs are observed because people take additional time to mentally prepare a new task set when switching between tasks. The purpose of the present study was simply to replicate the basic pattern of task-switching costs, and was not intended to test more detailed predictions of the task-set reconfiguration hypothesis. One avenue for research is to manipulate variables of interest that test more specific predictions of the reconfiguration view.
One major prediction of the reconfiguration hypothesis is that time is needed to activate task-set representations. In the present study, the task cue and stimulus appeared simultaneously on each trial, and subjects were instructed to respond as quickly and accurately as possible. Subjects took longer in the task-switch condition presumably because they needed more time to load the new task-set representation. This possibility could be tested by manipulating how much time participants have to prepare for the upcoming task. For example, on some trials the task cue could appear simultaneously with the stimulus, and on other trials the task cue could appear several seconds before the stimulus, thus allowing participants enough time to activate the new task-set. According to the reconfiguration hypothesis, task-switch costs should be eliminated when people have enough time to prepare and activate the new task set.
Indeed, the idea that advance preparation could reduce the switch cost was investigated by Rogers and colleagues (1995). They designed a study model in which switch and non-switch tasks were presented in one trial block, using what is called an “alternating runs paradigm.” They incrementally increased the preparation time between task cue and display of target for each trial and measured any changes in reaction time. Rogers used disparate task cues: a letter for Task 1 and a number for Task 2. They found that increased preparation time reduced task switch costs, but did not eliminate them entirely.
The fact that advance preparation does not eliminate switch-costs has important implications for the the reconfiguration hypothesis. First, time alone is not sufficient for activating the task-sets necessary for performance. It may be that mental preparation only partly activates task set representations, and other cues from the environment are necessary to complete task set activation. For example, it seems that seeing both a task cue and a stimulus (and not just a task cue alone) might be required for complete task set activation. An interesting avenue for future research is to investigate the differences between mental preparation and active preparation on task performance.
Below are two complete versions of the manuscript. The first is the original version, and the second is the edited version.
Rapid changes in technology, social interface, and work-place cost cutting have accelerated task switch demands on our society. For example, the ubiquity of cell-phone use seems to result in an almost irresistible instinct to respond to constant interruptions at any time and any place. This creates pressure to adapt to frequent interruption of any current task – even while driving or walking. Does this result in a reduction of response time leading to an increasing risk of avoidable accidents? Another example is the implementation of open office configurations (commonly known as the “bull-pen model”) to reduce overhead costs for businesses. This set-up requires a worker to switch quickly from one task to another due to an inability to control interruptions. Multi-tasking has become a desirable skill in the workplace, but do the costs of repeated task switching degenerate productivity and increase error thus decreasing any cost benefits of the open office model? Because of the intensification of modern task-switching, it is becoming increasingly relevant to determine if rapid task-switching negatively impacts task performance.
Laboratory studies have demonstrated that repeated task switching results in increased error and reaction time. It has been proposed that cognitive reconfiguration between tasks may cause Task Switch Costs: a delayed reaction to stimuli and a reduction in accuracy. The cognitive reconfiguration that is necessary to adapt to novel procedure results in delayed and accurate transition from one task to another. (Monsell, 2003; Rogers, et al., 1995).
Rogers and colleagues (1995) investigated if task preparation time may mitigate task set reconfiguration costs. They designed a study model in which switch and non-switch tasks were presented in one trial block, using what is called an “alternating runs paradigm.” They incrementally increased the preparation time between task cue and display of target for each trial and measured any changes in reaction time. Rogers used disparate task cues: a letter for Task 1 and a number for Task 2. They found that increased preparation time reduces time switch costs, but contend this decrease is minimal.
Monsell (2003) reports that a residual shadow of the previous task procedure, or ‘residual cost’ creates interference with execution of the new task. Monsell also used the alternating runs paradigm, reasoning that this model is more useful than an alternate study structure that runs separate trial blocks for task-repeat and task-switch tasks. In this study, Monsell examined whether practice before trial execution would reduce task reconfiguration costs, and reported that practice produced only a nominal reduction in reaction time.
The present study is a laboratory experiment. We also utilized the alternating runs model used by Monsell and Rogers to collect data and compare measures of response time during repeated task and task switch trials. Unlike the experiment models used in the studies designed by Rogers and colleagues (1995) and that of Monsell (2003), we chose to use identical targets for both tasks: in this case, a letter for Task 1, and a number for Task 2. We measured the effects of task-switch on reaction time and error rates and chose to limit our analysis to reaction time only.
We expected to find data that supports previous findings that task switch trials result in greater costs in reaction time when compared with non-switch trials.
Twenty-nine Brooklyn College undergraduate students participated this study to fulfill a requirement for an Experimental Psychology course. Experimenters also served as subjects in this study.
