problem solving zeigarnik effect

There’s a Science-Backed Reason You Have Your Best Ideas in the Shower

According to Fast Company , your mind tends to remember unsolved or interrupted problems better than projects you've already finished—the aforementioned Zeigarnik effect. So when you're stumped by a complex puzzle—a tricky riddle, for instance, or a complicated workflow issue at work—and take a break from trying to reason it out, the idea lingers in your brain (kind of like all those unclosed apps chugging along in the background on your phone). And when you let your mind drift , it gives your brain the chance to connect insights from your actions or surroundings with your unsolved problems in order to come up with a solution.

The reason those puzzle pieces often come together in the shower is because nothing in there requires your full attention.

The reason those puzzle pieces often come together in the shower is because nothing in there requires your full attention. It's quiet, you're relaxed, and you're basically just left with your wandering thoughts. So you may glance at your shampoo bottle, which was recommended by your co-worker Ali, and— aha! You realize that Ali is the perfect person to lead that new project.

So the next time you're trying to be creative or solve a problem, stop thinking so hard. Roll out your yoga mat , take a walk , read a book , and let the answer come to you.

Probiotics have been linked to brain fog —so should you kick your kombucha habit? Or check out the buzzy eating plan that will seriously boost your brain .

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The Zeigarnik Effect and Memory

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

How it Works

At a Glance

The Zeigarnik effect is why it’s harder to forget about work you haven’t finished yet compared to tasks you’ve completed.

Have you ever found yourself having intrusive thoughts about something you haven’t finished? Maybe a half-done work project is keeping you up at night or the suspenseful plot of a novel you’re reading keeps circling your thoughts. There is a reason why it's so hard to stop thinking about uncompleted and interrupted tasks, and psychologists called it the Zeigarnik effect.

In this article, we’ll go over a simple explanation of the Zeigarnik effect in psychology and why you tend to better remember unfinished tasks than completed ones.

The Zeigarnik Effect

The Zeigarnik effect is something you probably experience more often than you realize. When you start working on a task but do not complete it, thoughts of the unfinished work will probably continue to pop into your mind even when you've moved on to other things. These thoughts urge you to go back and finish the task you started. The effect is also why you keep thinking about a page-turner novel you’re halfway through or a video game you haven’t yet beat.

In your day-to-day life, you have multiple tasks that command your attention. But you may find that these things you’ve left unfinished tend to creep into your mind even when you’ve started working on something else.

Soap operas and serialized dramas also take advantage of the Zeigarnik effect. For example, an episode may end, but the story is clearly not over. These “cliffhangers” leave viewers eager to see what happens next, and thanks to the Zeigarnik effect, they will be motivated to come back and watch the next episode to find out.

It’s also common to experience the Zeigarnik effect in school. Before an exam, you probably were able to remember quite a bit of the material you’d just been studying After an exam, however, you probably would have had a much harder time remembering all of the things that you’d studied. Since you no longer have immediate use for it, the information may feel like it got flushed out of your memory.

Zeigarnik Effect Examples

You probably experience the Zeigarnik effect often in your daily life. For example:

You went grocery shopping and took your kids to school, but you can’t stop thinking about the laundry you didn’t finish folding.
You replied to a bunch of emails but didn’t get to them all before the end of the workday on Friday. You think about the ones you have left all weekend.
You tune in to the season finale of your favorite TV show and it leaves with a major cliffhanger. You’re still thinking about what happened weeks later.
You have a notification that you haven’t checked, and even though you have to work, you’re distracted by the unread text.
You fill out a job application and get a message saying your profile is only 75% complete. You don’t feel like you can move on to another task until you finish setting it up (100%).

History of the Zeigarnik Effect

The Ziegarnik effect was first observed and described by a Russian psychologist named Bluma Zeigarnik, a student of influential theorist Kurt Lewin . While sitting in a busy restaurant in Vienna, Ziegarnik noted that the waiters had better memories of unpaid orders. Once the bill was paid, however, the waiters had trouble remembering the exact details of the orders.

Zeigarnik's Research

In a series of experiments, participants were asked to complete simple tasks such as placing beads on a string, putting together puzzles, or solving math problems. Half of the participants were interrupted partway through doing these tasks.

After an hour-long wait, Zeigarnik asked the participants to describe what they had been working on. She discovered that the people who had been interrupted in their work were twice as likely to remember what they had been doing as the people who had been able to complete the tasks.

In another version of the experiment, Zeigarnik found that adults were able to remember the unfinished tasks 90% more often than they could finish tasks. Zeigarnik's initial studies were described in a paper titled "On Finished and Unfinished Tasks" that was published in 1927.

Further Research Exploring the Effect

In the 1960s, memory researcher John Baddeley further explored Zeigarnik’s findings in an experiment. Participants were given a limited time to solve a set of anagrams. If they could not solve the anagram before the time was up, they were given the word answer.

Later, when the participants were asked to recall the anagrams, they were better able to remember the words that they had not solved than the ones they had. The findings supported Zeigarnik's observations that people have a better memory for unfinished tasks or interrupted information.

Conflicting Research

Not all research has found support for the effect, however. Some studies have failed to show the same effect and other researchers have found that there are a variety of factors that can influence the strength of the effect. For example, studies have shown that motivation can play a major role in how well people remember information.

How Does It Work?

Short-term memory is limited in both capacity and duration. Typically, we can only retain so many things in our memory. Even then, we need to keep rehearsing information to hold on to it. This process requires quite a bit of mental effort. The harder you try to keep it in your memory for the short term, the harder you’ll have to work to get it to stay put.

Waiters, for example, have to remember a lot of details about the people at the tables they are serving. Information about what food and drinks patrons order needs to stay in their memory until the customers have finished their meals.

To deal with data overload, people often rely on mental tricks that help them remember a great deal of information. The Zeigarnik effect is one example. We hold on to information in the short term by constantly pulling it back into our awareness. By thinking of uncompleted tasks often, we’re more likely to keep remembering them until they get done.

The Zeigarnik effect does not just affect memory in the short term. Unfinished tasks, such as goals that we still want to reach, can also continue to intrude into our thoughts over longer stretches of time. For example, if you have not finished college, your unfinished degree may loom large in your memory for years.

The Zeigarnik effect tells us a lot about how memory works . Once information is perceived, it is often stored in sensory memory for a brief time. When we pay attention to information, it moves into short-term memory. Many short-term memories are quickly forgotten, but through the process of active rehearsal, some information can move into long-term memory .

Zeigarnik suggested that failing to complete a task creates underlying cognitive tension. This makes more mental effort and rehearsal necessary to keep the task at the forefront of our awareness. Once it’s done, however, our mind can let go of the extra effort.

How to Make the Zeigarnik Effect Work for You

More than just being an interesting observation about how the human brain works , you can use the Zeigarnik effect to your advantage. There are some real-world applications of the Zeigarnik effect that you can use today.

Common sense might tell you that finishing a task before you stop is the best way to approach a goal. However, the Zeigarnik effect suggests that being interrupted during a task may be an effective strategy for improving your ability to remember information.

Get More Out of Your Study Sessions

If you are studying for an exam, break up your study sessions rather than cramming the night before the test. By studying the information that you need to learn in increments, you will be more likely to remember it until test day.

If you are struggling to memorize something important, momentary interruptions might work to your advantage. Rather than simply repeating the information, review it a few times, and then take a break. While you are focusing on other things, you will find yourself mentally returning to the information that you were studying.

Overcome Procrastination

Procrastination is related to the Zeigarnik in some familiar ways. We often put off tasks until the last moment, only to finish them in a frenzied rush at the last possible moment to meet a deadline. Unfortunately, this habit leads to stress and poor performance.

One way to overcome procrastination is to put the Zeigarnik effect to work for you. Start by taking the first step, no matter how small. Once you’ve started—but not finished—your work, you will find yourself thinking about the task until you complete it. You may not finish it all at once, but each small step you take puts you closer to your goal.

This approach can motivate you to finish and lead to a sense of accomplishment once you finally complete a task and can put your mental energies elsewhere.

Generate Interest and Attention

Advertisers and marketers use the Zeigarnik effect to encourage consumers to buy their products. Creatives like filmmakers and TV writers also use the effect. For example, think about how movie trailers are designed to attract your attention by teasing you with some, but not all, info about the plot and characters. The trailer draws your attention but leaves you wanting more. To find out what happens, you’ll have to see the movie.

TV episodes often end with a moment of high action, a cliffhanger moment, which leaves the fate of characters or the outcome of the situation unresolved. To relieve the tension created by cliffhanger endings, viewers have to remember to watch the next episode when it comes out.

Promote Mental Well-Being

The Zeigarnik effect is not always beneficial. When you do not complete tasks, they may weigh heavily on your mind and create stress. The stressful, invasive thoughts can lead to anxiety and affect your sleep.

That said, the Zeigarnik effect has a way of getting you to resolve the stress. The repeated thoughts you’re having will motivate you to finish what you’ve started, and this can relieve stress and improve your self-esteem , and self-confidence.

The Zeigarnik effect started with a simple observation of how restaurant waiters manage to remember many customer orders. Since then, research has shown that we tend to better recall unfinished tasks than completed ones. While many factors can influence the Zeigarnik effect and its strength, you can often use it to your advantage. For example, by taking deliberate breaks while studying, you may find that you can better remember important details you’ll need for a test.

Zeigarnik B. On finished and unfinished tasks . In: Ellis WD, ed. A Source Book of Gestalt Psychology. Kegan Paul, Trench, Trubner & Company; 1938:300-314. doi:10.1037/11496-025

Baddeley AD. A Zeigarnik-like Effect in the Recall of Anagram Solutions . Quarterly Journal of Experimental Psychology . 1963;15(1):63-64. doi:10.1080/17470216308416553

Kodden B. The Art of Sustainable Performance: The Zeigarnik Effect . In: The Art of Sustainable Performance. In: The Art of Sustainable Performance . Springer International Publishing; 2020:67-73. doi:10.1007/978-3-030-46463-9_10

Oyama Y, Manalo E, Nakatani Y. The Hemingway effect: How failing to finish a task can have a positive effect on motivation . Thinking Skills and Creativity . 2018;30:7-18. doi:10.1016/j.tsc.2018.01.001

Syrek CJ, Antoni CH. Unfinished tasks foster rumination and impair sleeping—Particularly if leaders have high performance expectations . Journal of Occupational Health Psychology . 2014;19(4):490-499. doi:https://doi.org/10.1037/a0037127

The Corsini Encyclopedia of Psychology, Volume 4 . United Kingdom, Wiley, 2010.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Zeigarnik Effect Examples in Psychology

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

Key Takeaways

The Zeigarnik effect refers to the tendency for interrupted tasks, in some circumstances, to be recalled better than completed tasks.
Name after the Russian psychologist Bluma (Wolfovna) Zeigarnik (1901-88), who first reported it in the journal Psychologische Forschung in 1927.

Origins of the Zeigarnik Effect

The Zeigarnik Effect is the tendency for tasks that have been interrupted and uncompleted to be better remembered than tasks that have been completed.

Bluma Zeigarnik (1927) first saw this effect in waiters, who seemed to remember orders only so long as the order was in the process of being served and promptly forgot the order as soon as it was finished.

This small observation of waiters came to become the starting point of a series of experiments by Zeigarnik (Denmark, 2009).

At the time when Zeigarnik studied the Zeigarnik effect, she was supervised by the notable Gestalt theorist Kurt Lewin and was frequently exposed to the writings of Gestalt psychologists such as Wolfgang Köhler, Kurt Koffka, and Max Wertheimer (MacLeod, 2020).

In short, Gestalt psychology is a school of psychology that emerged in the early 20th century that emphasizes that the whole of human behavior is not deducible from the analysis of parts of that behavior in isolation.

Zeigarnik was strongly influenced by the field theory of her supervisor, Kurt Lewin, in her studies of the Zeigarnik Effect. Lewin postulated a theory of psychological tensions where tensions were forms of energetics (Marrow, 1969).

These “psychic tensions” provided people with the mental energy to prepare for and bring about behavior, and this behavior released the tension.

Zeigarnik attributed the same principle as the cause of the Zeigarnik effect, and her dissertation ultimately connected Lewin’s psychic field theory to observations of behavior in waiters.

Zeigarnik’s Initial Experiments

In her experiments on the Zeigarnik effect, Zeigarnik (1927) asked participants to complete a series of anywhere between 15 and 22 tasks.

Some involved tactile tasks (such as stringing beads), while others involved applying mental abilities to, for example, solve a puzzle.

Zeigarrnik allowed half of the participants to complete their tasks and interrupted the other half of these participants partway through, asking the participants to move on to something else.

She removed the tasks from the subject’s view and, after an hour’s delay, asked the participants to recall the activities they had been involved in. For example, in her first experiment, Zeigarnik gave 32 adults 22 tasks, such as thread winding, paper folding, multiplication, drawing, and counting backward.

The tasks were intended to take from 3-5 minutes and were interrupted when the patient “was most engrossed” in the task.

Zeigarnik’s initial studies confirmed her initial hypothesis. Zeigaarnik conducted four such experiments.

In the first experiment, which she considered to be her main experiment, she tested participants individually, and the number of unfinished tasks (designated I) that participants recalled was significantly higher than the number of tasks that participants completed (designated C).

In fact, participants were twice as likely to remember incomplete tasks than complete ones (Zeigarnik, 1927; Denmark, 2009). She replicated this experiment with 15 individual adults and in group situations with 47 adults and 45 adolescent children.

Zeigarnik, in subsequent experiments, examined the recall ratio for tasks interrupted at different times and found that tasks that were interrupted at the middle or toward the end were more likely to be recalled than those interrupted near the beginning of work on them. A

As the participants grew nearer and nearer to completing each task they were interrupted in, they became increasingly more likely to remember these incomplete tasks over completed ones.

According to Zeigarnik’s hypothesis, participants were more likely to remember incomplete tasks because they spurred “psychic tension.”

Once someone completes the task, this relieves psychic tension, and thus, they can release it from their memory, and the person no longer uses significant cognitive effort to remember the task (Zeigarnik, 1927).

Zeigarnik also found that people who expressed high levels of ambition were more likely to remember incomplete tasks (that is to say, a high I/C ratio) than those who have average levels of ambition (a low I/C ratio).

If participants believed that an incomplete task represented a failure, they were also more likely to remember incomplete tasks than those who had not.

Zeigarnik carried out two further small experiments (with 12 adults) to challenge alternative interpretations to her theory, for example, interrupting tasks but then allowing participants to immediately resume completing half of them.

Again, she observed that participants were ultimately the most likely to recall tasks that she never allowed to be completed.

In her second of these small experiments, she told six participants that six of the interrupted tasks would be resumed and that another six would not be, though no tasks were ever resumed.

Once again, Zeigarnik found that, regardless of whether or not she said the tasks would be resumed, all of the tasks that were interrupted were more likely to be remembered by participants than those that the participant was able to complete (MacLeod, 2020).

Replicability

Psychologists generally agree that the Zeigarnik effect is sensitive to a number of factors that are difficult to control in a laboratory experiment.

For example, the Zeigarnik effect is less likely to appear if a participant is ego-involved in the task, the effect is more likely to appear if the interruption of the tasks does not seem to be an intentional part of the experiment, and the effect is more likely to appear if the participant has not come to the conclusion that the task is impossible or beyond their ability (Denmark, 2009).

There are two features of Zeigarnik’s methodology that have been little discussed but which may have influential implications.

In her studies, Zeigarnik only recorded responses produced before participants hesitated, as she considered only that portion of recall to be related to her tension hypothesis. Zeigarnik also observed that uncompleted tasks tended to be recalled first (MacLeod, 2020).

This idiosyncratic methodology may be one reason why some researchers, beginning with the work of Schlote (1930), have been unsupportive of the Zeigarnik effect.

A series of psychologists have criticized the replicability of the Zeigarnik effect. Hovland (1951), for example, stated that “few investigators could unequivocally reproduce Zeigarnik’s findings” and argued that findings differed dramatically depending on participant personality.

A review by Butterfield (1964) concluded that the Zeigarnik effect is far from being the invariable result in ITP [interrupted task paradigm].

Frequently, more complete than uncompleted tasks are recalled,” and many psychologists since then (such as Atkinson (1953) have claimed that there is no “universal pattern” as to whether or not and which sort of participants recalled more incomplete than completed tasks (MacLeod, 2020).

Despite conflicting accounts as to the validity of the Zeigarnik effect, the phenomenon nonetheless remains an extensively researched topic, with studies aimed at measuring the effect with those with intellectual disabilities and in people with obsessive-compulsive disorder, as well as studies analyzing the relationship with memory and ego-states (Heinz, 1997; House and McIntosh, 2000; Mantyla and Sgaramella, 1997).

Implications for Everyday Life

Productivity and learning.

Psychologists have examined the implications of the Zeigarnik effect on learning. Generally, educators believe that if a learning task is interrupted and resumed later, then the information learned during that task is more likely to be remembered.

An educator may recommend, for example, that one studies a subject in small intervals, and taking breaks midway through memorizing a concept may lead to better recall.

Others may suggest, for example, that in order to avoid procrastination, one can take the very first step toward completing a task as soon as possible before resuming it later.

Applications have ranged as far from general advice on exam preparation to designs of outdoor classroom experiences. For example, Hiramatsu et al. (2014) recount the development of a learning system for outdoor school trips in Japanese elementary and secondary schools.

The researchers developed a mobile application that gave students quizzes on field trips on topics that they were not aware that they would be quizzed on beforehand but which they had studied in school.

Those who answered the quizzes showed more interest and recall of the objects shown in those quizzes than those not shown in the quizzes.

Those who had been given an incomplete preparatory lecture were more likely to recall and answer quiz questions correctly than those who were not.

Advertisers have long used the Zeigarnik effect as a method of catching the attention and memory of viewers. For example, in one study of the potential for the Zeigarnik effect in advertising, Heimbach (1972) carried out a series of trials.

In one such trial, the researchers prepared 30-minute television programs with four tests and five filler commercials, each existing in program breaks. Some commercials were shown in their entirety, while others were interrupted.

Immediately after finishing the television program, the researchers asked participants to identify the type of product, the brand name of the product, and a detailed description of the contents of each of the nine commercials shown to them.

All in all, despite the researcher’s hypothesis, there was little support for the application of the Zeigarnik effect to broadcast advertising.

However, a more thoroughly controlled experiment conducted later by Heimbach showed that, indeed, interrupted commercials were more likely to be remembered than those that were not (Heimbach, 1972).

Atkinson, J. W. (1953). The achievement motive and recall of interrupted and completed tasks. Journal of Experimental Psychology, 46 (6), 381.

Butterfield, E. C. (1964). The interruption of tasks: Methodological, factual, and theoretical issues. Psychological Bulletin, 62 (5), 309.

Denmark, F. L. (2010). Zeigarnik effect. The Corsini encyclopedia of psychology , 1-1.

Hartmann, H. (1933). An Experimental contribution to the psychology of obsessive-compulsive neurosis: on remembering completed and uncompleted tasks.”. Essays on Ego Psychology , 404-418.

Heimbach, J. T., & Jacoby, J. (1972). The Zeigarnik effect in advertising. ACR Special Volumes.

Hiramatsu, Y., Ito, A., Fujii, M., & Sato, F. (2014). Development of the learning system for outdoor study using Zeigarnik effect. Paper presented at the International Conference on Learning and Collaboration Technologies.

