# data[].attributes.attachment_link.title
Interactive Brain | How the brain works & the impact of injury

# data[].attributes.attachment_link.description
Take an interactive journey to see how the brain works and what impact an injury can have

# data[].attributes.headline
From "Differentiated Instruction and Implications for UDL Implementation"

# data[].attributes.subject
From "Differentiated Instruction and Implications for UDL Implementation"

# data[].attributes.body
Figure 2. Three primary principles guide UDL—and provide structure for the Guidelines.

# data[].attributes.headline
Willis, "What You Should Know about your Brain"

# data[].attributes.subject
Willis, "What You Should Know about your Brain"

# data[].attributes.attachment_link.readable_text
Should mobile devices be encouraged in class? Trinity College, CC BY-NC-ND

It's hard not to notice the connection of today's youth to technology.

Fused to their smartphones around the clock, they prefer screens to paper and text message to speech; they consider leaving voicemail an act of interpersonal aggression.

They seem to focus differently too: skimming and sampling their way through multiple streams of data, they look like they're taking it in all at once.

Some educators call them "digital natives," reflecting the idea that tech is at the core of who they are and how they function.

If living with technology really has rewired this generation for multitasking, what implications does this have for how we educate them? Should we tolerate - or maybe even encourage - mobile devices in class? And should we worry when we see students keeping an eye on social media or other diversions while doing homework?

Why attention matters for learning

As a professor who specializes in course design, I deal with these questions frequently as I help fellow faculty devise better strategies. In this work, I draw on my research background in cognitive psychology, a specialization focusing on mental processes such as reasoning, memory and attention.

Of those processes, attention is one that I tell teachers to be particularly attuned to. Research shows that memory - especially working memory, which holds information we're using in the present moment - is deeply intertwined with attention.

Without sustained focus, we retain surprisingly little, and that window of focus is much narrower than we may realize.

So, are the minds of digital natives - or any heavy tech users - better or worse when it comes to attention? It's a complicated question, partly because attention works in some paradoxical ways.

The function of attention is to prioritize where we put our limited cognitive resources at any given moment. One thing attention does is keep irrelevant information at bay. Like a bouncer at an exclusive nightclub, its job is to ensure that only the most important and relevant elements pass through into conscious awareness.

But at the same time, a well-functioning attention system has to stay open to information that's in the background and could be potentially useful. That mental bouncer has to constantly scan the crowd for anyone who might turn out to be a great addition to the party, pick them out and usher them inside.

What technology does to the ability to pay attention

Technology seems to have a bigger impact on that second side of attention.

Certain kinds of tech use - habitually consuming lots of online media at once, or playing certain kinds of video games - appear to boost the ability to efficiently pick up on peripheral information while keeping up with a main task.

And although long-term studies are lacking, there is a growing body of research on how tech-saturated environments shape the minds and brains of kids.

It's unlikely that video games or online media damage kids' ability to pay attention. But comparative research across high-tech and low-tech societies suggests that information processing is different in kids who grow up working with digital rather than physical tools. Neither group is better or worse across the board, but this research suggests that high-tech kids may be less inclined to learn by watching for extended periods of time.

That said, we should be cautious about concluding that today's students have developed the ability to juggle as much technology as they want. Technology has not reshaped the basic ways in which our brains process information.

As cognitive psychologist Daniel Willingham points out, the impact of things like video games is relatively subtle. All they can do is tinker around the edges of our mental systems, rather than altering them at the core.

As Willingham says, something as fundamental as attention couldn't be deeply reshaped without a major overhaul of the brain - something that would be a function of evolution, not life experiences.

All of this means that college teachers should be skeptical of claims that their game- and tech-obsessed students process information completely differently. Compared to earlier generations, today's students are probably no more able to learn while simultaneously engrossed in Twitter, Facebook and texting.

College students and technology

There are other pitfalls to consider.

Research with young adults in college suggests that they are neither as enthusiastic about technology nor as adept at using it as we may assume. Sometimes, they intentionally opt for lower-tech approaches; in one study of California college students, most said that they preferred paper over a browser for their own studying.

This is something my faculty colleagues commonly observe as well - that although their students may use technology like social media casually throughout the day, it doesn't translate to other tech-based tasks, such as navigating a course's online homework system.

Students also get into trouble if they assume - because of the "digital nativism" idea, or simple lack of self-awareness - that they can master demanding coursework while engaged in digital distractions.

Evidence suggests that multitasking - such as tweeting in class - reduces learning, or at the very least makes it take longer.

Even sitting next to a classmate surfing the web on his or her own laptop hampers learning.

In sum, despite that appearance of being fused to their devices, today's students aren't immune to the distraction those devices cause. And they don't necessarily want technology in every corner of their educational lives.

How college teachers can help

College teachers need to include lots of tech support for online assignments and other kinds of educational technologies, because even students who have grown up with computers still get stuck when using them in new contexts.

Teachers must also avoid the trap of adding tech to a class just because they assume digital native students want it. Educational technology can be highly effective, but only when it is tightly coupled to the teacher's goals.

Even better, teachers can help students understand for themselves how attention works - knowledge that everyone ought to have in today's distracting era. Without vilifying technology, teachers can work to raise students' awareness about how attention impacts learning more than we may realize, no matter what our age.

The effects of technology on cognition are intriguing, but they don't justify teaching millennials as if they were a new species. Teachers need to think twice about tolerating devices in class, except as part of structured activities that link to the lesson at hand.

# data[].attributes.attachment_link.title
Can millennials pay attention to classwork while texting, tweeting and being on Facebook?

# data[].attributes.attachment_link.description
From the look of it, the millennials appear to be very comfortable with technology. But are they as immune to the effects of digital distraction as some might assume - especially in the classroom?

# data[].attributes.body
It's the Attention, Stupid!

# data[].attributes.attachment_link.title
Online Learning Consortium Institute: Workshops: Log in to the site

# data[].attributes.body
the long read

# data[].attributes.attachment_link.title
The surprising habits of original thinkers | Adam Grant | TED

# data[].attributes.attachment_link.provider_name
Faculty Focus | Higher Ed Teaching & Learning

# data[].attributes.attachment_link.readable_text
I was wrapping up a presentation on memory and learning when a colleague asked, "How do we help students learn in courses where there's a lot of memorization?" He explained that he taught introductory-level human anatomy, and although the course wasn't all memorization, it did challenge students' capacity to retain dozens of new terms and concepts.

The question itself is tricky because most teaching professionals are heavily invested in the idea that learning isn't about being able to regurgitate facts on an exam. We also worry, and with good reason, that emphasizing rote learning steals time and effort away from the deeper thinking that we want students to do. But in my work I've come to realize that memorization deserves some airtime because it is one important route to building content knowledge and expertise. Furthermore, acquiring content knowledge doesn't have to detract from critical thinking, reasoning, or innovation-rather, it can complement all these. Cognitive scientists are making new discoveries all the time about the connections between memory and processes such as drawing inferences, making predictions, and other skills that make up the ability to think like an expert in a discipline.

So what techniques target this specific teaching and learning challenge? We're all familiar with mnemonics such as the first-letter technique-using a word such as RICE (rest, ice, compression, elevation) or a catchy phrase (Every Good Boy Does Fine) to remember a sequence of information. Mnemonics are one good tool, but what else can we offer?

