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What is MBE?

Mind, Brain, and Education Science is a newly emerging discipline that incorporates developmental psychology, cognitive neuroscience, and educational psychology to answer the question: "How do we learn best?" The answers to this question are found through an intersecting study of the above-mentioned disciplines and their broader fields. The answers will, eventually, come to inform pedagogy, in the hope of creating a learning environment for students that works with the natural functioning of their brains and minds.

Five Well-Established Concepts of MBE Science

As quoted from Mind, Brain, and Education Science by Tracey Tokuhama-Espinosa:

1. Human brains are as unique as faces.

2. All brains are not equal because context and ability influence learning.

3. The brain is changed by experience.

4. The brain is highly plastic.

5. The brain connects new information to old.

A Few Major MBE Players (Alphabetical Order)

1. Antonio Damasio: Professor of Neuroscience at the University of Southern California and Director of the Brain and Creativity Institute; he studies decision-making and emotions.

2. Kurt Fischer: Founder of the Mind, Brain, and Education Program at Harvard; first President of the International MBE Society; Editor of the MBE Journal; he studies cognitive and emotional development.

3. Mary Helen Immordino-Yang: Current (2016-2018) President of the International MBE Society; Associate Professor of Education and Psychology at University of Southern California; she studies the psychology and neurobiology of social emotional, self-awareness, and its impact on learning.

4. Mark R. Rosenzweig: Professor Emeritus of Psychology at the University of California, Berkeley; he is best known for discovering neuroplasticity-the brain's ability to change and grow.

5. David Sousa: Member of the Cognitive Neuroscience Society and world-renowned educational consultant on the brain and learning.

6. Tracey Tokuhama-Espinosa: Educational researcher and professor who seeks to advance the field of MBE. She has two main books on MBE: one acts as a "comprehensive guide" to MBE, while the other focuses on "50 practical applications" of MBE.

MBE Papers & Guides

Mind, Brain, and Education: Building a Scientific Groundwork for Learning and Teaching, by Kurt W. Fischer
Excerpt from the abstract: The primary goal of the emerging field of Mind, Brain, and Education is to join biology, cognitive science, development, and education in order to create a sound grounding of education in research. The growing, worldwide movement needs to avoid the myths and distortions of popular conceptions of brain and genetics and build on the best integration of research with practice, creating a strong infrastructure that joins scientists with educators to study effective learning and teaching in educational settings. Science and practice together provide many potentially powerful tools to improve education.

Mind, Brain, and Education: The Birth of A New Science, by Michel Ferrari and Hazel McBride
Abstract: In this paper, we examine the history of the emerging discipline of Mind, Brain, and Education (MBE) and explore the benefits as well as the difficulties involved in integrating neuroscience into educational policy and practice. We examine the power of neuroscience to impact practice and document the rise of neuromyths. History is on the side of the new discipline of MBE, but there is still much important work to be done to make neuroscientific findings accessible, comprehensible, and relevant to educators.

The Science of Learning, by Deans for Impact
About: The Science of Learning summarizes existing cognitive-science research on how students learn, and connects it to practical implications for teaching. The report is a resource for teacher-educators, new teachers, and anyone in the education profession who is interested in how learning takes place.

Mind, Brain, and Education: Implications for Educators, Edited by Lynn Butler-Kisber
Statement of Purpose: LEARNing Landscapes (TM) is an open access, peer-reviewed, online education journal supported by LEARN (Leading English Education and Resource Network). Published in the autumn and spring of each year, it attempts to make links between theory and practice and is built upon the principles of partnership, collaboration, inclusion, and attention to multiple perspectives and voices. The material in each publication attempts to share and showcase leading educational ideas, research and practices in Quebec, and beyond, by welcoming articles, interviews, visual representations, arts-informed work, and multimedia texts to inspire teachers, administrators, and other educators to reflect upon and develop innovative possibilities within their own practices.

Mind, Brain, and Education: The Students at the Center Series, by Christina Hinton, Kurt Fischer, and Catherine Glennon
From the Introduction: This paper considers student-centered learning approaches in light of mind, brain, and education research. Student-centered approaches to learning comprise a research-based framework for education that aims to help students from a wide range of backgrounds master the skills necessary for college and the 21st-century knowledge economy (JFF 2011). In particular, it is intended to support underserved youth who are often excluded from higher education. The approaches begin with a common set of rigorous standards. Students can reach these standards through learning experiences tailored to their needs and interests, which may include informal learning outside of school. Students are empowered to take responsibility for their own learning, with teachers
and other professionals as facilitators.

Learning About Learning: What Every New Teacher Needs to Know, by The National Council on Teacher Quality
Letter of Support: We have spent much of our professional lives researching learning and cognition. In the course of that work, we have
used and reviewed many textbooks of educational psychology and instructional methods - textbooks that are required reading in teacher preparation programs. We have been consistently frustrated by the lack of discussion in many of these textbooks of teaching strategies that are backed up by strong evidence, and by a frequent overemphasis on strategies for which evidence is anecdotal at best.
Teaching aspiring teachers how to maximize student learning and retention is the paramount task of their training. It is therefore of real consequence that the guidance given by textbooks on these topics makes only passing reference
to essential knowledge about learning.

MBE Links for Further Study & Networking

Bella Abdurachmanov is an educational writer and writing instructor. As editor of The Learning Mind, a project of Kars4Kids, she studies the science behind how people learn best. Bella teaches college writing and creative writing at Touro College, hoping to impart a love of learning to her students. She enjoys writing, in all of its forms, as a spiritual endeavor and means to self-growth.

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Mind, Brain, and Education Science is a newly emerging discipline that incorporates developmental psychology, cognitive neuroscience, and educational psychology to answer the question: "How do we learn best?" The answers to this question are found through an intersecting study of the above-mentioned disciplines and their broader fields.

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coalition for psychology in schools and education

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The top 20 teaching and learning principles

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The APA-supported Coalition for Psychology in Schools and Education has condensed the most important psychological science on PreK-12 teaching and learning into 20 principles.

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By Applied Learning Sciences Team

If you are reading these words, it is very likely you have spent some time as a learner in at least one educational setting - a classroom, for example, or an online course, or even a more informal setting such as an after-school club.

Take a moment to picture one of those settings. What do you see?

Perhaps what you notice in your mind's eye is the arrangement of the room: desks or tables, bookshelves, whiteboards, and various materials and technologies. Maybe what you picture are learners engaged in various activities and the educators who are supporting those learners. Or possibly you are envisioning something else entirely - such as the actual content itself, or inter-personal interactions, or that feeling you have when you haven't quite studied enough for a test.

If you can picture these things, you are already very well-acquainted with some of the foundational issues that are at the heart of learning science. As a discipline, learning science is both as old as the hills, and so new that it is still labeled an emerging field.

How we learn, and how to apply that knowledge of how we learn, has long been a point of human fascination. In ancient times, Socrates and Aristotle spent much of their lives developing theories of knowledge and learning - and their influence is still felt in education today. In the nineteenth and twentieth centuries, other influential researchers, ranging from Jean Piaget to Benjamin Bloom to David Kolb to Lev Vygotsky, began to shape educational practice through the application of their theories and research.

But learning sciences, as a standalone discipline, is relatively new. Definitions are still emerging, but most agree that learning science is an interdisciplinary field focused on the development of effective learning methodologies and solutions. The empirical and theoretical underpinnings of neuroscience, cognitive science, instructional design, data analytics, anthropology, linguistics, computer science, psychology, and education have formed the foundation of the discipline. We expect that the discipline will continue to evolve, especially with the introduction of programs and institutes designed specifically to build a learning work force (e.g. the Science of Learning Institute at Johns Hopkins University or METALS at Carnegie Mellon University).

But how does this all relate to the educational setting in your mind's eye - or to actual practice?

Picture that imaginary learning setting one more time. Look once at the seating arrangements. Look at what the learners are doing. Take a peek at the materials they are using.

The learning sciences allow us to ask fundamental questions about every single aspect of the classroom, and then draw from a broad and deep base of research to answer those questions in ways that enhance our practice and empower our learners. This truly is the intersection of where the science of learning meets the art of teaching - because learning science offers us the power to apply empirical validation to our decision-making in education.

For us at McGraw-Hill Education, it is worth noting that there is one more very important, and very exciting, aspect of learning sciences. When we ask those fundamental questions, conduct that research, and apply it to practice, we place ourselves squarely in the space in which the greatest innovation may take place.

It is already happening. Educators and researchers are already asking incredible questions, and the learning sciences are already informing equally incredible solutions. As a learning science company, we will build on this momentum, explore this remarkable space, and adapt it further to improve learning and instruction.

Why?

The answer is everywhere we look: because learning, and understanding how that learning happens, really does change everything.

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What is Learning Science? - Inspired Ideas - Medium

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If you are reading these words, it is very likely you have spent some time as a learner in at least one educational setting - a classroom, for example, or an online course, or even a more informal setting such as an after-school club. Take a moment to picture one of those settings.

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by Allison Posey

Emotions are not just messy toddlers in a china shop, running around breaking and obscuring delicate cognitive glassware. Instead, they are more like the shelves underlying the glassware; without them cognition has less support.

-Mary Helen Immordino-Yang and Antonio Damasio,

We Feel, Therefore We Learn

The brain actively predicts how to budget energy based on previous experiences.

Brain networks related to emotion are widespread and diffuse, activating the body's physiological and cognitive processes.

Learning needs sufficient activation.

Clarify the intended goal for each part of a lesson.

Ensure learning goals are relevant and meaningful for students.

Design the context in a flexible way, with resources and options for active learning that align with the intended goal.

Educator Dilemma: Drew was in my neuroscience elective, a three-week, intensive class for "gifted and talented" high school students. His motivation to learn the content was insatiable; in fact, we had to make rules for mandatory study breaks so that he would eat, exercise, and socialize. After the first week of class, Drew had read the entire college-level textbook, and it was evident he understood many of the topics from the rich conversations he initiated. He was curious about long-term potentiation and understanding neural changes that occurred during classical conditioning. However, when I collected his first test, it was completely blank. He had not answered a single question. I wondered what had happened. How could he have learned so much information but been unable to share any of it on an assessment? I asked myself, "Why is his test blank? He knows the content, so is this a problem of engagement?" In what ways does this educator dilemma resonate with you?

Emotion: The Activator of Physiology

In every moment of every day, your brain and body are subconsciously working to appraise your current situation. Are you basically OK in this moment? How much energy does your nervous system need to expend to handle the demands of the situation? Consider what is happening in your current surroundings: Is it loud or dark? Is there someone to interact with? Is there food to eat? Do you need to run away? Do you need to learn something? Now reflect on your body's internal state: Are you hot or cold? Do you need to shift your weight to relieve pressure on your back? Is your heart rate sufficient for the oxygen levels in your blood? Are you digesting food? Thankfully you don't need to actively monitor many of these states, but your nervous system does in every moment of every day.

In classic neuroscience, this appraisal of the body has been described as being like a loop. Incoming sensory information from the external environment (and internal body systems) was sent to, processed, and evaluated by the brain, which led to an appropriate motor output response. For example, if there was a buzzing mosquito near your arm, the sensory input was perceived and processed by your brain, which coordinated a response to shoo it away. This process maintains the body in a state of balance, or homeostasis.

Current understanding of the brain shows a much more dynamic process in play than this simple loop. It turns out that your brain is a phenomenal prediction machine. Instead of just waiting for incoming sensory information, it is actively appraising each moment to anticipate what you may need. For example, when I go out into the backyard in the summer, my brain is already predicting that the outdoor environment may include a lot of mosquitos, and it is evaluating the energy that will be required to maintain homeostasis within this context. It may begin to release glucose to my muscles, preparing for a lot of swatting. I may perceive the presence of a mosquito and shoo at it, only to find that there is not one there. My brain directs attention to pertinent stimuli from the environment to help achieve my goals, and it ignores unnecessary information, such as the sound of my neighbor's leaf blower or cars driving by. My nervous system predicts and budgets energy appropriately for that situation based on previous experiences and the goals of the situation.

Shifting to a school context, imagine you enter a classroom and are told, "Today we are going to research and deliver an oral report on spiders." How do you appraise this situation? Based on prior experiences, your brain begins to make predictions. It may evaluate this situation positively: "I have a pet spider and I love talking in front of the class." Others may appraise this situation negatively: "I dislike spiders. I hate talking in this class because kids tease me and this teacher does not like me." Your appraisal is based on your past experiences as well as experiences in this particular context. It is aligned to the targeted goal or task at hand. The appraisal occurs at both a conscious and a subconscious level, and you may identify the emotion in different ways, perhaps as being "excited and ready to go" or "slightly agitated and negative." Your brain predicts the energy demands it may require for this task in this context, and this prediction influences what you pay attention to, how you execute the tasks, and ultimately how you engage and learn in that moment.

Describe the context (environment). What are your students doing?

What previous experiences have students had in this context that may influence how their brains are making predictions about the energy they need to expend to achieve the intended learning goal?

Ultimately, at the core of these appraisals are emotions. Emotion networks are widespread throughout the brain and intricately connect to the body's physiological systems as well as to higher-order cognitive networks. Emotion networks connect with survival centers that influence heart rate, breath rate, and blood pressure. If you are nervous about presenting the spider report in front of peers, you may perceive an increase in activity in these systems and describe the situation by noting, "My palms are sweaty and I feel nervous." The physiological arousal that accompanies emotion also activates attention systems and motivates the body to take action. It recruits motor systems that move muscles and direct actions. Brain networks involved in memory formation, goal setting, planning, reasoning, and problem solving are recruited. These lead to observable behaviors. At the center of this abundance of activity is the emotional appraisal.

Each learner in a classroom will appraise and feel each situation differently based on the goals, the context, and the individual's previous experiences. Each will have unique brain activation and physiological responses, even if behaviors appear consistent. For example, some students may start in on the task and seem to be working in a fairly consistent way, even though their appraisals, internal processes, and predictions vary in a multitude of ways: one student may have just had a fight with a friend and may not be paying any attention to the lesson, another may have never done an oral report before and be overwhelmed, and another may be an experienced actor who is completely comfortable in front of an audience and cannot wait to give the report. For an educator, this variability can be a challenge and seem overwhelming to address in the classroom. How can you possibly be aware of all of the different appraisals and subsequent emotions of each learner in each situation? How can we design to address this range?

Designing for Emotion

Every learning event in our classrooms is appraised in this conscious and unconscious "is this good or bad for me" way. The appraisal causes a shift in physiology-perhaps heart rate and blood pressure increase, pupils dilate, and palms sweat. Individuals may consciously recognize these shifts ("I feel nervous"), or the shifts may be subconscious and manifest in an upset stomach. In addition, higher-order brain systems are activated, such as memory and planning centers. ("Remember that last oral presentation when I used index cards and practiced in front of the mirror? That worked great!" or "Last time I stuttered and no one could hear me. What options can help me this time?") As each learner progresses through the day, the brain predicts and appraises every situation as it relates to previous experiences and the current context. In this way, the brain budgets the body's energy resources to drive perception and prepare for action.