House, R. D., & McIntosh, E. G. (2000). The Zeigarnik effect in a sample of mentally retarded persons. Perceptual and motor skills, 90 (2), 702-702.

Hovland, C. I., & Stevens, S. (1951). Handbook of experimental psychology.

MacLeod, C. M. (2020). Zeigarnik and von Restorff: The memory effects and the stories behind them. Memory & cognition, 48 (6), 1073-1088.

Mäntylä, T., & Sgaramella, T. (1997). Interrupting intentions: Zeigarnik-like effects in prospective memory. Psychological Research, 60 (3), 192-199.

Marrow, A. J. (1977). The practical theorist: The life and work of Kurt Lewin: Teachers College Press.

Schlote, W. (1930). Über die Bevorzugung unvollendeter Handlungen: JA Barth.

Zeigarnik, B. (1927). Das Behalten erledigter und unerledigter Handlungen [On finished and unfinished tasks]. Psychologische Forschung, 9, 1-85.

Zeigarnik, B. (1938). On finished and unfinished tasks .

Further Information

McGraw, K. O., & Fiala, J. (1982). Undermining the Zeigarnik effect: Another hidden cost of reward. Journal of Personality, 50 (1), 58-66.

Burke, W. W. (2011). A perspective on the field of organization development and change: The Zeigarnik effect. The Journal of Applied Behavioral Science, 47 (2), 143-167.

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The Zeigarnik Effect: The Reason You Feel Constantly Overwhelmed

You know how having too many open Chrome tabs bogs your computer down?

The same happens to your brain.

Unfinished tasks keep “running” in the background.

It’s called the Zeigarnik Effect.

Here’s how it works and what to do about it…

Quick History of the Zeigarnik Effect:

The Zeigarnik Effect is named after Dr. Bluma Zeigarnik .

While sitting in a busy restaurant in Vienna, she noticed the servers had better memories of unpaid orders vs. paid ones.

Once the customers paid, the servers struggled to remember the exact details of the orders.

Because our brains are wired to remember unfinished tasks better than completed ones.

Like a to-do list, once we finish a task, our brain checks it off to free up mental bandwidth.

But this also means that, the more unfinished tasks we have, the more resources our brain dedicates to keeping track of them.

Examples of the Zeigarnik Effect

The Zeigarnik Effect is why storytellers use cliffhangers. We keep reading/watching to find out what happens next because our brains are driven to seek closure. Once resolved, we can forget and move on. Until then, we lean in with rapt attention.

The Zeigarnik Effect is why we suck at “multi-tasking.” What most people call “multi-tasking” is actually what psychologists call “task-switching.” True multitasking is known as parallel processing—where you simultaneously do multiple things at the exact same time.

The reason what most people call multi-tasking isn’t true parallel processing is because we rapidly switch our attention between different tasks. For example, you can’t simultaneously check emails and do deep work—you rapidly switch between reading and replying to a few emails, then switch back to working on your “main” task for the day.

Because when we’re doing something and get interrupted, our brain keeps that “tab” open—draining our CPU.

The Zeigarnik Effect is one reason perfectionists struggle with anxiety. They obsess over details and having unrealistic expectations. Aka, they drown in unfinished tasks. So the Zeigarnik Effect keeps their brains bogged down, poorly focused, and stressed out. They can’t let things go.

The Zeigarnik Effect is one reason we struggle to fall asleep. For many of us, bedtime is the one time we’re not surrounded with distractions. So our brain goes into overdrive refreshing and resurfacing all the tabs we’ve kept open throughout the day.

So what can you do about the Zeigarnik Effect to stop feeling overwhelmed?

Strategies to Overcome the Zeigarnik Effect

Here are three simple strategies that help me overcome the Zeigarnik Effect:

Ditch the To-Do List

Switch from a to-do list to a Needle Movers List .

To-do lists are never-ending. Like fighting a hydra, for every task you complete, two sprout in its place.

A Needle Mover list only has the 1-3 highest ROI tasks for the day. Way easier to remember, track, and complete.

I write mine out on a post-it note on my desk because I can’t fit much more than a few things on it. Plus, it’s satisfying to crumple it up and throw it away once I finish them.

Give Yourself Permission to Forget

Build what Tiago Forte calls a “Second Brain,” which is somewhere you can save information to come back to later. A second brain can be a notebook, a program like Notion (which is what I use), or anything else that works for you. Check out Tiago’s book, Building a Second Brain (aff) to learn more about how to develop your own system.

The more tasks and information you can offload, the fewer your brain will have to constantly monitor.

You don’t build a Second Brain to remember.

You build it so you can forget.

Stop Overcomplicating Everything

The more complex something is, the more moving parts you have to keep track of.

So the simpler you can make a task, the less it’ll take up your mental bandwidth.

A simple question to ask to help you stop overcomplicating everything is, “What is my actual goal here?” Then only focus on tasks related to that.

Final Thoughts on the Zeigarnik Effect

The Zeigarnik Effect unintentionally forces our brain to dedicate more memory and attention to unfinished tasks.

So if you want to improve your focus and free up your memory, minimize and offload unfinished tasks.

That way, you can stop bogging your brain down with remembering unfinished tasks and use it for what it’s best at:

Being creative, solving problems, and understanding complex ideas.

You’ve probably heard of Shiny Object Syndrome and how it causes distractions. But have you heard of Shiny Object Syndrome’s little brother, Yak Shaving?

Check out this article next to learn what it is and how it keeps you constantly distracted…

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Zeigarnik Effect

Reviewed by Psychology Today Staff

The Zeigarnik Effect is the power of unfinished business or interrupted or uncompleted activity to hold a privileged place in memory . Unfinished tasks create a cognitive burden, weigh more heavily on the mind, and are more easily recalled than completed tasks.

The Zeigarnik Effect explains why people are haunted by unfulfilled goals and may be more apt to recall what they haven’t achieved than what they have. Some have speculated that the cognitive burden of unfinished work causes some to see themselves negatively and contributes to such problems as impostor syndrome .

Understanding the Zeigarnik Effect
Harnessing the Zeigarnik Effect

Research has also established that unfulfilled goals can create intrusive thoughts when doing unrelated tasks and lead to poor performance of those tasks. However, creating specific plans for the unfulfilled goals eliminates the interference. Drafting and committing to a completion plan, researchers say, releases the cognitive burden and frees cognitive resources for other pursuits. Score 1 for to-do lists.

The Zeigarnik Effects may explain the power of unprocessed negative events to undermine intimate relationships. Such events may be preferentially remembered and mentally replayed until they undermine trust and put partners on a downward cascading path of suspicion and hostility.

The effect was first described by Soviet psychologist Bluma Zeigarnik in 1927. She was inspired to study the phenomenon after her professor, prominent psychologist Kurt Lewin, noticed that a waiter could remember in detail restaurant orders that had not yet been paid but could not remember the details of the orders once everyone had paid.

After designing experiments to observe the effect and explore the mental components, Zeigarnik found that an uncompleted task creates tension that makes its elements cognitively accessible until the task is discharged. Her experiments involved the assigning of tasks to study participants and, under controlled conditions, interrupting them at the halfway point or allowing them to complete the tasks undisturbed, then, an hour later, asking the participants to recall the details of their tasks.

The Zeigarnik Effect can be harnessed to boost recall . If you are trying to learn or memorize a body of facts, take time out in the middle of the task to focus on some other activity. Then, return to the learning task. Taking short breaks—of a few minutes to one hour—during a series of study sessions improves learning.

Yes. Taking the first step on a project, no matter how small, can create enough tension when the task is interrupted to motivate the resumption of the task.

The Zeigarnik Effect makes a powerful case against multitasking. Focusing on one task at a time will avert intrusive thoughts of unfinished work that will only create delays in finishing all the tasks. Completion of each task approached sequentially instead of simultaneously will clear mental space for the next task.

Why is it so hard to turn off a video game at the end of the night? What can we do when we spend hours telling ourselves "just one more minute and then I'll stop"?

When you're stymied trying to accomplish a new task, surprisingly the solution might lie in creating a detailed plan for other unrelated, unfinished tasks.

My joy following vaccination against COVID-19 seemed outsized, so I looked to psychology to explain the intensity of reaction. The Zeigarnik Effect and repression brought insight.

Feeling like you're running out time? A few tips for successfully closing out the year and find peace.

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How to solve complex problems (by not focusing on them)

The Zeigarnik effect can do something stunning when we scatter our attention and let our mind wander.

[Photo: Edgar Chaparro /Unsplash]

BY Chris Bailey 4 minute read

Simple decisions are best made using cold, hard logic. This way, we can work through the incremental steps that lead to an answer. But the same isn’t true for complex decisions, ones that require more creativity in meshing together a web of interconnected ideas. These decisions can be impossible to work through with logic and reason alone. That’s why we need to tap into the proven power of our subconscious mind.

We’re wired to remember what we’re in the middle of more than what we’ve completed, a phenomenon known in psychology circles as the Zeigarnik effect , named after Bluma Zeigarnik, the first person to study this concept. As a result, uncompleted tasks and decisions weigh more heavily on our minds than ones we’ve finished—focus comes when we close these distracting open loops. While annoying during attempts to focus, the Zeigarnik effect can do something stunning when we scatter our attention and let our mind wander.

Chances are you’ve experienced a few eureka moments. Maybe they struck while you were taking a shower, getting the mail, or walking through an art gallery. Your brain suddenly found the solution to a problem you hadn’t thought about in a few hours. In that instant, the puzzle pieces satisfyingly slid together and locked into place.

Two things were likely true in that moment: First, your insight was a response to a problem you’d been stuck on. Second, your mind was likely wandering while you did something that didn’t require your full attention. I call this mode of mind wandering “scatterfocus.”

Thanks to the Zeigarnik effect, we store any problems currently stumping us at the front of our minds. As a consequence, we connect each new experience to these unresolved problems, desperate to unearth novel solutions.

When doing something mindless and habitual, potential insight triggers come from two places: our wandering minds and the external environment.

Here’s an example.

Let’s say I invite you to my secret productivity-experiment lair. I offer you a seat, set a timer for 30 minutes, and ask you to solve this seemingly simple problem: The number 8,549,176,320 is the most unique 10-digit number. What makes it different? Let’s imagine you can’t solve the problem in the allotted time—not unreasonable, given that this is a tricky test. The question continues to weigh on your mind after you’ve left.

By now you’ve reached an impasse and have encoded the problem to memory. You see those digits whenever you close your eyes. (Naturally, the better you remember a complex problem, the greater your odds of coming up with a creative solution.)

Thanks partly to the Zeigarnik effect, your mind will automatically connect new experiences to this problem. You return to work with the number imprinted on your brain. You find your mind returning to it periodically, sometimes even against your will. In fact, odds are your mind will wander more often than usual—our thoughts drift more when we’re chewing on a complex problem—which causes you to make a higher-than-normal number of mistakes in your work.

Later in the day, you’re doing an activity that takes you into habitual scatterfocus mode: alphabetizing your bookshelf. You’re putting away the book The 80/20 Principle by Richard Koch. Your mind processes where the book will be shelved.

Okay, ignore the word “the.” First value is 8, so I’ll put it with the other books that start with a number. Huh, the first number in Chris’ experiment was also an 8. The solution hits you like a lightning bolt. 8,549,176,320. Eight, five, four, nine . . . A, B, C, D, Eight, Five, Four, G, H . . . The number has every digit arranged in alphabetical order!

This is a straightforward example of an insight trigger—usually they are more subtle, nudging your mind to think in a different direction to restructure the mental dots that represent a problem. I designed this example to illustrate a simple concept: A wandering mind connects the problems we’re tackling with what we experience and where our minds wander.

Look back at some of the greatest eureka moments in history. In addition to reaching an impasse with their problems, some famous thinkers arrived at solutions to them after being spurred by an external cue. Archimedes figured out how to calculate the volume of an irregular object when he noticed his bathwater overflowing. Newton came up with his theory of gravity when he saw an apple fall from a tree—probably the best-known insight trigger in history. For his habitual scatterfocus routine, renowned physicist and Nobel laureate Richard Feynman would sip 7UP at a topless bar, where he could “‘watch the entertainment’,” and, if inspiration struck, scribble equations on cocktail napkins.”

Our minds also wander to some fascinating places by themselves. One study found our mind wanders to think about the past 12% of the time, the present 28% of the time, and the future 48% of the time. Connecting all three of these mental destinations helps us piece together ideas and solutions to problems we’re incubating.

Insight triggers are remarkable. You may see a bird picking at a chip packet, which leads you to realize you should throw out the chips you’ve been snacking on so you can lose those final 10 pounds. Intentionally daydreaming while making breakfast, you recall how you resolved a past work dispute and realize you can use the same technique today. The more we purposefully let our mind wander and the richer our environment, the more insights we unearth.

Think about the moments when your most creative ideas struck. Wherever you were, you likely weren’t focused on them. If you’re stuck on a creative problem right now, don’t actively try to work through it. Get up, let your mind wander, and take a look around instead.

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Zeigarnik's sleepless nights: How unfinished tasks at the end of the week impair employee sleep on the weekend through rumination

Affiliations.

1 Work and Organizational Psychology, University of Trier.
2 Arbeitsbereich Arbeits- und Organisationspsychologie, University of Hagen.
3 AG Angewandte Psychologie in Arbeit, Gesundheit und Entwicklung, Ruhr-Universität Bochum.
PMID: 27101340
DOI: 10.1037/ocp0000031

It is almost common sense that work stress leads to sleep impairment, but the question of how work-related stressors impair employee sleep remains open. This study focuses on the role of rumination as the underlying mechanism for sleep impairment. Specifically, the authors contribute to recent research differentiating affective rumination from problem-solving pondering and examine the impact of both forms of rumination on the stressor-sleep relationship. Following theories of rumination and the Zeigarnik effect, they focus on unfinished tasks as a key onset for rumination. Unfinished tasks have received much research attention in the memory context but have been neglected as a stressor that can impact recovery. Drawing on theory, differential indirect links between unfinished tasks and sleep through affective rumination versus problem-solving pondering are examined. Further, the number of unfinished tasks extending over a 3-month period may impair employee sleep more than unfinished tasks within the acute phase. In this study, intraindividual links in a diary study supplemented by depicting between-person effects of unfinished tasks over a period of 3 months are examined. The authors matched 357 Friday and Monday observations over a 12-week interval for 59 employees. The results of the multilevel analysis suggest that the within-person relationship between unfinished tasks and sleep is mediated by affective rumination. Although problem-solving pondering was negatively related to sleep impairment, the indirect effect was not significant. Finally, beyond the acute effect, the authors found higher levels of unfinished tasks over 3 months are related to increased sleep impairment on the weekend. (PsycINFO Database Record

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What is the Zeigarnik Effect?

The Zeigarnik Effect is a psychological phenomenon that suggests people remember uncompleted or interrupted tasks better than completed ones. In other words, it’s harder to forget about work you haven’t finished yet. This effect was first studied by Bluma Zeigarnik , a Soviet psychologist, in the 1920s. She observed that waiters in a restaurant remembered incomplete orders better than those they had already served.

The Zeigarnik Effect suggests that the brain treats finished and unfinished tasks differently when it comes to memory. When people start a task but don't finish it, it creates a cognitive tension or dissonance that makes them more likely to remember the task and be motivated to finally complete it. The tension dissipates once the task is completed and no longer requires your attention, at which point the task is less likely to be remembered.

This phenomenon has implications in various areas, including productivity, learning, and motivation. For example, it suggests that leaving your remaining tasks partially completed can help maintain focus and motivation to finish them. It has also been used in advertising, where advertisers might create curiosity gaps or unfinished narratives to engage audiences and make them more likely to remember their messages.

How the Zeigarnik Effect works

The Zeigarnik Effect works by creating a state of mental tension or cognitive dissonance when a task is left incomplete. This tension arises because the brain perceives an inconsistency between the desire to complete the task and the current state of unfinished business. As a result, the brain remains actively engaged, making it easier to remember unfinished tasks and process the interrupted task until it is resolved.

Several mechanisms contribute to the Zeigarnik Effect:

Attentional bias: Incomplete tasks tend to capture more attention than completed ones. The brain prioritizes information that is perceived as unresolved or pending, leading to increased mental focus on unfinished tasks.
Memory encoding: The brain prioritizes the encoding of information related to incomplete tasks into working memory . This prioritization guarantees that the task remains accessible and easily retrievable until it is completed.
Motivational arousal: The tension created by unfinished tasks can serve as a motivational cue, driving individuals to seek closure by completing the task. The desire to alleviate the discomfort associated with unresolved tasks motivates action toward task completion.
Selective rehearsal: Individuals may engage in selective rehearsal or mental rehearsal of incomplete tasks, repeatedly reviewing and planning strategies for task completion. This rehearsal process helps maintain task-related information in working memory and facilitates progress toward resolution.

Benefits of the Zeigarnik Effect

The Zeigarnik Effect can offer several benefits in various aspects of life, including memory effects productivity, learning, and motivation:

Increased task completion: The Zeigarnik Effect motivates individuals to complete tasks they have started, but not yet finished. The mental tension associated with unfinished tasks drives individuals to seek closure, leading to a higher likelihood of task completion.
Enhanced memory retention: Incomplete tasks are more likely to be remembered than completed tasks. Leveraging the Zeigarnik Effect can improve memory retention of important information or tasks, so that they remain accessible and easily retrievable until resolved.
Improved focus & attention: Unfinished tasks tend to capture more attention and mental focus. Using the Zeigarnik Effect can help individuals prioritize tasks and maintain focus on important goals, reducing distractions and increasing overall productivity.
Motivational tool: The tension created by unfinished tasks serves as a powerful motivational cue. Harnessing the Zeigarnik Effect can help individuals stay motivated and engaged in pursuing their goals, even when faced with obstacles or challenges.
Enhanced problem-solving: Unresolved tasks stimulate problem-solving processes as individuals seek ways to overcome obstacles and achieve closure. Leveraging the Zeigarnik Effect can encourage creative thinking and adaptive problem-solving strategies.

Best practices for managing the Zeigarnik Effect

Harnessing the Zeigarnik Effect effectively involves leveraging its principles to enhance productivity, motivation, and cognitive performance. Here are some best practices for applying the Zeigarnik Effect in daily life:

1. Use task segmentation

Divide bigger tasks or projects into smaller, more manageable pieces. This approach creates multiple opportunities to experience the Zeigarnik Effect, as each incomplete subtask can serve as a motivator for continued progress.

2. Set clear goals

Having a clear understanding of what needs to be accomplished helps focus attention and motivation, making it easier to leverage the Zeigarnik Effect.

3. Prioritize tasks strategically

Identify high-priority tasks or tasks with impending deadlines and leave them incomplete strategically. The urgency associated with this task interruption can amplify the Zeigarnik Effect, driving increased motivation and focus toward completion.

4. Create to-do lists

Maintain to-do lists or task trackers to document your tasks and track progress. Including both interrupted and completed tasks and incomplete tasks on your list can help capitalize on the Zeigarnik Effect by keeping unfinished tasks top of mind.

5. Use visual cues

Use visual cues, such as calendar events, sticky notes, reminders, or progress charts, to visually represent incomplete tasks and urgency around them. These cues serve as constant reminders of unfinished business, enhancing motivation and focus.

6. Practice-focused work sessions

Create dedicated time blocks in your calendar to work on specific tasks without interruption. During these sessions, immerse yourself fully in the task at hand and avoid multitasking to maximize the Zeigarnik Effect's impact on your attention and motivation.