Learning isn't about being able to regurgitate facts on an exam, but memorization deserves some airtime because it is one important route to building content knowledge and expertise

For starters, we should convey to students (and keep in mind ourselves) that memorization challenges nearly everyone because the mind simply isn't set up to take in reams of disjointed facts. Our memory systems evolved to be picky about what we remember, selecting the information that is most relevant to our goals and discarding the rest. Understanding this helps us and our students have realistic expectations about the level of effort needed to succeed.

It also helps to understand how we create memories for words, because many memorization tasks involve scientific terms, foreign language vocabulary, and the like. How we do this has been hotly debated among researchers, but one view is that we rely on a special piece of mental machinery called the phonological loop that holds speech sounds.

If you've ever muttered a phone number to yourself while you ran around looking for a pencil to write it down with, you've experienced the phonological loop in action. But recycling phone numbers isn't the phonological loop's main reason for existing. Rather, its real job kicks in when we run into a word we haven't heard before. When this happens, the phonological loop grabs onto the sequence of sounds and keeps it fresh while other memory systems set up representations of what the new word means. The catch is that phonological loop capacity varies fairly substantially from individual to individual. This means that words of three, four, or more syllables overwhelm some people's ability to learn new words on the fly.

With these two points in mind-that memory doesn't handle disjointed facts well and new vocabulary hinges on one sometimes-shaky mechanism within memory-here are some ways to help students manage memorization:

Emphasize context and purpose. Ask yourself why students are being asked to memorize these facts in the first place. If students can readily answer that question and if they can picture future situations in which they will use the information, they will be better primed to remember it.

Break down new vocabulary words, especially those that are more than two syllables long. Allow time to rehearse and remember the first couple of syllables before tacking on later ones. Also keep in mind that this process will be easier for some students than others due to wide variation in phonological loop capacity; you may want to design in a "mastery learning" or other individualized approach for vocabulary so that students can move through at their own pace.

Visualize information. For most people, imagery is highly memorable, perhaps because so much of the brain is devoted to visual processing. Memory champions, such as the ones populating the best seller Moonwalking with Einstein, use elaborate visualization strategies to achieve incredible feats of memorization. Similarly, strategies such as the keyword mnemonic work by linking word sounds to images (such as using an image of a cowboy on a horse to remember the Spanish word "caballo," which sounds a bit like "cowboy").

Take advantage of the "Big Three" applied memory principles-testing, spacing, and interleaving. Briefly, these refer to the facts that quizzes are a great way to study and that we do best when we spread out our study sessions and alternate between different topics. Tackling a big memory project such as human anatomy means we need every advantage we can get, and dozens of research studies have supported these three as producing the biggest memory payoff for the time invested.

Avoid the rereading trap. Students tend to fall into passively reviewing material, and in doing so they miss the key advantage of techniques such as testing: retrieval practice. Retrieval practice strengthens memory, but it works only when we actively challenge memory. Flash cards, a favorite student strategy, are fine as long as students use them to actively quiz themselves.

It's okay to expect students to learn the facts and terms needed to become experts in a discipline. With an understanding of memory and some strategies to share, we can make this side of learning productive and maybe even painless-but certainly at least less painful.
Michelle Miller is a professor in the Department of Psychological Sciences at Northern Arizona University.
Reprinted from The Teaching Professor, 28.9 (2014): 3. © Magna Publications. All rights reserved.

# data[].attributes.attachment_link.title
Helping Students Memorize: Tips from Cognitive Science

# data[].attributes.attachment_link.description
Although there is more to learning than regurgitating facts on an exam, memorization is an important route to building content knowledge and expertise.

# data[].attributes.attachment_link.provider_name
Retrieval Practice: A Powerful Strategy to Improve Learning

# data[].attributes.attachment_link.readable_text
It's what you say and how you say it.

How can we demystify the science of learning in every lesson, every day?

Here are a few key phrases to adopt in your classroom that make retrieval, spacing, interleaving, and feedback concrete, engaging, and familiar. Download more key phrases from our library and spread the word (or the phrase), too!

Emphasize no-stakes retrieval practice

# data[].attributes.attachment_link.title
Demystify the science of learning with these key phrases

# data[].attributes.attachment_link.description
It's what you say AND how you say it! How can we demystify the science of learning in every lesson, every day? Here are key phrases to adopt in your classroom that make retrieval, spacing, interleaving, and feedback concrete, engaging, and familiar. Download more key phrases from our librar

# data[].attributes.attachment_link.title
Feats of memory anyone can do | Joshua Foer

# data[].attributes.attachment_link.provider_name
Faculty Focus | Higher Ed Teaching & Learning

# data[].attributes.attachment_link.readable_text
I was wrapping up a presentation on memory and learning when a colleague asked, "How do we help students learn in courses where there's a lot of memorization?" He explained that he taught introductory-level human anatomy, and although the course wasn't all memorization, it did challenge students' capacity to retain dozens of new terms and concepts.

The question itself is tricky because most teaching professionals are heavily invested in the idea that learning isn't about being able to regurgitate facts on an exam. We also worry, and with good reason, that emphasizing rote learning steals time and effort away from the deeper thinking that we want students to do. But in my work I've come to realize that memorization deserves some airtime because it is one important route to building content knowledge and expertise. Furthermore, acquiring content knowledge doesn't have to detract from critical thinking, reasoning, or innovation-rather, it can complement all these. Cognitive scientists are making new discoveries all the time about the connections between memory and processes such as drawing inferences, making predictions, and other skills that make up the ability to think like an expert in a discipline.

So what techniques target this specific teaching and learning challenge? We're all familiar with mnemonics such as the first-letter technique-using a word such as RICE (rest, ice, compression, elevation) or a catchy phrase (Every Good Boy Does Fine) to remember a sequence of information. Mnemonics are one good tool, but what else can we offer?

Learning isn't about being able to regurgitate facts on an exam, but memorization deserves some airtime because it is one important route to building content knowledge and expertise

For starters, we should convey to students (and keep in mind ourselves) that memorization challenges nearly everyone because the mind simply isn't set up to take in reams of disjointed facts. Our memory systems evolved to be picky about what we remember, selecting the information that is most relevant to our goals and discarding the rest. Understanding this helps us and our students have realistic expectations about the level of effort needed to succeed.

It also helps to understand how we create memories for words, because many memorization tasks involve scientific terms, foreign language vocabulary, and the like. How we do this has been hotly debated among researchers, but one view is that we rely on a special piece of mental machinery called the phonological loop that holds speech sounds.

If you've ever muttered a phone number to yourself while you ran around looking for a pencil to write it down with, you've experienced the phonological loop in action. But recycling phone numbers isn't the phonological loop's main reason for existing. Rather, its real job kicks in when we run into a word we haven't heard before. When this happens, the phonological loop grabs onto the sequence of sounds and keeps it fresh while other memory systems set up representations of what the new word means. The catch is that phonological loop capacity varies fairly substantially from individual to individual. This means that words of three, four, or more syllables overwhelm some people's ability to learn new words on the fly.