The first step in designing learning experiences for this range of emotional appraisal is to clarify the intended learning goal. For example, the focus of this learning experience may primarily be to deliver an oral report, with less emphasis on learning facts about spiders or learning to compile information into a report. Those other skills may be of focus in other lessons and may actually be a barrier for some to achieve the oral component. Once the target goal is clarified, focus on it! Identify the subskills required to effectively deliver an oral report and share those expectations with students. What does it mean to deliver a superb oral report? What do "expert" presenters do? Clarify the expectations so students know what they are going for. When you zero in on the goal, the brain knows what to focus on and how to predict and direct energy for attention, planning, and action.

Once you have clarified the goal for the task, reflect on the relevancy of the goal. Why does it matter? What is the purpose? Is there a meaningful connection you can make so that students will care? Because emotions are at the core of the brain's appraisals, connecting the goals to authentic and relevant experiences will help students activate their physiology and appraise the situation positively. It will invite curiosity, so they might take notice or want to explore more.

Finally, it is important to offer a few choices related to the identified goal. Because each learner brings unique experiences and backgrounds to each situation, offering even two options can help the nervous system appraise the situation in a more positive way. For example, if the goal is to deliver an effective oral report and meaningful connections are made to how this skill is relevant, then consider how you can offer a few options for students to select as they work to achieve that goal. For example, there may be the option to deliver the presentation to the class or to make a video of their presentation, like a news report. Another option might be to allow them to select any topic for the oral report. You might offer the option to use a graphic organizer and to preview the rubric that will be used to assess the oral delivery. Whatever the options are, the focus of the materials and methods should align with the intended goal, so learners are supported to get to high-level skills and can more positively appraise the context as one that is encouraging them to engage in the learning.

The Physiology of Emotion

When you feel an emotion-any emotion, such as disgust, excitement, or anger-your body goes through a range of physiological changes associated with the sympathetic nervous system, including changes in heart rate, galvanized sweat response, pupil dilation, blood pressure, digestion, and breath rate. Glucose, or sugar, can be triggered to be released from storage to deliver the energy needed for your muscles to move your body, whether it is to avert the oral report and talk with friends or to practice the presentation. A number of chemicals help prepare the body for action. Adrenaline mobilizes the body, increasing heart rate and blood pressure, and enlarging blood vessels-a combination that gives the skin a "blushing," reddish hue. It helps "tag" events for memory, so years later you might recall this spider presentation. Cortisol, a stress hormone, releases blood sugar that mobilizes muscles and helps with metabolism and immune function. It travels throughout the brain and body and therefore has a broad impact. Note that in prolonged amounts, cortisol is the chemical that can be actually become toxic to the hippocampus, the region of the brain fundamental to learning.

This physiology of emotional activation has remained fundamentally the same over thousands of years of evolution: the system that helped us survive on the savannah is the same system that is active today in our classrooms. However, today, instead of responding to an approaching tiger, students respond to events such as taking tests, reading, public speaking, researching, or choosing where to sit for lunch. The physiology of a student in a classroom can be as intense as it would be if he were escaping a tiger. In fact, physiological measures from a group of elementary school students reading a passage aloud to their peers were found to be as extreme as someone running from a tiger: stress hormones were high, galvanized skin responses peaked, and heart rate soared. The teacher, reflecting on this research, was understandably surprised and said, "I never thought of asking students to read aloud to the class as being like tigers in the room for some students."

Similarly, a teacher's physiology will be activated differently throughout the course of a school day, making predictions tied to previous experiences with different students, job responsibilities, or interactions with parents and colleagues. Both real and imagined stimuli spark changes in physiology, initiating changes that prepare the body to achieve the goals. Worrying about the upcoming spider presentation or parent-teacher conference can have similar effects on the physiology as the actual event. We learn to describe these changes in brain and body physiology in these different contexts as emotions, and we engage in the situations accordingly.

1. In what ways do you clarify goals, make them relevant, and offer a few options to support those goals?

2. What events might be "like tigers" for your learners during your lessons or in your context?

An Activation Model

A little activation of physiology is good for learning-in fact, it is necessary to kick-start the body to attention and action. However, as in the so-called Goldilocks syndrome, too little activation can result in apathy and boredom, whereas too much activation can lead to stress and anxiety. Returning to the example of the oral report on spiders, one student may appraise the task as incredibly boring. That student's heart rate and breath rate might be low, and cognitive networks such as attention and memory would be minimally active. Without enough activation, undesired behaviors may result, such as an incomplete draft of the speech, a dearth of research, or a poor delivery.

By contrast, another student's appraisal of this assignment may result in stress. He might be uncomfortable thinking about spiders and is nervous at the thought of presenting in front of the class. His heart rate and sweat level may increase; cognitive centers may not function as efficiently as they normally would. Too much activation of physiology diminishes cognitive processes such as attention and memory. This state leads to less conceptual flexibility or creativity; emotions overrule cognition. It may also result in undesirable actions, such as standing speechless during the presentation, or having shaking muscles and a cracking voice, or delivering the speech poorly. Notice that sometimes the observed behavior-in this case, poor delivery-may be similar for the two students, even though their emotional appraisals differ. Most students will fall on a continuum somewhere between these extremes, and the challenge for educators is that each student's activation system will be different in response to events in the classroom.

A "one-size-fits-all" lesson is unlikely to address the range of emotional appraisal and subsequent physiological activation of the students in the class. When we have students do the same task in the same way and at the same time, we are not designing for the range of students we know we have. To overcome this challenge, educators should follow the suggestions mentioned earlier: offer a clear goal, a relevant connection that addresses the question "why does this matter," and a few options within the lesson to support the intended lesson goal. These elements guide the nervous system toward the appropriate sensory and motor actions and support engagement for the learning task. As shown in Figure 1.1, which illustrates the "activation model," finding the right point on the spectrum between low emotional activation (boredom) and high emotional activation (anxiety) by offering relevant options for attaining the goal is the key to student cognition. Having an option to work with a peer, to use a graphic organizer, to see a model example, or to get to choose the animal to research can shift appraisal of the task. However, it is essential to have the learning target (goal, objective) clarified so that you are able to optimize the flexible options that you offer. Rubrics and assessments should focus on and highlight the necessary skills to achieve the goals, and even though learners begin in different places, each can progress toward the same goal. One educator's experience illustrates the potential benefits: "My learning goal was for students to write a comparative essay. I decided to let students choose their topics, instead of using my topics. One student who had not written much all year actually wrote two pages comparing pancakes and waffles."

Figure 1.1. Activation Model

Here is an example of how a 5th grade teacher incorporated a clear goal into her lesson so she could integrate targeted, flexible options to support the range of student activation during the learning event. As she had done before, Ms. McNabb had the local science museum bring a stargazing bubble to her classroom. Having the bubble in the classroom was usually a memorable event, but she started to think about the range of activation of her students and how to incorporate clear goals and purposeful options into the experience to enhance the learning. Many students became very excited when they saw the bubble and eagerly entered, but some were more reluctant to go into the dark, enclosed space with their energized classmates.

First, Ms. McNabb clarified the learning goal and discussed with the students why it mattered: "Have any of you ever told stories about the patterns you see in the stars at night? Today we will find a few different constellations so you can tell what season it is and even find your own zodiac constellation by looking at the stars." Then she realized that not all students necessarily had to enter the bubble; some could choose to watch the lesson through a window in the bubble or quietly use headphones and a video to learn the constellations. She recorded the stargazing session and posted it to her website so students could watch the video on their own, which helped students who were absent during the class period as well as those who missed some of the information during the session. She also invited students to create their own stories about the constellations instead of just learning the ancient tales. She noted that these goal-driven options in the lesson led to deeper levels of observation, discussion, and learning. More students seemed engaged.

These small modifications in lesson design optimized options and relevancy in support of the target goal. Some of these options were already present in the lesson, but the activation model helped Ms. McNabb reflect on why some of her teaching strategies worked (or did not work). She thought more about how the purposeful options for the clear, relevant goal helped students be in an active, focused state for learning. Not all students needed to do the same thing at the same time.

Understanding how the brain is an active predictor helps us understand why we need to set clear, meaningful goals. Thinking about the activation model helps us reflect on why we need to provide a few purposeful options to support the range of learners in our teaching contexts.

Invite learners to use the activation model themselves to reflect and share their thoughts about their learning choices. How do they initially appraise a learning goal? How do options for relevancy and learning choices change their appraisal and engage them more meaningfully in the task? Learners can become more metacognitive about options that work best or that do not work well for them as they pursue the goal.

In summary, based on previous experiences, a learner's brain appraises the energy and resources it may need to expend in a given context. Emotion networks are central to both physiology and cognitive centers; emotion is at the core of learning. Having a clear goal that matters and relevant options in the environment can shift emotional appraisal into a more active, directed state for learning.

1. What previous experiences might students have in your subject area or class that affect how they are predicting and appraising your context?

2. How can you work to clarify goals and to make them relevant?

3. How can you use the activation model and plan a few flexible options for the range of learners?

How Emotions Tie the Knot for Learning

An adult sticks her tongue out at a baby, and the baby mimics the action by sticking out her tongue as well. The adult coos with praise. This interaction between the child and the adult included an emotional response, and we know that learning took place because the next time the baby saw the same adult, she stuck out her tongue again. Learning happened through the interaction of the baby with the adult, and even in such a simple example, it is clear that emotion played a key role.

Learning includes the process of gaining information from the environment or body systems (the child sees the adult stick out her tongue), actively trying out and incorporating the skills or behaviors (the child tries to stick out her tongue), and then applying them in novel circumstances (the child sticks out her tongue again, even without the adult initiating). The child in this example was actively engaged in the new experience, not just passively receiving information. The next time the child was in a similar context, the brain had anticipated and appraised the situation, which led to purposeful actions and behaviors.

For learning to occur, there must be an active interaction between the learner and the learning environment, including the work spaces in the room, the materials and methods of a lesson (e.g., books, technologies, handouts, rubrics, and assessments), and their peers and teachers. The learning environment can include scaffolds or tools and resources that students can use when first learning something but are intended to be gradually released once the learner becomes more proficient at the skill. Like the scaffolding on a building, learning scaffolds are meant to be removed over time. For example, a graphic organizer is a scaffold for writing that may help an individual achieve the writing goal. Over time, the graphic organizer is needed less, but initially it may be important to an individual's appraisal of the writing task. We can apply what we have learned about emotions and learning to this simple scenario, and you can extend it to think about your own classroom. How does designing a simple change in the learning environment-that connects to the intended learning goal, such as a graphic organizer for writing-influence a student's interaction with the environment and appraisal of the situation? How might this encourage more engagement and even risk-taking in the learning?

We learn emotions in much the same way that we learn words and concepts. Take, for example, how we learn the meaning of a dog. We perceive key features of a dog: two ears, four legs, and fur, and it barks. We interact with and share this understanding using language, pictures, and real examples to describe and highlight different kinds of dogs. We learn that a dog can be distinguished from a wolf or cat, which also have two ears, four legs, and fur. We learn best about dogs when we are able to have a contextualized, active experience with them (i.e., petting, smelling, and playing with them). Similarly, when we learn about our emotions, we begin to recognize the changes that take place in our brain and body due to interactions with the environment. We connect these changes to previous experiences we may have had, and we share a deeper meaning and understanding of emotions when we use a common language and share examples and experiences. For example, consider phrases such as "When I read this, I felt sad and there was a lump in my throat," "I was proud of the way she stood up to the bully, and I wanted to cheer," or "The last time I saw that, I laughed and felt happy." Notice how the words for the emotions connect to changes in physiology and are contextualized.

In the classroom, we may hear, "I was nervous when I presented in front of the class. My heart was racing, and I didn't want to talk." When we begin to clarify language for emotion in a learning situation and connect it with a goal, we can then start to design options to support the activation in the context. "Having a friend present with me helped me feel calmer" or "Practicing first with a video camera helped my voice be less shaky." The more we co-construct a language to describe emotions in connection to our body activation in a learning environment, the more effectively we can incorporate "emotional design" for learning.

Chapter Summary

Emotions are essential for learning. The brain's emotion networks connect with basic body survival systems such as heart rate and blood pressure, and they also connect with higher-order cognitive systems such as memory and attention. Perception, action, and behaviors always involve emotion networks. In any situation, the nervous system makes predictions about the energy demands it will have to support and appraises the situation by asking, "Is this good or bad for me?" This appraisal directs perception and action both consciously and subconsciously.

Each learner's system activates in a unique way-too little activation can lead to boredom or apathy, and too much activation can lead to stress or anxiety. The challenge for educators is to support the wide range of activation in a context with a range of learners. To do this, first identify a clear learning target and make it relevant and meaningful for your learners. Then proactively integrate or scaffold a few options into the environment that support learners' progress and achievement of that goal.

Revisiting the Educator Dilemma

Let's return to the anecdote at the start of this chapter, about Drew, a student who demonstrated a high level of comprehension of complex information but did not do well on a written assessment of this material. Using what was discussed in this chapter about the brain's predictive appraisals based on previous experience and the central role of emotion for cognition, there are several ways to reflect on this educator dilemma.

Here is how this teacher thought about some of the strategies from this chapter; how can you make connections from this chapter to your teaching?

Know your learners will have a range of background experiences that influence their appraisal of a situation and engagement in the task. Drew's previous test-taking experiences influenced how his nervous system made predictions and appraised this situation. The testing environment might be "like a tiger" for his physiology, and his body system may be too activated. This can limit memory and cognitive flexibility so he is not able to demonstrate his full understanding in this context.

Clarify the goal. In this case, was the goal to take a written test or to demonstrate knowledge? Depending on the goal, the options available will vary. If the goal were to demonstrate understanding, then it could be through any means. However, the teacher in this example determined the goal to be an understanding of the concepts through writing (a skill that scientists "in the real world" need).

Integrate purposeful options in the learning environment to support the goal. For example, there could be

A practice opportunity, which could be completed and reflected upon collaboratively and with formative feedback before the assessment.

Flexible seating or time allotted.

Headphones made available as an option to reduce distractions from the environment.

An opportunity to use notes, textbooks, and other resources. (After all, when scientists communicate information in writing, they are able to use their resources.)

Make the learning task relevant. Clarify why the task is important. For this example, discuss why it is important to be able to communicate what you know with others in a written form. Have students brainstorm when they may be in this kind of situation in the world outside school.

Note that the options should be available for all learners, not just Drew. Such availability allows all learners to build a repertoire of strategies to become experts about their strengths, challenges, and weaknesses. Ultimately, the educator will determine which options are offered and works with students to select strategies that help achieve the goals. This educator dilemma may be used as a model for thinking about learning and the brain through the lens of emotion.