7. Reward progress

Celebrate incremental progress and milestones as you work toward your task deadlines. Rewarding yourself for progressing on and completing subtasks can reinforce positive behavior and motivation, making it easier to leverage the Zeigarnik Effect.

8. Maintain momentum

Once you start working on a task, aim to maintain momentum and make consistent progress. Continuously engaging with tasks helps sustain the Zeigarnik Effect and prevents procrastination or loss of motivation.

Examples of the Zeigarnik Effect in daily life

The Zeigarnik Effect manifests in various aspects of daily life, influencing behavior, cognition, and motivation. Here are some examples:

To-do lists: When you create a to-do list and leave tasks unfinished, those incomplete tasks tend to linger in your mind, prompting you to prioritize and complete them. The feeling of mental tension associated with unfinished tasks motivates you to work through the list.
Unanswered messages or emails: When you receive messages or emails but don't respond to them immediately, they may continue to occupy your thoughts until you address them. The unresolved nature of these communications triggers the Zeigarnik Effect, prompting you to prioritize responding to them.
Half-read books or articles: If you start reading a book or article but don't finish it, you're more likely to remember where you left off and feel compelled to return to it later. The incomplete reading creates a sense of cognitive dissonance, motivating you to seek closure by finishing what you started.
Incomplete projects: Whether it's a work project, a DIY home improvement task, or a personal project, leaving tasks unfinished can lead to increased mental focus and motivation to complete them. The Zeigarnik Effect encourages you to actively work toward resolving incomplete projects to alleviate the tension associated with their unfinished status.
Unfinished conversations: When conversations are interrupted or left unresolved, they may continue to occupy your thoughts until they are completed. You might find yourself revisiting the conversation in your mind or feeling the need to follow up to clarify or resolve any lingering issues.
Unfinished chores: If you start cleaning or organizing but don't finish, the unfinished task may nag at you until you complete them. The Zeigarnik Effect can motivate you to return to the chores and complete them to restore a sense of order and closure.
Study sessions: When you interrupt studying with a short break, the unfinished task (studying) stays in your mind, making it more likely you'll remember the material later. This "mental tension" can actually boost recall compared to uninterrupted studying.

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The Action | Andrew's Writings

Actionable Ideas #8: Zeigarnik Effect, Preventable Problems, 21 Times

In these actionable ideas articles, I highlight three ideas that you can convert into immediate action to drive meaningful value in your work and life.

How can we leverage unfinished business to be more productive? How do we improve our problem solving skills by focusing on problem prevention? and why should we aim to repeat ourselves 21 times to get a point across? Let’s dive in:

Idea #1: Zeigarnik Effect aka Unfinished Business

Themes : productivity

The Zeigarnik Effect states that people remember unfinished or interrupted tasks better than completed tasks. Dr. Z coined the effect when she was dining and noticed the waitstaff’s ability to recall long lists of unfinished orders, but unable to remember any of the completed orders. The idea is that the mind develops a task-specific tension that allows that unfinished task to stay top of mind more than completed tasks.

Action item: after you’ve completed a task, start the next task and then stop. This trick is often used by writers to keep the momentum going for their next writing session – after finishing writing a page, start the next one and then stop while it’s incomplete. This will be a jumping off point for when you come back to write.

Idea #2: Preventable Problems Paradox

Themes : problem solving, process, culture

The preventable problems paradox states that any complex organization will over time tend to incentivize problem creation more than problem prevention. Articulated by Shreyas Doshi in this fantastic twitter thread , he states that this is largely because problem prevention is often not visible or feels boring, while problem solving is very visible and feels critical.

Action item : promote a culture of pre-mortems so that you are consistently thinking about future problem prevention. Bake it into the initial part of any task or project, alongside the initial problem you are trying to solve. Do this both in work and your personal life so that you are thinking through every dimension of your execution plan.

Idea #3: 21 Times Rule

Themes : communication, influence, alignment, culture

UNH Connect - Online - February Career Speakers Series

In her book Rise , Patty Azzarello writes “there is a well tested marketing principle… for your audience to understand and internalize your message well enough to act on it, it takes them hearing or seeing your message seven times. And for every one time they see or hear it, they have to be exposed to it three times. That’s 21 times!” In order to drive a point home, you must over-communicate. Use spaced repetition to get your point across and inspire action in others.

Action item : remember this rule as you try to socialize and communicate an idea to an audience. Maybe it is a new project that just started – make sure its known, bring it up as often as possible, but of course do this tactfully. 21 is a rough guideline, the key here is to over communicate with the understanding that most people forget (or weren’t paying attention the first time!) You can follow Patty’s playbook here

There may be opportunity to combine and play around with these. For example, maybe you can harness the Zeigarnik Effect and keep incomplete tasks top of mind for your team, while also communicating the core strategy with the 21 times rule. You may also be able to make a culture of pre-mortems top of mind with the 21 times rule, and avoid the preventable problems paradox.

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Memory for incomplete tasks: A re-examination of the Zeigarnik effect

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Zeigarnik and von Restorff: The memory effects and the stories behind them

Published: 06 April 2020
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Two of the best known eponymous phenomena in memory research were carried out as dissertations in the same era at the same university, each supervised by an influential researcher working within the Gestalt framework. Both examined the influence of unexpected events on memory. Bluma Zeigarnik ( Psychologische Forschung, 9 , 1–85, 1927 ) first reported that memory is better for interrupted tasks than for completed tasks, a phenomenon long known as the Zeigarnik effect . Hedwig von Restorff ( Psychologische Forschung, 18 , 299–342, 1933 ) first reported that memory is better for isolated than for non-isolated pieces of information, a phenomenon long known as the von Restorff effect . In this article, I present: (1) a biographical sketch of the researcher behind each phenomenon, (2) a description of their dissertation research, and (3) an evaluation of the current status of each phenomenon.

The Future of Memory

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Introduction

In psychology, as in many sciences, numerous phenomena are named after the researcher(s) who first explored them. Sometimes they are given the most general labels (e.g., the Yerkes-Dodson Law – Yerkes & Dodson, 1908 ), sometimes their labels are more specific (e.g., the Müller-Lyer illusion –Müller-Lyer, 1889 ), but most often they are simply known as “effects” (e.g., the Stroop effect – Stroop, 1935 ). In the domain of memory, few longstanding phenomena are named for individuals, but surely two of the best known are the Zeigarnik effect and the von Restorff effect. The Zeigarnik ( 1927 ) effect refers to the observation of better memory for interrupted and hence incomplete tasks than for completed tasks; the von Restorff ( 1933 ) effect refers to the observation of superior memory for isolated information over non-isolated information.

Who were Zeigarnik and von Restorff? How did they come to study their eponymous phenomena? What did they actually do? How have their findings held up over time? The purpose of this article is to answer these questions. In considering each researcher and her famous phenomenon, I will begin with a biographical sketch, then outline the motivation behind the research, and then describe what was, in fact, actually done. I will conclude with a summary of the status of the phenomenon since the original work.

As it turns out, their stories are linked in multiple ways and together provide a compelling portrait of a time and place in the history of psychological science. Both were women in research at a time when men dominated the field. They did their work in the same research institute just a few years apart. Their famous findings come from their dissertations, published in the same journal. Both dissertations examined the influence of unexpected events on memory. Both were supervised by influential scientists and for each the driving force behind their dissertation was their supervisor’s “field theory,” deriving from Gestalt psychology. Neither ever pursued the phenomenon that her dissertation made famous. And for both, their work was carried out during one of the most dramatic periods in history – in the shadow of Hitler’s rise to power in Germany, which would greatly influence the course of their lives.

Bluma Vulfovna Zeigarnik

This sketch was compiled from several sources, including Marrow ( 1969 ), Nikolaeva ( 2011 ), and the websites https://www.thescienceofpsychotherapy.com/bluma-wulfovna-zeigarnik/ and Wikipedia : https://en.wikipedia.org/wiki/Bluma_Zeigarnik . It relies especially, though, on a portrayal of his grandmother’s life written by Andrey Volodya Zeigarnik ( 2007 ), and based on his interviews with his father, Vladimir, and his uncle, Yurii. Formerly a chemist in Russia, Andrey Zeigarnik now is a professional photographer who has lived in Israel since 2017 (see http://www.zeigarnik.ru/ ).

Zhenya-Bluma Gerstein was born on 9 November 1901 in Prienai, a small town in southern Lithuania. She was the only child of Volf and Ronya Gerstein, educated Jews who owned a store in Prienai. Because Russian was the official state language, Bluma’s education was in Russian, but her parents spoke Yiddish at home so she learned that language as well. Sometime in her youth, the family moved to Minsk, capital city of Belarus.

In 1916, Bluma enrolled at a girls’ school in Minsk – in the fifth grade, having skipped the first four. Instruction was in Russian, but she also studied French, German, and Latin. Of her time enrolled at that school, 4 years were actually spent at home due to a life-threatening case of meningitis. When the worst of her illness had passed, she worked with private tutors. In 1918, she graduated from the seventh grade with a gold medal. She wanted to continue her education, but universities required the higher-level qualifications of boys’ schools for admission. Bluma decided to take the boys’ school final exams and obtained high scores, resulting in her becoming one of the first women in Russia to attend university.

Like many other successful psychologists (e.g., D. O. Hebb, B. F. Skinner), Bluma’s interest in the discipline arose through studying literature, in her case through the influence of one of her teachers in Minsk. Consequently, she began to study in preparation for university, spending considerable time in the library. It was there that she met Albert Zeigarnik, whom she married in 1919. Because she was only 18 and because Albert was not well-to-do, her parents initially disapproved of the marriage. Eventually, however, they accepted it, even providing the support for Bluma and Albert to study in Europe. In 1922, Bluma (henceforth Zeigarnik) enrolled in the Department of Philosophy at the Friedrich Wilhelm University of Berlin (since 1949, the Humboldt University of Berlin); Albert became a student at the Polytechnic Institute of Berlin.

The university had, at that time, a stellar group of researchers in the Psychological Institute, so Zeigarnik was exposed to the thinking of Gestalt psychologists Wolfgang Kőhler, Kurt Koffka, and Max Wertheimer, all students of Carl Stumpf (for a history of Gestalt psychology, see Wagemans et al., 2012 , b ). But she was especially taken with the teachings of another Stumpf student, Kurt Lewin (for a biography, see Marrow, 1969 ), Footnote 2 just 10 years her senior and with a devoted and impressive group of students assembled around him. She began working with him on studies aimed at extending Gestalt principles from perception more broadly to cognition. The conceptual basis of the work is described in the next section of this article.

After graduating from the university in 1925, Zeigarnik continued working with Lewin, and published her famous dissertation titled “The memory of completed and uncompleted actions” in 1927 in Psychologische Forschung , the journal created by the Gestaltists in 1922 (and, since 1974, published in English as Psychological Research ). An English translation was published subsequently as a book chapter (Zeigarnik, 1938 ). She was awarded her doctoral degree (with honors) in 1927; she was 26 years old. Figure 1 presents a photograph of her 5 years before her dissertation (age 21 years); Fig. 2 presents a photograph of her dissertation document.

Bluma Zeigarnik (age 21 years), from the Zeigarnik family archive with the permission of her grandson, Andrey Zeigarnik. This photo appeared as Fig. 2 in his biography of his grandmother (Zeigarnik, 2007 )

Bluma Zeigarnik’s dissertation document, from the archives of Humboldt University of Berlin (Phil. Fak. 01, Nr. 664, Blatt 344)

For the next 4 years, Zeigarnik stayed on at the university as a part-time research scientist. Then in 1931, because communism appealed to them, Albert and his brothers decided that it was time to return to Soviet Russia. They saw this as a “homecoming,” not realizing what awaited them in Stalinist Russia. They moved to Moscow, where Zeigarnik became a scientist at the Institute of Higher Nervous Activity, soon to become part of the All-Union Institute of Experimental Medicine. It was there that she worked with the second great influence of her scientific life, Lev Vygotsky (for biographies, see van der Veer, 2014 , and Yasnitsky, 2018 ). In 1933, when Lewin visited Moscow on his way back to Germany overland from Japan, Zeigarnik organized Lewin's visits to Vygotsky's home, introducing her two mentors and including her colleague and close friend, Aleksandr Luria. It was Zeigarnik who told Lewin of Hitler’s rise to Chancellor, which finally convinced Lewin that, as a Jew and a free thinker, he must leave Germany: That year, he accepted a position at Cornell University in the USA. [Other major figures in Gestalt Psychology – Max Wertheimer (The New School for Social Research, 1933) and Kurt Koffka (Smith College, 1927) – had already left for America, and Wolfgang Köhler would follow 2 years later (Swarthmore College, 1935).] When Vygotsky died of tuberculosis 4 years later at the age of 37 years, and with Lewin having moved to the USA, Zeigarnik lost her two major inspirations, a source of deep regret throughout her life.

A visit to Lazarett Hospital in Berlin during the 1920s, in particular to Kurt Goldstein’s clinic (see Goldstein, 1939 ; for his influence, see Teuber, 1966 ), had generated in Zeigarnik a continuing interest in clinical psychology, to which she turned in the 1930s. Her focus became clinical neuropsychology, and medical psychology more generally. Albert and Bluma’s first child, a son named Yurii, was born in 1934. Then, in 1935, she was awarded the academic degree of Candidate of Biological Sciences, a major boost to her career since her German Ph.D. was not respected in Soviet science. But in 1936, a government decree – the resolution “On Pedological Perversions in the System of the People’s Commissariat of Education” – rendered much of Soviet psychology forbidden ideologically (see Bauer, 1959 ), and little is known about Zeigarnik’s work for the next 4 years. Finally, though, in 1940, she published her research on post-traumatic dementia, work that she had begun in the early 1930s.

In 1938, her research group became part of the Institute of Psychiatry of the Ministry of Health, and Zeigarnik began to work as a psychologist-neurologist. Albert and Bluma’s second son, Vladimir, was born in 1939. In 1940, she traveled to Lithuania to see her mother for the first and last time since the 1920s, her father having already died. But that same year, Albert was arrested on charges of being a German spy and sentenced to 10 years in prison camp. Footnote 3 With two small children and inadequate income, these were difficult years for Zeigarnik, who spent considerable time going to the Lubyanka – the KGB headquarters in Moscow – to try to find out what was happening to her husband. In that time, she was supported by a host of brave friends, notably Aleksandr Luria and Susanna Rubinshtein.

As a consequence of this difficult time, her grandson Andrey (Zeigarnik, 2007 ) describes Zeigarnik as having changed from open to very guarded and fearful, in part because of her concern for her sons. She would never talk about her past in the West and held, at least on the surface, a very Marxist perspective that she mandated as well for her family. In 1941, Zeigarnik was reassigned to work at a clinic for nervous diseases in Chelyabinsk where she was a senior research scientist focusing on the rehabilitation of head injury victims.

In 1943, Zeigarnik and her sons returned to Moscow to find that their apartment had been ransacked by an individual housed there for those 2 years by the government. She also suffered another loss: Her second dissertation, based on her medical research during World War II, was stolen from her home by a co-worker at the Institute of Psychiatry. Fearing reprisals if she accused the co-worker of plagiarism, she destroyed all material related to that work. Eventually, life was restored to order and she became head of a laboratory at the Institute of Psychiatry. In 1949, she also began to teach courses on psychopathology at Moscow State University. But at the end of the 1940s, Soviet psychology was again censored by the government at the same time as a state anti-Semitic campaign began against “cosmopolitanism,” Stalin’s barely disguised attack on Jews (see Pinkus, 1988 ). Demoted from head of laboratory in 1950, by 1953, Zeigarnik had lost her job, basically for being a Jew. Luria, Rubinshtein, and other friends helped her to survive in this period, financially and in terms of work. It was not until 1957, well after Stalin’s death and with Khrushchev’s rise to power, that Zeigarnik returned to the position of head of the psychopathology laboratory at the Institute of Psychiatry. She remained there until 1967, while also teaching at Moscow State University.

In 1958, she completed her third doctoral dissertation, earning the degree of Doctor of Pedagogical Sciences. In 1965, she was given the title Professor of Psychology, and in 1967 she was elected chair of the Faculty of Psychophysiology and Neuropsychology of Moscow State University. In the following years, she published several monographs, among them Thought Disorder in the Mentally Ill ( 1959 ), Pathology of Thinking ( 1965 ), Introduction to Pathopsychology ( 1969 ), Personality and Pathology of Activity ( 1971 ), Experimental Abnormal Psychology ( 1972 ) and Foundations of Psychopathology ( 1973 ). In 1978, she was awarded the First Degree Lomonosov Prize for her work on the psychology of mental disorders and the rehabilitation of the mentally ill. In 1983, she was chosen to receive the Kurt Lewin Award from the Society for the Psychological Study of Social Issues, but her government refused permission for her to travel to the West to receive it.

Figure 3 presents a picture of Zeigarnik later in life. Throughout the 1980s, she remained in her position at Moscow State University, lecturing and supervising many graduate students. During that time, she published her monographs The Theory of Personality of K. Lewin ( 1981 ), Theories of Personality in Foreign Psychology ( 1982 ), and Pathopsychology ( 1986 ). Ironically, her last book (Zeigarnik, 2001 ), published posthumously, was about her dissertation work. Nikolaeva ( 2011 ) details her work and her influence in Russia. It was also during this very active time, however, that Zeigarnik became seriously ill from the long-term consequences of anemia. She died on 24 February 1988, at the age of 86 years.

Bluma Zeigarnik later in life, from the Zeigarnik family archive with the permission of her grandson, Andrey Zeigarnik. This photo appeared as Fig. 3 in his biography of his grandmother (Zeigarnik, 2007 )

As her grandson, Andrey, wrote (Zeigarnik, 2007 , p. 265), “She was a tiny, fragile-looking woman, hardly coming up to the shoulder of a person of average height. Understandably, she never gave lectures from behind the podium, because she would not have been visible. But she perceived herself to be taller than her actual physical size. Every time she bought a coat or a dress, she would choose a size too large.” He went on to describe her devotion to her grandchildren and graduate students, her loyalty, her clinical acumen, her sense of humor, and her “extraordinary kindness.” Clearly, she led an extraordinary life – a complete life – but certainly one with memorable interruptions.

The foundation of Zeigarnik’s dissertation

With Kurt Lewin as her supervisor, Zeigarnik was strongly influenced by his field theory (e.g., Lewin, 1939 ), a theory closely linked to Gestalt theory. This linkage is hardly surprising given Lewin’s colleagues in the Psychological Institute, including Kurt Koffka and Max Wertheimer. Although he became one of the most prominent social psychologists, Lewin was very much influenced by Gestalt psychology, writing that “The fundamental ideas of Gestalt theory are the foundation of all our investigations in the field of the will, of affection, and of the personality” (Lewin, 1935 , p. 240). As further evidence of this influence on his thinking, Lewin also writes that he collaborated for over a decade with another colleague, Wolfgang Köhler, Footnote 4 one of the architects of Gestalt theory and the eventual Director of the Institute – and later von Restorff’s dissertation supervisor.

In Lewin’s conceptual system, a person was “a complex energy field in which all behavior could be conceived of as a change in some state of a field during a given unit of time. He postulated a theory of psychological tensions in which tensions function as a form of energetics” (Marrow, 1969 , p. 30). These tensions (or needs or wants), existing in the “psychic field” (as opposed to the physical field), Footnote 5 provided the mental energy to prepare for and ultimately to bring about behavior. Carrying out the behavior released the tension (for more on his theory, see Burnes & Cooke, 2013 ). It is easy to see how this led to the core idea motivating Zeigarnik’s dissertation research.