With these two points in mind-that memory doesn't handle disjointed facts well and new vocabulary hinges on one sometimes-shaky mechanism within memory-here are some ways to help students manage memorization:

Emphasize context and purpose. Ask yourself why students are being asked to memorize these facts in the first place. If students can readily answer that question and if they can picture future situations in which they will use the information, they will be better primed to remember it.

Break down new vocabulary words, especially those that are more than two syllables long. Allow time to rehearse and remember the first couple of syllables before tacking on later ones. Also keep in mind that this process will be easier for some students than others due to wide variation in phonological loop capacity; you may want to design in a "mastery learning" or other individualized approach for vocabulary so that students can move through at their own pace.

Visualize information. For most people, imagery is highly memorable, perhaps because so much of the brain is devoted to visual processing. Memory champions, such as the ones populating the best seller Moonwalking with Einstein, use elaborate visualization strategies to achieve incredible feats of memorization. Similarly, strategies such as the keyword mnemonic work by linking word sounds to images (such as using an image of a cowboy on a horse to remember the Spanish word "caballo," which sounds a bit like "cowboy").

Take advantage of the "Big Three" applied memory principles-testing, spacing, and interleaving. Briefly, these refer to the facts that quizzes are a great way to study and that we do best when we spread out our study sessions and alternate between different topics. Tackling a big memory project such as human anatomy means we need every advantage we can get, and dozens of research studies have supported these three as producing the biggest memory payoff for the time invested.

Avoid the rereading trap. Students tend to fall into passively reviewing material, and in doing so they miss the key advantage of techniques such as testing: retrieval practice. Retrieval practice strengthens memory, but it works only when we actively challenge memory. Flash cards, a favorite student strategy, are fine as long as students use them to actively quiz themselves.

It's okay to expect students to learn the facts and terms needed to become experts in a discipline. With an understanding of memory and some strategies to share, we can make this side of learning productive and maybe even painless-but certainly at least less painful.
Michelle Miller is a professor in the Department of Psychological Sciences at Northern Arizona University.
Reprinted from The Teaching Professor, 28.9 (2014): 3. © Magna Publications. All rights reserved.

# data[].attributes.attachment_link.title
Helping Students Memorize: Tips from Cognitive Science

# data[].attributes.attachment_link.description
Although there is more to learning than regurgitating facts on an exam, memorization is an important route to building content knowledge and expertise.

# data[].attributes.attachment_link.readable_text
How well do students learn when a lesson is mainly in PowerPoint? Henrik Berger Jørgensen, CC BY-NC-ND

For a brief period in the history of teaching, using PowerPoint automatically qualified you as a tech-savvy professor - an innovator who wouldn't settle for the usual combination of staticky black-and-white overhead films and hand-scrawled chalkboard notes.

Now, it's hard to believe that PowerPoint was once considered innovative by anyone. Popular criticism includes everything from tongue-in-cheek comments about death by PowerPoint to serious claims that it fundamentally degrades how we think and communicate.

But much of today's college instruction isn't in face-to-face classrooms, a setting in which PowerPoint was traditionally used. It's in the burgeoning field of online learning.

So if more learning is moving online, does that mean that teaching with PowerPoint is becoming a thing of the past?

Surprisingly, the answer is no.

Passive learning through PowerPoint?

Even though there's little research that directly addresses whether PowerPoint affects learning in college students, critics have questioned its value in educational settings.

Some ask whether PowerPoint might indirectly undermine the quality of teaching by reinforcing a passive learning approach.

We know that lecturing is less effective than alternative methods. It therefore makes sense for teachers in face-to-face classrooms to question how much of their class time ought to be spent on slideshows.

But the fact is "slideshows" remain a popular method for presenting content in today's online courses.

Technically, these are often not PowerPoints, but decks generated using other types of specialized programs.

And they may differ from standard in-class PowerPoint presentations in important ways.

For example, taking advantage of the increased flexibility of the online environment, they give students more control over how quickly to go through the material and when to backtrack. They can also have more interactive features, such as quizzes, that ask students to apply material while they are learning it.

Even so, the basic - and flawed - idea is the same: put the material in front of students, and learning will happen.

What's wrong with slideshows in online courses

As a psychologist specializing in teaching techniques and course design, when I talk to faculty about teaching effectively with technology, I sometimes tell them to follow the ABS principle: anything but slideshows.

I'm only half-kidding with that blanket statement.

After all, we learn with the same brains in online environments as we do face-to-face; the principles of learning don't change just because the medium changes. And today's learning theorists agree: active involvement trumps passive viewing.

Students need to grapple with challenging problems, practice skills, discuss and defend viewpoints. But for this kind of active learning to happen, instructors need to ensure they do not rely too heavily on slides.

There are alternatives: simulations, problem-based learning, even educational games are all proven methods for drawing students in. They also transplant well into online learning.

Using slideshows the right way

So do slideshows have any place in a well-designed online course? Possibly.

They can be used strategically for things they are best at: giving students an overview of new material, or providing a refresher on concepts students will need for an upcoming activity.

Slides are also great for for integrating visual illustrations. This is important because visuals - diagrams, figures, photos and the like - have a powerful impact on learning.

Visual information is almost always more memorable than sound, text or other modalities.

This isn't because of the now-debunked idea that some people are "visual learners," but more likely stems from how the brain codes images. There are separate systems for representing verbal and visual information in the brain. When we save information in both places, it is easier to recall.

Teachers don't have to stick with static images, either. Even basic animations can illuminate conceptual relationships - such as cause and effect, or the unfolding of a process over time - in ways that text can't.

Furthermore, as University of California, Santa Barbara researcher Richard Mayer has discovered, visuals and the spoken word pair up in powerful ways, so that audio plus visuals produce better learning than either alone.

Research also tells teachers some things not to do with visuals. Instructors should avoid purely decorative graphics, as these can actually hamper learning.

They should also eschew reading text verbatim, instead using a conversational, natural speaking style for voiceovers and verbal explanations.

What this means in the larger context of online learning

In sum, slideshows can be a useful part of online instruction, when used for the right things and designed in the right way. But they shouldn't be the main, or the only, method of instruction - any more than lectures should dominate face-to-face classes.

But it's not just instructors who need to hear this message.

Publishing and educational technology companies, who provide many of the tools that educators rely on, can do more to develop products that push beyond familiar formulas and draw on the latest learning science.

We need tools that make it easy to create assignments that ask students to apply what they have learned, in scenarios that are as realistic and challenging as possible.

These learning tools also need to be adaptive, adjusting the material and pace to the individual learner. This kind of educational technology does exist, but far more can be done to expand the available options.

Teaching in the age of technology comes with its own set of opportunities as well as challenges. And online education presents educators and tech developers with a rare opportunity to fundamentally rethink what we do.

Will we use it to explore new avenues for learning, or will we fall back on the the same old techniques that don't work well in face-to-face classrooms?

# data[].attributes.attachment_link.title
Learning from PowerPoint: is it time for teachers to move on?

# data[].attributes.attachment_link.description
Slideshows, when designed right, can be a useful part of online instruction. But they shouldn't be the main, or the only, method of instruction.