Teacher Connection: 7th Grade Language Arts

Katie Novak, a 7th grade language arts teacher, thinks of building her lessons as being comparable to building a closet of shoes. She knows there will be a range of student emotions and that at any point students can demonstrate different strengths and challenges. For every lesson, activity, or discussion, she clarifies the goal and makes it authentic. She then shares options and strategies (the "closet of shoes") that are available for all students to use as they work toward the goal. She designs options into the classroom for all students to use ("a closet of shoes available for everyone").

Dr. Novak shares with her students how different shoes are more appropriate depending on the goal. If the goal is to go hiking in the snow, winter boots are a good choice. However, there can be choice within the category of "boots." Some will prefer boots with fuzzy, warm insides, while others might choose boots with a tough waterproof surface. Either boot will help students reach the goal of hiking outside in winter. When the goal changes-for example, if the goal is now to go out for a fancy dinner-different shoes become more relevant, and students are empowered to make choices that help them reach the goal. One of her students remarked, "Don't you like different shoes? Well, kids like different ways of learning. It is the same thing." Dr. Novak fills her classroom and lesson "closets of shoes" with options and lets students choose what works best for them for that particular activity.

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by Allison Posey Emotions are not just messy toddlers in a china shop, running around breaking and obscuring delicate cognitive glassware. Instead, they are more like the shelves underlying the glassware; without them cognition has less support. -Mary Helen Immordino-Yang and Antonio Damasio, We Feel, Therefore We Learn The brain actively predicts how to budget energy based on previous experiences.

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The Adult Learning Theory - Andragogy - of Malcolm Knowles

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Are you interested in Adult Learning Theory? Check the The Adult Learning Theory - Andragogy - of Malcolm Knowles to find out more.

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Calm Sleep Stories | Stephen Fry's 'Blue Gold'

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Whether you're coming from the original CourseSites, from Educator Preview, or you're brand new to CourseSites - this is your destination to try out Blackboard's Ultra experience and teach courses online, for free.

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To teach effectively, you have to reach your students, students who come to the classroom with varied backgrounds, expectations, abilities, and learning styles. This understanding is at the heart of the philosophy of practice known as universal design for learning (UDL). UDL is part of a larger movement of universal design, which works to increase access for all through designs that-from the beginning-consider needs of diverse people, not just adapting when a situation requires it. For instructors, UDL means designing your course with accessibility in mind-regardless of whether you've been approached by a student who may "require" such adjustments.

Why Consider UDL?

According to the Journal of Postsecondary Education and Disability Winter 2011 edition, eleven percent of undergraduates report some type of disability - and the proportions have swapped such that the majority of disabilities are now non-apparent (e.g., mental, emotional, psychiatric condition/depression, ADHD, specific learning disabilities/dyslexia), compared to more apparent disabilities (e.g. mobility impairment). Meanwhile, between sixty and eighty percent of students with disabilities don't actually contact campus university services for "official" accommodation requests/reporting - which means many students in your classrooms may fall in this category without your knowledge.

This is also an issue of social justice. By putting disability and access considerations at the center of our teaching, we join efforts to make higher education accessible to all of our students.

Key Principles of UDL UDL in Your Course Additional Resources

Key Principles of UDL

Many of you have heard about the increase in classroom technologies, but in their publication Universal Design for Learning in Postsecondary Education (PDF) Rose, et al. remind us that it's important to distinguish UDL from use of "assistive technologies" that are available (from "low-tech" wheelchairs, eyeglasses, or ASL interpreters to more "high tech" computerized support-spell-check, text-to-speech). UDL is about making material that is physically accessible to all, but also about accessible pedagogies.

Based in neuroscientific research and primary brain networks, UDL is grounded in three key principles that are core to learning:

Provide Multiple Means of Representation

Provide Multiple Means of Action and Expression

Provide Multiple Means of Engagement

Principle #1: Provide multiple means of representation

Principle #2: Provide multiple means of action and expression

Principle #3: Provide multiple means of engagement

Brain Networks Activated: Recognition

Brain Networks Activated: Strategic

Brain Networks Activated: Affective

Key word: what. The what of learning

Key word: how. The how of learning

Key word: why. The why of learning

Description: How we gather facts and categorize what we see, hear, and read. Identifying letters, words, or an author's style are recognition tasks.

Description: Planning and performing tasks. How we organize and express our ideas. Writing an essay or solving a math problem are strategic tasks.

Description: How learners get engaged and stay motivated. How they are challenged, excited, or interested. These are affective dimensions.

The principle put another way: Principle #1: Present information and content in different ways.

The principle put another way: Principle #2: Differentiate the ways that students can express what they know.

The principle put another way: Principle #3: Stimulate interest and motivation for learning.

Adapted from the National UDL Center
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UDL In Your Course

While several starters and tips for incorporating UDL into your course(s) are listed below, an especially helpful resource for checklists and recommendations on UDL is the Access Project, a project of Colorado State University's Department of Occupational Therapy. On their website, the Project hosts many UDL materials, including A Concise Introduction to UDL, How Do You Teach? (a UDL Checklist for professors), and a more in-depth UDL Quick Tips chart.

Your Syllabus-Consider how you address principles of UDL and access in your syllabus:

Do you include a disability or accessibility statement (your approach to disability and accommodations in the classroom)? Do you point this out to students in your in-class syllabus review?

Consider what tone you are setting with including such a statement. Since it is your classroom, perhaps create your own, tailored statement. Even if it's an addition to your institution's existing statement, this can help you to set the tone of your course.
For Georgetown's recommended statement and other examples, see the Teaching Commons Georgetown Policies page, and select "Disabilities Accommodations." Consider the placement of such statement(s) on the syllabus, and how you might highlight these during class.

Additional suggested practices for UDL & syllabus design

Assessment - Consider how UDL might impact your class assessment approach(es):

How can you best assess students' mastery? Have you considered combinations of assessments that are balanced to provide opportunities for students to use their different strengths (e.g., written assignments, creative submissions, electronic portfolios, real-time data)?

Within each assignment, are instructions clear? Are the layout and format of the assignment easy to navigate?

Check out our Designing Assessment page for more ideas and suggestions.

And if you'd like help with some self-assessment of how well you're doing with these ideas and issues, feel free to email CNDLS to set up a conversation or a Mid-Semester Teaching Feedback session.

Course Materials-Consider the accessibility of your course content and information:

Providing "multiple means of representation" involves preparing your course materials (whether print, digital, etc.) to be accessible to all students.

Creation of accessible materials:
The Access Project (also referenced earlier) has many guides that provide information on how to design materials that are in line with universal design principles for Microsoft products (Word, PowerPoint) and other applications (e.g., Adobe).

Class Time-Consider how you vary your approaches within the classroom, from methods of content delivery to student participation:

Present information in multiple formats, including text, graphics, audio, and video.

Try having your lectures supported by handouts or a few PowerPoint slides.

Davies et al. of Colorado State University, in Measuring the Effectiveness of Universal Design for Learning Intervention in Postsecondary Education, recommend the use of video clips, as long as you are sure to both introduce the video and summarize/debrief main points with the class.

Group discussions: There are many ways to use and tailor discussion activities to increase student engagement:

Providing time for individual reflection and writing prior to the group discussion may allow more students to be involved and gather their thoughts.

Using small groups: a "Think-Pair-Share" exercise with many pairs or small groups discussing ideas can also encourage more low-stakes participation.

Offering online formats: a forum for online discussion or blogging provides another space for students to participate and demonstrate comprehension.

Use classroom technologies such as clickers (Clickers - CNDLS technology page) and Poll Everywhere (Poll Everywhere - CNDLS technology page) for engagement and/or real-time interaction and feedback.

Use classroom technologies such as clickers and Poll Everywhere for engagement and/or real-time interaction and feedback.

Provide a bit of structure at the beginning of each class: outline key points, connect them to course objectives, and give a preview of the material to be covered in that day.

Check out our Planning and Leading Class, Teaching with Technologies, and Active Learning pages for more ideas.

Remember, key principles of UDL include providing multiple means of expression and of engagement-offer students a variety of ways to express themselves and to interact with course material.
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Additional Resources

SoTL Note: These resources are helpful for learning more about UDL, but much of the current literature focuses on K-12 education, so there's a need for additional empirical, peer-reviewed research. If you're considering Scholarship of Teaching and Learning (SoTL), learn more at our SoTL Teaching Commons page.
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Please reach out to us at cndls@georgetown.edu if you'd like to have a conversation with someone at CNDLS about these or other teaching issues.

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To teach effectively, you have to reach your students, students who come to the classroom with varied backgrounds, expectations, abilities, and learning styles. This understanding is at the heart of the philosophy of practice known as universal design for learning (UDL).

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I taught the first online learning course at the University of Washington in 1995. My co‑instructor was Dr. Norm Coombs, at the time a professor at the Rochester Institute of Technology. We designed the course to be accessible to anyone, including students who were blind, were deaf, had physical disabilities, or had multiple learning preferences. Norm himself is blind. He uses a screen reader and speech synthesizer to read text presented on the screen. We employed the latest technology of the time-email, discussion list, Gopher, file transfer protocol, and telnet (no World Wide Web yet!). All online materials were in a text-based format, and videos, presented in VHS format with captions and audio description, were mailed to the students. When asked if any of our students had disabilities, we were proud to say that we did not know. Why? Because no one needed to disclose a disability since all of the course materials and teaching methods were accessibly designed.

Technology has changed dramatically since I first taught online, but the basic principles that can guide the design of accessible courses have not. The term UD was coined by Ronald Mace, an architect, product designer, and wheelchair user whose work led to the creation of the Center for Universal Design (CUD) at North Carolina State University and its seven principles of UD. UD is defined as "the design of products and environments to be usable by all people, to the greatest extent possible, without the need for adaptation or specialized design." The UD definition, principles, and guidelines were created to make any application accessible, usable, and inclusive and, thus, are a logical choice to underpin practices that ensure that online courses meet the needs of potential students with a wide variety of characteristics that include those related to gender identity, race, ethnicity, culture, marital status, age, abilities, interests, values, learning preferences, socioeconomic status, and religious beliefs.

For a history of UD, the basic principles of UD and those that later evolved to address issues specific to the design of learning activities and IT, consult my book Creating Inclusive Learning Environments in Higher Education: A Universal Design Toolkit and other resources presented in the Center for Universal Design in Education, which is hosted by DO-IT Center at the University of Washington-where DO-IT stands for Disabilities, Opportunities, Internetworking, and Technology. For resources specific to applications of UD to online learning, including accessibility checkers, legal issues, technical details, and promising practices, consult .

A statement about how students can request disability-related accommodations should be included in the syllabus. Then instructors can apply the 20 tips I list below, as they begin to work toward making their online courses more inclusive. The complementary video, 20 Tips for Instructors about Making Online Learning Courses Accessible, may be viewed online, along with a for further background and directions for implementing these tips.

Tips

Nine tips for course materials follow. Consult Accessible Technology at uw.edu/accessibility for details on the design, selection, and use of accessible IT as well as accessibility checkers that help you identify accessibility problems in materials you use or create:

1. Use clear, consistent layouts, navigation, and organization schemes to present content. Keep paragraphs short and avoid flashing content.

2. Use descriptive wording for hyperlink text (e.g., "DO-IT website" rather than "click here").

3. Use a text-based format and structure headings, lists, and tables using style and formatting features within your Learning Management System (LMS) and content creation software, such as Microsoft Word, and PowerPoint and Adobe InDesign and Acrobat; use built-in page layouts where applicable.

4. Avoid creating PDF documents. Post most instructor-created content within LMS content pages (i.e., in HTML) and, if a PDF is desired, link to it only as a secondary source of the information.

5. Provide concise text descriptions of content presented within images (text descriptions web resource).

6. Use large, bold, sans serif fonts on uncluttered pages with plain backgrounds.

7. Use color combinations that are high contrast and can be distinguished by those who are colorblind (color contrast web resource). Do not use color alone to convey meaning.

8. Caption videos and transcribe audio content.

9. Don't overburden students with learning to operate a large number of technology products unless they are related to the topic of the course; use asynchronous tools; make sure IT used requires the use of the keyboard alone and otherwise employs accessible design practices.

Eleven tips for inclusive pedagogy follow; many are particularly beneficial for students who are neurodiverse (e.g., those on the autism spectrum or who have learning disabilities). Consult Equal Access: Universal Design of Instruction for more guidance.

1. Recommend videos and written materials to students where they can gain technical skills needed for course participation.

2. Provide multiple ways for students to learn (e.g., use a combination of text, video, audio, and/or image; speak aloud all content presented on slides in synchronous presentations and then record them for later viewing).

3. Provide multiple ways to communicate and collaborate that are accessible to individuals with a variety of disabilities.

4. Provide multiple ways for students to demonstrate what they have learned (e.g., different types of test items, portfolios, presentations, single-topic discussions).

5. Address a wide range of language skills as you write content (e.g., use plain English, spell out acronyms, define terms, avoid or define jargon).

6. Make instructions and expectations clear for activities, projects, discussions and readings.

7. Make examples and assignments relevant to learners with a wide variety of interests and backgrounds.

8. Offer outlines and other scaffolding tools and share tips that might help students learn.

9. Provide adequate opportunities to practice.

10. Allow adequate time for activities, projects, and tests (e.g., give details of all project assignments at the beginning of the course).

11. Provide feedback on project parts and offer corrective opportunities.

These tips apply to both synchronous and asynchronous teaching. Additional tips for synchronous presentations (e.g., speak all content presented visually, turn on the caption feature of your conferencing software, do not require students to have their cameras on) can be found in Equal Access: Universal Design of Your Presentation.

Acknowledgments

DO-IT (Disabilities, Opportunities, Internetworking, and Technology) serves to increase the success of individuals with disabilities. This publication was partially funded through DO-IT's AccessCyberlearning project that is supported by the National Science Foundation (NSF Grant #1550477). Any questions, findings, and conclusions or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of the NSF. More information about DO-IT can be found at uw.edu/doit.

Copyright © 2022, 2021, 2020, 2019, 2018, 2015, University of Washington. Permission is granted to copy these materials for educational, noncommercial purposes provided the source is acknowledged.

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20 Tips for Teaching an Accessible Online Course

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scald=5808:sdl_editor_representation {"link":"","linkTarget":""}] I taught the first online learning course at the University of Washington in 1995. My co‑instructor was Dr. Norm Coombs, at the time a professor at the Rochester Institute of Technology.

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As an educational technologist, I continue to learn each day. When I develop new presentations or explore a new passion, I create a page of resources. This site will serve as the entry point to these resources, ideas, tips, and tricks. The links to the pages are below.

If you have any comments or ideas to share, or want to find out how you can have me come speak to your school, district, or conference attendees, in-person or virtually, please drop me a line! Social networks are in the footer.

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As an educational technologist, I continue to learn each day. When I develop new presentations or explore a new passion, I create a page of resources. This site will serve as the entry point to these resources, ideas, tips, and tricks. The links to the pages are below.