The story has often been related of how Lewin arrived at the insight that then became the basis of Zeigarnik’s dissertation. Best known is Boring’s ( 1957 , p. 734) version: “There is a story related to Zeigarnik’s experiment. Lewin and his friends were in a restaurant in Berlin, in the sort of prolonged conversation which always surrounded Lewin. It was a long time since they had ordered and the waiter hovered in the distance. Lewin called him over, asked what he owed, was told instantly and paid, but the conversation went on. Presently Lewin had an insight. He called the waiter back and asked him again how much he had been paid. The waiter no longer knew.” Boring’s emphasis is on the waiter remembering the total amount of the bill, not on remembering what was ordered.

There is, however, another version told by Donald MacKinnon, who was at the particular gathering. Marrow ( 1969 , p. 27-28) quotes MacKinnon’s description: “[S]omeone called for the bill and the waiter knew just what everyone had ordered. Although he hadn’t kept a written reckoning, he presented an exact tally to everyone when the bill was called for. About a half hour later, Lewin called the waiter over and asked him to write the check again. The waiter was indignant. ‘I don’t know any longer what you ordered’, he said. ‘You paid your bill’.” MacKinnon’s emphasis is on the waiter remembering what each person had ordered, not on remembering the total amount of the bill. These differing stories fit very nicely with another idea being developed around the same time – Bartlett’s ( 1932 ) perspective on memory as being fundamentally reconstructive. The crux, though, is that the waiter remembered some aspect of the interaction better when the interaction was not yet completed; this was the undischarged tension in Lewin’s field theory.

Zeigarnik’s dissertation connected Lewin’s field theory to his observation about the waiter’s memory. Applied to task performance, in field theory the idea was that the intention to perform a task created a kind of need (or “quasi-need”) that resulted in a kind of psychological tension. That tension, which would be released when the task was completed, would remain active when the task was interrupted and not completed. Could that residual tension influence later memory? That was the question that Zeigarnik addressed in her dissertation: “What is the relation between the status in memory of an activity which has been interrupted before it could be completed and of one which has not been interrupted?” (Zeigarnik, 1938 , p. 300).

What Zeigarnik did

Over the period 1924–1926, Zeigarnik undertook an impressive series of experiments to answer this question. In her first experiment, 32 adults were given 22 tasks to do. These tasks were quite different from each other, examples being thread winding, paper folding, multiplication, drawing a vase, and counting backwards. Each task was intended to take about 3–5 min (although there was no time limit), with the instructions to “complete as rapidly and correctly as possible” (Zeigarnik, 1938 , p. 300). Half of the tasks were allowed to go to completion whereas half were interrupted. Interruption occurred just when the participant “was most engrossed” in the task and consisted of presenting the next task and saying “Now do this, please” (Zeigarnik, 1938 , p. 303). After presentation of all 22 tasks, participants were immediately asked to recall as many of the tasks as they could.

Although Zeigarnik did not number her experiments, I will do so for clarity. The results of her first four experiments were very clear – and remarkably consistent – as shown in Table 1 . In Experiment 1, which she considered her main experiment, she tested participants individually and their recall strongly favored interrupted tasks, whether indexed in terms of the participants or the tasks. In Experiment 2, using a different set of tasks, she replicated with a sample of 15 individual adults. She also twice replicated the finding in group testing situations, once with 47 adults – Experiment 3 – and once with 45 children (mean age of 14 years) – Experiment 4. In all four of these experiments, the proportion of participants who recalled the interrupted tasks best was consistently around .8. Throughout her experiments, Zeigarnik’s preferred statistic was the ratio of interrupted tasks recalled (IR) to completed tasks recalled (CR), a measure which shows superior recall of interrupted tasks to the extent that its value is greater than unity. Zeigarnik also saw this measure as having the virtue of eliminating individual differences in memory. This ratio hovered close to 2.0 across all four experiments.

Zeigarnik also carried out two small experiments (both with 12 adults) to challenge potential alternative interpretations; these are also shown in Table 1 . In Experiment 5, the issue was whether interrupted tasks might be “a shock” so she interrupted all 18 tasks but allowed participants to immediately resume completing half of them whereas the other half were not completed; she observed her standard result favoring recall of the incomplete tasks. In Experiment 6, her concern was whether participants might assume that interrupted tasks would be resumed later and hence preferentially retain them. She therefore told participants for six interrupted tasks that they would be resumed and for six interrupted tasks that they would not be resumed, and included six completed tasks as well. In fact, no interrupted tasks were resumed. Both interrupted conditions showed her typical advantage over the completed condition.

Pursuing the concern about people anticipating resumption of interrupted tasks, Zeigarnik conducted two further experiments, each with 12 participants. In Experiment 7, using 20 tasks, she informed participants for the interrupted half of the tasks that they would be resumed; in Experiment 8, using 18 different tasks, she informed participants for the interrupted half of the tasks that they would not be resumed. She argued that if her previous findings hinged on participants anticipating resumption of interrupted tasks, then her effect should be enhanced in Experiment 7 but reduced in Experiment 8. Neither outcome transpired: Her effect was virtually unchanged, leading her to discard the “anticipated resumption” explanation of superior recall of interrupted tasks.

Two features of Zeigarnik’s studies have received little attention over the years but could nevertheless be influential. The first is procedural: She included in her recall data only responses produced before the first hesitation, responses she considered to be spontaneous. She saw only that portion of recall as relating to the tension hypothesis; subsequent recall, she thought, would presumably involve more extensive search, which would not speak to her hypothesis, grounded as it was in the field theory idea of a continuing tension. (It is worth noting, however, that Zeigarnik ( 1927 , p. 13) states that she did not find a difference between pre-hesitation recall and total recall.) Relatedly, she reported an observation about the order of recall – that the incomplete tasks tended to be recalled first. Possibly, then, output interference (see, e.g., Smith, D’Agostino, & Reid, 1970 ) – interference imposed by earlier-recalled items on yet-to-be-recalled items – could have played a role in her observed data pattern. The likelihood of a hesitation would be expected to increase as recall progressed so that, if completed tasks were recalled later, they would be less likely to be counted.

Zeigarnik did report some contrasting findings that she saw as also being consistent with her “continuing tension” account. In Experiment 9, seven participants who were fatigued when performing the tasks (but rested when recalling them) actually recalled more completed tasks; in contrast, eight participants who were fresh when performing the tasks (but fatigued when recalling them) showed no effect. Zeigarnik saw fatigue as undermining maintenance of the tension required to produce her effect. She also thought that the tension should dissipate with time and showed that in two further experiments. In Experiment 10, eight participants went through the entire procedure twice, the first time recalling one day later and the second time recalling immediately. They showed her typical pattern immediately but the effect disappeared after the 1-day retention interval. In Experiment 11, 17 participants were tested both immediately and then again after 1 day: The typical recall advantage for interrupted tasks was observed immediately but shrank by more than half over 24 h (despite the repeated recall). Zeigarnik was, however, ahead of her time (see McGeoch, 1932 ) in suggesting that what occurred in time – not time itself – was crucial. Therefore, in Experiment 12, she manipulated context change (see Sahakyan & Kelley, 2002 , for a similar logic applied to intentional forgetting). For six participants called to an important telephone call just after all of the tasks had been administered, who consequently experienced a context change between study and test, she actually found completed tasks to be better recalled. She further tested this context change idea with two small groups: four participants interrupted in such a way as to easily return to the experimental situation still showed her effect, albeit about half as large as usual, whereas three participants interrupted in such a way as to make returning to the experimental situation difficult actually recalled more completed tasks.

Zeigarnik even brought back 14 of the 32 participants from Experiment 1 after 3–6 months and carried out the procedure again with new tasks; she reported a correlation of .9 between the two occasions, which she saw as evidence of consistent individual differences. With regard to such differences, she noted that “ambitious” participants who wanted to succeed showed an enhanced advantage for the interrupted tasks. Her conclusion that “The strength with which such tension systems arise and persist evidently varies greatly between different individuals” (p. 314) foreshadowed the empirical work of other researchers that would follow, much of which would pursue her phenomenon by exploring individual differences.

In her conclusion, Zeigarnik ( 1938 , p. 313) wrote that “The experiments reported here have shown that unfinished tasks are remembered approximately twice as well as completed ones … because at the time of report there still exists an unsatisfied quasi-need. This quasi-need corresponds to a state of tension whose expression may be seen not only in desire to finish the interrupted work but also in memorial prominence as regards that work.”

Zeigarnik’s dissertation was the first to put Lewin’s ideas to a strong test. Lewin held her dissertation in high regard, stating in his book A dynamic theory of personality (Lewin, 1935 , p. 240) that “All later investigations [in his laboratory] are built upon this” and describing her work as “of unusual conceptual clearness with great psychological acuity.” He also traced the influence of Zeigarnik’s dissertation on other work that followed closely in his laboratory. In 1928 , Maria Ovsiankina showed that when a period of relatively few demands was provided following interruption of a task, that task tended to be resumed: The tension had not been resolved. Shortly thereafter, Gita Birenbaum ( 1930 ) showed that intentions relating to the primary task were rarely forgotten, unlike less central goals (e.g., the content of a letter would be remembered whereas the date written on that letter would be more likely to be forgotten). Footnote 6 Zeigarnik’s dissertation clearly inspired a body of work aimed at putting Lewin’s field theory to continuing and stringent empirical test.

Status of the Zeigarnik effect today

The phenomenon, for many years referred to as “the interrupted task paradigm” or by similar designations, would appear to have acquired its creator’s name around the middle of the last century: In his dissertation on motivation, Atkinson ( 1953 ) uses the term Zeigarnik effect for the first time. As of the writing of the current article, Zeigarnik’s dissertation article has been cited 367 times according to PsycInfo and 1,155 times according to Google Scholar.

Beginning with work by Schlote ( 1930 ), whose results were not supportive of Zeigarnik’s, numerous researchers carried out related studies, enticed by this provocative phenomenon and by Lewin’s increasing influence. In particular, a study by Marrow ( 1938 ) was often pointed to. He followed Zeigarnik’s logic, testing 30 participants using a series of 20 paper-and-pencil tasks including circling vowels in a paragraph, adding numbers, listing recently read books, and the like. He obtained an IR/CR ratio of 1.77, with 25 participants showing I > C, three showing I = C, and two showing C > I. His results clearly bolstered Zeigarnik’s findings and generalized them to a different type of material. But as it turned out, many results over the ensuing years were not as supportive of Zeigarnik’s reported phenomenon.

Prentice ( 1944 ) was the first to discuss the thorny complications with this line of research. There were sufficient problems that by 1950 , Sears (p. 113) questioned the value of the interrupted task paradigm in no uncertain terms: “When a research operation requires as much discussion of its ‘psychological meaning’ as interruption does, it is time to find a new operation.” Hovland ( 1951 , p. 677) summarized that “there have been a number of failures to confirm the appearance of the phenomenon.” Alper ( 1952 , p. 78) concurred: “Few investigators could unequivocally reproduce Zeigarnik’s findings,” and argued that findings differed dramatically depending on the participant’s personality.

After almost 40 years, the work that followed Zeigarnik’s dissertation was reviewed by Butterfield ( 1964 ) and by van Bergen ( 1968 ). In his review, Butterfield ( 1964 , p. 309) concluded that “the Zeigarnik effect is far from being the invariable result in ITP [interrupted task paradigm]. Frequently, more completed than incompleted tasks are recalled.” Instead, Butterfield summarized the numerous studies that had investigated individual differences factors in memory for interrupted tasks, concluding that there was no universal pattern but that what was observed might vary as a function of what could be broadly described as motivation, intriguing given that investigating motivation was what initially led to Zeigarnik’s work.

The first and most influential illustration of this “no universal pattern” conclusion was provided by Atkinson ( 1953 ) in his dissertation where he observed that, when provided with skill-oriented instructions, individuals high in need achievement showed better memory of incomplete tasks – the Zeigarnik effect – whereas those low in need achievement showed the reverse. He argued that the goal of the individual dictated the outcome: “When the goal is to experience feelings of success and personal accomplishment, then persistence of the interrupted activity in recall and subsequent resumption of it are instrumental to attainment of that goal. When, however, the goal is to avoid feelings of failure, non-recall of past failures and presumably non-resumption of previously failed activities are instrumental to the avoidance of renewed feelings of failure” (Atkinson, 1953 , p. 387). Weiner, Johnson, and Mehrabian ( 1968 ) suggested that high-need achievement individuals showed a Zeigarnik effect because they chose to rehearse the interrupted tasks, agreeing with Caron and Wallach ( 1957 ) that the effect, when it occurred, was due to differential learning, not differential recall. The simple interpretation was that highly motivated individuals want to remedy their failures whereas less motivated individuals want to forget their failures.

Given this pattern of results in individual differences studies, it is not surprising that the Zeigarnik effect has been difficult to replicate with unselected participants: Butterfield ( 1964 ; see p. 315) reports a host of studies that have failed to find better memory for the incomplete tasks. He also underscores the important observation, made earlier by Osgood ( 1953 , p. 587), that: “inability to show that I and C tasks are equally well learned is a crucial shortcoming of ITP as a measure of retention since the original learning opportunity is frequently shorter for I than for C tasks” (Butterfield, 1964 , pp. 315-316). Indeed, one could certainly imagine that the longer exposure to the completed tasks than to the incomplete tasks might be expected to produce better, not worse, memory for the completed tasks, if total time on task is relevant (see, e.g., Cooper & Pantle, 1967 ).

Van Bergen ( 1968 ) was inspired by Cartwright’s ( 1959 , p. 33) statement that “when Zeigarnik’s original conditions have been exactly reproduced the same findings have been obtained” and by a clear belief in the importance of replication, particularly based on her Chapter 3 summary of the post-Zeigarnik literature. Consequently, she began her own precise replication in 1961. She tested 34 participants, mostly university students, at her home. In trying to replicate exactly, she faced a number of problems, notably when precisely the interruption should occur, but used her judgment. Her results showed absolutely no difference in recall up to the first hesitation. Using Zeigarnik’s preferred ratio – IR/CR – the value was .88, and the number of participants showing I > C was nine, the number showing I = C was five, and the number showing C > I was 20. Van Bergen reports that the pattern was unchanged when all of the recall data were examined, and that her participants did not systematically recall the interrupted tasks first. She then tried to precisely replicate Marrow’s ( 1938 ) study as well. Again, there was no advantage for the interrupted tasks: She observed an IR/CR ratio of .88 and the number of participants showing I > C was three, I = C was six, and C > I was 11. Substituting a new experimenter, she very closely replicated this pattern in another experiment with 25 participants. She carried out several additional experiments, one with only three of the 20 tasks being interrupted, another using a different set of materials, and another testing children: All failed to produce any evidence of Zeigarnik’s pattern.

In her dissertation, Van Bergen ( 1968 , p. 267) stated in her final paragraph that “the problem of the selective recall of uncompleted and completed tasks must be regarded as one of those ‘questions which seem to lead nowhere’,” and argued that this phenomenon, which she referred to as a “non-problem,” should be “discarded.” She made this argument on two principal bases. The first was the seven new experiments comprising her dissertation (chapter 5), where she reported that “in none of them was this phenomenon verified” (p. 220). The second was her comprehensive review of the replication attempts in the literature (chapter 6), where she reported that “Of the studies which were intended to show a Zeigarnik effect, less than half actually did so” (p. 249). In fact, by actual count, less than a third of the 44 papers that she considered as replication attempts reported a Zeigarnik effect. She even went on to assert, notwithstanding Butterfield’s analysis, that “The studies on personality variables did not offer a substantial contribution to the clarification of the study of selective recall” (p. 249).

There have been isolated studies relating to the Zeigarnik effect since the late 1960s, but very few (e.g., Seifert & Patalano, 1991 ). It is likely that the detailed reconsiderations provided by Butterfield ( 1964 ) and by van Bergen ( 1968 ) go a considerable way to explaining why the phenomenon, despite being well known in cognitive psychology and indeed in other disciplines, is rarely cited in modern textbooks. At best, it would appear to hinge on certain individual difference characteristics; at worst, it is simply not replicable. Yet the core idea has remained better known than the criticisms. As one illustration, in the idea of “need for cognitive closure” (Kruglanski & Webster, 1996 , p. 263), Zeigarnik’s core idea can be seen to live on as “a desire for definite knowledge on some issue … [that] … represents a dimension of stable individual differences as well as a situationally evocable state”. Footnote 7 Her phenomenon may, then, be an instance of a provocative and appealing idea that, once let out of the box, is nearly impossible to put back in. As Butterfield ( 1963 , p. 56, quoted by Van Bergen, 1968 , p. 267) concluded in his dissertation, task interruption “has become one of those instances in the history of psychology when a technique rather than a concept is the focus of intense experimentation.” There are certainly many precedents in Psychology and indeed in other sciences.

Hedwig Ida Auguste von Restorff

This sketch was compiled from a number of sources, initiated from an encyclopedia entry written by David Murray ( 2012 ) and then using original documents from von Restorff’s years at Friedrich Wilhelm University provided by the university.

Hedwig von Restorff was born 14 October 1906 in Berlin, Germany. Footnote 9 Her parents were Elisabeth Marie Karoline Juliane von Plessen (born 14 December 1886) and Major Reinhold Louis Wilhelm von Restorff (born 27 March 1869). She had one brother, Wilhelm Louis Gustav von Restorff, born just over 5 years after her on 27 January 1912. Von Restorff’s schooling began at a private elementary school (a Lyceum) in Zossen, about 50 km south of Berlin and then at another Lyceum in Heiligengrabe, about 120 km north of Berlin, where she earned her certificate in 1922. She was then admitted to the equivalent of high school at the State Augusta School in Berlin, obtaining her certificate in science in 1925. A year later, she obtained her certificate as a Lyceum teacher. Two years later, while in university, she passed additional exams in Greek and Latin at the provincial school college in Berlin, obtaining an additional high school certificate.

Von Restorff was admitted in 1926 to Friedrich Wilhelm University to study new languages, later specializing in philosophy, psychology, and natural science. She continued her studies in Jena in 1928 and in Berlin until 1932. In Berlin, von Restorff worked with the influential group of Gestalt psychologists in the Psychological Institute, where her dissertation was supervised by the Director, a major figure in Gestalt psychology, Wolfgang Köhler (for a biography, see Ley, 1990 ). The famous study (von Restorff, 1933 ; no English translation has ever been published, but see Köhler & von Restorff, 1995 ) was in fact her dissertation, titled “On the effects of the formation of a structure in the trace field.” The work was done collaboratively with Köhler, and she was awarded the PhD magna cum laude (with honors) on her birthday at age 27. Like Zeigarnik’s dissertation, von Restorff’s dissertation appeared in Psychologische Forschung . Figure 4 presents a picture of her around the time of her dissertation; Fig. 5 presents a photograph of her dissertation document. Intriguingly, her curriculum vitae at the time indicate that she attended lectures by Köhler, Wertheimer, and Lewin, with the inclusion of Lewin’s name the only evidence that I have found of any overlap with Zeigarnik.