# data[].attributes.body
Humor: John Cleese on why people do not recognize their. . . . Quotes Dunning.

# data[].attributes.attachment_link.readable_text
Cite this guide: Chick, N. (2013). Metacognition. Vanderbilt University Center for Teaching. Retrieved [todaysdate] from https://cft.vanderbilt.edu/guides-sub-pages/metacognition/.

Thinking about One's Thinking | Putting Metacognition into Practice

Thinking about One's Thinking

Metacognition is, put simply, thinking about one's thinking. More precisely, it refers to the processes used to plan, monitor, and assess one's understanding and performance. Metacognition includes a critical awareness of a) one's thinking and learning and b) oneself as a thinker and learner.

Initially studied for its development in young children (Baker & Brown, 1984; Flavell, 1985), researchers soon began to look at how experts display metacognitive thinking and how, then, these thought processes can be taught to novices to improve their learning (Hatano & Inagaki, 1986). In How People Learn, the National Academy of Sciences' synthesis of decades of research on the science of learning, one of the three key findings of this work is the effectiveness of a "'metacognitive' approach to instruction" (Bransford, Brown, & Cocking, 2000, p. 18).

Metacognitive practices increase students' abilities to transfer or adapt their learning to new contexts and tasks (Bransford, Brown, & Cocking, p. 12; Palincsar & Brown, 1984; Scardamalia et al., 1984; Schoenfeld, 1983, 1985, 1991). They do this by gaining a level of awareness above the subject matter: they also think about the tasks and contexts of different learning situations and themselves as learners in these different contexts. When Pintrich (2002) asserts that "Students who know about the different kinds of strategies for learning, thinking, and problem solving will be more likely to use them" (p. 222), notice the students must "know about" these strategies, not just practice them. As Zohar and David (2009) explain, there must be a " conscious meta-strategic level of H[igher] O[rder] T[hinking]" (p. 179).

Metacognitive practices help students become aware of their strengths and weaknesses as learners, writers, readers, test-takers, group members, etc. A key element is recognizing the limit of one's knowledge or ability and then figuring out how to expand that knowledge or extend the ability. Those who know their strengths and weaknesses in these areas will be more likely to "actively monitor their learning strategies and resources and assess their readiness for particular tasks and performances" (Bransford, Brown, & Cocking, p. 67).

The absence of metacognition connects to the research by Dunning, Johnson, Ehrlinger, and Kruger on "Why People Fail to Recognize Their Own Incompetence" (2003). They found that "people tend to be blissfully unaware of their incompetence," lacking "insight about deficiencies in their intellectual and social skills." They identified this pattern across domains-from test-taking, writing grammatically, thinking logically, to recognizing humor, to hunters' knowledge about firearms and medical lab technicians' knowledge of medical terminology and problem-solving skills (p. 83-84). In short, "if people lack the skills to produce correct answers, they are also cursed with an inability to know when their answers, or anyone else's, are right or wrong" (p. 85). This research suggests that increased metacognitive abilities-to learn specific (and correct) skills, how to recognize them, and how to practice them-is needed in many contexts.

Putting Metacognition into Practice

In " Promoting Student Metacognition," Tanner (2012) offers a handful of specific activities for biology classes, but they can be adapted to any discipline. She first describes four assignments for explicit instruction (p. 116):

Preassessments-Encouraging Students to Examine Their Current Thinking: "What do I already know about this topic that could guide my learning?"

The Muddiest Point-Giving Students Practice in Identifying Confusions: "What was most confusing to me about the material explored in class today?"

Retrospective Postassessments-Pushing Students to Recognize Conceptual Change: "Before this course, I thought evolution was... Now I think that evolution is ...." or "How is my thinking changing (or not changing) over time?"

Reflective Journals-Providing a Forum in Which Students Monitor Their Own Thinking: "What about my exam preparation worked well that I should remember to do next time? What did not work so well that I should not do next time or that I should change?"

Next are recommendations for developing a "classroom culture grounded in metacognition" (p. 116-118):

Giving Students License to Identify Confusions within the Classroom Culture: ask students what they find confusing, acknowledge the difficulties

Integrating Reflection into Credited Course Work: integrate short reflection (oral or written) that ask students what they found challenging or what questions arose during an assignment/exam/project

Metacognitive Modeling by the Instructor for Students: model the thinking processes involved in your field and sought in your course by being explicit about "how you start, how you decide what to do first and then next, how you check your work, how you know when you are done" (p. 118)

To facilitate these activities, she also offers three useful tables:

Questions for students to ask themselves as they plan, monitor, and evaluate their thinking within four learning contexts-in class, assignments, quizzes/exams, and the course as a whole (p. 115)

Prompts for integrating metacognition into discussions of pairs during clicker activities, assignments, and quiz or exam preparation (p. 117)

Questions to help faculty metacognitively assess their own teaching (p. 119)

Weimer's " Deep Learning vs. Surface Learning: Getting Students to Understand the Difference " (2012) offers additional recommendations for developing students' metacognitive awareness and improvement of their study skills:

"[I]t is terribly important that in explicit and concerted ways we make students aware of themselves as learners. We must regularly ask, not only 'What are you learning?' but 'How are you learning?' We must confront them with the effectiveness (more often ineffectiveness) of their approaches. We must offer alternatives and then challenge students to test the efficacy of those approaches." (emphasis added)

She points to a tool developed by Stanger-Hall (2012, p. 297) for her students to identify their study strategies, which she divided into " cognitively passive" ("I previewed the reading before class," "I came to class," "I read the assigned text," "I highlighted the text," et al) and " cognitively active study behaviors" ("I asked myself: 'How does it work?' and 'Why does it work this way?'" "I wrote my own study questions," "I fit all the facts into a bigger picture," "I closed my notes and tested how much I remembered," et al). The specific focus of Stanger-Hall's study is tangential to this discussion, ¹ but imagine giving students lists like hers adapted to your course and then, after a major assignment, having students discuss which ones worked and which types of behaviors led to higher grades. Even further, follow Lovett's advice (2013) by assigning "exam wrappers," which include students reflecting on their previous exam-preparation strategies, assessing those strategies and then looking ahead to the next exam, and writing an action plan for a revised approach to studying. A common assignment in English composition courses is the self-assessment essay in which students apply course criteria to articulate their strengths and weaknesses within single papers or over the course of the semester. These activities can be adapted to assignments other than exams or essays, such as projects, speeches, discussions, and the like.

As these examples illustrate, for students to become more metacognitive, they must be taught the concept and its language explicitly (Pintrich, 2002; Tanner, 2012), though not in a content-delivery model (simply a reading or a lecture) and not in one lesson. Instead, the explicit instruction should be "designed according to a knowledge construction approach," or students need to recognize, assess, and connect new skills to old ones, "and it needs to take place over an extended period of time" (Zohar & David, p. 187). This kind of explicit instruction will help students expand or replace existing learning strategies with new and more effective ones, give students a way to talk about learning and thinking, compare strategies with their classmates' and make more informed choices, and render learning "less opaque to students, rather than being something that happens mysteriously or that some students 'get' and learn and others struggle and don't learn" (Pintrich, 2002, p. 223).