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Apps for Education

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Database of educational and workforce mobile apps for all devices, grade levels, college, and careers in over 150 program areas; PreK-Careers

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Attention and Memory: video duration for learning 

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Attention and Memory: video duration for learning 

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Kathryn E. Perez, Monitoring Editor

Educational videos provide an important content-delivery tool in many classes. Effective use of video is enhanced when instructors consider cognitive load, student engagement, and active learning. This essay reviews literature relevant to these elements and suggests practical ways instructors can use these principles when using video as an educational tool.

Abstract

Educational videos have become an important part of higher education, providing an important content-delivery tool in many flipped, blended, and online classes. Effective use of video as an educational tool is enhanced when instructors consider three elements: how to manage cognitive load of the video; how to maximize student engagement with the video; and how to promote active learning from the video. This essay reviews literature relevant to each of these principles and suggests practical ways instructors can use these principles when using video as an educational tool.

Video has become an important part of higher education. It is integrated as part of traditional courses, serves as a cornerstone of many blended courses, and is often the main information-delivery mechanism in online courses. Several meta-analyses have shown that technology can enhance learning (e.g., Means et al., 2010; Schmid et al., 2014), and multiple studies have shown that video, specifically, can be a highly effective educational tool (e.g., Allen and Smith, 2012; Kay, 2012; Lloyd and Robertson, 2012; Rackaway, 2012; Hsin and Cigas, 2013; Stockwell et al., 2015). Video may have particular value for student preparation in biology classes, in part because students may find it more engaging ( Stockwell et al., 2015) and because it can be well suited to illuminating the abstract or hard-to-visualize phenomena that are the focus of so many biology classes (e.g., Dash et al., 2016; see Video Views and Reviews features in CBE-Life Sciences Education for other examples). The medium is not inherently effective, however; Guo et al. (2014) have shown that students often disregard large segments of educational videos, while MacHardy and Pardos (2015) demonstrate that some videos contribute little to student performance. What, then, are the principles that allow instructors to choose or develop videos that are effective in moving students toward the desired learning outcomes? Consideration of three elements for video design and implementation can help instructors maximize video's utility in the biology classroom:

Together, these elements provide a solid base for the development and use of video as an effective educational tool.

COGNITIVE LOAD

One of the primary considerations when constructing educational materials, including video, is cognitive load. Cognitive load theory, initially articulated by Sweller (1988, 1989, 1994), suggests that memory has several components. Sensory memory is transient, collecting information from the environment. Information from sensory memory may be selected for temporary storage and processing in working memory, which has very limited capacity. This processing is a prerequisite for encoding into long-term memory, which has virtually unlimited capacity. Because working memory is very limited, the learner must be selective about what information from sensory memory to pay attention to during the learning process, an observation that has important implications for creating educational materials.

Based on this model of memory, cognitive load theory suggests that any learning experience has three components. The first of these is intrinsic load, which is inherent to the subject under study and is determined in part by the degrees of connectivity within the subject. The common example given to illustrate a subject with low intrinsic load is a word pair (e.g., blue = azul); grammar, on the other hand, is a subject with a high intrinsic load due to its many levels of connectivity and conditional relationships. In an example from biology, learning the names of the stages of mitosis would have lower intrinsic load than understanding the process of cell cycle control. The second component of any learning experience is germane load, which is the level of cognitive activity necessary to reach the desired learning outcome-for example, to make the comparisons, do the analysis, and elucidate the steps necessary to master the lesson. The ultimate goal of these activities is for the learner to incorporate the subject under study into a schema of richly connected ideas. The third component of a learning experience is extraneous load, which is cognitive effort that does not help the learner toward the desired learning outcome. It is often characterized as load that arises from a poorly designed lesson (e.g., confusing instructions, extra information) but may also be load that arises due to stereotype threat or imposter syndrome. These concepts are more fully articulated and to some extent critiqued in an excellent review by deJong (2010).

These definitions have implications for design of educational materials and experiences. Specifically, instructors should seek to minimize extraneous cognitive load and should consider the intrinsic cognitive load of the subject when constructing learning experiences, carefully structuring them when the material has high intrinsic load. Because working memory has a limited capacity, and information must be processed by working memory to be encoded in long-term memory, it is important to prompt working memory to accept, process, and send to long-term memory only the most crucial information ( Ibrahim et al., 2012).

The cognitive theory of multimedia learning builds on the cognitive load theory, noting that working memory has two channels for information acquisition and processing: a visual/pictorial channel and an auditory/verbal-processing channel ( Mayer, 2001; Mayer and Moreno, 2003). Although each channel has limited capacity, the use of the two channels can facilitate the integration of new information into existing cognitive structures. Using both channels maximizes working memory's capacity-but either channel can be overwhelmed by high cognitive load. Thus, design strategies that manage the cognitive load for both channels in multimedia learning materials promise to enhance learning.

These theories give rise to several recommendations about educational videos (see Table 1 ). Based on the premise that effective learning experiences minimize extraneous cognitive load, optimize germane cognitive load, and manage intrinsic cognitive lead, four effective practices emerge.

TABLE 1.

Element to consider

Recommendation

Rationale

Examples

Packaging video with interactive questions.

May increase germane cognitive load, improve memory via the testing effect, and improve student self-assessment.

Integrate questions into videos with HapYak or Zaption, as described by Obodo and Baskauf (2015)

Follow short videos with interactive questions within an LMS, as done by Keithly and colleagues (2015), or within Google Forms, as done by Caudel and colleagues (2015)

Use guiding questions.

May increase germane cognitive load, reduce extraneous cognitive load, and improve student self-assessment.

Senchina (2011) provides guiding questions for videos designed to introduce physiology students to professional ethics related to experimenter-subject interactions, such as the following: "Observe the subject's behavior and responsiveness during the dehydration period. What changes as the subject becomes dehydrated? What problems does he have? Observe the experimenters' behavior and responsiveness as dehydration progresses. What do they do differently? Why?"

Make video part of a larger homework assignment.

May increase student motivation, germane cognitive load, and student self-assessment.

Package videos with a series of questions or problems that ask students to apply the concepts from the videos. iBiology Education videos (e.g., What Can You Learn with a Light Microscope?) provide one example ( iBiology, 2016)

Signaling, which is also known as cueing ( deKoning et al., 2009), is the use of on-screen text or symbols to highlight important information. For example, signaling may be provided by the appearance of two or three key words ( Mayer and Johnson, 2008; Ibrahim et al., 2012), a change in color or contrast ( deKoning et al., 2009), or a symbol that draws attention to a region of a screen (e.g., an arrow; deKoning et al., 2009). By highlighting the key information, signaling helps direct learner attention, thus targeting particular elements of the video for processing in the working memory. This can reduce extraneous load by helping novice learners with the task of determining which elements within a complex tool are important, and it can also increase germane load by emphasizing the organization of and connections within the information. Mayer and Moreno (2003) and deKoning et al. (2009) have shown that this approach improves students' ability to retain and transfer new knowledge from animations, and Ibrahim et al. (2012) have shown that these effects extend to video.

The benefits of signaling are complemented by segmenting, or the chunking of information in a video lesson. Segmenting allows learners to engage with small pieces of new information and gives them control over the flow of new information. As such, it manages intrinsic load and can also increase germane load by emphasizing the structure of the information. Segmenting can be accomplished both by making shorter videos and by including "click forward" pauses within a video, such as using YouTube Annotate or HapYak to provide students with a question and prompting them to click forward after completion. Both types of segmenting have been shown to be important for student engagement with videos ( Zhang et al., 2006; Guo et al., 2014) and learning from video ( Zhang et al., 2006; Ibrahim et al., 2012).

Weeding, or the elimination of interesting but extraneous information that does not contribute to the learning goal, can provide further benefits. For example, music, complex backgrounds, or extra features within an animation require the learner to judge whether he or she should be paying attention to them, which increases extraneous load and can reduce learning. Importantly, information that increases extraneous load changes as the learner moves from novice toward expert status. That is, information that may be extraneous for a novice learner may actually be helpful for a more expert-like learner, while information that is essential for a novice may serve as an already known distraction for an expert. Thus, it is important that the instructor consider his or her learners when weeding educational videos, including information that is necessary for their processing but eliminating information that they do not need to reach the learning goal and that may overload their working memory. Ibrahim et al. (2012) has shown that this treatment can improve retention and transfer of new information from video.

Finally, the utility of video lessons can be maximized by matching modality to content. By using both the audio/verbal channel and the visual/pictorial channel to convey new information, and by fitting the particular type of information to the most appropriate channel, instructors can enhance the germane cognitive load of a learning experience. For example, showing an animation of a process on screen while narrating it uses both channels to elucidate the process, thus giving the learner dual and complementary streams of information to highlight features that should be processed in working memory. In contrast, showing the animation while also showing printed text uses only the visual channel and thus overloads this channel and impedes learning ( Mayer and Moreno, 2003). In another example, using a "talking head" video to explain a complex process makes productive use only of the verbal channel (because watching the speaker does not convey additional information), whereas a Khan Academy-style tutorial that provides symbolic sketches to illustrate the verbal explanation uses both channels to give complementary information. Using both channels to convey appropriate and complementary information has been shown to increase students' retention and ability to transfer information ( Mayer and Moreno, 2003) and to increase student engagement with videos ( Guo et al., 2014; Thomson et al., 2014).

STUDENT ENGAGEMENT

Another lens through which to consider educational video is student engagement. The idea is simple: if students do not watch videos, they cannot learn from them. Lessons on promoting student engagement derive from earlier research on multimedia instruction and more recent work on videos used within MOOCs (massive open online courses; see Table 1 ).

The first and most important guideline for maximizing student attention to educational video is to keep it short. Guo and colleagues examined the length of time students watched streaming videos within four edX MOOCs, analyzing results from 6.9 million video-watching sessions ( Guo et al., 2014). They observed that the median engagement time for videos less than 6 minutes long was close to 100%-that is, students tended to watch the whole video (although there are significant outliers; see the paper for more complete information). As videos lengthened, however, student engagement dropped, such that the median engagement time with 9- to 12-minute videos was ∼50%, and the median engagement time with 12- to 40-minute videos was ∼20%. In fact, the maximum median engagement time for a video of any length was 6 minutes. Making videos longer than 6-9 minutes is therefore likely to be wasted effort. In complementary work, Risko et al. (2012) showed 1-hour videos to students in a lab setting, probing student self-reports of mind wandering four times in each lecture and testing student retention of lecture material after the lecture. They found that student report of mind wandering increased and retention of material decreased across the video lecture ( Risko et al., 2012).

Another method to keep students engaged is to use a conversational style. Called the personalization principle by Mayer, the use of conversational rather than formal language during multimedia instruction has been shown to have a large effect on students' learning, perhaps because a conversational style encourages students to develop a sense of social partnership with the narrator that leads to greater engagement and effort ( Mayer, 2008). In addition, some research suggests that it can be important for video narrators to speak relatively quickly and with enthusiasm. In their study examining student engagement with MOOC videos, Guo and colleagues observed that student engagement was dependent on the narrator's speaking rate, with student engagement increasing as speaking rate increased ( Guo et al., 2014). It can be tempting for video narrators to speak slowly to help ensure that students grasp important ideas, but including in-video questions, "chapters," and speed control can give students control over this feature-and increasing narrator speed appears to promote student interest.

Instructors can also promote student engagement with educational videos by creating or packaging them in a way that conveys that the material is for these students in this class. One of the benefits for instructors in using educational videos can be the ability to reuse them for other classes and other semesters. When creating or choosing videos, however, it is important to consider whether they were created for the type of environment in which they will be used. For example, a face-to-face classroom session that is videotaped and presented within an online class may feel less engaging than a video that is created with an online environment as the initial target ( Guo et al., 2014). A video's adaptability can be enhanced, however: when reusing videos, instructors can package them for a particular class using text outside the video to contextualize the relevance for that particular class and lesson.

ACTIVE LEARNING

As biology educators, we have abundant evidence that active learning in the classroom provides clear advantages over passive encounters with course material through lecture (e.g., Knight and Wood, 2005; Haak et al., 2011; Freeman et al., 2014). Similarly, elements that promote cognitive activity during video viewing can enhance student learning from this medium (see Table 1 ).

Schacter and Szpunar (2015) propose a conceptual framework for enhancing learning from educational videos that identifies online learning as a type of self-regulated learning. Self-regulation of learning requires students to monitor their own learning, to identify learning difficulties, and to respond to these judgments; in other words, it requires students to actively build and interrogate mental models, practicing metacognition about the learning process. Novices within a field, however, have difficulty accurately judging their understanding, often overestimating their learning ( Bjork et al., 2013). This problem may be enhanced when new information is delivered via video, which students report as easier to learn and more memorable than text ( Salomon, 1994; Choi and Johnson, 2005). Incorporating prompts for students to engage in the type of cognitive activity necessary to process information-to engage in active learning-can help them build and test mental models, explicitly converting video watching from a passive to an active-learning event. The means to do this can vary, but the following strategies have demonstrated success in some contexts.

Package Video with Interactive Questions

Szpunar et al. compared the test performance of students who answered questions interpolated between ∼5 min video lectures and students who did unrelated arithmetic problems between the videos, finding that the students in the interpolated question group performed significantly better on subsequent tests of the material and reported less mind wandering ( Szpunar et al., 2013). Students who received the interpolated questions also exhibited increased note taking, reported the learning event as less "mentally taxing," and reported less anxiety about the final test. These results suggest that interpolated questions may improve student learning from video through several mechanisms. First, they may help to optimize cognitive load by decreasing extraneous load (i.e., anxiety about an upcoming test) and increasing germane load (i.e., note taking, reduced mind wandering). Further, interpolated questions may produce some of their benefit by tapping into the "testing effect," in which recall of important information strengthens students' memory of and ability to use the recalled information ( Roediger and Karpicke, 2006; Brame and Biel, 2015). Finally, interpolated questions may help students engage in more accurate self-assessment ( Szpunar et al., 2014), an important benefit for a medium that students may perceive as "easier" than text. Tools like HapYak and Zaption can also allow instructors to embed questions directly into video and to give specific feedback based on student response. This approach has similar benefits to interpolated questions in increasing student performance on subsequent assessments ( Vural, 2013) and has the additional benefit of making the video interactive (see following section).

Use Interactive Features That Give Students Control

Zhang and colleagues compared the impact of interactive and noninteractive video on students' learning in a computer science course ( Zhang et al., 2006). Students who were able to control movement through the video, selecting important sections to review and moving backward when desired, demonstrated better achievement of learning outcomes and greater satisfaction. One simple way to achieve this level of interactivity is by using YouTube Annotate, HapYak, or another tool to introduce labeled "chapters" into a video. This not only has the benefit of giving students control but also can demonstrate the organization, increasing the germane load of the lesson.

Use Guiding Questions

Lawson and colleagues examined the impact of guiding questions on students' learning from a video about social psychology in an introductory psychology class ( Lawson et al., 2006). Building on work from Kreiner (1997), they had students in some sections of the course watch the video with no special instructions, while students in other sections of the course were provided with eight guiding questions to consider while watching. The students who answered the guiding questions while watching the video scored significantly higher on a later test. Guiding questions may serve as an implicit means to share learning objectives with students, thus increasing the germane load of the learning task and reducing the extraneous load by focusing student attention on important elements. This strategy is often used to increase student learning from reading assignments (e.g., Tanner, 2012; Round and Campbell, 2013), and it can translate effectively to helping students learn from video.