Hedwig von Restorff at the time of her dissertation, from the archives of Humboldt University of Berlin (NS-Doz. 2, Nr. ZD I 0877)

Hedwig von Restorff’s dissertation document, from the archives of Humboldt University of Berlin (Phil. Fak. 01, Nr. 748, Blatt 105)

The rise of the National Socialist (Nazi) party to become the dominant force in the Reichstag, the German Parliament, following the July 1932 election cast a long shadow over the Psychological Institute. This may have been especially the case because the Psychological Institute was located in an annex to the Kunstgewerbe Museum, originally the Kaiser’s Palace. Köhler, an outspoken critic of the policies of the Nazis (see Crannell, 1970 ; Henle, 1978 ), fought to retain control of the Institute for himself and his colleagues and for his students/collaborators, who at the time included von Restorff, Karl Duncker, Footnote 10 and Otto van Lauenstein. Footnote 11 But in 1935, all three collaborators were dismissed, at which point Köhler wrote to a friend in the USA that “The government has decided in May [1935] to dismiss all the assistants who were trained by me and in June, during the term, they were suddenly forbidden to continue their work and their teaching: Duncker, von Lauenstein, and von Restorff,” adding that “I am not yet sure whether I shall be able to place them somewhere” (from a letter quoted by Henle, 1978 , p. 944). Because of these events, Köhler left Berlin for Swarthmore College in the USA in 1935. The Psychological Institute was then put under Nazi control, effectively ending its scientific influence.

Following her dismissal from the Institute, von Restorff briefly worked as a research assistant but then chose to enter medical school in 1935, studying at the Pharmacology Institute in the Friedrich Wilhelm University of Berlin. She was awarded her license to practice in 1939 and published a second dissertation in the realm of medicine in 1940. She then stayed on in the Pharmacology Clinic for a year. On 31 January 1942, she married another physician, Helmut Adolph Johannes Trendelenburg (born 1 November 1915, in Hötting, near Innsbruck, Austria), and around that time moved from Berlin, in the northeast of Germany, to Freiburg, in the southwest. Hedwig and Helmut had two sons during the war, Michael Friedrich Reinhold Trendelenburg (born 25 October 1942) and Christian Helmut Wilhelm Trendelenburg (born 3 October 1945). Near the end of the war, Helmut went missing, likely near Kaliningrad, Russia; the date of his death is listed in Geni as 16 April 1945. Von Restorff continued to practice medicine as a family physician in Freiburg until her untimely death on 6 July 1962 at Freiburg. Figure 6 presents a picture of her later in life. She was only 55 years old when she died – like those of her laboratory colleagues Duncker and von Lauenstein, her life, despite several isolated and distinctive highlights, was too short.

Hedwig von Restorff later in life, downloaded from http://www.buffinstituteofdesign.com/von-restorff-effect-in-phycology-of-design

The foundation of von Restorff’s dissertation

As already mentioned, von Restorff was a student of Wolfgang Köhler, a leading Gestalt psychologist and the Director of the Psychological Institute. Like Zeigarnik, von Restorff’s thinking was heavily influenced by the fundamental ideas of Gestalt psychology including, in the realm of perception, the important distinction between figure and ground – as Wallace ( 1965 ) and Hunt ( 1995 ) both have noted. One of her goals, therefore, was to extend these perceptual ideas to higher cognitive processes, such as memory, echoing Ebbinghaus’s ( 1885 ) goal half a century earlier. Her prediction was that the similarities of non-isolates (the ground) and their difference from an isolate (the figure) should be critical for memory. She couched this in terms of Köhler’s ideas about phenomenal experience and the phenomenal field (Köhler, 1929 , 1940 ), which English and English ( 1958 , p. 207) define as “everything, including itself, experienced by an organism at any moment.” The isolate stands out from its surrounding context, the field in which it resides. In this key respect, then, von Restorff’s theoretical context rested on a field theory, just as Zeigarnik’s had. Lindahl and Århem ( 2016 ) provide a detailed analysis of field theory.

As the reviews by Wallace ( 1965 ) and Hunt ( 1995 ) both noted, there were precursors to von Restorff’s dissertation, notably in the studies of Calkins ( 1894 , 1896 ), Jersild ( 1929 ), and van Buskirk ( 1932 ), all of which explored the value of vividness in learning. But von Restorff went beyond vividness, which she did not see as necessary to produce an isolation effect. What mattered, she argued, was salience: The non-isolates were similar in some way(s) and the isolate differed from them; this difference did not have to be physical. Indeed, this perspective is very much in keeping with Köhler’s concept of phenomenal field, which relates the physical world to the mental world. Moreover, in a point that is often overlooked, von Restorff saw the potential memory difference as more due to interference among the non-isolates – a cost – than to extraordinary memory for the isolate – a benefit – the obverse of how it has usually been described since then. Footnote 12

What von Restorff did

In her dissertation, von Restorff’s initial experiments used the isolation paradigm wherein one or more items – the isolate(s) – differed from all of the other items – the non-isolates (which she referred to as the “massed” items). Again, I number the experiments for easier reference; Table 2 presents the data. Experiment 1 involved presenting five lists of eight paired associates, each list made up primarily (four out of eight) of one of the following types of material: nonsense syllables, numbers, letters, non-letter keyboard characters, and colors. The other four pairs were different from the majority and from each other, with one from each of the other four types of material. Table 3 presents an example list. Each list was presented three times for study, with each display of a pair lasting 2 s. After a 6-min conversation break, recall was requested: This apparently was free recall of pairs, not cued recall of one member of a pair by the other. After 25 min, the next list was presented. Von Restorff reported that “The number of hits is higher in the isolated constellation than in the corresponding massed [non-isolated] constellation, regardless of type of material” (p. 301).

In Experiment 2 (all), von Restorff replicated the Experiment 1 pattern – virtually perfectly – with five groups of participants, each group receiving five successive lists featuring as the non-isolates only one of the five types of material used in Experiment 1. Those five groups were not all treated identically, however, so it is worthwhile to differentiate a couple of them. In Experiment 2a, only one list was presented per day: Overall performance improved relative to when all lists occurred on the same day but the isolate advantage remained quite constant. In Experiment 2b, the presentation rate was decreased from 2.0 to 1.5 s per pair and there was a longer filled delay: This time, overall performance decreased but again the isolate advantage persisted unchanged.

In Experiment 3, she reduced the number of isolates from four to two, expecting a more pronounced advantage for the isolates. There now were six pairs from one set of materials and one each from two of the other sets (deleting letters and colors). Presentation rate was 1.5 s with three repetitions of a list, and lists were spaced several days apart. Her results strongly supported her prediction of an enhanced effect when lists contained fewer isolates.

Next, von Restorff moved to yes/no recognition instead of recall. Footnote 13 In Experiment 4, she presented 15 individual items, consisting of three isolates and 12 non-isolates, with the isolates never occurring in the first two or the last two serial positions. There was only one study cycle: All items were displayed simultaneously with a metronome indicating every 1.5 s to go on to the next item. Otherwise, her procedure closely followed that of Experiment 1. Remarkably, to avoid interference at test, the recognition test consisted of only the 15 studied items – there were no distractors – although participants were told prior to test that some items would be familiar and some would not be. Footnote 14 She concluded that “tests of recognition yield smaller differences than do tests of reproduction” (von Restorff, 1933 , p. 310), and found it interesting that this was true despite the ratio of isolates to non-isolates being smaller in the recognition experiment than in the recall experiments. Experiment 5 was a replication with 18-item lists in which the three isolates were presented in serial positions 4–6. Despite overall greater recognition, the pattern was identical. Experiment 6 was most like what we usually think of today as the von Restorff paradigm: There was only one isolate. Lists consisted of either 19 syllables and one number, or the reverse, with the lone isolate in the middle of the list. Participants studied one list each day with a 10-min retention interval. The isolation advantage was, as she had anticipated, dramatically increased by using only a single isolate.

Experiments 7–9 were all aimed at the role of distinctiveness and continued the use of only a single isolate. In Experiment 7, participants studied three 10-item lists. A list was studied, there followed 10 min of studying a text, and then there was free recall of the list followed by recall of the text. This procedure was repeated three times in succession. The first studied list was always the comparison condition and contained a number and a syllable in positions 2 and 3 plus eight other unique items. The next two lists (order counterbalanced) contained either one syllable and nine numbers or the reverse. Placing the isolate early in the list was done to minimize the degree to which it would be perceived as unusual or salient. Footnote 15 Relative to the early recall experiments, the isolate advantage was enhanced here, and von Restorff noted that the isolate was almost always recalled very early. In Experiment 8, she moved to lists of eight pairs with only one list per day, otherwise following the same procedure as Experiment 7. This greatly enhanced the isolate advantage. Finally, in Experiment 9, using a large sample of school children (half boys and half girls), she reproduced the result of Experiment 8.

There are several more experiments in the dissertation, but their focus is specifically on proactive and retroactive interference, which von Restorff saw as underlying her phenomenon. In fact, as Hunt ( 1995 ) emphasized, her explanation of the isolate advantage focused more on the cost to the non-isolates than on the benefit to the isolate(s). In her view, the similarity of the non-isolates to each other led to interference among them, interference not experienced by the isolate. She related her findings to the earlier results of Ranschburg ( 1902 , 1905 ), who showed that homogeneous lists were harder to learn and remember than heterogeneous lists. In von Restorff’s own words ( 1933 , p. 316), the non-isolates “may be functionally disadvantaged because they belong to a subgroup” whereas the isolate(s) “remain independent (isolated) in the list.”

This dissertation is truly a tour de force. In the end, von Restorff tied her results back to the Gestalt concept of fields, concluding (p. 342) that “items which are not presented in such a monotonous massing achieve much higher values of accurately reproduced items than those in massed positions. This detrimental effect is not only based on the conglomeration of similar items, but also on the field formation and the absorption of items into fields, which benefit from the uniform progression of lists.” In so doing, she firmly established a finding that remains one of the best-known phenomena in the entire memory literature.

Status of the von Restorff effect today

Like Zeigarnik’s phenomenon, von Restorff’s acquired its name around the middle of the last century: Jenkins and Postman ( 1948 ) still called it the isolation effect, whereas Green ( 1956 ) first called it the von Restorff effect. As of the writing of the current article, von Restorff’s dissertation article has been cited 236 times according to PsycInfo and 817 times according to Google Scholar, including citations in new empirical work that continues to emerge on her phenomenon (e.g., Chee & Goh, 2018 ).

By the 1960s, the literature was sufficiently extensive for Wallace ( 1965 ) to publish a review article specifically on the von Restorff effect. He divided published studies into three major groupings: (1) physical distinction of an item (e.g., the isolate printed in red with all others in black) versus no distinction (e.g., all items in black), (2) a different type of item (e.g., the isolate is a nonsense syllable with all others being words) versus no difference (e.g., all items are nonsense syllables), or (3) equivalent numbers of items from two sets, with placement of an item from each set amid a sequence of items from the other set (see Siegel, 1943 ). Wallace then reviewed the evidence with this distinction in mind, and considered possible explanations. He took it as a given that the isolation effect resulted from differential encoding and did not consider possible differential retrieval. As he asserted in his abstract, “isolation facilitates learning of the isolated item” (p. 410). In his summary of theory, he cited such factors as surprise, organization, and attention at the time of learning as key to the effect, admitting that these were “relatively unrefined concepts” (p. 422) and concluding that “At the theoretical level, the von Restorff phenomenon remains a controversial one” (p. 423). There was, however, no such controversy at the empirical level.

Thirty years later, Hunt ( 1995 ) characterized the extant explanation of the von Restorff effect as resting on the salience and the consequent differential attention to the isolate, similar to the view with which Wallace had concluded his review. Hunt’s own account, however, differed markedly from this prevailing view, incorporating instead a central tenet of von Restorff’s own explanation: the importance of the similarity among the non-isolates and the difference of the isolate from them. His idea of distinctiveness (see also Hunt, 2013 ) aligned with von Restorff’s analysis: Because she had observed the isolation effect even when the isolate was the second item in the list (a finding that Hunt replicated; see also Kelley & Nairne, 2001 ), the isolate could not yet have been perceptually distinct. Hunt clearly saw an important role not just for encoding but also for retrieval: “Distinctiveness enhances memory by facilitating discriminative processes at retrieval” (Hunt, 1995 , p. 110).

Unlike the Zeigarnik effect, von Restorff’s phenomenon is routinely described in textbooks on memory and cognition, and continues to generate novel studies. From a survey of the literature, the effect is clearly robust and readily replicable, although as always in experimental psychology the critical issue is determining an appropriate control condition. Recent work is helping to refine our understanding of the mechanism(s) underlying the effect. To illustrate, returning to Wallace’s ( 1965 ) first two categories of isolation effects – a physical change versus a change in item type – Bireta and Mazzei ( 2016 ) argued that benefitting from physical isolation is automatic whereas benefitting from semantic/categorical isolation requires attention. Schmidt and Schmidt ( 2017 , p. 194) agreed that attention during study is essential for conceptual isolates, but also pointed to a “favorable retrieval environment” as important. Chee and Goh ( 2018 ) went so far as to suggest that the effect is entirely at retrieval, with the cues for the non-isolates “overloaded” relative to those for the isolate (see Watkins & Watkins, 1975 , for the concept of cue overload). There also are studies emerging that examine the underlying brain mechanisms (e.g., Elhalal, Davelaar, & Usher, 2014 ; Kamp, Brumback, & Donchin, 2013 ).

My perspective, influenced by those of Kolers (see, e.g., Kolers & Roediger, 1984 ) and Tulving ( 1983 ) and others, is that we cannot really separate encoding from retrieval – that every act of “encoding” involves retrieval and that every act of “retrieval” involves encoding. Perhaps the best summary at this juncture, then, is to say that there both encoding and retrieval contribute to the von Restorff effect, in line with most memory phenomena more generally.

Since von Restorff’s ( 1933 ) classic study, there have been many studies published that in some way make contact with her phenomenon. Many have simply reported some version of an isolation effect; considerably fewer have been directed squarely at trying to explain the effect. But in all of them, there has never been any doubt that the effect is a truly robust one. Her phenomenon is still of significant interest in memory research today both because of its robustness and because its boundary conditions and its explanation continue to invite exploration.

That both Zeigarnik and von Restorff worked in the same institute in Berlin within a few years of each other, were very influenced by Gestalt ideas, studied the influence of unexpected events on memory, and published their famous works only 6 years apart in the same journal, are only a few of the remarkable coincidences in their lives. Neither really pursued the work that bears her name, linking them as well to Stroop, who carried out his research in the same period but also did not pursue it (MacLeod, 1991b ). Footnote 16 Yet despite their proximity in space and time, there is no evidence to suggest that Zeigarnik and von Restorff ever even met. With respect to their scientific contributions, whereas Zeigarnik’s phenomenon now appears not to be a general one and thus is rarely pursued in the literature or included in textbooks, von Restorff’s phenomenon is highly robust, continuing to attract research attention and to be a staple in textbooks.

I thank Henry Roediger for bringing this source to my attention.

When I contacted Andrey Zeigarnik, her grandson, I asked him what was known about his grandfather. He replied in an email on 21 August 2019 that “Albert died 2 years after imprisonment. This is the official version and we don't know the truth. Russia still has unpredictable history.”

Lewin’s unsent letter to Köhler ( 1933 /1986), which would have been dangerous for them had it fallen into Nazi hands, indicates their closeness.

Lewin’s concept of field, like that of his Gestalt colleagues, had its roots in the 19th-century concept of field in physics (see McMullin, 2002 ). Lewin’s concept was broader, however, than the Gestalt perceptual/cognitive perspective, referring also to personality and motivation.

Birenbaum’s work fits well with fuzzy trace theory (e.g., Brainerd & Reyna, 2004 ), with its distinction between gist and detail.

A more recent concept would apparently make the opposite prediction from Zeigarnik’s: Under the fading-affect bias, the affect deriving from positive events persists in memory longer than does the affect associated with negative events (see Skowronski, Walker, Henderson, & Bond, 2014 ), assuming that incompletion is conceived of as negative event.

Murray ( 2012 ) gives her birthdate as 14 December 1906, but the curriculum vitae that she submitted with her dissertation lists her date of birth as 14 October, as does Geni, the family tree website ( https://www.geni.com/people/Hedwig-Ida-Auguste-Dr-phil-et-med/6000000033525189271 ). Interestingly, von Restorff’s mother is listed in Geni as having been born on 14 December ( https://www.geni.com/people/Elisabeth-Marie-Karoline-Juliane/6000000033525169940 ), likely the source of the confusion.

Famous for proposing the idea of functional fixedness in problem solving (Duncker, 1945 ), Duncker was exiled from Germany in 1935 and initially took up an assistantship with Frederick Bartlett in England. Shortly thereafter, he immigrated to the USA where he took a position as assistant to Wolfgang Köhler at Swarthmore College. Suffering from continuing depression, for which he had been under treatment, Duncker committed suicide at age 37 (see Schnall, 1999 ).

Because he was Jewish, von Lauenstein had been dismissed from the Institute in 1934, but Köhler successfully fought to have him reinstated later in the year. Von Lauenstein’s major contribution was, like von Restorff’s, in the domain of memory: He explored “the time error” in psychophysics, essentially the influence of retention interval (e.g., von Lauenstein, 1938 ). Pratt described (in the preface to Köhler, 1969 , p. 15) what happened to von Lauenstein after the dissolution of the Psychological Institute. He moved to England in 1937 and was to move to Rutgers University in 1939. But having returned to Germany in the summer of 1939, when war began in September, he informed colleagues at Rutgers that he was not permitted to leave Germany. While serving in the German Army, he was seriously wounded yet was forced back to active duty near the end of the war. He never returned.

I thank Reed Hunt for bringing this point to my attention (see also Hunt, 1995 ).

Participants were actually permitted to respond “unsure,” but von Restorff ultimately treated these responses as “No” responses.

Wallace ( 1982 ) also used this technique and reviewed the very few studies that had used it before he did; he apparently was unaware of von Restorff’s earlier use of the procedure.

Kelley and Nairne ( 2001 ) went one step farther and showed that an isolate in the first position, where it could not be seen as an isolate as study began, nevertheless produced a von Restorff effect.

Ironically, Stroop ( 1935 ) actually used as the neutral verbal condition in his third experiment a character that he saw as letter-like but having no meaning. The character he chose was the swastika (see MacLeod, 1991a ).

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Author Note

Preparation of this article was supported by Natural Sciences and Engineering Research Council (NSERC) of Canada Discovery Grant A7459. I am grateful to Tom Harding in the University of Waterloo Library for the considerable assistance that he provided in contacting sources in Germany. I also thank my colleague Igor Grossmann for translating several German documents. And I thank Douglas Hintzman, Reed Hunt, Henry Roediger (and the members of the Roediger laboratory), and an anonymous reviewer for helpful suggestions on earlier versions of this article.

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MacLeod, C.M. Zeigarnik and von Restorff: The memory effects and the stories behind them. Mem Cogn 48 , 1073–1088 (2020). https://doi.org/10.3758/s13421-020-01033-5

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Study Strategies: The Zeigarnik Effect

The Zeigarnik Effect is a psychological phenomenon that can revolutionize how you approach studying. Just like the Pomodoro Technique , it offers a structured approach to learning, helping you retain information more effectively. Named after Dr. Bluma Zeigarnik, the psychologist who first identified it in the 1920s, this effect taps into the power of incomplete tasks to keep our brains engaged and focused.

How does it work?

The Zeigarnik Effect suggests that our minds tend to remember unfinished or interrupted tasks better than those we’ve completed. Think about it: have you ever found yourself fixating on a problem or assignment you haven’t finished? That’s the Zeigarnik Effect in action. Our brains have a natural tendency to seek closure, which keeps us mentally engaged until we complete the task at hand.