Metacognition instruction should also be embedded with the content and activities about which students are thinking. Why? Metacognition is "not generic" (Bransford, Brown, & Cocking, p. 19) but instead is most effective when it is adapted to reflect the specific learning contexts of a specific topic, course, or discipline (Zohar & David, 2009). In explicitly connecting a learning context to its relevant processes, learners will be more able to adapt strategies to new contexts, rather than assume that learning is the same everywhere and every time. For instance, students' abilities to read disciplinary texts in discipline-appropriate ways would also benefit from metacognitive practice. A literature professor may read a passage of a novel aloud in class, while also talking about what she's thinking as she reads: how she makes sense of specific words and phrases, what connections she makes, how she approaches difficult passages, etc. This kind of modeling is a good practice in metacognition instruction, as suggested by Tanner above. Concepción's " Reading Philosophy with Background Knowledge and Metacognition " (2004) includes his detailed "How to Read Philosophy" handout (pp. 358-367), which includes the following components:

What to Expect (when reading philosophy)

The Ultimate Goal (of reading philosophy)

Basic Good Reading Behaviors

Important Background Information, or discipline- and course-specific reading practices, such as "reading for enlightenment" rather than information, and "problem-based classes" rather than historical or figure-based classes

A Three-Part Reading Process (pre-reading, understanding, and evaluating)

Flagging, or annotating the reading

Linear vs. Dialogical Writing (Philosophical writing is rarely straightforward but instead "a monologue that contains a dialogue" [p. 365].)

What would such a handout look like for your discipline?

Students can even be metacognitively prepared (and then prepare themselves) for the overarching learning experiences expected in specific contexts. Salvatori and Donahue's The Elements (and Pleasures) of Difficulty (2004) encourages students to embrace difficult texts (and tasks) as part of deep learning, rather than an obstacle. Their "difficulty paper" assignment helps students reflect on and articulate the nature of the difficulty and work through their responses to it (p. 9). Similarly, in courses with sensitive subject matter, a different kind of learning occurs, one that involves complex emotional responses. In " Learning from Their Own Learning: How Metacognitive and Meta-affective Reflections Enhance Learning in Race-Related Courses" (Chick, Karis, & Kernahan, 2009), students were informed about the common reactions to learning about racial inequality (Helms, 1995; Adams, Bell, & Griffin, 1997; see student handout, Chick, Karis, & Kernahan, p. 23-24) and then regularly wrote about their cognitive and affective responses to specific racialized situations. The students with the most developed metacognitive and meta-affective practices at the end of the semester were able to "clear the obstacles and move away from" oversimplified thinking about race and racism "to places of greater questioning, acknowledging the complexities of identity, and redefining the world in racial terms" (p. 14).

Ultimately, metacognition requires students to "externalize mental events" (Bransford, Brown, & Cocking, p. 67), such as what it means to learn, awareness of one's strengths and weaknesses with specific skills or in a given learning context, plan what's required to accomplish a specific learning goal or activity, identifying and correcting errors, and preparing ahead for learning processes.

--------

1 Students who were tested with short answer in addition to multiple-choice questions on their exams reported more cognitively active behaviors than those tested with just multiple-choice questions, and these active behaviors led to improved performance on the final exam.
References

Adams, Maurianne, Bell, Lee Ann, and Griffin, Pat. (1997). Teaching for diversity and social justice: A sourcebook. New York: Routledge.

Bransford, John D., Brown Ann L., and Cocking Rodney R. (2000). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press.

Baker, Linda, and Brown, Ann L. (1984). Metacognitive skills and reading. In Paul David Pearson, Michael L. Kamil, Rebecca Barr, & Peter Mosenthal (Eds.), Handbook of research in reading: Volume III (pp. 353-395). New York: Longman.

Brown, Ann L. (1980). Metacognitive development and reading. In Rand J. Spiro, Bertram C. Bruce, and William F. Brewer, (Eds.), Theoretical issues in reading comprehension: Perspectives from cognitive psychology, linguistics, artificial intelligence, and education (pp. 453-482). Hillsdale, NJ: Erlbaum.

Chick, Nancy, Karis, Terri, and Kernahan, Cyndi. (2009). Learning from their own learning: how metacognitive and meta-affective reflections enhance learning in race-related courses . International Journal for the Scholarship of Teaching and Learning, 3(1). 1-28.

Flavell, John H. (1985). Cognitive development. Englewood Cliffs, NJ: Prentice Hall.

Hatano, Giyoo and Inagaki, Kayoko. (1986). Two courses of expertise. In Harold Stevenson, Azuma, Horishi, and Hakuta, Kinji (Eds.), Child development and education in Japan, New York: W.H. Freeman.

Lovett, Marsha C. (2013). Make exams worth more than the grade. In Matthew Kaplan, Naomi Silver, Danielle LaVague-Manty, and Deborah Meizlish (Eds.), Using reflection and metacognition to improve student learning: Across the disciplines, across the academy. Sterling, VA: Stylus.

Salvatori, Mariolina Rizzi, and Donahue, Patricia. (2004). The Elements (and pleasures) of difficulty. New York: Pearson-Longman.

Schoenfeld, Alan H. (1991). On mathematics as sense making: An informal attack on the fortunate divorce of formal and informal mathematics. In James F. Voss, David N. Perkins, and Judith W. Segal (Eds.), Informal reasoning and education (pp. 311-344). Hillsdale, NJ: Erlbaum.

Weimer, Maryellen. (2012, November 19). Deep learning vs. surface learning: Getting students to understand the difference. Retrieved from the Teaching Professor Blog from http://www.facultyfocus.com/articles/teaching-professor-blog/deep-learning-vs-surface-learning-getting-students-to-understand-the-difference/.

This teaching guide is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Photo credit: wittygrittyinvisiblegirl via Compfight cc

Photo Credit: Helga Weber via Compfight cc

Photo Credit: fiddle oak via Compfight cc

# data[].attributes.attachment_link.description
by Nancy Chick Print Version Cite this guide: Chick, N. (2013). Metacognition. Vanderbilt University Center for Teaching. Retrieved [todaysdate] from https://cft.vanderbilt.edu/guides-sub-pages/metacognition/. Thinking about One's Thinking | Putting Metacognition into Practice Thinking about One's Thinking Metacognition is, put simply, thinking about one's thinking. More precisely, it refers to the processes used to plan, monitor, and assess...

# data[].attributes.attachment_link.readable_text
It doesnt matter what subject you teach, differences in students performance are affected by how much they engage in deliberate practice. Researchers who have investigated expert and novice performance have uncovered important distinctions between deliberate practice and other activities, such as play or repetition.

Deliberate practice is not the same as rote repetition. Rote repetition - simply repeating a task - will not by itself improve performance. Deliberate practice involves attention, rehearsal and repetition and leads to new knowledge or skills that can later be developed into more complex knowledge and skills. Although other factors such as intelligence and motivation affect performance, practice is necessary if not sufficient for acquiring expertise (Campitelli & Gobet, 2011).

Define practice:

"Deliberate practice (DP) occurs when an individual intentionally repeats an activity in order to improve performance. The claim of the DP framework is that such behavior is necessary to achieve high levels of expert performance." (Campitelli & Gobet, 2011, p. 280).

"Deliberate practice consists of activities purposely designed to improve performance." (Gobet & Campitelli, 2007, p. 160).