Make Video Part of a Larger Homework Assignment

MacHardy and Pardos (2015) have developed a model relating educational video characteristics to students' performance on subsequent assessments. One observation from their analysis of Khan Academy videos was that videos that offered the greatest benefits to students were highly relevant to associated exercises. This result is supported by results observed in a "teaching-as-research" project at Vanderbilt University (for background on teaching as research, see www.cirtl.net). Specifically, Faizan Zubair participated in the BOLD Fellows program, in which graduate students develop online learning materials for incorporation into a faculty mentor's course and then investigate their impact in teaching-as-research projects. Zubair developed videos on that were embedded in a larger homework assignment in Paul Laibinis's chemical engineering class and found that students valued the videos and that the videos improved students' understanding of difficult concepts when compared with a semester when the videos were not used in conjunction with the homework ( Zubair and Laibinis, 2015; see also Summary).

The important thing to keep in mind is that watching a video can be a passive experience, much as reading can be. To make the most of our educational videos, we need to help students do the processing and self-evaluation that will lead to the learning we want to see.

SUMMARY

Video may provide a significant means to improve student learning and enhance student engagement in biology courses ( Allen and Smith, 2012; Kay, 2012; Lloyd and Robertson, 2012; Rackaway, 2012; Hsin and Cigas, 2013; Stockwell et al., 2015). To maximize the benefit from educational videos, however, it is important to keep in mind the three key components of cognitive load, elements that impact engagement, and elements that promote active learning. Luckily, consideration of these elements converges on a few recommendations:

Keep videos brief and targeted on learning goals.

Use audio and visual elements to convey appropriate parts of an explanation; consider how to make these elements complementary rather than redundant.

Use signaling to highlight important ideas or concepts.

Use a conversational, enthusiastic style to enhance engagement.

Embed videos in a context of active learning by using guiding questions, interactive elements, or associated homework assignments.

REFERENCES

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MacHardy Z, Pardos ZA. Evaluating the relevance of educational videos using BKT and big data. In: Santos OC, Boticario JG, Romero C, Pechenizkiy M, Merceron A, Mitros P, Luna JM, Mihaescu C, Moreno P, Hershkovitz A, Ventura S, Desmarais M, editors. Proceedings of the 8th International Conference on Educational Data Mining, Madrid, Spain. 2015. [http://educationaldatamining.org/EDM2015/index.php?page=proceedings (accessed 15 October 2016) Google Scholar]

Mayer RE. Multimedia Learning. New York: Cambridge University Press; 2001. [Google Scholar]

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Salomon G. Hillsdale, NJ: 1994. Interaction of media, cognition, and learning. [Google Scholar]

Zubair F, Laibinis P. Interactive videos to enhance student understanding of thermodynamic efficiency. 2015. Presentation at the American Institute of Chemical Engineers, held 11 November 2015 in Salt Lake City, UT.

Articles from CBE Life Sciences Education are provided here courtesy of American Society for Cell Biology

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Effective Educational Videos: Principles and Guidelines for Maximizing Student Learning from Video Content

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Educational videos provide an important content-delivery tool in many classes. Effective use of video is enhanced when instructors consider cognitive load, student engagement, and active learning. This essay reviews literature relevant to these elements ...

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Sleep health: can we define it? Does it matter? - PubMed - NCBI

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Kolb’s Learning Styles & Experiential Learning Cycle

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Kolb’s Learning Styles theory identifies four types of learners: converging, diverging, assimilating, and accommodating. These styles are part of his Experiential Learning Cycle, which involves four stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. The cycle emphasizes learning through experience, reflection, conceptualization, and testing new ideas.

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Arianna Huffington: How to succeed? Get more sleep

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In this short talk, Arianna Huffington shares a small idea that can awaken much bigger ones: the power of a good night's sleep. Instead of bragging about our sleep deficits, she urges us to shut our eyes and see the big picture: We can sleep our way to increased productivity and happiness -- and smarter decision-making.

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Testing makes perfect, finds memory retrieval research

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Students who were repeatedly tested on material significantly outperformed those who repeatedly studied it.

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Marc Effron, president of the Talent Strategy Group, looked at the scientific literature behind high performance at work and identified eight steps we can all take to get an edge. Among those steps is taking care of your body - sleep, exercise, and nutrition. But the most important is sleep. He offers some practical advice on getting more and better rest, and making time to exercise. Effron is the author of the new book, 8 Steps to High Performance: Focus On What You Can Change (Ignore the Rest).
Download this podcast TRANSCRIPT
SARAH GREEN CARMICHAEL: Welcome to the HBR IdeaCast from Harvard Business Review. I'm Sarah Green Carmichael.

All of us want to perform to our utmost level at work - whether we're a new employee or the CEO.

But a lot of the self-help books and articles out there don't start with science, says our guest today, Marc Effron.

MARC EFFRON: To me, high performance is someone who delivers both results and behaviors at the 75 percentile, compared to their peers, over time. So it's not that you had a good month or good year but year in year out you are delivering results and behaviors better than most of the other people that you work with.

In his new book, Effron actually looks at eight different ways that you can become a high performer at work - with the studies to back up those claims.

Those include things like setting big goals, relying on yourself to develop new skills instead of expecting your company to help you, and growing your network.

But the step I found most interesting and surprising - and that Effron says is entirely within our control - is taking care of your physical body.

Marc Effron is president at The Talent Strategy Group, and the author of the book "8 Steps to High Performance: Focus on What You Can Change (Ignore the Rest)."

Marc, thanks so much for being here.

MARC EFFRON: My pleasure. Happy to be here, Sarah.

SARAH GREEN CARMICHAEL: You know, I must have read hundreds of leadership books while I've been hosting this show and not a whole lot of them talk about the importance of physical well-being. Why was that something that you felt was really important to talk about?

MARC EFFRON: Well, I didn't go into writing the book with a preconceived notion that I should write about the body and how the body contributes to high performance. I sorted through thousands of academic articles that said that they had some nugget of proof in them that connected something with something else. And it just so happened that there was a lot that popped up around sleep; a little bit around exercise. And so it caused me to include this one of the eight steps because it really emerged as a very powerful force in our performance.

SARAH GREEN CARMICHAEL: Yeah, it's interesting that you say there's a lot about sleep and not as much about some of the other things, because I too have looked for research on say, nutrition or exercise, and there really isn't as much research on that. Do you know why that's the case? Is it just harder to measure that other stuff or is sleep really that much more important?

MARC EFFRON: Well, there have been plenty of experiments done on exercise and performance, so it's not for lack of looking. In fact, that was probably the area where I was most surprised, being a bit of a gym rat myself. I was looking for some great proof that that would make me even more successful if I just spent one more day in the gym. And unfortunately, if you go through all of the academic literature, there just really isn't anything there. Obviously if you eat right and exercise you will be in great shape and probably a happier individual, but scientists could find no link between those two things and your performance at work.

SARAH GREEN CARMICHAEL: You know, I - as someone who doesn't always eat very well and definitely doesn't exercise enough, I find that oddly reassuring. So, you kind of break sleep into two different parts: quantity and quality. So let's start with quantity. We all know we're supposed to get six to eight hours of sleep, but is there a number that's the absolute minimum? Like, if six to eight just feels impossible. Is there a number that you should aim for as the absolute least you can get and still be effective?

MARC EFFRON: Yeah. That's part of the big challenge, Sarah, is that the advice on sleep is really all over the place. In fact, the National Sleep Foundation says it's somewhere between six and 10 hours. That's a four hour gap where you could do a lot of other things.

And if you look across the collection of research on sleep, it really narrows in on about six to seven hours being the ideal. That ideal is going to vary for everyone. The science also says we each likely have a set sleep point; that minimum number of hours that we need, and we can try as hard as we want to get less than that and train ourselves, it's not going to work.

So the challenge with quantity is that there's not a great set of research into what's the exact right number. We at least have a range, but we can likely narrow it down to that six to seven hour

Point.

SARAH GREEN CARMICHAEL: I remember a few years ago there was a fascinating Wall Street Journal article about what they called the "sleepless elite," this tiny, tiny, tiny fraction of people who really can function, on not very much sleep at all. I think the problem is that a lot of us think we're in this group and we're not. We really do need more sleep. But why is it so tempting to think that we can sort of cut corners there?

MARC EFFRON: Well, I think any high performer wants to do whatever he or she can do to continue being a high performer. And so we're always looking for a shortcut and while we've got those wasted seven hours, we're just lying in bed - we're not working at all during that time.

Therefore, why don't I just take one of those hours and apply it to work? It's a very tempting thought, until you dig into the research and you find out it just doesn't work that way. In fact, I remember reading that article and thinking, "I bet they have a couple of hints at the end about how I can become one of those sleepless elites."

They did not and there are certainly no hints in the academic literature to help people do that. We are unfortunately somewhat hardwired into our sleep patterns; both the number of hours we need, but also the time that we wake up. So those advocating for, you know, a 4:00 AM wake up and get a great start to the day - it's likely that their brains are hardwired to that and it would be very difficult for somebody else to shift and engage in that exact same pattern.

SARAH GREEN CARMICHEL: So what happens to our brains when we don't get enough sleep?

MARC EFFRON: Well, this is what's interesting is you would naturally think, "Well, if I don't get enough sleep, I'm not going to show up as smart, as clever as I would if I got a full night's sleep." But what the science says is, actually, that's not what deteriorates. It's our more fundamental skills that deteriorate.

We'll forget people's names. We won't remember where the car keys are. We might not be alert on the way to work and swerve or get into an accident. So it's actually not the higher motor functions, the higher cognitive functions that suffer, it's the much more fundamental ones. So you'll still be just as bright in that meeting, you'll just be really crabby or irritable.

SARAH GREEN CARMICHAEL: Which can be a really bad thing if you're supposed to be running the meeting.

MARC EFFRON: It does not contribute to success in some cases.

SARAH GREEN CARMICHAEL: So it's not just the quantity of sleep you get - the number of hours. You also make the case that it's the quality of sleep that really can affect your mood and your performance at work. So I'm wondering to hear a little bit more about maybe some things you do to make sure that the high quality of sleep is there?

MARC EFFRON: And that was one of the most interesting findings when I was sorting through all those academic papers, was the fact that it is quality, not quantity that matters most. And the challenge for anyone who lives a real life is that the recommendations around getting high quality sleep sounds something like: you should go to bed in a completely dark room with no device reading beforehand, no animals in the room, the room set to about 65 degrees and no other disturbances.

And if you're a hermit, that's probably a lovely way to get a full night's sleep. But for those of us who probably have all of those things going on, it makes it a bit challenging. If nothing else, it at least lays out: here's what's required to get higher quality sleep. And the two big ones are a dark room and a cold room. Those are actually pretty controllable, and those can certainly help to improve the quality. You can get five hours of quality sleep, which will allow you to be a higher performer than 10 hours of poor quality sleep.

SARAH GREEN CARMICHAEL: I don't know if this is a study you ran across, but I do remember reading once that people, when you make your bed, you get higher quality sleep. And that is a study that, regardless of how scientific it is, I always cite as a chronic bed maker.

MARC EFFRON: Well Sarah, there is what we call a replication crisis in this field. And I'm just going to guess that mountain might not stand up twice. But it doesn't hurt, I'm sure.

SARAH GREEN CARMICHAEL: It was probably done by someone who was nagging their spouse to make the bed.

MARC EFFRON: Exactly, or who was in the sheet business - one of the two.

SARAH GREEN CARMICHAEL: Exactly. So if you, for whatever reason, have not managed to get enough sleep or you have not managed to be in that dark, cool room, are there some ways that you can make up for it after the fact?

MARC EFFRON: Yeah, I think there are a few different strategies. One is: recognize that if you do not get either the quality or the quantity of sleep that you want, you are not going to be your best. Let someone in the office that you interact with regularly, and who you trust, know that. Not as an excuse, not: "Hey Sarah, I had a bad night's sleep, so please put up with me being a jerk today." But: "Hey Sarah, I had a bad night sleep. We've got that big meeting today. Can you make sure that I'm well-paced, I'm not rushing my words? Give me a little hand signal if I'm being too crabby?"

Ask others for help with recovering from that. But also, the science is really clear that two things help a lot. One is naps. Now, not everybody has the luxury of taking a nap, but the science is super clear that a 10 minute nap - not five, not 15, not an hour - a 10 minute nap can make up for as much as one hour of lost sleep.

So if you can find some place to put your head down for 10 minutes, it has amazingly restorative benefits. And then our old favorite caffeine. Caffeine is proven to increase alertness - not a surprise to any of us.

And so if you don't get that good quality or quantity of sleep, come into the office. Let your trusted friend know, "Hey, keep an eye on me today. I want to make sure I am a high performer even though I haven't had a great night's sleep." If you can find time to take a 10 minute nap to get that boost and a couple of cups of coffee or tea, that's a magical recipe for faking that you actually have had a great night's sleep.

SARAH GREEN CARMICHAEL: Is there a danger with caffeine that it actually makes your sleep problem worse? Like if you have caffeine because you didn't sleep, but then caffeine keeps you awake so then you get another night's bad sleep - are you kind of in a vicious cycle.?

MARC EFFRON: Yeah, there's definitely a challenge there. And most of us underestimate the half life of caffeine. So. people often will say, "I can have a double espresso and fall right asleep." Well, that's great, but quality of sleep is a very likely going to suffer, with caffeine speeding up your brain and your body. And so what the science would suggest is that caffeine within about six hours of bedtime is probably going to have a negative effect on both quality and quantity of sleep.

SARAH GREEN CARMICHAEL: What if you are someone who is chronically sleep-deprived? Maybe you have a new baby at home and they keep waking up no matter what you do, but for whatever reason, you are not in a position where you can really improve your sleep quality and it it's not just like, oh, today's a bad day. It's like every day is a bad day. What would your advice be to someone in that situation?

MARC EFFRON: I think I would go with one of the first things that I mentioned, which is you simply need to let other folks know, try as I might, I am not going to be at my sharpest over the next week, month, year - whatever that period is and ask for people to - if it's a long time understand, hey, I've got a new baby at home, you know, try as I might, I can't control their crying, therefore... But also let them know: here's what it's going to end. So no one wants to have an employee come to them and say for the next 12 years I'm probably not going to show up to well. So it's probably helpful to say here's the period during which I think I probably won't be as sharp as I'd like to be, but then also recognize things like ating - well while it might not be a high performance strategy - is certainly going to give you the fuel to perform well during the hours that you are at work.

And we've already talked about caffeine, a well-proven strategy around that. And that's part of the challenge with any of these elements of being a high performer is sometimes we simply can't get that particular step done. That's why there are eight steps in total. If you say, "Guess what, I'm, I'm just not going to get the sleep that I need." There are still seven other things you can do that can help you be a higher performer.