Here’s how you can harness the Zeigarnik Effect to supercharge your study sessions:

Break it down : Just like the Pomodoro Technique divide work into manageable intervals, break your study material into smaller tasks. Instead of trying to tackle everything at once, focus on one concept or chapter at a time.

Embrace interruptions : Contrary to what you might think, interruptions can actually be beneficial when studying. Instead of seeing them as distractions, use them strategically to keep your brain active and engaged. For example, pause midway through a reading or problem-solving session to jot down questions or ideas that come to mind.

Mix it up : Interleaved Practice is another technique that complements the Zeigarnik Effect. Instead of studying one subject exclusively, alternate between different topics or skills. This keeps your brain on its toes, forcing it to retrieve information from different parts of your memory.

Reflect and review : After each study session, take a few moments to reflect on what you’ve learned and what you still need to work on. This not only reinforces your memory but also sets the stage for the Zeigarnik Effect to kick in during your next session.

By incorporating the Zeigarnik Effect into your study routine, you can enhance your focus, retention, and overall learning experience. So, the next time you sit down to study, remember: it’s okay to leave tasks unfinished—they might just help you learn better in the long run.

See also: The Pomodoro Technique and Interleaved Practice .

Writers as diverse as Charles Dickens, William Shakespeare and even J.K Rowling have demonstrated the power of cliffhangers in fiction, but why limit this device to stories? Apply the Zeigarnik Effect to improve your learning, productivity, relationships, and even your mental health.

The Zeigarnik Effect describes your tendency to remember interrupted or incomplete tasks more than completed ones.

A TRIP TO A CAFE.

This model can be traced back to the 1920s when Bluma Zeigarnik, a Soviet psychologist, was sitting in an Austrian Cafe. Zeigarnik noticed that the waiters in the cafe consistently memorised complex food orders from patrons but, as soon as the order was delivered, the waiters seemed to forget the order. Zeigarnik’s resulting hypothesis was that, because our brains are so goal-focused, unfinished tasks would remain in our memory longer than finished ones.

In a sense, such unfinished tasks create a ‘cognitive tension’ in our minds that demand ongoing focus and thought. See the Origins section below for how she researched and tested this hypothesis.

GOALS VS PLANS.

While Zeigarnik’s original thesis focused on incomplete goals, recent research has discovered that making a plan to complete an unfinished goal will reduce the Zeigarnik Effect . In other words, simply making a plan or scheduling an unfinished task might be enough to create a sense of ‘mental completion’, allowing you to forget the unfinished task.

View Limitations below for other complicating factors with how this model plays out, including motivation and perceived difficulty of tasks.

APPLICATIONS.

There are many potential applications of this mental model, including:

Learning . Use the Zeigarnik Effect to interrupt your learning and create an ‘unfinished state’ with important topics to better embed them into memory.

Innovation . Combine this model with Focused and Diffuse Thinking to consciously seed ‘incomplete’ problems and key questions into your subconscious mind for more diffuse and creative problem-solving. See the canvas at the bottom of this page for more.

Task motivation . Strategically interrupt important work at key moments to ensure that it remains front of mind and that you are driven to return and complete it as a result.

Mental health . Apply the mental model in reverse, working to close 'open loops' and achieve a sense of ‘closure’ that will support you to be more present and reduce overwhelm and anxiety.

Marketing and communication . Use cliffhanger styled storytelling techniques to ‘hook’ an audience and keep them engaged in your narrative. This might include posing a question, a problem or a surprising idea that you answer and address later.

UX . Create gradual goals and feedback for users to give them a sense of progress and encourage them to strive for completion, e.g. ‘Your profile is 40% complete, why not finish it now?’

Relationships . Remember that rule about not going to bed angry with one another? According to the Zeigarnik Effect, it’s a thing. You don’t even have to resolve everything, just identify a plan or a time where you can address issues and points of conflict.

IN YOUR LATTICEWORK.

We’ve already mentioned how the Zeigarnik Effect is a wonderful companion model with Focused and Diffuse Thinking and Deep Work for innovation and problem-solving. In fact, see the canvas at the bottom of this page for a practical guide of combining these three models.

Beyond that, in a learning context, consider combining it with Spaced Retrieval . Apply your understanding of Activation Energy to this model for improved task completion, for example, consider interrupting work to keep it in your mind and ensure you return to it, even as you’re reducing Activation Energy by providing yourself with an easy re-entry point.

Continuing with the theme of tasks and productivity, use the Zeigarnik Effect to better understand the power of Kanban and other productivity methods that allow you to capture tasks while defining your focus. Compare this to a long list of unsorted tasks that will give you a constant sense of incompletion and potential overwhelm and anxiety as a result.

Finally, consider how you will combine this model with Temporal Landmarks . Such landmarks can help you to develop a mental sense of completion, even if nothing else has changed. So leverage the 'fresh starts' that the morning, beginning of a week, or new year provides.

AND, JUST FOR FUN.

Use the canvas.

See the canvas at the bottom of this page for practical help in combining the Zeigarnik Effect with Focused and Diffuse Thinking and Deep Work for a more effective working week.

Strategically interrupt to learn and remember.

Consider what information and priorities you want in your memory and interrupt your reading or work accordingly to apply the Zeigarnik Effect .

Strategically complete tasks to forget and clear your mind.

Protect your mental health! By the same token as the previous point, consider things you are carrying in your mind that you don’t want there and consider how you might create a sense of completion so you can let go of them cognitively. Remember that you don't necessarily need to actually complete them, you need to seek that 'mental completion' with a plan or sense of closure. And yes, I am talking about that old family clash from your childhood that still gets under your skin!

Create plans and schedule tasks.

As noted, recent research has demonstrated that creating plans or scheduling tasks helps to shift unfinished tasks in your mind. This can help you focus, be more present, and reduce anxiety.

Make a start on tasks you want to prioritise.

Getting started on a task not only helps you overcome Activation Energy , it also helps you to better fix that task in your mind, maximising your chance of completing it.

Use cliffhangers in pitches, communication, stories and marketing.

Hook your audience with something they are interested in and curious about. Then, delay the reveal to maintain interest and engagement.

Solve complex problems by taking breaks.

Combine the Zeigarnik Effect with Focused and Diffuse Thinking to solve complex problems. Purposefully interrupt your work on a complex challenge to seed that problem in your mind, then go for a walk or just relax to let your subconscious mind, and Diffuse Thinking , to continue to work on it.

Understand & work with your brain.

Create impactful journeys.

Just for fun...

Learning & memory hacks.

While there has been considerable research supporting the Zeigarnik Effect, there have been studies that have not replicated it and/or point to complicating factors.

Zeigarnik herself pre-empted such criticism, by suggesting that a number of factors would influence the magnitude of the effect, including the timing of the interruption, the motivation of the person, how tired they were, and how difficult they believed the task was.

I’ve already noted the research asserting that planning and scheduling can have a similar effect to completing a task from a cognitive perspective. This research was conducted by Baumeister and Masicampo in 2011 who, for example, found that study participants did worse on a brainstorming task when they were not allowed to finish a warm-up task. However, they were able to improve their brainstorming simply by making a plan to complete the warm-up task.

Other research by McGraw and Fiala in 2006, suggested that reward expectancy impacted the Zeigarnik Effect . Specifically, demonstrating that participants promised a reward would less likely to return to a task than those that were not promised a reward.

Kids and learning.

One of the studies that Bluma Zeigarnik ran involved children and learning. She gave 138 children tasks involving puzzles and maths.

Group A were allowed to finish without interruption. An hour later 12% of them remembered the tasks.

Group B were interrupted. An hour later 80% of them remembered the tasks.

Other experiments saw similar results with adults.

The story of Bluma Zeigarnik sitting in an Austrian Cafe in the 1920s to spark her hypothesis has some variations in the retelling. Some describe the waiters forgetting orders after the food was delivered, elsewhere it’s about when the bill was paid. Either way, it does seem that such an experience piqued her curiosity.

It led to a series of experiments, one of which was described in the In Practice section. Many of these experiments involved participants completing simple tasks such as puzzles, a maths problem, or making a clay figure. Her results consistently revealed up to 90% more recall from those who were interrupted in their task completion for both children and adults alike.

Zeigarnik published her results in 1927 in a paper entitled On Finished and Unfinished Tasks .

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PMC11192818

Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experience

Kelsey r. allen.

1 Department of Brain and Cognitive Sciences, MIT and Center for Brains, Minds, and Machines, Cambridge, MA USA

Kevin A. Smith

Laura-ashleigh bird.

2 Department of Psychology, Durham University, Durham, UK

Joshua B. Tenenbaum

Tamar r. makin.

3 MRC Cognition Brain Sciences Unit, University of Cambridge, Cambridge, UK

4 Institute of Cognitive Neuroscience, University College London, London, UK

Dorothy Cowie

Associated data.

The data have been made available on a permanent third-party archive https://github.com/k-r-allen/embodied_experience . The stimuli for the study can be found at https://sites.google.com/view/virtualtoolsgame and in the Supplemental Materials.

The code has been made available on a permanent third-party archive https://github.com/k-r-allen/embodied_experience .

‘Embodied cognition’ suggests that our bodily experiences broadly shape our cognitive capabilities. We study how embodied experience affects the abstract physical problem-solving styles people use in a virtual task where embodiment does not affect action capabilities. We compare how groups with different embodied experience – 25 children and 35 adults with congenital limb differences versus 45 children and 40 adults born with two hands – perform this task, and find that while there is no difference in overall competence, the groups use different cognitive styles to find solutions. People born with limb differences think more before acting but take fewer attempts to reach solutions. Conversely, development affects the particular actions children use, as well as their persistence with their current strategy. Our findings suggest that while development alters action choices and persistence, differences in embodied experience drive changes in the acquisition of cognitive styles for balancing acting with thinking.

Supplementary Information

The online version contains supplementary material available at 10.3758/s13423-023-02400-4.

Introduction

Everyday experience is both constrained and enabled by the bodies we inhabit. Taller people can reach further, while people with two fully functioning hands can manipulate multiple objects at the same time. ‘Embodied cognition’ (Wilson, 2002 ) suggests that such constraints play a fundamental role in shaping our cognitive and perceptual experiences. Many versions of embodiment theory suggest that these effects reach even further, into how we reason about those experiences. Supporting this view, researchers have shown that when individuals’ bodies or skills are altered, e.g., through temporary training or by being born with limb differences, this can change their perceptual capacities (Aglioti et al., 2008 ; Hagura et al., 2017 ), spatial cognition (Makin et al., 2010 ), body representation (Maimon-Mor et al., 2020 ), or motor skills (Maimon-Mor et al., 2021 ). Here we ask if these effects of embodiment can be broader, by testing whether differences in embodied experience (through limb differences) affect the ways that people think about acting in the world, even when their capacities for action are made equal.

Prior studies have rarely addressed how a lifetime of embodied experience affects the use of “cognitive styles”: the individual differences in how people allocate cognitive resources to thinking about and acting in the world (Messick, 1976 ; Kozhevnikov, 2007 ). Through short-term direct manipulations of people’s bodies or accessible actions, researchers have shown that people are sensitive to action costs – the amount of effort required to perform actions – for both motor planning (Izawa et al., 2008 ) and motor behaviors (Prévost et al., 2010 ). Priming people with different action costs in a perceptual decision-making task can even affect their decisions in a setting where action costs no longer apply (Hagura et al., 2017 ), suggesting that action costs can generalize beyond the immediate task. However, these studies transpire on the scale of minutes, and typically demonstrate behavioral effects where costs are manipulated within a task but do not show that long-term and generalized action costs are learned from embodied experience.

To study the effect of embodied experience over longer time-scales, researchers have investigated the perceptual and motor capabilities of individuals born with limb differences. However, the tasks used to study these capabilities often require judgments related to absent body parts, and therefore differences in behavior might be driven by differences in sensorimotor experience or available information. For example, while people with congenital limb differences are slower to judge whether a picture is of a left or right hand (Maimon-Mor et al. , 2020 ; though cf. Vannuscorps and Caramazza , 2016 ; Vannuscorps et al. , 2012 ), they lack first-person experience of that hand.

Here we test the hypothesis that growing up with a different body may affect the everyday cognitive styles individuals use to solve problems in their environments, even when the capabilities tested are divorced from particular bodily differences. For example, if people with congenital limb differences have learned that actions are in general more costly – perhaps as a result of difficulties using artifacts designed for people with two hands (see Fig. 1 ) – we might expect that they will differ in how they approach physical problems generally, even when action costs are equated. Such cognitive styles for action have been observed previously on shorter time-scales: e.g., individuals adapt their motor plans to their own levels of sensorimotor uncertainty and variability (Harris and Wolpert, 1998 ; Gallivan et al., 2018 ; Körding and Wolpert, 2004 ), including becoming more persistent (Leonard et al., 2017 ), or spending more time thinking before acting (Dasgupta et al., 2018 ). The differences in cognitive styles might be expected to emerge early in infancy, when experience begins driving motor skill acquisition (Adolph et al., 2018 ) but might also develop throughout childhood alongside more precise motor planning, control and tool use (Berard et al., 2006 ; Chicoine et al., 1992 ; Adalbjornsson et al., 2008 ).

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Individuals with limb differences must engage in independent motor problem-solving for many everyday behaviors, such as opening a jar. These experiences may change their cognitive styles for motor tasks in general. For example, with two hands, opening a jar can be accomplished by using one hand to stabilize the jar while the other one twists the lid ( A ). With a single hand ( B ), opening a jar can be accomplished by using one’s arm and torso to stabilize the jar. The Virtual Tools game ( C ) equalizes action possibilities and costs for individuals with different types of limbs by creating a virtual action space. (i) The aim is to move the red ball into the green goal. A participant selects a tool from three options (shapes in colored boxes) and places it in the scene (ii). Once placed, physics is “turned on” and objects can fall under gravity or collide with each other (iii); the blue and red lines represent the observed motion trajectories for the tool and the ball, respectively

To test the influence of embodied experience on the learning of cognitive styles, we studied behavior in a virtual physical problem-solving task where all participants had equal capabilities to interact with the world. This “Virtual Tools game” (Allen et al., 2020 ) requires people to use virtual objects as tools to solve a physical problem (e.g., getting the red ball into the green goal, Fig. 1 C) using a single limb to control a cursor, thus equating action costs. Allen et al. ( 2020 ) provide a set of performance metrics for this task that measure the cognitive styles participants use.

We chose participant groups to represent a diverse range of embodied experience: children (5 to 10-year-olds) and adults born with limb differences, and age-matched children and adults born with two hands. Children have less embodied experience than adults, while individuals with limb differences have dramatically different kinds of experience. By using a virtual task with simple controls, we equate manipulation capabilities and instead study how embodied experience affects the cognitive styles that support action planning and reasoning more generally.

We tested whether cognitive styles are affected by life experience, as indexed by age (children versus adults) and by limb differences. We first predicted that those having to devise unique solutions to everyday physical problems due to growing up with a limb difference might use different, perhaps even more efficient, ways of solving the virtual puzzles. To assess this, we considered the key outcome measures of attempt type, thinking time, attempts to solution, time to solution, and solution rate introduced by Allen et al. ( 2020 ). Second, given that tool use capabilities develop throughout childhood (Beck et al., 2011 ; Keen, 2011 ), we expected solution rates to improve with age. Finally, we tested whether differences in cognitive styles between those with and without limb differences would emerge early because compensatory behavior evolves early, or whether differences might grow with development as motor and cognitive skills develop. Overall, we found that participants with limb differences do use a different set of cognitive styles than those without – spending more time thinking while interacting less with the world. While performance does improve with age, we did not find evidence that the thinking/acting difference between participants with and without limb differences changes over development.

Participants

We recruited a total of 145 participants across four groups: 40 adults without limb differences (Adult-NLD), 35 adults with limb differences (Adult-LD), 45 children with no limb differences (Child-NLD), and 25 children with limb differences (Child-LD). LD and NLD participants were well matched for age (Child-LD mean: 7.91 years old, sd: 1.84, range 5.08–10.72; Child-NLD mean: 7.94 years old, sd: 1.74, range 5.02–10.56; Adult-LD mean: 40.7 years old, sd: 15.5, range 19–76; Adult-NLD mean: 41.2 years old, sd: 15.2, range 18–70). We extensively liaised with the limb-difference community, including through existing volunteer databases and two UK charities who support children with a limb difference and their families. We included participants with congenital upper limb anomalies, as summarized in Tables S2 and S3 in the Supplemental Material. Information on limb differences was self-reported but verified for a subset of participants (88% and 55% in children and adults, respectively). We recruited children and adults without limb differences to match the education level and age of the special population over the same period. We recruited a larger sample of children without limb differences to give us a greater understanding of the range of typical performance in a two-handed population. Two-handed children were recruited through an existing university volunteer families database and affiliated Facebook page. The experiment was approved for adults under protocols approved by UCL (9937/001), and all adults provided informed consent. The experiment was approved for children under a protocol approved by the ethics committee at Durham University (PSYCH-2019-08-30T10_08_45-mnvj24), and informed consent was provided by the legal guardian.

As with Allen et al. ( 2020 ), the experiment was run online on participants’ personal computers at home. All participants were provided with an identifying code used to link their performance with individual information (e.g., specific limb differences). The experiment progressed through two stages: motor pre-test, and Virtual Tools game. We also collected several additional demographic and clinical details (see below Table Table1 1 ).

Proportion of children and adults with limb differences who have an affected right or left arm, or both arms, and associated ages of groups in years

Age group	Affected limb(s)			Mean (SD) age
	Right arm	Left arm	Both
Children	0.32	0.44	0.24	7.91 (1.84)
Adults	0.42	0.55	0.03	40.7 (15.5)

All participants were given the same experiment, with only three exceptions that differed between children and adults: (1) children received simplified instructions for all stages, (2) adults played one additional Virtual Tools level that we removed from the children’s experiment due to excessive challenge (see Section S3 in the Supplemental Material), and (3) adults were given a more extensive questionnaire that included additional questions about the strategies they had used and video games they had played before.

Motor pre-test

The motor pre-test was used to measure participants’ ability to control the cursor. Each trial began with a star centered in a 600x600 px area on the screen. Once the star was clicked, a 10- px radius circle appeared in a random position either 150 px or 250 px from the center. Participants were instructed to click on the circle as quickly and accurately as possible. Participants completed ten motor test trials (five at each distance from the center).

On each trial we measured (a) reaction time, and (b) the distance (in px ) between the center of the circle and the cursor click location. As a measure of participants’ basic motor accuracy, we took the median of both of those measures across all ten trials; we used the median to avoid skew from outlier trials, and found in pilot testing that this was a relatively stable measurement.

Virtual Tools game

On each level of the Virtual Tools game, participants were presented with a scene and three “tools” (see Fig. 1 C-i), along with a goal condition (e.g., “get the red object into the green goal area”). Participants could accomplish this goal by clicking on a tool and then an unobstructed part of the game area to place that tool (Fig. 1 C-ii). They could choose the location of the tool but its orientation was fixed to how it was displayed on the screen. As soon as the tool was placed, physics was “turned on” and objects could fall under gravity or collide with each other depending on the specific tool placement made (Fig. 1 C-iii). If the goal was not accomplished, participants could press a button to reset the scene to its initial state. Participants could attempt to solve the level as many times as they liked but were limited to a single tool placement for each attempt. Participants could move onto the next level once they had accomplished the goal, or after 60 s had passed.