Benefits of deliberate practice

Practice is important for teaching and learning in at least five ways:

1. Practice greatly increases the likelihood that students will permanently remember new information (Anderson, 2008).

2. Practice increases student facility or automaticity (learning to apply elements of knowledge automatically, without reflection). Automaticity is usually only achieved through extensive rehearsal and repetition. Automaticity frees up students cognitive resources to handle more challenging tasks (Brown & Bennett, 2002; Moors & De Houwer, 2006).

3. When students practice solving problems, they increase their ability to transfer practiced skills to new and more complex problems. This is true in childhood (Glover, Ronning, & Bruning, 1990) and adult years (Li, Schmiedek, Huxhold, Röcke, Smith, & Lindenberger, 2008).

4. Practice helps students acquire expertise in subject matter and, therefore, it helps to distinguish novices from experts in given subjects (Ericsson, Krampe, & Clemens, 1993)

5. Cognitive gains from practice often bring about motivation for more learning (Kalchman, Moss, & Case, 2001).

Teachers should think of practice not as rote repetition, but as deliberate, goal-directed rehearsal paired with reflection on problem-solving processes. For example, when teachers have students practice identifying phonemes, the ultimate goal is for students to read with fluency and comprehension. Although fluent reading may be too complex a task for beginning readers to tackle, the more manageable task of identifying phonemes may scaffold students learning to achieve the ultimate goal of reading. That is, teachers should always design practice activities with the goal of transferring knowledge to new and more complex problems in mind.

General recommendations for practice and instruction

Research (Anderson, 2008; Campitelli & Gobet, 2011; Ericsson, Krampe, & Clemens, 1993) suggests several conditions that must be in place in order for practice activities to be most effective in moving students closer to skillful performance. Each of these conditions can be met with carefully designed instruction:

Because practice requires intense, focused effort, students may not find it inherently enjoyable. Therefore, teachers can encourage students to practice more by pointing out every time that practice has actually improved their performance. Motivate your students to practice by:

Expressing confidence in students' ability to succeed in solving practice problems.

Designing activities that maximize opportunities for students to succeed.

Teachers should design practice tasks with students existing knowledge in mind. When students succeed at practice problems, the benefits of practice are maximized. But when students become frustrated with unrealistic or poorly designed practice problems, they often lose motivation, will not receive the full benefits of the practice they have done, and will be less motivated to attempt future practice problems.

Students receive the greatest benefits from practice when teachers provide them with timely and descriptive feedback.

Students should have repeated opportunities to practice a task through practicing other tasks like it.

What type of feedback is most effective?

Feedback is most effective when it contains descriptions of how students work meets performance criteria and what students can do to improve. This kind of descriptive feedback is more effective than feedback consisting of vague, general comments (e.g., "nice work or "needs improvement).

Feedback should also be focused on the learning process. That is, teachers should focus their feedback on helping students reflect on their problem solving skills, as well as on progress they have made.

Do's:

1. Vary practice activities.

2. Distribute practice over extended periods of time.

3. Provide clear instructions on performance expectations and criteria.

4. Before asking students to practice independently, model the problem-solving process that you expect students to use.

5. Break complex problems into their constituent elements, and have students practice on these smaller elements before asking them to solve complex problems independently.

6. Guide students through sample practice problems by using prompts that help them reflect on problem-solving strategies.

7. Provide students with fully completed sample problems as well as partially completed sample problems before asking them to apply new problem-solving strategies on their own.

8. Wait until students actually need more information to solve a complex problem before you give them more information. This strategy - known as "just-in-time instruction" - helps keep the amount of information that students must hold in their short-term memories to a manageable level as they practice (Linneman & Plake, 2006; Simkins & Maier, 2008).

9. Provide plenty of opportunities for students to practice applying problem-solving skills before you test them on their ability to use those skills.

Don'ts:

1. Don't ask students to practice complex problem solving without providing them with plenty of guidance and feedback.

2. Don't overload students by presenting information in redundant formats.

3. Don't give your students complex practice problems before they have the skills they need to succeed.

Research suggests that when teachers construct problems for students to practice, they should keep in mind the limitations of human memory.

Moving information to permanent storage is often explained as a multistore model of memory (Atkinson & Shiffrin, 1968; 1971; Baddeley, 1996; 2002). According to this model, our brains have three memory storage systems: sensory memory, short-term memory and long-term memory (Baddeley, 1996; 2002; Carlson, Sullivan, & Schneider, 1989). Learning occurs when we move information from working memory to long-term memory, and practice helps with this process.

Deliberate practice (sometimes called 'rehearsal') keeps the information in our short-term memory long enough for it to move to long-term memory. Once it is in long-term memory, it can be built upon to create more and more complex associations (Baddeley, 2002).

Most researchers agree that there are forms of Short-Term Memory (STM) for different modalities, such as visual STM, auditory STM and motor STM. For example, Baddeley (1992; 1996; 2002) suggests that short-term memory is a dynamic place where sounds and images are turned into verbal and pictorial models. STM is also thought to have a "central executive" that manages the information it is holding. This central executive also controls our awareness of that information (Baddeley, 2002).

The implication for teachers is that they should present material in multiple modalities. If a child who is learning to read hears a word, sees the word and a picture of the word, there will be more learning. The child can get the visual information from the visual STM and additional auditory information in the auditory STM. These two sensory sources of information supplement and complement one another (Liao, 2014). However, teachers must be careful not to have too much extraneous information in the classroom. Over stimulation can put too much demand on working memory (Pass & Kester, 2006).

Short-term or working memory

Working memory or short-term memory, is stored for very short periods of time - minutes at most. Information in our short-term memory is generally housed in the temporal lobe of the brain (the hippocampal region). It allows you to remember phone numbers and also information that comes from long-term memory right before you need to use it. Short-term memory is also the memory system that is used when we cram for a test at the last minute. Attention difficulties, distractions and overload of too much information all negatively impact our short-term memory and cause it to fail. We all have a limited ability to focus on too much stimuli at the same time, and if we don't process the information, we lose it. Practice helps us increase our ability to access information rapidly and automatically.

Practice also frees our brains to process more challenging information and problems. For example, if a child has memorized the multiplication table and can retrieve it automatically, working memory is freed to do more complicated computations.

Practice can improve children's working memory and enhance learning of academic skills, such as reading comprehension and mathematical reasoning (e.g., Dahlin, 2011; Gersten, Fuchs, Williams, & Baker, 2001; Holmes, Gathercole, & Dunning, 2009), verbal learning (Liao, Kronemer, Yau, Desmond, & Marvel, 2014) and motor skills (Gomes, Ugrinowitsch, Marinho, Shea, Raisbeck, & Benda, R., 2014).

And practice using visual spatial tasks can improve visual-spatial working memory (Melby-Lervag & Hulme, 2012; Shipstead, Redick, & Engle, 2012). Likewise, children can be taught a rehearsal or practice strategy to help them improve their reading comprehension (e.g., Gersten, Fuchs, Williams, & Baker, 2001; Rose, Cundick, & Higbee, 1983). Moreover, rehearsal is easier to learn and less demanding than other strategies (Turley-Ames & Whitfield, 2003).