SARAH GREEN CARMICHAEL: So I want to talk a little bit more about some of these things that are beyond sleep. But staying in the physical realm, you mentioned that you're kind of a gym rat and you were really hoping to find strong evidence that working out would improve your performance at work. What did you find? You said you didn't quite find that, but you must've found a couple things. I'm curious to know what you found.

MARC EFFRON: There is a lot of research and about the best that I could find that talked about exercise and its impact on performance was: One, more vigorous - meaning weights, not aerobic exercise - tended to yield slightly higher performance benefits on the same day only. And exercising in the morning tended to yield higher performance benefits then exercising at night. That was about it. So if you combine those two little factoids, what you get is some really hard exercise early in the morning can give you a very small performance boost during the rest of the day.

SARAH GREEN CARMICHAEL: So I do think there is some really good research that shows that there is a connection between even moderate amounts of exercise and mental health. So could you then make a transitive leap that moderate amounts of exercise improve mental health and having better mental health surely helps you at work or is that too much of a stretch?

MARC EFFRON: Yeah, I think you get to what we call a three way interaction: If A causes B, does B cause C? It's a lot easier to measure both of those things separately. So if you show up to work without good mental health, then it's probably going to impact your performance. But it's a bit of a stretch to be able to connect all of those things together.

SARAH GREEN CARMICHAEL: And sort of the third leg of this whole stool is diet, right? The stuff that we eat. It seems like there's really not a lot of evidence out there though, that what you eat really affects your performance at work. Did that surprise you?

MARC EFFRON: It did. I really thought I'd find some magical connection to at least what we know about, you know, decent balanced diets and higher performance. And again, it wasn't for lack of studies being out there. But it was more of a stretch to say food leads to higher performance, rather than food leads to good health and good health might be a platform or a foundation for higher performance. We're back to that three way interaction where it probably contributes, but it probably contributes in such a small way that it's going to be really difficult to kind of parse that out of everything else that we're studying.

SARAH GREEN CARMICHAEL: And maybe, you know, individual results might vary. If you're someone who has pizza for lunch and then notices that you fall into a food coma, maybe that's just you and you can learn from that, but that doesn't mean that it's like, science.

MARC EFFRON: And I would also guess - I didn't find anything that proved this - but I would guess there's far more downside risk on food than there is upside. So if I eat a perfect diet, will I be a higher performer? Probably not according to the science. But if I eat a really horrible diet, will I have health problems that will likely take me away from work or reduce my focus on my performance? Absolutely.

SARAH GREEN CARMICHAEL: And maybe this is something that should go without saying, we all have our own definitions of high performance, but I'm curious to know how did you define high performance and what do you mean when you use that term?

MARC EFFRON: To me, high performance is someone who delivers both results and behaviors at the 75th percentile, compared to their peers, over time. And a few magical phrase s in there: One is "compared to their peers." So all performance is relative. You might be great, but how great are your peers? So you're better than three out of four of your peers - and over time - so it's not that you had a good month or a good year, but year in, year out, you are delivering results and behaviors better than most of the other people that you work with.

SARAH GREEN CARMICHAEL: So that kind of reminds me of that old joke about the lawyers where they're running from the tiger, and it's like, "I don't have to outrun the tiger, I just have to outrun you."

MARC EFFRON: One of my favorite jokes.

SARAH GREEN CARMICHAEL: It's a piece of life wisdom, like I keep coming back to.

MARC EFFRON: But it's true - performance is relative. And I think that's part of the challenge with a lot of the changes that we've seen in performance management systems at companies recently is there was a lot of noise with people saying, "Well, I did a good job and I want a prize."

Well that might be great, Marc, that you want a prize, but Sarah did a better job, so she's going to get a better prize. So it's not simply that you're doing a good job. Life is relative. It's always going to be relative.

And that's what high performers understand, is it's not just about me being good, it's about me understanding what is the highest possible standard for the work that I'm doing and how can I meet that highest possible standard because somebody else is trying really hard to get there.

SARAH GREEN CARMICHAEL: There's so much about work performance and being better than your peers that is out of your control. You don't really control who your peers are. You don't really sometimes control what your company culture is, although you can try. And one of the things you wrote in the chapter on managing your physical health is how you manage your body is a completely controllable factor. And that is both appealing, but I also have to admit that it doesn't always feel that way to me as a person with a body. I'm just curious to know sort of how it feels to you.

MARC EFFRON: Sure. Well, I would suggest all of these steps are completely controllable. Now, that still means you're working in an ecosystem where other things are happening. So you might set big goals, and somebody else sets bigger goals. You might set big goals and your boss doesn't like those goals.

So it's not saying that you can completely control the outcome, but you can completely control that step. And part of it - again, let's go back to the exercise piece. If you say you want to exercise an hour every other day, will something else need to not get done in that hour? Absolutely.

And that could be talking to your friends, it could be reading a book, it could be enjoying your favorite show on TV. All those things would simply get sacrificed, but it's making choices to say, "In the 24 hours that you have to spend, how would you like to spend them?" High performers say I'm going to spend more of those hours on things that deliver high performance and willingly sacrifice other things that might be very enjoyable, but that I don't see adding up to that higher performance.
SARAH GREEN CARMICHAEL: And don't sacrifice your sleep!

MARC EFFRON: Keep at least six hours.

SARAH GREEN CARMICHAEL: Yes. Well, Marc, this has been really fun. Thank you so much for spending some time with us.

MARC EFFRON: My pleasure. I enjoyed it.

SARAH GREEN CARMICHAEL: That was Marc Effron. He's president at The Talent Strategy Group, and author of the new book "8 Steps to High Performance: Focus on What You Can Change (Ignore the Rest)."

This episode was produced by Mary Dooe and Curt Nickisch. We got technical and production help from Alexie Rodriguez. Adam Buchholz is our audio product manager.

Thanks for listening to the HBR IdeaCast. I'm Sarah Green Carmichael.

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The Science Behind Sleep and High Performance

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Marc Effron, president of the Talent Strategy Group, looked at the scientific literature behind high performance at work and identified eight steps we can all take to get an edge. Among those steps is taking care of your body - sleep, exercise, and nutrition. But the most important is sleep.

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See how experiences shape the brain and body together, allowing them to adapt to the outside environment and develop new skills. Plus, learn how the brain forms memories by storing and processing billions of pieces of information every second.

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We are cognitive psychological scientists interested in research on education. Our main research focus is on the science of learning. (Hence, "The Learning Scientists"!). Our Vision is to make scientific research on learning more accessible to students, teachers, and other educators.

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Learning and Memory - Queensland Brain Institute - University of Queensland

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Queensland Brain Institute Queensland Brain Institute

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TedED: Hippocampus and Memory. H.M.'s contributions 

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TedED: Hippocampus and Memory. H.M.'s contributions 

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What happens when you remove the hippocampus? - Sam Kean

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Nobody goes to graduate school expecting a walk in the park - long hours spent in the lab, in the the library, and sitting in front of a computer screen are just an accepted part of daily life. For many students, it is the most energetically demanding time of their lives, not just for the hours put in, but the cognitive resources required to think critically and absorb complex material. While such an assiduous lifestyle should perhaps warrant more rest than normal, sleep often takes a backseat to projects, reading, and deadlines.

Sure, every student knows sleep is important. But how important is it, really?

According to Northern Illinois University professor and Industrial-Organizational psychologist Dr. Larissa Barber, sleep is the single most important health behavior we engage in. In a 2010 study, she and her colleague found that healthy sleep patterns play a critical role in self-regulation - an executive function in the brain that controls our behaviors ( Stress and Health, 2010). It helps us to override urges, initiate behaviors, and persist. It tells us to get out of bed and get to class on time, to start working on that thesis/dissertation, and to keep trudging through that dense journal article, despite the impulse to incessantly check e-mail and Facebook. Without adequate sleep, our brains are less equipped to control our behaviors and direct them towards our goals, which can spell trouble for burdened graduate students.

"I would say that graduate school is probably the ultimate self-regulatory exercise," Barber said. "Almost every critical performance outcome in graduate school requires self-regulatory resources, or energy."

Not only is self-regulation important for knowing when to buckle down and focus, or get yourself out of bed in the morning, it's also critical for managing emotions and stress. Sleeping less can cause people to feel more threatened by stressful events, which in response, can lead to dysfunctional coping.

"Things that usually rate as minor stressors all of a sudden seem like a big deal," Barber said. "So, sleep can often mean the difference between you crying after receiving negative feedback from your advisor on a paper, or calmly evaluating the information as constructive or motivating."

How much sleep is enough? While most people think of the standard as 8 hours, studies show that performance decrements don't appear until you hit below 7 hours, with less-than-6 hours showing a large effect. But personality and individual differences do play a role in a person's "magic number." On average, people need between 6 and 8 hours of sleep. However, people who score high on extraversion (talkative, out-going, assertive) tend to recharge a bit faster than others. Same goes for older people, and people with high metabolisms.

But 7 hours a night may not get you off the hook. It's not just the amount of sleep that matters for positive well-being and performance outcomes; sleep routines are just as important. Barber's study found that in order to see stress levels decline and self-control improve, people need to get enough sleep consistently over the course of the work week.

Further evidence for the importance of sleep routines comes from a study of the effect of staying up late and sleeping late on the weekends, which is a common practice for young adults. CUNY researchers found that a delayed sleep schedule on the weekend can seriously hurt cognitive performance and overall mood in the beginning of the week, through "Sunday night insomnia" and the "Monday morning blues" (Psychology and Health, 2001). Scientists suggest that a later wake-up time prevents exposure to morning light which throws our circadian rhythm off kilter, and subsequently shifts our body's sleep schedule.

Barber finds it troubling that people commonly overlook routines in favor of just catching up on sleep at a later date.

"People often think of sleep in compensatory terms, like a credit card - I'll just pay it back later!" she said. "But they forget what I call the 'hidden interest rates,' which is circadian rhythm disruption. Poor Monday always gets a bad reputation, but the reality is that we set ourselves up for Monday failure via our weekend sleep habits."

In addition to getting enough sleep and getting it routinely, high-quality rest is also an important factor to consider. Studies indicate that sleep hygiene is one of the greatest predictors of overall sleep quality (Hong Kong Med J, 2010). This refers to general behavioral practices and environmental factors that allow for a sound sleep, like diet, exercise, light, temperature, noise, as well as sleep schedule and pre-sleep activities. On the flip side, poor sleep quality is associated with significant psychological distress, depression, confusion, and generally lower life satisfaction. Poor sleep hygiene, in specific, is linked to a higher prevalence of insomnia and chronic sleep difficulties.

In a 2010 study, Chinese researchers found that despite its importance in sleep quality, the level of sleep hygiene knowledge among university students was "relatively inadequate" (Hong Kong Med J, 2010). A more recent study found that although sleep disturbances are often comorbid with most psychiatric disorders, and can exacerbate symptoms, graduate clinical psychology students receive little training in the assessment of sleep and treatment of sleep disorders (Training and Education in Professional Psychology, 2012).

So give your sleep hygiene the attention it deserves, and don't treat it like an afterthought. It has a far more profound effect on your well-being than your probably even realize.

Take a moment to educate yourself on sleep hygiene with these research-based tips.

- Don't take naps

- Avoid sleep medications (prescription or over-the-counter)

- Use the bed only for sleeping, and lie down only when sleepy. If you can't fall asleep within 10 minutes, do something else not work or school-related

- Avoid caffeinated drinks at least 4 hours before bedtime. This includes the obvious culprits like coffee, but also includes chocolate milk, certain teas, and some vitamin drinks

- Avoid nicotine and alcohol 4 hours before bedtime

- Avoid glowing screens before bedtime: TV, phone, tablets, computer, etc. The light interferes with with your body's ability to wind down

- Don't stay up late to do work. Research shows that staying up 17-19 consecutive hours is similar to having a blood alcohol limit over the legal limit. So, working late at night is like writing a paper while drunk. You're better off waking up early and starting on it first thing in the morning

- Find out your sleep need and stick to it

- Don't sleep with your phone on (or in the same room). Remove the temptation to check it altogether

- Put a notebook next to your bed to write down things you think of before bedtime (including brilliant thesis/dissertation ideas) to prevent you from worrying about remembering it the next day or getting up to check into it

- Enlist "sleep allies" to help you stick to your schedule, such as a spouse or roommate. Anyone who can tell you (or text you), "Shut it down," at a certain time of night

- Put your sleep time in your calendar and protect it like you would class time or your dissertation defense day. It's non-negotiable (only in case of emergency)

- Manage your time well overall. If you find yourself sacrificing sleep, then you have too many things on your high-priority list (that are not sleep) and you need to re-evaluate (a) what all you are trying to do and (b) how you are doing it

TAKE THE QUIZ: Sleep Hygiene Index
(Assessment of Sleep Hygiene Using the Sleep Hygiene Index, Journal of Behavioral Medicine, 2006)Always (5), frequently (4), sometimes (3), rarely (2), never (1).
Sum the items for your score. The higher your score, the more maladaptive your sleep hygiene status is.1. I take daytime naps lasting two or more hours.
2. I go to bed at different times from day to day.
3. I get out of bed at different times from day to day.
4. I exercise to the point of sweating within 1 hour of going to bed.
5. I stay in bed longer than I should two or three times a week.
6. I use alcohol, tobacco, or caffeine within 4 hours of going to bed or after going to bed.
7. I do something that may wake me up before bedtime (for example: play video games, use the internet, or clean).
8. I go to bed feeling stressed, angry, upset, or nervous.
9. I use my bed for things other than sleeping or sex (for example: watch television, read, eat, or study).
10. I sleep on an uncomfortable bed (for example: poor mattress or pillow, too much or not enough blankets).
11. I sleep in an uncomfortable bedroom (for example: too bright, too stuffy, too hot, too cold, or too noisy).
12. I do important work before bedtime (for example: pay bills, schedule, or study).
13. I think, plan, or worry when I am in bed.

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Sleep and Grad School: How Important Is It For Students?

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Graduate school is energetically taxing - you're spending long hours in the lab, sprinting across campus, synthesizing ideas, and absorbing complex material. With routines like this, sleep often takes a backseat. But how important is sleep for graduate students, and how can it be improved?

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Memory, Attention, and Distraction [Internet]. Salt Lake City (UT): Genetic Science Learning Center; 2016 [cited 2018 Oct 11] Available from https://learn.genetics.utah.edu/content/memory/

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Hidden Brain Podcast: Eyes Wide Open Part 1

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Hidden Brain Podcast: Eyes Wide Open Part 1

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subscribe to Hidden Brain podcast

Staying up until the early hours of the morning with friends. Skipping sleep to prepare for an exam. These are fairly standard aspects of student life for many young people.

But these sorts of all-nighters pale in comparison to the stunt Randy Gardner pulled when he was 17.

"You don't need sleep. That was the thinking in the 1960s, and that was the thinking I had," said Randy Gardner.