The task-switching experiment was administered on a desktop computer operating METACARD software. The experiment design utilized the numbers 1,2,3,4,5,6,7,8, and 9 as target stimuli with a task cue of either odd/even or greater than or less than 5. The response keys were as follows: z = odd, x = even and n <5, m > 5.
The experiment was a within subject design with task switch and task repeat as the independent variables and response time and error rates as the two dependent variables.
We utilized a within subject study model. There were 100 trials per subject. Each trial block consisted of 50 task switches and 50 task repeat conditions. Both conditions were presented in a single trial block utilizing an alternating runs paradigm. METACARD software generated randomized task repeat and task switch conditions within each trial block. The subjects were instructed to respond as quickly as possible using the designated response keys to indicate whether task cue was odd/even or greater/less than 5 based on the instruction of the task cue displayed above the target stimulus.
A fixation cross was displayed for 500 milliseconds followed by a simultaneous display of the task cue and one number The task cue and stimulus number remained onscreen until the subject responded. The subject was tasked to respond correctly to the task cue instruction more quickly as possible utilizing the correct response key to report if the number was odd/even, or greater/less than 5. The METACARD program recoded response time and accuracy.
A paired sample t-test was used to analyze and compare the reaction time means between task repeat versus task switch conditions. The task switch reaction times were significantly higher for task-switch (M = 1447.16 ms; SD = 447.43), than those for the task-repeat condition (M = 1257.01 ms; SD = 288.41), t(28)= -4.326 0.00 ˂.05 The results of this experiment are presented in Figure 1.
The mean reaction time for the task switch condition was greater than that of the task repeat trials. The task-switch condition resulted in a much greater standard deviation than task-repeat, indicating that task-switch reaction times had greater discrepancy between subjects. This may suggest that cognitive reconfiguration times may vary greatly between individuals. The single sample t-test result of 0.00 ˂.05 indicates a significant correlation between task-switch and increased TRC. The overall data analysis provides support for the significant impact of task-switching on reaction time.
The results of our current study are constant with the findings of those conducted by Monsell (2003) and Rogers (1995). Also, we have demonstrated that using the same target stimulus between tasks, and our decision to display the task cue with the target do not significantly reduce response time. Our study supports our prediction that our findings would be consistent with previous studies.
Some problems with our study may have contributed to possible augmentation of our results. Subjects were not alone in the room in which the trials were conducted. Experimenters were engaged in conversation during the subject’s trials. This might have interfered with the subject’s ability to concentrate on the task, thus increasing the difficulty responding quickly to the task-switch condition.
In this study, the experimenters also participated as the subjects. This might have introduced a bias that may have skewed the data.
The sample group was small, restricted to 28 subjects. This added another probable confound. A larger sample group would have contributed to the power of this study.
Because of changing societal demands and the accelerated evolution in technologies, there is increasing pressure to engage in task-switching. In fact, one could argue that rapid task-switching is becoming more relevant to daily function in society. Future studies should explore how repeated cognitive task reconfiguration impacts functionality in real world situation which can be done in vivo, or by developing simulations of these conditions in the laboratory. Prolonged task switch reconfiguration processing might cause more mental fatigue than concentrating one task at a time. It is relevant to investigate how environmental distractions (noise, peripheral activity) affect task reconfiguration costs. Another interesting study could be requiring a subject to alternate between texting on a cell-phone and attend to walking while negotiating various obstacles. Exploring if and how the effects of repeated task-switch configuration impacts the daily lives of people adapting to increasing demands for cognitive reconfiguration is a meaningful next step.
Everyday situations often require people to juggle the demands of multiple tasks. For example, many people leave their cell-phones on throughout the day allowing incoming calls of messages to interrupt ongoing activities. Cell phone use can be a major source of distraction for other tasks like walking or driving. Indeed, many states have made texting while driving illegal because texting while driving is assumed to seriously impair driving performance. The notion that switching between tasks can impair performance is a general phenomenon, termed task-switching costs (for a review see, Monsell, 2003). The purpose of the present experiment is to validate a computerized laboratory procedure for measuring task-switching costs.
The influence of task-switching on performance has been a topic of interest for several decades (for early work see, Jersild, 1927). Task-switching effects are commonly investigated by having participants complete at least two different tasks. For example, on each trial a participant could be presented with a single task cue and stimulus. In the procedure used in the present research, the stimulus was a number between 1 and 9 (excluding the number 5). The task cue “odd/even” instructed subjects to determine whether the number was odd or even by pressing one of two response keys. The task cue “magnitude” instructed subjects to determine whether the number was larger or smaller than five by pressing one of two response keys. In this design, the participant completes several trials and the task order is randomized by the computer. On each trial, the participant is instructed to make their response as quickly and accurately as possible.