Long-term memory

When working-memory is stored, it becomes long-term memory. Long-term memory is the depository of memories throughout the brain that results from the ongoing chemical processes that changes neuronal connections. The formation of long-term memories takes place over the span of days, weeks and in some cases years. As the memories are consolidated, neuron connections become more and more efficient, and in the process, connections that are no longer useful are discarded from lack of use.

Although the capacity of our long-term memory is apparently limitless (as far as we know, no one has ever stored all that the brain is capable of storing), working memory can typically hold only seven items (plus or minus 2) at any given time, and these items "decay" rapidly (Miller, 1956; Baddeley, 2002).

Research on the brains of rats has shown that relatively short periods of practice can enhance the plasticity of the brain and actually change its structure (Rioult-Pedotti, Friedman, Hess, & Donoghue, 1998). Brain imaging has shown that even a brief lesson on new words changes brain circuits (Allami, Brovelli, Hamzaoui, Regragui, Paulignan, & Boussaoud, 2014; Abdullaev & Posner, 1998; Raichle et al., 1994) and retrieval (Habib, Nyberg, & Tulving, 2003).

Experts and novices differ in the amount and structure of information stored in their long-term memories. These cognitive differences occur in large part because of differences in the amount of deliberate practice in which each has engaged (Cantor & Engle, 1993).

Extensive and deliberate practice makes it possible for students to access and apply increasingly complex information without explicitly thinking about it. In other words, it increases automaticity (see earlier discussion of automaticity) (Anderson, 2008; Case, 1985, 1991). Automaticity leaves students' working memories free to process new information (Kotovsky, Hayes, & Simon, 1985). Since working memory can be overloaded at any time, 'savings' from automaticity become very important. When there is too much information in our working memory it will fail, and the chemical processes that transfer information to our long-term memories also fail. Long-term learning is enhanced by a distribution process in which information is repeated, allowing time with time in between practice sessions, rather than crammed practice.

Reviews and tests are forms of practice that can improve learning. Tests (or quizzes) that are given immediately after a learning exercise give children opportunities to practice. Because learning is recent, students tend to do well on these tests. However, their success does not ensure long-term retention. Teachers can provide time during class to give students additional practice in taking tests. Some tests, such as tests with open-ended answers have been shown to enhance learning because they involve students in the "retrieval" of information from long-term memory. Tests are more effective when they are given at spaced intervals and when they are given frequently (Cull, 2000; Dempster; 1991; Rodriguez, Michael, 2004;Roediger & Karpicke, 2006).

Chunking information, or combining small bits together in short-term memory, is another strategy that students can use to increase their STM holding capacity. Although chunking gives a student more capacity in their STM, its usefulness depends on how much knowledge a person already has. The more knowledge one has on a topic, the more likely that one can "chunk" incoming information into larger and more meaningful chunks. This is why knowledge itself adds to one's ability to solve complex problems. Children who use chunking are more likely to be strategic about their learning. Teachers can help children become more strategic by explicitly teaching them strategies like chunking.

Example of chunking information

The following digits can be seen as a random list of numbers: 14921776. Random or meaningless information taxes the limited capacity of short-term memory (STM). However, if a student "chunks" the information into two dates (1492: Columbus discovered America; and 1776: the year the Declaration of Independence was signed) it is learned as 1492 1776. This leaves room in the short-term memory for more information.

Chunking allows several units of information to be compressed into a single meaningful unit or chunk (e.g., whereas a random number string above represents 8 discrete units to be stored in short-term memory (near its maximum capacity), the two meaningful dates represent only two chunks of information to be stored. This leaves space in the STM for more information to be held. And when more information is held in STM, there is more information available for transfer to long-term memory where it can be permanently stored (Gobet et al., 2001).

Chunking can develop experts' schema. Information stored in the brain in larger chunks of integrated concepts are known as "schemas." For example, a novice chess player who does not yet have complex schemas for chess playing has to devote his or her limited working memory to processing large amounts of information while playing (Anderson, 1996).

An expert, on the other hand, can easily access schemas from long-term memory (deGroot, 1966; Cantor & Engle, 1993). With practice, a novice player can construct chess-playing schemas and therefore increase the amount of information stored in long-term memory. In this way, practice accessing and applying knowledge helps one to transfer that knowledge into a "permastore" of long-term memory so that he or she can more efficiently utilize space available in the working memory (Elo, 1978; Kotovsky & Fallside, 1989).

Theories explaining expert performance

1. Many researchers (e.g., Ericsson et al., 1993; Baddeley, 2002) argue that differences in the amount of practice account for the majority of performance differences between individuals, resulting from differences in the amount and structure of knowledge stored in long-term memory.

2. Other researchers emphasize innate, genetic differences or talent as the main factor accounting for differences between expert and novice performance (Mosing, et.al., 2014; Plomin, DeFries, McClearn, & Rutter, 1997).

3. More recently, researchers have emphasized an interaction between innate and behavioral factors in accounting for differences in performance levels (Gobet & Campitelli, 2007).

Overall, the research supports the view that deliberate practice plays a role in performance differences and can reduce performance differences between individuals who are thought to possess talent and those who do not (Chase & Ericsson, 1981). See Gobet and Campitelli (2007), Mosing, et.al., (2014) and Macnamara, et.al., (2014) for a comprehensive review of this debate.

What can a teacher do to maximize the positive effect of practice?

When teachers assign students to complete practice problems that are structurally similar but different in surface features, it helps them discriminate between relevant and irrelevant information in a given problem. This outcome makes it more likely that students will be able to transfer knowledge gained from practice to new and more complex problems (Paas, 1992). As with all scaffolds, they should be diminished gradually as learners become increasingly more independent.

Students' problem-solving skills also increase when teachers distribute practice over time rather than "cramming" practice into short periods (Bahrick & Hall, 2005). Assignments involving practice are more effective when they are shorter, more frequent and distributed over longer periods of time (Cooper, 2007; Cooper, Robinson, & Patall, 2006; Epstein & Van Voorhis, 2010). This distributive effect is particularly important because it also makes it more likely that students will recall information over longer periods of time. Therefore, distributed practice helps students transfer information into their long-term memory.

Teachers should also have students begin homework during class. By doing so, teachers can monitor students to ensure they understand and can solve practice problems correctly before they practice independently. If teachers are unable to monitor this initial practice, they should explain to their students where they can obtain the information (textbooks, web sites, handouts, etc.) they need to complete the assigned practice problems correctly.

Reviews and tests are a form of practice that can improve learning. Tests provide opportunities for practice that enhance learning. Open-ended test questions also provide practice in retrieving information. Both reviews and tests are most effective when they are well spaced and frequent (Dempster, 1991).

Teachers can train students to reflect on their thinking when solving problems by providing them with lists of questions. This kind of "strategy instruction" helps students to construct schemas more efficiently by facilitating a metacognitive awareness of the problem-solving process. That is, students learn more about how they learn and this helps them with new learning.

Teachers can increase students' abilities to transfer existing problem-solving knowledge to new problems when they prompt students to deliberately reflect on problem-solving strategies they have used before. (Renkl, Stark, Gruber, & Mandl, 1998; Rosenshine & Meister, 1992; Stark, Mandl, Gruber, & Renkl., 2002).

Is homework always effective practice?

