Re: Multitasking Pro APK
Hien Mondesir
The negative impact of chronic, heavy multitasking might be particularly detrimental to adolescent minds. At this age, brains are busy forming important neural connections. Spreading attention so thin and constantly being distracted by different streams of information might have a serious, long-term, negative impact on how these connections form.
Moisala M, Salmela V, Hietajärvi L, et al. Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. NeuroImage. 2016;134:113-121. doi:10.1016/j.neuroimage.2016.04.011
Doing more than one task at a time, especially more than one complex task, takes a toll on productivity. Although that shouldn't surprise anyone who has talked on the phone while checking E-mail or talked on a cell phone while driving, the extent of the problem might come as a shock. Psychologists who study what happens to cognition (mental processes) when people try to perform more than one task at a time have found that the mind and brain were not designed for heavy-duty multitasking. Psychologists tend to liken the job to choreography or air-traffic control, noting that in these operations, as in others, mental overload can result in catastrophe.
Although switch costs may be relatively small, sometimes just a few tenths of a second per switch, they can add up to large amounts when people switch repeatedly back and forth between tasks. Thus, multitasking may seem efficient on the surface but may actually take more time in the end and involve more error. Meyer has said that even brief mental blocks created by shifting between tasks can cost as much as 40 percent of someone's productive time.
Understanding the hidden costs of multitasking may help people to choose strategies that boost their efficiency - above all, by avoiding multitasking, especially with complex tasks. (Throwing in a load of laundry while talking to a friend will probably work out all right.) For example, losing just a half second of time to task switching can make a life-or-death difference for a driver on a cell phone traveling at 30 MPH. During the time the driver is not totally focused on driving the car, it can travel far enough to crash into an obstacle that might otherwise have been avoided.
The scientific study of multitasking over the past few decades has revealed important principles about the operations, and processing limitations, of our minds and brains. One critical finding to emerge is that we inflate our perceived ability to multitask: there is little correlation with our actual ability. In fact, multitasking is almost always a misnomer, as the human mind and brain lack the architecture to perform two or more tasks simultaneously. By architecture, we mean the cognitive and neural building blocks and systems that give rise to mental functioning. We have a hard time multitasking because of the ways that our building blocks of attention and executive control inherently work. To this end, when we attempt to multitask, we are usually switching between one task and another. The human brain has evolved to single task.
The first of these networks is thought to support the coding of a task goal and the selection of task-relevant information. This means identifying a task goal, selecting relevant information, and disregarding irrelevant information that does not help us to achieve the goal. In the context of multitasking, we know that the brain has a hard time processing and completing two or more tasks at once: the inherent ways the dorsal and ventral attention systems interact with the frontoparietal control network makes this so. When we approach a task, a goal representation in the frontoparietal control network is thought to guide top-down attentional allocation, mediated by the dorsal attention network, to select information that is relevant to achieve the task goal. This information can include external sensory information or internal thoughts.
The MMI score from the Nass lab represents the mean number of media with which an individual multitasks during a typical consumption hour. A high MMI score means an individual engages in a lot of media multitasking (e.g., checking email while also perusing Facebook and watching Netflix), and a low score means he or she does not (e.g., checking email without any secondary media). In the 2009 study, heavier and lighter media multitaskers were asked to perform a set of cognitive tasks that place demands on attention, control, and memory. This study initiated a rapidly evolving literature that seeks to answer the fundamental question: does media multitasking in everyday life impact our minds and brains, affecting performance even when we are single tasking?
For example, one recent study from our lab measured media multitasking, working memory, and long-term memory in undergraduate students at Stanford University. In one part of the study, subjects were asked to keep simple visual objects in memory over a brief (one second) delay and to make a memory-dependent decision after the delay. This is a standard task to measure working memory capacity (that is, the amount of information that can be held active in mind). After this came a long-term memory test: the subjects were shown the same objects they had seen during the working memory task along with novel ones and had to indicate which were seen earlier and which were new. We found that heavier media multitasking (as measured by the MMI) was associated with worse performance on both the working memory and long-term memory tasks. This was the case whether using an extreme groups approach (comparing the top 25 percent of individuals who were HMMs to the bottom 25 percent as LMMs) or a continuous approach (using data from all subjects, including light, intermediate, and heavy media multitaskers).
These results add to a growing body of work documenting the relationship between media multitasking and cognitive operations linked to sustained attention and working memory. To build on our prior work, one approach we have recently taken is to measure attention lapses at the trial and subject level while individuals of varying media multitasking habits perform single cognitive tasks. By trial level, we mean moment-to-moment (i.e., state-level) attention lapses during a task that could help predict why an individual performed well at one moment and less well at another; by subject level, we mean individual (i.e., trait-level) differences in attention lapsing that could help to predict differences in task performance across people. Previous work has shown that fluctuations in pupil diameter from eye tracking data and fluctuations in alpha and theta oscillatory power from electroencephalographic (EEG) data reliably track attention lapses during various single tasks.
In this ongoing project, we are measuring moment-to-moment changes in pupillary response and alpha and theta oscillatory power to quantify attention lapsing, with the aim of testing the relationship between lapsing, task performance, and MMI status. We are also learning more about which indices of sustained attention may relate to MMI status. (Here, we should note that while we and others have identified sustained attention as a potential mechanism behind performance differences between HMMs and LMMs, not all studies come to this conclusion. As we review in a recent publication, the discrepancy between studies could be due to differences in: (a) multitasking and performance measurement methodology, (b) demographics of the measured samples, (c) statistical power, and/or (d) analytic approach, among other reasons.)
There are a number of potential practical consequences of media multitasking in everyday life, one being academic outcomes. While we have primarily examined the relationship of MMI status and single tasking and multitasking performance in the lab, new translational studies offer a complementary perspective. For example, recent work has shown that students learn less when texting or using social media while attending lectures. Reading proficiency and homework accuracy have also been shown to decrease as individuals multitask with instant messaging and various computer programs.
In computing, multitasking is the concurrent execution of multiple tasks (also known as processes) over a certain period of time. New tasks can interrupt already started ones before they finish, instead of waiting for them to end. As a result, a computer executes segments of multiple tasks in an interleaved manner, while the tasks share common processing resources such as central processing units (CPUs) and main memory. Multitasking automatically interrupts the running program, saving its state (partial results, memory contents and computer register contents) and loading the saved state of another program and transferring control to it. This "context switch" may be initiated at fixed time intervals (pre-emptive multitasking), or the running program may be coded to signal to the supervisory software when it can be interrupted (cooperative multitasking).
Multitasking does not require parallel execution of multiple tasks at exactly the same time; instead, it allows more than one task to advance over a given period of time.[1] Even on multiprocessor computers, multitasking allows many more tasks to be run than there are CPUs.
Multitasking is a common feature of computer operating systems since at least the 1960s. It allows more efficient use of the computer hardware; when a program is waiting for some external event such as a user input or an input/output transfer with a peripheral to complete, the central processor can still be used with another program. In a time-sharing system, multiple human operators use the same processor as if it was dedicated to their use, while behind the scenes the computer is serving many users by multitasking their individual programs. In multiprogramming systems, a task runs until it must wait for an external event or until the operating system's scheduler forcibly swaps the running task out of the CPU. Real-time systems such as those designed to control industrial robots, require timely processing; a single processor might be shared between calculations of machine movement, communications, and user interface.[2]
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