In 1963, Randy stayed awake for eleven days - breaking the world record for going without sleep. Today, at 71, he offers his wisdom about staying up past your bedtime - and describes how his teenage stunt came back to haunt him decades later.
Hidden Brain is hosted by Shankar Vedantam and produced by Maggie Penman, Jennifer Schmidt, Rhaina Cohen, Parth Shah, and Renee Klahr. Our supervising producer is Tara Boyle. You can also follow us on Twitter @hiddenbrain, and listen for Hidden Brain stories each week on your local public radio station.

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Eleven Days Without Sleep: The Haunting Effects Of A Record-Breaking Stunt

# data[].attributes.attachment_link.description
subscribe to Hidden Brain podcast Staying up until the early hours of the morning with friends. Skipping sleep to prepare for an exam. These are fairly standard aspects of student life for many young people. But these sorts of all-nighters pale in comparison to the stunt Randy Gardner pulled when he was 17.

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"Nature" Journal Article (2018): Rest to Learn! Awake Quiescence Aids Memory: 

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"Nature" Journal Article (2018): Rest to Learn! Awake Quiescence Aids Memory: 

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Abstract

Newly encoded memories are labile and consolidate over time. The importance of sleep in memory consolidation has been well known for almost a decade. However, recent research has shown that awake quiescence, too, can support consolidation: people remember more new memories if they quietly rest after encoding than if they engage in a task. It is not yet known how exactly this rest-related consolidation benefits new memories, and whether it affects the fine detail of new memories. Using a sensitive picture recognition task, we show that awake quiescence aids the fine detail of new memories. Young adults were significantly better at discriminating recently encoded target pictures from similar lure pictures when the initial encoding of target pictures had been followed immediately by 10 minutes of awake quiescence than an unrelated perceptual task. This novel finding indicates that, in addition to influencing how much we remember, our behavioural state during wakeful consolidation determines, at least in part, the level of fine detail of our new memories. Thus, our results suggest that rest-related consolidation protects the fine detail of new memories, allowing us to retain detailed memories.

Introduction

In order for labile new memories to be remembered over the longer term they must be consolidated, i.e. strengthened and stabilised, over time1,2. Research in humans and non-human animals suggests that an important mechanism of consolidation is the automatic neural reactivation of newly encoded memories in the minutes that follow their formation3,4,5,6,7,8,9,10,11,12,13,14,15. Memory reactivation is observed especially during periods of post-encoding sleep and awake quiescence (quiet resting), where the magnitude of reactivation predicts subsequent memory3,4,6,9,16, and the electrophysiological disruption of reactivation results in reduced memory11. Sleep and awake quiescence may be especially conducive to memory reactivation due to reductions in sensory input and task engagement, which could otherwise impact this process.

Behavioural research in humans supports this hypothesis, demonstrating better memory retention after post-encoding sleep19,20,21,22 and post-encoding awake quiescence18,23,24,25, relative to a filled period of wakefulness. While the effect of sleep in memory is well established, research into the effects of awake quiescence is still in its infancy. To date the latter research has shown that humans retain more words and associative information after a 15-30 minute delay and a 7-day delay26,27 when they rest quietly in the minutes immediately after memory encoding than when they engage in an unrelated perceptual task. This effect cannot be explained away by intentional rehearsal during the rest period, because people remember more memories after awake quiescence than task conditions, even when the memories cannot be rehearsed intentionally26,27. Thus, converging human and animal research indicates that awake quiescence supports the offline consolidation of new memories.

It is not yet known how exactly this rest-related consolidation benefits new memories, and whether it affects the fine detail of new memories. Rest-related consolidation could increase the number of retained memories without necessarily supporting the fine detail of individual memories. Therefore, in the study reported here, we examined how a brief period of awake quiescence after encoding affects the fine detail of new memories. To this end, we applied the robust and sensitive 'Mnemonic Similarity Task' (MST) to probe the fine detail of new memories. The MST, originally designed to assess pattern separation, is sensitive to manipulations during memory encoding and consolidation30,31. In this task, participants are first sequentially presented a set of photos of everyday items (targets). Memory for the presented items is subsequently examined via a visual recognition test, where participants are sequentially presented a set of photos of everyday items again, but asked to respond "old", "similar", or "new" to indicate whether each presented item is (i) identical to a photo presented during encoding (target, correct response = "old"), (ii) visually similar, but subtly different, to a photo presented during encoding (lure, correct response = "similar"), or (iii) a new photo that was not presented during encoding (foil, correct response = "new"). An equal number of target, lure and foil photos are presented. Two memory measures are typically extracted from the MST: a standard recognition score, and a Lure Discrimination Index (LDI) score. The standard recognition score is calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse targets, while rejecting foils. A high standard recognition score can indicate good gist-based memory, but it need not imply that memories are fine in detail32,33. The LDI score is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate lures from targets, while controlling for any response biases28,34. Thus, if a participant holds detailed representations of encoded target photos, they should demonstrate superior ability to correctly identify subtle visual differences in subsequently presented lures, and thus discriminate them from targets (i.e. respond "similar" to lures, rather than "old"). However, if a participant holds less detailed representations of encoded target photos, they should be less likely to correctly identify subtle visual differences in subsequently presented lures, and thus incorrectly mistake lures for target items (i.e. respond "old" to lures, rather than "similar"). Thus, a higher LDI score should indicate memories of fine detail, where subtle visual differences between previously encoded targets and presented lures can be identified.

Using the MST, Borota et al.30 recently demonstrated a positive dose-dependent effect of post-encoding caffeine administration on LDI scores. They hypothesised that caffeine promotes the consolidation of encoded (target) items, thus supporting participants' ability to discriminate these viewed (target) items from similar (lure) items during subsequent testing30. Similarly, we anticipated that if rest-related consolidation protects the fine detail of newly formed memories, then the MST LDI score should reveal this, because people who experience post-encoding awake quiescence should be better able to discriminate targets from lure items.

In our between-subjects study (see Fig. 1), sixty young adults were exposed to a subset of 60 photos of everyday items from the MST within the context of an incidental encoding task (indoor/outdoor judgement making task), before experiencing either: (i) no delay between item encoding and the subsequent testing phase ('No delay group', N = 20), (ii) 10 minutes of awake quiescence, i.e. resting quietly in dimly-lit room ('Awake quiescence delay group', N = 20), or (iii) 10 minutes of a perceptual task, i.e. playing a spot-the-difference game ( 'Perceptual task delay group', N = 20). Participants' memory for the earlier presented items was then probed via the MST memory test (see above), during which participants were presented 90 photos of everyday items: 30 were old, i.e. were identical to those presented during encoding (targets), 30 were similar (visually similar items from the same semantic category) to the remaining photos presented during encoding (lures), and 30 were new to those presented during encoding (foils) - see Fig. 1 for examples. Using the responses in the MST memory test we calculated a standard recognition score - to measure gist memory - and an LDI score - to measure the fine detail of memories. We also measured response times during the MST memory test and during the encoding task. We hypothesised that, if post-encoding awake quiescence protects the fine detail of new memories, participants' LDI scores should be superior following the awake quiescence delay than the perceptual task delay.

Experimental paradigm. Participants underwent three phases: (i) encoding, (ii) delay, and (iii) testing. During encoding, participants were presented 60 photos of a range of unique everyday items from the Mnemonic Similarity Task database (e.g. Stark et al. 2013). Participants incidentally encoded these items via the performance of a judgment making task, where they were required to respond whether a presented item would typically be found indoors or outdoors. Each item was presented for 2000 ms and was followed by a 500 ms inter-stimulus crosshair (+). Following encoding, participants completed one of three delay conditions: ( A) no delay (N = 20), ( B) 10 minutes of awake quiescence (N = 20), or ( C) 10 minutes of an engaging perceptual task (spot-the-difference game) (N = 20). In the subsequent testing phase, participants were presented 30 of the 'old' items presented during encoding (targets), along with 30 'similar' items that were visually similar objects from the same semantic category to the remaining 30 items presented during encoding (lures), and 30 'new' items that were visually and semantically different to the items presented during encoding (foils). There was no limit on the time to respond during testing.

Results

Our three delay condition groups were well matched in their demographic information (see supplementary information). Participants' responses in the encoding task (indoor/outdoor judgment-making task) were also matched across groups (see supplementary information). Importantly, significant group differences emerged in the subsequent memory test (Mnemonic Similarity Task, MST). Participants in the delay groups differed significantly in their LDI scores, which was calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils28, (F(2,53) = 6.954, P = 0.002, ηρ² = 0.208) - see Fig. 2D. Specifically, the LDI score was significantly higher in the awake quiescence delay than the perceptual task delay (t(35) = 4.454, P < 0.001), and this finding survived the Bonferroni correction (α = 0.017; 0.05/3 comparisons). LDI scores did not differ significantly between the (i) no delay and awake quiescence delay groups (t(36) = −1.602, P = 0.118), or (ii) no delay and perceptual task delay groups (t(35) = 1.876, P = 0.069) - see Fig. 2D.

Mnemonic Similarity Test (MST) response data for the no delay (N = 19), 10 min awake quiescence delay (N = 19), and 10 min perceptual task delay (N = 18) groups showing the proportion of old, similar, and new, responses to ( A) 'old' targets, ( B) 'similar' lures, and ( C) 'new' foils. Lure Discrimination Index (LDI) scores for the three groups are also shown ( D) - this measure is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate similar lures from old targets, while controlling for any response biases. Standard recognition measure ( E) - this measure is calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse old targets, while rejecting new foils. The awake quiescence delay group's ability to discriminate between targets and lures was significantly better than that of the perceptual task delay group, as shown in the groups' ( D) LDI scores and ( B) specific responses to lures. The latter (see red box) shows that the perceptual task delay group provided a significantly higher proportion of (incorrect) old responses to lure items than those in the awake quiescence delay group. In contrast, the awake quiescence delay group provided a significantly higher proportion of (correct) similar responses to lure items than those in the perceptual task delay. In all cases, error bars show the standard error of the mean. Non-corrected significance levels: *p < 0.05, **p < 0.01, ***p < 0.001.

If the benefit of awake quiescence delay over perceptual task delay in the LDI measure reflects a difference in participants' ability to correctly discriminate lures from targets, an interaction should be observed between delay condition group (quiescence vs. task) and response type to lure items ("old" vs. "similar"). Specifically, a greater proportion of correct "similar" responses to lures (i.e. correct identification of lures) would be expected in the quiescence group and a greater proportion of incorrect "old" responses to lures (i.e. incorrectly mistaking lures for targets) would be expected in the task group. In line with this prediction a 2 × 2 ANOVA revealed a significant interaction between delay condition group and response type to lure items (F(1,35) = 9.030, P = 0.005, ηρ² = 0.205). Pairwise comparisons of the proportion of "old" and "similar" responses to lure items - see Fig. 2B - confirmed that participants in the perceptual task delay provided a significantly higher proportion of (incorrect) "old" responses to lure items than those in the awake quiescence delay (t(35) = −2.395, P = 0.022). In contrast, participants in the awake quiescence delay provided a significantly higher proportion of (correct) "similar" responses to lure items than those in the perceptual task delay (t(35) = 3.584, P = 0.001). Moreover, participants in the awake quiescence delay provided a significantly smaller proportion of (incorrect) "new" responses to lure items (t(35) = −3.141, P = 0.003) and (incorrect) "similar" responses to foil items (t(35) = −2.613, P = 0.013) than the perceptual task delay. No other comparisons reached significance (all P > 0.05). When using a Bonferroni-corrected alpha level of 0.006 (0.05/9 comparisons), only the differences in "similar" responses to lures and "new" responses to lure items remained significant.

Performance on standard recognition, which was calculated as the proportion of "old" responses to target items minus the proportion of "old" responses to foils, was near ceiling across groups (see Fig. 2E). This indicates that all participants encoded and retained (at least) gist memory of most presented target items. Although there was no significant main effect of delay group in this measure (F(2,53) = 2.600, P = 0.084, ηρ² = 0.089), those who experienced no delay between encoding and testing were significantly better at discriminating targets from foil items than those who experienced 10 mins of a perceptual task (t(35) = 2.145, P = 0.039), though this finding did not survive the Bonferroni correction (α = 0.017; 0.05/3 comparisons). There were no significant differences in standard recognition performance between (i) the no delay and awake quiescence delay groups (t(36) = 1.424, P = 0.163), or (ii) the awake quiescence delay and perceptual task delay groups (t(35) = 1.033, P = 0.309).

As reported above, the LDI and standard recognition scores of the MST rely on the subtraction of probabilities. While this method has been shown to be sensitive to various experimental manipulations28,29,30,34, concerns have been raised regarding the method by which these measures are calculated33. These concerns relate to the use of proportional values in measurement calculation due to inconsistencies across studies and an inability to directly compare against signal detection theory analyses33. Thus, we examined whether our results held when using signal detection theory by computing d' scores using the standard formula: z(hit rate)-z(false alarm rate). Specifically, we computed traditional memory sensitivity d' scores for the standard recognition measure [z(proportion of old responses to targets)-z(proportion of old responses to foils)] in order to show how well old items were discriminated from new items. We also computed target vs. lure discrimination scores [z(proportion of old responses to targets)-z(proportion of old responses to lures)] to show how well old targets were discriminated from similar lures. See Table 1 for group means of these two measures. Critically, analysis of these new signal detection theory scores replicated the findings of our proportional scores. All significant results in proportional analyses were significant in our signal detection theory analyses (see below).

Table 1 Signal detection theory scores for the no delay, 10 min quiescence and 10 min task delay condition groups.

For the traditional memory sensitivity d' scores, we found no significant main effect of delay condition group (F(2,53) = 2.656, P = 0.080, ηρ² = 0.091). Pairwise comparisons revealed no significant difference in d' scores between the quiescence and task groups (t(35) = 0.743, P = 0.463), or no delay and quiescence groups (t(36) = −1.627, P = 0.112). We did find a significant difference between the no delay and task groups (t(35) = −2.265, P = 0.030), though this finding did not survive the Bonferroni correction (α = 0.017; 0.05/3 comparisons).

For the target vs. lure discrimination scores, we found a significant main effect of delay group (F(2,53) = 3.249, P = 0.047, ηρ² = 0.109). Pairwise comparisons confirmed a significant difference between the quiescence and task delay groups (t(35) = 2.405, P = 0.022). There was no significant difference between the no delay and quiescence delay groups (t(36) = −0.249, P = 0.804), or no delay and task delay groups (t(35) = 1.850, P = 0.072). These findings are in keeping with our results when participants' ability to discriminate lures from targets was computed using proportional scores (Lure Discrimination Index, LDI; see above), though it is noted that, unlike when using the LDI measure, the significant difference between the quiescence and task delay groups did not survive Bonferroni correction (α = 0.017; 0.05/3 comparisons).