Following Allen et al. ( 2020 ), participants were initially given instructions about how the game functioned, including the difference between static (black) and moving (blue/red) objects, goal areas, and how to place tools and reset the level. For familiarization with the interface and physics, participants were given one introductory level that required them to place tools at least three times without a goal, followed by two simple levels that they were required to solve that were not analyzed. For adults, this process was identical to that of Allen et al. ( 2020 ); children received simplified instructions (see Fig. S1 in the Supplemental Material).

In the main task, participants were asked to solve 14 different levels, each with a different set of three tools, designed to probe knowledge of diverse physical principles (e.g., support, collisions, tipping; see Fig. 2 ). The solutions to each level are determined by the physics of the game, rather than being decided by the experimenters. Solution (“truth”) maps are provided in the supplement (Fig. S2 ). As in Allen et al. ( 2020 ), on each level, we recorded all attempts from each participant – defined as the tool chosen and where it was placed; the time elapsed between the start of the level and the time of the attempt; and whether the level was solved. Examples of participants’ play in different levels is shown in Fig. 3 .

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The 14 levels of the Virtual Tools game (Allen et al., 2020 ) that participants played. These cover a wide variety of physical action concepts including “balancing,” “launching,” “catapulting,” “supporting,” and “tipping.” To play the game, please see https://sites.google.com/view/virtualtoolsgame

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Examples of different participant trajectories through different levels. Each panel is an individual attempt by a participant, labeled by the attempt number. The starting positions of objects are shown as more transparent, while their final positions after physics is “turned on” are shown as opaque. Available tools for the levels depicted are shown on the right. ( A ) Participants with limb differences tend to spend more time between attempts while taking fewer overall attempts to solve levels. In this level, the goal is to knock the container over so that the ball touches the ground. An adult with a limb difference first uses the hook object to try to do this but by the third attempt realizes they can place an object underneath the container to tip it over. An adult without a limb difference persists with placing objects above the container in rapid succession until they are ultimately successful. ( B ) In this level, the goal is to get the red ball into the container. Children often do so by placing a tool very close to the ball, while adults are more likely to drop a tool from further up. Both this child and adult are ultimately successful. ( C ) Adults tend to perseverate with attempt types more than children. In this level, the goal is to get the red ball into the container, which can be achieved by placing a tool on the platform next to the block. Before finding solutions, this child switched between attempt types (from dropping a block at the bottom, to putting a wedge on the wrong side of the platform, to finally putting it on the correct side of the platform) more than the adult (who focused on the same attempt type but tried this attempt type many times before being successful)

For the main analyses, we used four different overall performance metrics defined by Allen et al. ( 2020 ) to measure different facets of performance: (1) whether the level was solved (solution rate), (2) time until the solving attempt was performed (time to solution), (3) how many attempts were taken until the level was solved (attempts to solution), (4) times to the first attempt and the average time between attempts (thinking time). We also analyzed the specific kinds of attempts taken (attempt type) using the same methodology introduced in Allen et al. ( 2020 ) to cluster and classify participants. All measures were automatically extracted from the recorded tool placements and timings.

Additional measures

After both the motor test and Virtual Tools task, participants were given a short questionnaire to ask what device they used to control the cursor, and, for participants with limb differences, how the cursor was controlled. Additionally, for the adult participants we included the questions asked in Allen et al. ( 2020 ), including prior video game experience, and free-form responses about strategies they had used on the task. In separate surveys, we gathered demographics and interface information (Section S2.2 in the Supplemental Material), limb-differences information (Section S2.3 and Tables S2 and S3 ), and verbal and nonverbal IQ (for a subset of children only; Section S2.1 ). Age was used as a covariate in our analyses, while gender, device, and limb usage were studied as possible moderators (see Section S8 in the Supplemental Material). Free-form strategy descriptions were read in order to discover non-standard ways of solving the levels but were not directly analyzed.

Finally, in exploratory analyses of attempt types, we also directly analyze the kinds of errors different groups of participants made in Section S6 . These errors include using an unworkable tool, placing the tool above vs. below the correct object, or placing it at the wrong precise location relative to the correct object.

For the motor test, performance was analyzed using linear models to predict the dependent variable (either median reaction time or click distance) as a function of both age group and limb difference group, controlling for the effect of differences due to age in years separately for adults and children.

For all of the Virtual Tools performance metrics, variables were modeled using linear mixed effect models, using random intercepts for participants and levels. Additionally, we included age in years and median motor test response times as covariates, parameterized separately for children and adults. We had prespecified these as covariates since we believed that they would account for general performance differences; nonetheless analyses without covariates produced similar results (see Section S7 in the Supplemental Material). For all but the solution metric, we conditioned our analyses only on successful levels, as we were interested in the mental processes that led to solutions, and not processes that might be indicative of frustration or perseverance; however, analyzing all levels produced a qualitatively similar pattern of results (see Section S9 in the Supplemental Material).

In some analyses we attempt to differentiate the type of participants’ attempts, either to test whether the type of attempt is different across groups, or to test whether participants are switching types between attempts. Measuring attempt type directly in this game is challenging, as different combinations of tools / positions can have overall similar effects (see, e.g., Fig. 3 . Which of these should be considered the same type of attempt?). We therefore resort to an indirect measure which nonetheless provides intuitive notions of attempt type. Specifically, we use the classification methodology of (Allen et al., 2020 ) that groups attempts using nonparametric clustering. This allows the “type of attempt” to be defined by the data, grouping placements together in ways that can allow for custom definitions of “similar” across levels without requiring any explicit definitions. To compare attempt types across groups, we applied a leave-one-out classification analysis where, for each participant, we formed probability distributions over the tool identities and spatial positions by all other members of their group and those from members of the other group, then calculated the relative likelihood that the tool placement for a given level was a member of the correct group. To investigate whether attempt types change from attempt to attempt, we formed clusters over tool spatial positions using all attempts from all participants within a level. Two consecutive attempts were considered a “switch” if each attempt came from a separate cluster.

To determine clusters for each different level, we aggregated all attempts across all participants, taking the [ x , y ] spatial positions of each attempt as variables but ignoring the specific choice of tool. 1 We applied clusters separately for each participant group (children vs. adults, limb differences vs. no limb differences) but aggregated data across all participants from each group. Applying a Dirichlet Process mixture modeling package then gave us clusters for each level and group, as well as the probabilities that each attempt belonged to each cluster. We defined a “switch” as occurring if, for consecutive attempts, the cluster assigned as the highest likelihood was different for both of those attempts. Examples of discovered clusters for the data presented in Fig. 8 are shown in Fig. S6 .

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Comparison of tool placements on the first attempt for children and adults with and without limb differences. Each point represents an individual participant’s first attempt, with the position being where they placed the tool, and the color representing membership in “attempt type” clusters determined by Dirichlet Process Mixture Modeling over tool positions. Points of the same color therefore represent participants who chose first attempts belonging to the same attempt type

We will discuss in turn (1) the equating of basic motor abilities across groups, (2) participants’ overall performance metrics, (3) differences in cognitive styles that arise during solution finding, and (4) the detailed kinds of attempts each group made. For each section we will focus on the effect of embodiment but also note the effects of age where present.

Basic motor abilities across groups

We used the motor pre-test to examine whether there were group differences in cursor control which could affect performance on the Virtual Tools game. Children could control the cursor, with an average pixel error of 7.65 px (95% CI=[6.74, 8.55]) and reaction time of 3.04 s (95% CI=[2.67, 3.41]), albeit less accurately and more slowly than adults (error: 2.92 px , 95% CI=[2.52, 3.31]; RT: 1.91 s , 95% CI=[1.73, 2.09]). Exploratory analysis showed that children’s control improved linearly with age ( t ( 65 ) = 3.09 , p = 0.003 , Fig. 4 ).

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Reaction time on the motor task by participant age and group. Participants with limb differences were slightly faster on this task, suggesting that any differences in time to act or solve problems in the Virtual Tools game are not driven by differences in cursor control capabilities

Importantly, individuals with and without limb differences performed comparably on both motor error and reaction time. While there was a difference in median click-time between participants with and without limb differences, participants with limb differences were slightly faster (by 356 ms , 95% CI=[14, 698]; F (1,135) = 4.15, p = 0.044), though they clicked marginally further away from the target (by 0.79 px , 95% CI=[ - 0.14 , 1.72]; F (1,135) = 2.78, p = 0.098). There was no interaction found between age group and limb difference for either motor speed ( F (1,134) = 1.76, p = 0.19) or error ( F (1,134) = 0.001, p = 0.98). The differences found in motor control were relatively inconsequential for the Virtual Tools game – 0.79 px additional error would have little effect on 600 x 600 px game screens, and an extra 356 ms would be hard to detect with an average time between attempts of over 10 s . We therefore showed that both groups should have a level playing field for interacting with the Virtual Tools game.

Performance metrics across groups

In the Virtual Tools game, we initially tested whether limb difference and age affected overall solution rates or time (in seconds) to reach a solution. We found gross differences in solution rates between children and adults (adults: 85%, children: 77%; x 2 ( 1 ) = 11.20, p = 0.0008; Fig. Fig.5) 5 ) but no effect of limb difference ( x 2 ( 1 ) = 1.21, p = 0.27), nor any interaction between age and limb group ( x 2 ( 1 ) = 0, p = 0.99). Exploratory analysis showed that age in years additionally predicted success ( x 2 ( 2 ) = 12, p = 0.0025): while children’s solution rates improved with age (log-odds increase per year: 0.290, 95% CI =[0.038, 0.543]), adults’ worsened (log-odds decrease per year: 0.290, 95% CI =[0.006, 0.053]).

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Solution rate (percentage of levels solved by each participant) as a function of age for children ( left ) and adults ( right ) with limb differences (LD) and with no limb differences (NLD). Grey areas represent standard error regions on the regression lines

On time to solution, children were similarly slower than adults ( x 2 ( 1 ) = 40.0 , p = 2.6 ∗ 10 - 10 ; Fig. 6 B) but we found no effect of limb differences ( x 2 ( 1 ) = 0.41 , p = 0.52 ), nor was there an interaction between limb group and age group ( x 2 ( 1 ) = 0.22 , p = 0.64 ). Adults slowed down with age (average additional 1.16 s per year, 95% CI=[0.87, 1.46], ( x 2 ( 1 ) = 59.4 , p = 1.3 ∗ 10 - 14 ) , while children non-significantly sped up (2.64 s less per year, 95% CI=[ - 1.63 , 6.90], x 2 ( 1 ) = 1.47 , p = 0.23 ).

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The efficiency of finding solutions measured by number of attempts ( A ), time to solution as measured in seconds ( B ), time to first attempt ( C ), and time between attempts ( D ). Means with standard errors are shown. Participants with and without limb differences did not reliably differ on time to solution but participants with limb differences solved the levels in fewer attempts, and took more time until the first attempt and between attempts

Thus we found that while age causes noticeable changes in overall performance on the Virtual Tools game, limb differences do not. Nonetheless, participants might achieve similar overall levels of performance in different ways. We therefore next considered whether participants with or without limb differences might demonstrate distinctions on the more detailed performance metrics specified in Allen et al. ( 2020 ).

Cognitive styles in solution finding

We investigated whether there was a difference in the number of attempts that participants with and without limb differences took to solve each level. Participants with limb differences (LD) took fewer attempts on average to come to a solution than the participants with no limb differences (NLD; 83.9 % of the attempts, 95% CI=[ 72.2 % , 97.6 % ]; x 2 ( 1 ) = 5.19, p = 0.023; Fig. Fig.6A). 6 A). Conversely, they took more time for each attempt, including taking more time before the first attempt (3.79 s more, 95% CI = [1.99, 5.59]; x 2 ( 1 ) = 17.0 , p = 3.7 ∗ 10 - 5 ; Fig. Fig.6C), 6 C), and between all subsequent attempts (2.80 s more on average, 95% CI = [1.51, 4.10]; x 2 ( 1 ) = 17.9 , p = 2.3 ∗ 10 - 5 ; Fig. Fig.6D). 6 D). Again, we found differences by age, with children taking fewer attempts (89 % of the attempts as adults, x 2 ( 1 ) = 6.51 , p = 0.011 ); more time to the first attempt (6.1 s more, x 2 ( 1 ) = 13.4 , p = 0.00025 ); and more time between attempts ( x 2 ( 1 ) = 41.7 , p = 1.1 ∗ 10 - 10 ) but no evidence for an interaction between age and limb differences for any of these measures (number of attempts: x 2 ( 1 ) = 2.07, p = 0.15; time to first attempt: x 2 ( 1 ) = 0.10 , p = 0.76 ; time between attempts: x 2 ( 1 ) = 0.16 , p = 0.69 ). 2

Together, these results suggest that individuals born with limb differences learn a different cognitive style for physical problem-solving: they learn to spend more time considering the problem and less on gathering information from their attempts. While it is not possible to conclusively determine why individuals with limb differences spent more time on each attempt (i.e., it could relate to initiation costs (Khalighinejad et al., 2021 ) or habit formation from prior experience (Wong et al., 2017 )), this extra consideration time was connected at the group level to fewer overall actions being needed to solve the problem. We therefore tentatively interpret the difference in reaction time as “thinking” – some internal computation that supports solving problems with fewer numbers of attempts.

Attempt types

We first investigated whether there were differences in the types of first attempts taken by participants with and without limb differences, using the methodology described in the Methods: Analysis section to classify whether the types of attempts made by participants with limb differences better matched those of other participants with limb differences than those without, and vice versa. If this measure is on average reliably above chance on a level, this suggests that the two groups are beginning their solution search in different ways. However, we did not find statistically reliable effects. For qualitative differences between groups see Fig. 8 , and Section S10 of the Supplemental Material for further details. For direct analyses of “error” types (what kinds of errors each group makes), please refer to Section S6 of the Supplemental Material.

Using the same methodology, we found that across all levels, children’s and adults’ first attempt types can be differentiated (Fig. 7 B; note that in this cluster analysis comparing age groups, we cannot jointly model the effect of limb differences and age, so we report analyses separately for each group; NLD: t ( 84 ) = 4.91 p = 4 . 5 ∗ 10 - 6 ; LD: t ( 57 ) = 3.05, p = 0.0035). This analysis suggests that children are starting with some sort of different action than adults but it cannot tell us exactly what differs.

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Comparing tool placements across children and adults born with two hands. Bar plots show means and standard errors. ( A ) Examples of first attempts for both adults and children without limb differences on two levels. Each point shows an individual participant’s attempt, with the position being where they placed the tool, and the color representing which tool they chose (tools shown in colored boxes to the right of each level). ( B ) We tested whether we could classify participants’ age group based on attempt type for each level. The dashed line represents 50 % (chance). ( C ) The likelihood that children would “switch” attempt types. Please see Methods: Analysis and Section S5 in the Supplemental Material for details on how attempt type switching was measured

To explicitly test for what differs, we measured whether we could find differences between the two groups using more structured analyses of error types (e.g., using an unworkable tool, placing the tool above vs. below the correct object) but could not find reliable differences (see Section S6 ). However, we did find that adults on average placed their tool vertically farther from the object than children (average vertical distance in children: 74 px , adults: 91 px ; x 2 ( 1 ) = 5.20, p = 0.023), which is likely driving some of the differences. Nonetheless, we cannot make strong claims about why children and adults differ along this vertical dimension – e.g., children might just be more conservative, might be worse at understanding how gravity transfers into this environment, or might differ for a variety of other reasons. Furthermore, given the small absolute difference in vertical positions between groups, more work may be needed to understand this result.

Since children are often more exploratory than adults (Gopnik et al., 2001 ), we also tested whether they might be more likely than adults to try new attempt types over the course of a single level. Children’s and adults without limb differences’ attempts were used to form clusters describing different tool “attempt types” (see Methods: Analysis for details), and we assigned each attempt to one of these (Fig. 8 ). Within a level, children were more likely to try new attempt types than adults (children: 39 % attempt type switches, adults: 33 % ; x 2 ( 1 ) = 10.2, p = 0.0014), suggesting that their lower solution rates might be due to either increased exploration of inefficient attempt types, or giving up on promising attempt types early. Using a more structured measure of switching based on the same error types above (a “switch” being defined as changing the qualitative spatial position of the tool relative to an object in the scene, or switching which object in the scene to interact with), we similarly found a difference in switching behavior, with children switching 56 % of the time, and adults 47 % of the time ( p = 0.0009 ). While statistically significant, given the relatively small absolute difference in switching behavior, more work may be needed to investigate this result further.

We used a virtual physics problem-solving game to study how growing up in a different body affects a high-level cognitive task unrelated to body or hand representations. We found minimal differences in the specific kinds of actions used by individuals with and without limb differences, suggesting that fundamental aspects of physical problem-solving are not dependent on similar kinds of manipulation experience. However, we also found that individuals with limb differences, regardless of age, spent more time considering virtual physical problems, and took fewer attempts to find solutions. While congenital limb difference is not directly associated with cognitive differences (see Section S2.1 in the Supplemental Material), growing up with a limb difference may cause cascading effects on many aspects of development, relating to different opportunities to interact with the environment. For example, children with limb differences may be unable to solve problems by imitating their parents or peers, or may face challenges in using tools designed for two hands. In either case, they might come to appreciate the value of thinking more about solutions to physical problems before acting, and over time this could grow into a general cognitive style for interacting with the world. What is striking is that this learned cognitive style extends to a task in which action possibilities are equated across groups – indeed, individuals without limb differences were not slower to control the cursor in our task (see also (Maimon-Mor et al., 2021 )).

We call these differences in “cognitive styles,” as they fall under the definition of “consistent individual differences in preferred ways of organizing and processing information and experience” (Messick, 1976 ). However, while much of the previous research on cognitive styles has focused on how these individual differences might arise due to personality differences (Kozhevnikov, 2007 ), here we suggest that an alternative driver of different cognitive styles might be the body that people inhabit, which in turn affects the costs of interacting with the world.

Cognitive styles are often thought to be learned through experience, similarly to studies of motor learning in adults (Huang et al., 2012 ). The difference here is that the target of learning is not the motor plan itself but when to deploy those motor plans. While people’s information sampling has been shown to be sensitive to the costs of obtaining that information (Juni et al., 2016 ; Jones et al., 2019 ) and motor cost manipulations have been shown to affect the efficiency of motor reaching actions (Summerside et al., 2018 ), it has not previously been shown that motor differences directly affect the cognitive styles that people employ.

Our findings also bridge two different approaches to understanding human tool use. Tool use is theorized to be supported by specific sensorimotor knowledge of tool manipulation under the “manipulation-based” (embodied cognition) approach (Buxbaum and Kalénine, 2010 ; Gonzalez et al., 1991 ; van Elk et al., 2014 ), or generic physical knowledge under the “reasoning-based” approach (Allen et al., 2020 ; Osiurak and Badets, 2016 ). These theories have produced suggestions that there are distinct cognitive systems supporting different kinds of tool knowledge (Orban and Caruana, 2014 ; Goldenberg and Spatt, 2009 ). However, our results suggest a connection between the two systems: by its virtual nature and novel objects, the Virtual Tools game must rely on reasoning-based systems for tool use, yet we find that manipulation capabilities affect this reasoning. Thus we suggest that the development of the reasoning-based system is grounded in the embodied way that we interact with the world.