The primary question of interest was whether switching between tasks influences task performance as measured by reaction time and accuracy on each trial. The two important conditions were task-repetition and task-switch trials. Task-repeat trials refer to trial orders where the same task was repeated across trials (e.g., the previous trial involved an odd/even judgment and the current trial involved an odd/even judgment). Task-switch trials refer to trial orders where different tasks were completed across trials (e.g., the previous trial involved an odd/even judgment and the current trial involved a magnitude judgment). Typically, responses are slower and less accurate for task-switch trials than task-repeat trials, and this difference is termed the task-switching cost.
The task-set reconfiguration hypothesis provides one explanation of task-switching costs. According to this view, task performance in general relies on cognitive representations of the task at hand, referred to as task-sets. Task-sets can be likened to a recipe, they provide the instructions and and rules for performing a current task. A major assumption of the task-set reconfiguration hypothesis is that task-sets take time to activate. This assumption explains why task-switching produces a cost to performance in terms of speed and accuracy. For example, performance following a switch is slow because of the time taken to forget the previous task-set and activate the new task set. Performance following a task-repeat is not slowed because the same task-set can be applied. Furthermore, performance could become more error prone after a task switch because the new task-set may not be fully activated, and thus not able to accurately control all aspects of performance.
The purpose of the present experiment was to replicate the task-switching cost in a laboratory procedure. As mentioned previously, on each trial participants received a number between 1 and 9 (excluding 5) and a task cue indicating an odd/even or magnitude judgment. Trial order was randomized. The task-set reconfiguration hypothesis predicts that reaction times will be slower and less accurate for task-switch than repeat trials.
Twenty-nine Brooklyn College undergraduates participated to fulfill a requirement for an Experimental Psychology course.
The task-switching experiment was administered on a desktop computer operating METACARD software. The experiment design utilized the numbers 1,2,3,4,6,7,8, and 9 as target stimuli with a task cue of either odd/even or greater than or less than 5. The response keys were as follows: z = odd, x = even and n <5, m > 5.
The experiment was a within subject design with task switch and task repeat as the independent variables and response time and error rates as the two dependent variables. There were 100 trials per subject. Each trial block consisted of 50 odd/even trials, and 50 magnitude trials. Trial order was randomized for each subject. As a result, each subject received approximately 50% task-repeat trials, and 50% task-switch trials.
On each trial, a fixation cross was displayed for 500 milliseconds followed by a simultaneous display of the task cue and a target number. The cue and target remained onscreen until the subject responded. The subject was asked to respond as quickly and accurately as possible using the appropriate response buttons. The METACARD program recoded response time and accuracy.
A paired sample t-test was used to analyze and compare the reaction time means between task repeat versus task switch conditions. The task switch reaction times were significantly higher for task-switch (M = 1447 ms; SD = 447), than those for the task-repeat condition (M = 1257 ms; SD = 288), t(28)= -4.326 p ˂.05 The results of this experiment are presented in Figure 1.
The present findings were consistent with previous task-switching studies (Monsell, 2003; Rogers, 1995). Mean reaction times were slower for task-switch than repeat conditions, producing significant task-switching costs.
The pattern of task-switching costs observed here fit well with the predictions of the task-set reconfiguration hypothesis. Task-switch costs are observed because people take additional time to mentally prepare a new task set when switching between tasks. The purpose of the present study was simply to replicate the basic pattern of task-switching costs, and was not intended to test more detailed predictions of the task-set reconfiguration hypothesis. One avenue for research is to manipulate variables of interest that test more specific predictions of the reconfiguration view.
One major prediction of the reconfiguration hypothesis is that time is needed to activate task-set representations. In the present study, the task cue and stimulus appeared simultaneously on each trial, and subjects were instructed to respond as quickly and accurately as possible. Subjects took longer in the task-switch condition presumably because they needed more time to load the new task-set representation. This possibility could be tested by manipulating how much time participants have to prepare for the upcoming task. For example, on some trials the task cue could appear simultaneously with the stimulus, and on other trials the task cue could appear several seconds before the stimulus, thus allowing participants enough time to activate the new task-set. According to the reconfiguration hypothesis, task-switch costs should be eliminated when people have enough time to prepare and activate the new task set.
Indeed, the idea that advance preparation could reduce the switch cost was investigated by Rogers and colleagues (1995). They designed a study model in which switch and non-switch tasks were presented in one trial block, using what is called an “alternating runs paradigm.” They incrementally increased the preparation time between task cue and display of target for each trial and measured any changes in reaction time. Rogers used disparate task cues: a letter for Task 1 and a number for Task 2. They found that increased preparation time reduced task switch costs, but did not eliminate them entirely.
The fact that advance preparation does not eliminate switch-costs has important implications for the the reconfiguration hypothesis. First, time alone is not sufficient for activating the task-sets necessary for performance. It may be that mental preparation only partly activates task set representations, and other cues from the environment are necessary to complete task set activation. For example, it seems that seeing both a task cue and a stimulus (and not just a task cue alone) might be required for complete task set activation. An interesting avenue for future research is to investigate the differences between mental preparation and active preparation on task performance.