The groups' response times during the testing phase did not differ significantly (F(2,53) = 2.415, P = 0.099, ηρ² = 0.084) - see Fig. 3. However, we found that, overall, participants' responses differed significantly for item type (target vs. lure vs. foil) (F(2,106) = 33.914, P < 0.001, ηρ² = 0.390). As shown in Fig. 3, there was an overall increase in the time it took participants to respond between target and lure items (t(55) = −4.672, P < 0.001) and lure and foil items (t(55) = −3.940, P < 0.001). As a result, there was also a significant difference in response times between target and foil items (t(55) = −7.304, P < 0.001), where participants were slower to respond to the latter. All findings survived Bonferroni correction (α = 0.017; 0.05/3 comparisons). There was no significant interaction between item type and delay group (F(4,106) = 1.463, P = 0.219, ηρ² = 0.052).

Mean response times in the Mnemonic Similarity Test for the no delay (N = 19), 10 min awake quiescence delay (N = 19), and 10 min perceptual task delay (N = 18) groups, across all target, lure, and foil items ( left). Mean response times for each delay group broken down by responses to target, lure, and foil items ( right). In all cases, error bars show the standard error of the mean.

Discussion

Our results show that participants who rested quietly for 10 minutes (awake quiescence) after viewing photos of everyday items were subsequently better at discriminating these target items from visually similar lure items, relative to those who were engaged in an unrelated perceptual task for 10 minutes. This finding suggests that awake quiescence protects the fine detail of new memories, bestowing on them a higher resolution than they would otherwise have after a filled delay.

The effect of the awake quiescence delay, relative to the perceptual task delay, cannot be explained away by potential between-group differences in participants' background or encoding performance because the groups were well matched on these measures (see supplementary information for analyses). Furthermore, it is unlikely that this effect can be accounted for merely by intentional rehearsal of items during the 10-minute quiescence delay because the effect remained even when we excluded 4 participants who reported thinking about the items while resting (see supplementary information for analyses). In fact, the key group difference emerged in the LDI measure (see below), and it is unlikely that thinking about a presented item, e.g. a tree, during the rest period would have allowed participants to discriminate a similar lure tree from a previously encoded target tree (see Fig. 1). In addition, we observed no between-group differences in the time that it took participants to respond to items presented during testing. Therefore, it is also unlikely that the difference in memory performance between the awake quiescence and perceptual task delays can be accounted for by differences in the groups' ability to retrieve stored representations. Moreover, in light of concerns regarding the use of proportions in recognition memory measures33, results were replicated when analysing the scores computed via signal detection theory (see results for details).

We hypothesise that superior LDI scores in those who experienced awake quiescence, relative to the perceptual task, can be explained by better/increased consolidation during rest. In particular, our findings suggest that rest-related consolidation protects the fine detail of new memories. This, we propose, resulted in participants in the awake quiescence delay having a better subsequent ability to discriminate similar lures from encoded targets, relative to those in the task delay. Our consolidation hypothesis resonates with other recent findings that suggest that successful discrimination of target items and lures is dependent, not only on the successful encoding of memory representations, but also on the early-stage consolidation of these newly encoded representations30,31. Specifically, Borota et al. (2014) showed higher 24-hour MST LDI scores in participants who consumed caffeine immediately after encoding than in participants who consumed a placebo30. The superior LDI score could not be explained by a retrieval account because no superior LDI score was demonstrated in control participants who consumed the caffeine immediately prior to the 24-hour test only. Instead, Borota et al.30 hypothesise that caffeine enhances the consolidation of new memories, thus facilitating the discrimination of lures from target items. The mechanisms by which caffeine does so remain unknown30. Moreover, Doxey et al. (2018) recently reported higher MST LDI scores in participants who slept for an extended period after encoding than in participants who spent the equivalent time in active wakefulness (e.g. going about daily activities)35. They hypothesise that the benefit of sleep in the MST reflects superior consolidation during sleep than during active wakefulness35. In our study, it is possible that awake quiescence protected the fine detail of new memories by providing a state of reduced sensory input and task engagement. These factors are hypothesised to impact automatic consolidation-related neural reactivation, and their reduction might therefore be conducive to memory consolidation.

In contrast to the LDI measure, performance in the standard recognition measure, which probed participants' ability to endorse targets and reject foils, was not significantly different between delay groups (see Fig. 2E). We did find a significantly lower standard recognition score in the perceptual task delay than the no delay group, but this apparent forgetting did not survive corrections for multiple comparisons. Similar findings of a significant effect in the MST LDI, but not in standard recognition, have been reported in a range of studies applying the MST, including the aforementioned studies on the effects of post-encoding caffeine30 and sleep35, studies examining the effect of healthy and pathological ageing, hippocampal damage37, as well as acute exercise38. Together, these findings demonstrate that standard (old/new) recognition performance can be achieved via gist memory alone, in the absence of detailed target memories32,33, and that the various factors above do not affect gist memory substantially.

In our study, performance in the standard recognition measure was near ceiling across groups. Therefore, it cannot be inferred whether the conditions experienced during the delay period affected the consolidation of gist memory for target items. Nevertheless, this near-ceiling performance in the standard recognition measure provides greater insight into the scores observed in the LDI measure. It suggests that, irrespective of delay condition, participants retained gist memory for most presented targets. This was not the case for detail memory of the targets. Participants retained less detail memory (see Fig. 2B and D), and critically, our results indicate that the behavioural conditions experienced during the 10-minute delay significantly affected the consolidation of this detail memory of targets.

It is of note that whereas the awake quiescence delay group's LDI score was significantly higher than that of the perceptual task delay, neither groups' LDI scores differed significantly from that of the No delay group. However, Fig. 2D suggests tentatively that memory detail increased slightly during the awake quiescence delay and decreased more substantially during the perceptual task delay, albeit not significantly. It is possible that these effects would have been more robust and reached significance had we (i) imposed a longer delay between the encoding and test phase, and/or (ii) applied a within-subjects design, which would have provided more statistical power.

In summary, we provide the first evidence that rest-related consolidation protects the fine detail of new memories. This provides us with higher resolution memories and supports our ability to discriminate recently encoded memories from similar representations. Our findings also support the view that consolidation benefits from states of reduced sensory processing more generally, rather than being restricted to sleep1,16. Future work should examine the neurocognitive mechanisms of rest-related memory consolidation, with a focus on unveiling the factors implicated in the consolidation of detail memory.

Methods

Subjects

Sixty young adults (35 females, 25 males; mean age = 21.77 years, SD = 2.90, age range: 18-31 years) were recruited as participants. All participants had normal or corrected-to-normal visual acuity and no known premorbid psychiatric or neurological disorders that might affect performance. This research was approved by Heriot-Watt University's School of Life Sciences Research Ethics Committee (Ref: 2015-140) and all procedures adhered to the appropriate ethical principles for research in humans. All participants were briefed and provided their informed consent in writing prior to their participation. Twenty participants were pre-experimentally allocated pseudo-randomly to each of our three delay condition groups. Three participants (1 × awake quiescence, 2 × perceptual task) scored +2 SDs from their respective group means in our LDI measure, while one participant scored +2 SDs from their group mean in the standard recognition measure (1 × no delay), and were thus deemed as outliers and removed from our analyses. Thus, group Ns in the analyses reported below were as follows: no delay = 19; awake quiescence delay = 19; perceptual task delay = 18. It is however worth noting that no significant results changed when all recruited participants (N = 60) were included in our analyses.

Design

We employed a between-subjects design to examine retention of photographs of everyday items. The experiment took place in a single session, divided into an encoding phase, a delay phase, and a testing phase. Our experimental manipulation occurred during the delay phase (see Fig. 1).

Materials

Our paradigm used a modified (shortened) version of the Mnemonic Similarity Task28, which was combined with our sensitive memory paradigm that has been shown to detect positive rest-related effects in memory consolidation. The computerised task used to administer our procedure was developed and run using PsychoPy (version 1.83.01) via a Python-coded script39. The task was presented via a 22-inch wide screen computer monitor. Visual instructions were presented on the computer screen during the procedure. Stimuli (photos of everyday items) in the encoding and testing phases were presented in the centre of the screen on all occasions. Responses during encoding and testing phases were collected from participants via keyboard input.

Procedure

Encoding phase

Participants were pre-experimentally informed that they were taking part in a study investigating how humans make judgements about everyday items. They were not informed that they would experience one of three delay conditions or a subsequent memory test for the presented items. This incidental encoding procedure has been used previously28, and was particularly relevant in our study because it minimises the likelihood of mnemonic strategies, e.g. active rehearsal of items during the delay period, in particular the awake quiescence delay.

Participants were presented a total of 60 photos of a range of unique everyday items (see Fig. 1 for examples). Each item was presented as a standalone item on a white background. As in previous work28, each item was presented for a duration of 2 seconds, with an inter-stimuli crosshair (+) appearing in the centre of the screen for 0.5 seconds (total duration of encoding phase = 150 seconds). Thus, all participants received identical treatment and exposure to stimuli during encoding. When presented an item, participants were required to make a judgment as to whether they believed the item would typically be found indoors or outdoors. For example, if presented a photo of a sofa, this item would typically be found indoors, but if presented a photo of a tree, this item would typically be found outdoors. Participants input responses via the 'z' (indoors) and 'm' (outdoors) keys on the keyboard. Participants were instructed that some items may be ambiguous in their typical location (i.e. may be found indoors and outdoors), but that they should respond as quickly as possible and respond with their first instinct.

Delay phase

During the delay phase, participants experienced one of three delay conditions, where they either (i) moved immediately from the encoding phase to the testing phase (i.e. no delay) (N = 20), (ii) rested wakefully for 10 minutes between the encoding and testing phases (i.e. awake quiescence delay) (N = 20), or (iii) performed an unrelated perceptual task (a spot-the-difference game) for 10 minutes between the encoding and testing phases (i.e. perceptual task delay) (N = 20). Participants assigned to the awake quiescence condition were asked to sit quietly in the dimly-lit testing room and relax while the experimenter left the room to "set up the next section of the experiment"18. Care was taken to ensure that the testing room was devoid of any rich visual and/or audible sensory cues to minimise sensory information, and thus the disruption of consolidation. Participants assigned to the perceptual task delay were asked to play a visual spot the difference game18. Participants performed a total of 30 spot-the-difference trials in silence, each 20 second in duration. A trial consisted of the presentation of a pair of real-world photos on the computer screen (see Fig. 1 for examples). Photos were identical other than for two discrete differences. Participants were instructed to search for these two differences, and to silently point toward them if discovered. The experimenter sat behind the participant during this task and scored the number of differences that the participant correctly identified. There was no overlap between the contents of the spot-the-difference photos and the MST photos of everyday items. The no-delay condition was included as a baseline measure of recognition performance (i.e. immediate memory test).

Testing phase

Following the delay phase, all participants performed a modified (fewer items) version of the Mnemonic Similarity Task (MST)28, which probed the fine detail of memories for stimuli presented during the encoding phase. In this test, participants were sequentially visually presented 90 photos of everyday items in a random order which was the same across participants. Of these photos, 30 were old, i.e. were identical to those presented during encoding (targets), 30 were similar (visually similar items from the same semantic category) to the remaining photos presented during encoding (lures), and 30 were new to those presented during encoding (foils) - see Fig. 1 for examples. Participants were informed that, like in the first part of the study (Encoding phase), they would be visually presented a set of photos of everyday items in the centre of the computer screen. However, on this occasion, rather than performing an "indoor/outdoor" judgment-making task, their memory for the earlier presented items would be probed. They were instructed that presented items would be either (i) visually identical to those presented earlier (old targets), (ii) visually similar to those presented earlier (similar lures), or (ii) brand new and not presented earlier (new foils). Using the computer keyboard, they were asked to provide an 'old' (visually identical target item; 'z' key), 'similar' (visually similar lure item; 'v' key), or 'new' (new foil item; 'm' key) response. Participants were informed that there was no time limit to provide a response, and they should respond as accurately as possible. Onscreen instructions regarding which keys corresponded to which response (e.g. 'z' = old) were shown at all times during encoding and testing. To reduce the possibility of order presentation effects, target items were always the oddly numbered items presented during the encoding phase (i.e. 1, 3, 5, 7, 9...etc) and lure items were related to the evenly number items presented during the encoding phase (i.e. 2, 4, 6, 8, 10...etc). The same order of items was presented to all participants during encoding. These 30 targets and 30 lure items were then combined with 30 foil items and then ordered randomly using the random number generation tool in Microsoft Excel. The same random order of items was presented to all participants during testing.

Scoring

Performance in the MST was scored as in previous work28,30,34,40. Firstly, the total number of old, similar, and new responses for the 30 targets, 30 lures, and 30 target items was extracted and converted to proportion scores (each score/30). From these proportion scores, two key memory measures were calculated: (i) a standard recognition score, and a Lure Discrimination Index (LDI) score28. The standard recognition score is calculated as the proportion of "old" responses to targets minus the proportion of "old" responses to foils, and thus reflects a person's ability to correctly endorse targets, while rejecting foils. The LDI score is calculated as the proportion of "similar" responses to lures minus the proportion of "similar" responses to foils, and thus reflects a person's ability to discriminate lures from targets, while controlling for any response biases28,36. In addition to these proportional scores, we examined performance in line with signal detection theory by computing d' scores using the standard formula: z(hit rate)-z(false alarm rate). Specifically, we computed traditional memory sensitivity d' scores for the standard recognition measure [z(proportion of old responses to targets)-z(proportion of old responses to foils)] in order to show how well old items were discriminated from new items. We also computed target vs. lure discrimination scores [z(proportion of old responses to targets)-z(proportion of old responses to lures)] to show how well old targets were discriminated from similar lures. Finally, we extracted the time that it took participants to respond during encoding and testing to check for any potential group differences in encoding and/or retrieval.

Statistical analyses

Analyses were performed using SPSS Statistics 19 (copyright IBM Corp., NY, USA), with the alpha level set to 0.05. ANOVAs with between-subject factor delay condition (no delay vs. awake quiescence delay vs. perceptual task delay) were performed to examine group differences in encoding, LDI and standard recognition scores and test response times. Two-tailed independent t-tests were conducted to examine group differences between pairs of groups. Paired t-tests were conducted to examine within-subject differences in responses to different item types. Bonferroni corrections for multiple comparisons were applied post-hoc to avoid Type 1 error accumulation.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Additional information

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Acknowledgements

Thank you to Georgina Ottaway who assisted with data collection as a volunteer research assistant. Thanks go to all participants who gave their time to take part in our research and to three anonymous reviewers who provided valuable comments on an earlier version of this paper. Thanks to Alzheimer's Research UK Scotland Network and Alzheimer's Society, UK, who part-funded this project. Dr Michael Craig was supported by a Fellowship award from Alzheimer's Society, UK (grant number 316).

Author information

Affiliations

Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom

Michael Craig

& Michaela Dewar

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Contributions

Michael Craig and Michaela Dewar conceptualised the study. Michael Craig developed the computerised PsychoPy task. Michael Craig performed data collection. Michael Craig performed data analyses. Both authors contributed to writing the paper and approved the paper before submission.

Competing Interests

The authors declare no competing interests.

Corresponding author

Correspondence to Michael Craig.

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