Our results extend existing knowledge about children’s problem-solving and tool use. Children can use and select known tools by 2–3 years old (Keen, 2011 ) but do not reliably innovate new tools until 8–9 years (Beck et al., 2011 ), perhaps due to its increased cognitive demands (Rawlings & Legare, 2020 ). Children’s performance in our game is likely driven by these same planning and attentional skills, which underpin a useful balance between exploration and exploitation in a large solution space (Gopnik, 2020 ). Specifically, while children at this age can avoid perseveration (Rawlings & Legare, 2020 ; Cutting et al., 2011 ), our results suggest over-exploration may be an issue, as children switched attempt types more often than adults. The central difficulty with tool innovation and other physical problem-solving tasks at this age may be the need to search through large solution spaces, where children’s propensity for exploration (Oudeyer and Smith, 2016 ; Gopnik, 2020 ) comes at the cost of short-term gains in solution-finding.

Our findings suggest new interactions between embodiment, development, and cognitive styles and raise important questions for future work. The present study focused on a single task - the Virtual Tools game - because it is similar to manipulation tasks while still not requiring manipulation directly. Thus, we expected cognitive styles learned from lifelong differences in action costs and possibilities to carry over into this task, while still providing an equal playing field for all our participants.

One might ask how broadly these differences in cognitive styles would be expected to generalize, e.g. across domains and environments. Some previous work indirectly supports the notion of a broad effect across motor-related tasks. For example, individuals with a limb difference responded more slowly in motor planning (Philip et al., 2015 ) and hand laterality judgements (Maimon-Mor et al., 2020 ). These findings were previously interpreted as a consequence of having fewer available resources to accumulate evidence for tasks related to judgements about hands. However, in light of the present findings, and because the previous observed effects were also found for the intact hand, these results can be interpreted as a generally greater reliance on planning before acting for this population. Yet, these tasks all contain a visuospatial and motor component, so it is still unclear how broad this effect is, e.g., whether it transfers to abstract logical reasoning tasks or social interactions. It is also difficult to determine, based on our single study, whether the group differences we observed are directly or indirectly caused by different embodied experience considering the many developmental differences individuals growing up with a different body will experience. For example, life experiences for individuals born with different bodies may cause them to be generally more cautious, contemplative, or creative.

Thus, the current study provides a starting point for further investigation of how we learn to deploy our cognitive resources based on our embodied experiences. Being born with a different body does not change the fundamental ways in which people try to act on the world but it can change the styles they acquire in order to plan and act efficiently in their environments.

Open Practices Statement

This study was not preregistered. The data and analyses have been made available on a permanent third-party archive https://github.com/k-r-allen/embodied_experience . The stimuli for the study can be found at https://sites.google.com/view/virtualtoolsgame and in the Supplemental Materials.

Below is the link to the electronic supplementary material.

Acknowledgements

We thank Mathew Kollamkulam for collecting the data from the adult samples, Opcare, Limbo and Reach for assistance with participant recruitment, and all participating families and volunteers. The study was supported by a Wellcome Trust Senior Research Fellowship (215575/Z/19/Z) and a Medical Research Council grant (MC_UU_00030/10), awarded to TRM. KRA, KAS, and JBT were supported by National Science Foundation Science Technology Center Award CCF-1231216; Office of Naval Research Multidisciplinary University Research Initiative (ONR MURI) N00014-13-1-0333; and research grants from ONR, Honda, and Mitsubishi Electric. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.

Author Contributions

All authors contributed to the study conceptualization. KRA and KAS developed materials. LAB recruited participants under the supervision of TRM and DC. KRA and KAS analyzed the data. All authors contributed to the writing of the manuscript.

Open Access funding provided by the MIT Libraries. The study was supported by a Wellcome Trust Senior Research Fellowship (215575/Z/19/Z) and a Medical Research Council grant (MC_UU_00030/10), awarded to TRM. KRA, KAS, and JBT were supported by National Science Foundation Science Technology Center Award CCF-1231216; Office of Naval Research Multidisciplinary University Research Initiative (ONR MURI) N00014-13-1-0333; and research grants from ONR, Honda, and Mitsubishi Electric.

Availability of data and materials

Code availability, declarations.

The authors have no relevant financial or non-financial interests to declare.

The experiment was approved for adults under protocols approved by UCL (9937/001), and all adults provided informed consent. The experiment was approved for children under a protocol approved by the ethics committee at Durham University (PSYCH-2019-08-30T10_08_45-mnvj24), and informed consent was provided by the legal guardian.

Informed consent was obtained from all individual participants included in the study or, for the children, their legal guardians.

1 Tool orientation could not be controlled, so was not included as a variable

2 When testing for the effects of limb differences in children and adults separately, we found reliable differences in attempt timing in both children (first attempt: 4.13 s , 95% CI = [0.96, 7.30], x 2 ( 1 ) = 6.52, p = 0.011; between attempts: 2.57 s , 95% CI = [0.31, 4.84], x 2 ( 1 ) = 4.96, p = 0.026) and adults (first attempt: 3.29 s , 95% CI = [1.32, 5.26], x 2 ( 1 ) = 10.7, p = 0.0011; between attempts: 2.63 s , 95% CI = [1.26, 4.01], x 2 ( 1 ) = 14.2, p = 0.00017). However, while we found that adults with limb differences use fewer attempts than those without ( 75.1 % of the attempts, 95% CI = [ 59.1 % , 95.4 % ], x 2 ( 1 ) = 5.49, p = 0.019), children with limb differences took numerically fewer attempts than those without but this does not reach statistical significance ( 94.9 % of the attempts, 95% CI = [ 78.2 % , 115.0 % ], x 2 ( 1 ) = 0.29, p = 0.59). Thus, while we can claim that participants with no limb differences overall took more attempts, we did not have enough evidence to discriminate whether this is because these differences are consistent across age groups, or whether the distinction grows through development.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Kelsey R. Allen and Kevin A. Smith contributed equally to this work. Tamar R. Makin and Dorothy Cowie supervised equally for this work.

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The Zeigarnik Effect: How to Hack Your Procrastination and have Better Creative Ideas

On August 28th, 1963, Martin Luther King Jr was preparing to speak to 250,000 people on Capitol Hill.

I have a dream speech is one of the most famous speeches in history.

But Martin Luther King Jr winged it!

The original draft was called “Normalcy, never again.” King has been up till 3 am drafting and re-drafting the speech.

Noted gospel singer, Mahalia Jackson shouted to King from the crowd “ Tell them about the dream, Martin”

King departed from his prepared remarks and started improvising, punctuating his points with “I have a dream…”

King had used ‘dream’ references before in many of his speeches.

Strategic Procrastination

Have you ever been in the shower and the solution to a problem pops into your head? Or you had a Eureka moment with a creative idea?

It happens a lot, right?

This is the Zeigarnik Effect in play.

What is it and how can you use it to have better ideas?

“Once a task is finished, we stop thinking about it. But when it is interrupted and left undone, it stays active in our minds.” Adam Grant, Psychologist, and best selling author

It’s unfinished business. It's only later that the solution pops into our conscious minds or our creativity sparks into action.

This is John Cleese's go to creative hack

"Let your ideas bake" John Cleese

Brian Bates, a psych professor at Sussex University, on what trait separated the most creative architects from uncreative ones stated

"the creative architects deferred making decisions as long as possible. This is productive procrastination."

How was it discovered?

Psychologist Bluma Zeigarnik was eating in a restaurant in Vienna. She noticed that waiters had better memories for unpaid orders.

After the bills were paid they were unable to recall the orders with any clarity. The waiter's minds had simply stopped working on completed orders.

The research?

Participants were given simple tasks to complete such as placing beads onto string, puzzles, and maths problems.

Half of the participants were interrupted halfway through the experiment.

After an hour delay, Zeigarnik asked participants to recall what they had been working on.

Those that had been interrupted were twice as likely to recall -- and have alternative solutions - than those that had completed the task.

How to hack your creativity?

We all procrastinate.

We put off the tasks, leave it till the last minute and then have a mad panic to complete the task which creates a lot of stress.

This often produces poor work.

A better strategy is to:

Start early the creative task early

And then leave it.

The Zeigarnik effect will kick in and your subconscious will come up with better ideas/ solutions.

Complexity Bias

Many discount this as over-simplistic. Why?

Because humans have a complexity bias. We believe we need complex solutions to our problems.

This is because we tend to overthink everything.

Procrastination can have a huge impact on people's lives and careers, yet it's a fairly straightforward problem.

The problem? We haven't started the task.

The Solution? Start the task.

We think we need the motivation to start but that's wrong. We get the motivation to continue after we start.

The 2 Minute Rule

Boil down big tasks into smaller tasks and systemise them.

Productivity consultant and best-selling author, David Allen created this strategy. James Clear popularised in his book, Atomic Habits.

If it’s under 2 minutes do it now.

Simplify the process.

You’re not writing a blog post, you’re just writing two paragraphs.

Studying simply becomes opening your notes.

Doing the accounts is simply opening up the software.

The concept is to simplify the task.

Break it down into smaller pieces.

And just start the first piece.

Anyone can write a couple of paragraphs, or open their accounts software.

Don't wait for the motivation to start: it will never arrive.

Just start: and the motivation will come.

For creative projects:

Start early and stop. Leave unfinished work and allow your subconscious creativity to come up with new ideas and connect disparate ideas together to create something new.

Let the idea bake as John Cleese says.

For anything else:

Don’t wait for the motivation. It never arrives. Just start the project and then the motivation will come.

Somewhat predictably I have a newsletter. It’s got short creative hacks, creative business strategies, and mental models to build audiences and overcome creative blocks. It’s surprisingly good. You can sub here if you like.

DOI: 10.60027/ijsasr.2024.4545
Corpus ID: 270676049

Effect of Problem-based Learning on Mathematical Problem-solving Ability of Sixth-Grade Students in Leshan Primary School

Yuhua Yu , Danucha Saleewong , Kanreutai Klangphahol
Published in International Journal of… 21 June 2024
Mathematics, Education

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Implementation of the learning management model based on cognitive development theory to enhance mathematical problem-solving ability for prathomsuksa 6 students, effect of model-eliciting activities using cloud technology on the mathematical problem-solving ability of undergraduate students, effectiveness of problem-based learning combined with computer simulation on students’ problem-solving and creative thinking skills, development of problem-solving-based knowledge assessment instrument in electrochemistry, enhancing students' higher-order thinking skills (hots) through an inductive reasoning strategy using geogebra, effects of "handep" cooperative learning based on indigenous knowledge on mathematical problem solving skill., exploration of mathematics problem solving process based on the thinking level of students in junior high school, new approaches to problem‐based learning: revitalising your practice in higher education. edited by terry barrett and sarah moore. new york, n.y.: routledge, taylor and francis group, 2011. xiv + 295 pages. isbn 978‐0‐415‐87149‐5. $48.95., related papers.

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According to Fast Company, your mind tends to remember unsolved or interrupted problems better than projects you've already finished—the aforementioned Zeigarnik effect.So when you're stumped by ...
Why You Feel the Zeigarnik Effect
The stressful, invasive thoughts can lead to anxiety and affect your sleep. That said, the Zeigarnik effect has a way of getting you to resolve the stress. The repeated thoughts you're having will motivate you to finish what you've started, and this can relieve stress and improve your self-esteem, and self-confidence.
Why You Feel Overwhelmed: The Zeigarnik Effect
The Zeigarnik Effect is why we struggle with "multi-tasking." ... Being creative, solving problems, and understanding complex ideas. A version of this article also appears on coreywilkspsyd.com.
PDF Memory for Incomplete Tasks: A Re-examination of the Zeigarnik Effect
problem solving. By re-examining the Zeigarnik effect in terms of modern theories of problem representations, goals, and context effects, perhaps we can explain the circumstances under which the Zeigarnik effect will occur, and how it may function within a broader memory and problem solving cognitive architecture.
Zeigarnik Effect Examples in Psychology
The Zeigarnik Effect is the tendency for tasks that have been interrupted and uncompleted to be better remembered than tasks that have been completed. Bluma Zeigarnik (1927) first saw this effect in waiters, who seemed to remember orders only so long as the order was in the process of being served and promptly forgot the order as soon as it was ...
The Zeigarnik Effect: The Reason You Feel Constantly Overwhelmed
The Zeigarnik Effect is one reason perfectionists struggle with anxiety. They obsess over details and having unrealistic expectations. Aka, they drown in unfinished tasks. So the Zeigarnik Effect keeps their brains bogged down, poorly focused, and stressed out. ... Being creative, solving problems, and understanding complex ideas. ...
What Is the Zeigarnik Effect?
The Zeigarnik effect theory may point to a positive impact on mental health. First, the subconscious mind will urge your conscious mind to make a detailed plan when presented with a task. As soon ...
Zeigarnik Effect
The Zeigarnik Effect is the power of unfinished business or interrupted or uncompleted activity to hold a privileged place in memory. Unfinished tasks create a cognitive burden, weigh more heavily ...
Memory for incomplete tasks: A re-examination of the Zeigarnik effect
An important feature of human memory is the ability to retrieve previously unsolved problems, particularly when circumstances are more favorable to their solution. Zeigarnik (1927) has been widely ...
Zeigarnik effect
In psychology, the Zeigarnik effect, named after Lithuanian-Soviet psychologist Bluma Zeigarnik, occurs when an activity that has been interrupted may be more readily recalled.It postulates that people remember unfinished or interrupted tasks better than completed tasks. In Gestalt psychology, the Zeigarnik effect has been used to demonstrate the general presence of Gestalt phenomena: not just ...
When your mind sends you reminders: The Zeigarnik Effect
In such cases, the Zeigarnik Effect will help prioritise the problem, explains Sheen. "It allows your brain to engage in insightful problem-solving, which will generate a sense of accomplishment ...
How to solve complex problems (by not focusing on them)
Thanks partly to the Zeigarnik effect, your mind will automatically connect new experiences to this problem. You return to work with the number imprinted on your brain.
Zeigarnik's sleepless nights: How unfinished tasks at the end of the
Specifically, the authors contribute to recent research differentiating affective rumination from problem-solving pondering and examine the impact of both forms of rumination on the stressor-sleep relationship. Following theories of rumination and the Zeigarnik effect, they focus on unfinished tasks as a key onset for rumination.
Zeigarnik effect in Insight problem solving: Hemi-spheric difference in
People tend to exhibit enhanced memory performance for the unfinished tasks relative to the finished ones, which known as the Zeigarnik effect. Consistently, the impasse state in insightful problem solving were believevd to play an important role in maintaining the problem in one's mind and in catching the critical clue to overcome the main obstacles. More importantly, through hemi-visual ...
What is the Zeigarnik Effect?
Harnessing the Zeigarnik Effect can help individuals stay motivated and engaged in pursuing their goals, even when faced with obstacles or challenges. Enhanced problem-solving: Unresolved tasks stimulate problem-solving processes as individuals seek ways to overcome obstacles and achieve closure. Leveraging the Zeigarnik Effect can encourage ...
Actionable Ideas #8: Zeigarnik Effect, Preventable Problems ...
The Zeigarnik Effect states that people remember unfinished or interrupted tasks better than completed tasks. Dr. Z coined the effect when she was dining and noticed the waitstaff's ability to recall long lists of unfinished orders, but unable to remember any of the completed orders.
Memory for incomplete tasks: A re-examination of the Zeigarnik effect
A greater degree of success might be met in trying to account for Zeigarnik's original results and some subsequent manipulations in terms of a cognitive model of problem solving. By re-examining the Zeigarnik effect in terms of modern theories of problem representations, goals, and context effects, perhaps we can explain the circumstances under ...
Zeigarnik and von Restorff: The memory effects and the ...
Two of the best known eponymous phenomena in memory research were carried out as dissertations in the same era at the same university, each supervised by an influential researcher working within the Gestalt framework. Both examined the influence of unexpected events on memory. Bluma Zeigarnik (Psychologische Forschung, 9, 1-85, 1927) first reported that memory is better for interrupted tasks ...
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The Zeigarnik Effect is a psychological phenomenon that can revolutionize how you approach studying. Just like the Pomodoro Technique, it offers a structured approach to learning, helping you retain information more effectively. Named after Dr. Bluma Zeigarnik, the psychologist who first identified it in the 1920s, this effect taps into the ...
ModelThinkers
Solve complex problems by taking breaks. Combine the Zeigarnik Effect with Focused and Diffuse Thinking to solve complex problems. Purposefully interrupt your work on a complex challenge to seed that problem in your mind, then go for a walk or just relax to let your subconscious mind, and Diffuse Thinking, to continue to work on it.
Lifelong learning of cognitive styles for physical problem-solving: The
We used a virtual physics problem-solving game to study how growing up in a different body affects a high-level cognitive task unrelated to body or hand representations. We found minimal differences in the specific kinds of actions used by individuals with and without limb differences, suggesting that fundamental aspects of physical problem ...
Zeigarnik's Sleepless Nights: How Unfinished Tasks at the End of the
indirect effect for (b) problem-solving pondering is negative, as pondering reduces sleep impairment. Furthermore, Querstret and Cropley (2012) argue that certain combinations of problem-
Zeigarnik's sleepless nights: How unfinished tasks at the end of the
Specifically, the authors contribute to recent research differentiating affective rumination from problem-solving pondering and examine the impact of both forms of rumination on the stressor-sleep relationship. Following theories of rumination and the Zeigarnik effect, they focus on unfinished tasks as a key onset for rumination.
The Zeigarnik Effect: How to Hack Your Procrastination and ...
The Zeigarnick effect proves that our memory works better when a task is interrupted. It says absolutely nothing about creativity or problem solving. And yeah, the interrupted people came up with more alternative solutions to the problem, but they had an extra hour to think.
Effect of Problem-based Learning on Mathematical Problem-solving
The objective of this study was to (1) compare the students' mathematical problem-solving ability before and after learning through Using Problem-based Learning, (2) compare the mathematical problem-solving ability after learning through Problem-based learning with the determined criterion set at 70%, and (3) assess the significance level of ...

There’s a Science-Backed Reason You Have Your Best Ideas in the Shower

The Zeigarnik Effect and Memory

At a Glance

The Zeigarnik Effect

Zeigarnik Effect Examples

History of the Zeigarnik Effect

Zeigarnik's Research

Further Research Exploring the Effect

Conflicting Research

How Does It Work?

How to Make the Zeigarnik Effect Work for You

Get More Out of Your Study Sessions

Overcome Procrastination

Generate Interest and Attention

Promote Mental Well-Being

Zeigarnik Effect Examples in Psychology

Key Takeaways

Origins of the Zeigarnik Effect

Zeigarnik’s Initial Experiments

Replicability

Implications for Everyday Life

Further Information

Corey Wilks, Psy.D.

The Zeigarnik Effect: The Reason You Feel Constantly Overwhelmed

Quick History of the Zeigarnik Effect:

Examples of the Zeigarnik Effect

Strategies to Overcome the Zeigarnik Effect

Ditch the To-Do List

Give Yourself Permission to Forget

Stop Overcomplicating Everything

Final Thoughts on the Zeigarnik Effect

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1. Use task segmentation

2. Set clear goals

3. Prioritize tasks strategically

4. Create to-do lists

5. Use visual cues

6. Practice-focused work sessions

7. Reward progress

8. Maintain momentum

Examples of the Zeigarnik Effect in daily life

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Actionable Ideas #8: Zeigarnik Effect, Preventable Problems, 21 Times

Idea #1: Zeigarnik Effect aka Unfinished Business

Idea #2: Preventable Problems Paradox

Idea #3: 21 Times Rule

Memory for incomplete tasks: A re-examination of the Zeigarnik effect

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