There is no question that homework can provide students with opportunities for practice but there are more and less effective homework assignments. Research on homework (Cooper, 2001, 2006; Epstein & Van Voorhis, 2010) suggests that the relationship between time spent on homework and students' academic achievement varies with grade level. When parental involvement is well structured it can also enhance the effectiveness of homework on learning (Epstein & Van Voorhis, 2010; Marzano & Pickering, 2007).

How much time should be spent on homework?

Although high school students appear to benefit from at least two hours of homework per night, there is little or no relationship between time spent on homework and academic achievement for young (grades 1-3) students (Marzano, & Pickering, 2007). Younger students may benefit more from supervised in-school practice than from homework. Middle school students may benefit from one to two hours of homework per night (Cooper, 2007).

Will practice activities negatively affect students' motivation? Aren't practice activities the same as "drill and kill" instruction?

Although deliberate practice is often confused with rote learning, researchers emphasize that there is a difference between mere repetition and deliberate practice. For practice to be effective, appropriate instruction, guided practice, as well as descriptive and timely feedback must all be present.

Does practice with "authentic" problems increase learning?

Due to their relevance, "authentic" problems or "real-life" problems may motivate students. However, the positive effect of practice is only realized when teachers provide instructional supports that address the limitations of students' working memories (Anderson, Reder, & Simon, 1996, 2000; Sweller, van Merrienboer, & Paas, 1998; van Merrienboer, Kirschner, & Kester, 2003). If there is cognitive overload and the working memory is taxed beyond its capacity, students will lose motivation even if a problem is highly relevant to them.

Although "authentic" practice problems sound like they would motivate students with their real-world context; they may also include too many variables at the same time. Students' working memories are thus required to manage too many bits of information, and many of these bits may not be directly involved with the construction of the desired schema (Sweller & Chandler, 1994).

Is there such a thing as too much practice?

Merely repeating a task will not automatically improve performance. Deliberate practice is most effective when it involves intense concentration, rehearsal and reflection. For that reason, students are more likely to benefit from frequent, short practice sessions than from long "cramming" sessions. Teachers can foster this meta-cognitive learning process by providing students with frequent opportunities to discuss their problem-solving strategies (National Research Council, 2005).

Even in early grades when students are acquiring foundational knowledge, practice should not be confused with rote learning. For example, phonics instruction is most effective when it is practiced alongside more complex skills like identifying main ideas in stories (National Institute of Child Health and Human Development, 2000).

Does practice work for all academic subjects?

Practice is correlated with student achievement across all developmental levels and across all subjects. NICHD's (2000) analysis of strategies for reading instruction suggests that several kinds of practice activities are beneficial to students. Phonemic awareness practice, phonics practice and read-aloud practice all showed significant positive effects on student achievement in the early grades. Teachers should always keep long-term goals in mind when they are designing practice activities. The ultimate purpose of phonics instruction is not for students to identify phonemes, but rather, to improve their reading comprehension. The goal of practice is to transfer acquired skills to new and more complex problems. Therefore, practice activities should always be designed with that transfer process in mind.

Studies in mathematics education show that practice is effective when teachers design appropriate practice problems, distribute them over time, and provide students with sufficient feedback. Practice is ineffective when it is not appropriately designed, not well distributed, and when adequate feedback is not provided.

Deliberate practice is also effective in learning complex skills from musical fluency to athletic expertise.

When does the strategy work?

All students benefit from well-designed, developmentally-appropriate deliberate practice activities. A meta-analysis (Macnamara, Hambrick, & Oswald, 2014) showed that the effects of deliberate practice are robust for games, music and sports, and modestly effective in education.

Deliberate practice can also provide a bridge over the gaps that exist between different achievement levels. Achievement gaps often exist because of unequal opportunities for students to engage in appropriate deliberate practice rather than unequal learning abilities. As children grow older, they become better able to benefit from instruction on appropriate strategies like rehearsal, reflection and "chunking" information.

Special populations benefit from practice activities that are designed to meet their needs (Chapman, & Mudar, 2014; Nunes, Barros, Evans, & Burman, 2014; Rose, Cundick, & Higbee, 1983). For example, research on children with language processing problems suggests that their difficulties are due to sensory perception rather than an inability to learn. One reason that language-learning impaired students have difficulty learning to read is that their phonemic recognition and spatial discrimination perceptions are impaired. Appropriate practice activities have been shown to help these students compensate for their difficulties (Merzenich et al., 1996). Cooper's (2001) examination of homework practice suggests that students with learning disabilities benefit most from short, skill-reinforcing activities that are carefully monitored (see also Marzano & Pickering, 2007).

Description of research on the use of practice activities with children with learning disabilities

An extensive meta-analysis of interventions for students with learning disabilities found that practice-drill-review instructional strategies were generally effective across academic subjects (Swanson, Hoskyn, & Lee, 1999). Another meta-analysis of inclusion settings found that time-on-task correlated with student achievement across subject areas (Brophy & Good, 1986). Therefore, practice activities appear to be a key factor in student achievement across grade levels, skill levels and academic domains.

In summary, well-designed practice activities result from careful planning where teachers:

Model problem-solving processes.

Design partially-completed examples on which students can practice.

Sequence activities logically.

Space practice activities appropriately.

Monitor student practice providing guidance and feedback.

Is practice effective across the age span?

Children grow more strategic as they get older. Younger children are less strategic - are less likely to rehearse, practice, or chunk spontaneously or to know how to do these things well. They are also less likely to monitor their own knowledge or to use metacognitive strategies. Younger children are less likely than older children to know what they need to rehearse or practice more. Therefore, teachers of younger children should introduce them to strategies such as rehearsal and practice, and in this way can help students improve their learning.

Deliberate practice is effective across all school age levels. Fundamental practice skills are more appropriate for earlier grades than it is for later grades. Teachers can focus younger students on the skills they will need later when they tackle more complex tasks. For example, phonemic awareness instruction is more effective for preschool students than for older elementary school students (NICHD, 2000), but it remains a skill that is very useful in later grades.

Teachers should design developmentally appropriate practice problems for students. Students' memories and attention spans develop with age (Gomez-Perez & Ostrosky-Solis, 2006; Lechuga, Moreno, Pelegrina, Gomez-Ariza, & Bajo, 2006). In general, older students have greater attention spans and greater memory capacity than younger students. This may account for developmental differences seen in the benefits of homework (Cooper, 2001; Muhlenbrook, Cooper, Nye, & Lindsay, 1999) and age differences in the amount of practice that learners engage in outside the classroom (Ericsson et al., 1993). Teachers should plan the type and amount of practice students are expected to do with these developmental differences in mind.

Research on expert performance shows that experts increase the amount of deliberate practice they engage in over time. For example, research by Ericsson and colleagues (1993) on expert violinists revealed that in early childhood they practiced no more than five hours per week. However, by their early twenties, they practiced around thirty hours per week. Because deliberate practice is so taxing, it should be balanced with sufficient periods of rest. Younger learners tire more quickly and lose their motivation to persist if they are not allowed sufficient rest (Ericsson et al., 1993; NICHD, 2000).

Elementary

Early elementary students benefit little or not at all from homework (Campitelli & Gobet, 2011; Cooper, 2001; Halford, Maybery, & Bain, 1986).

Expert violinists practiced fewer than 5 hours per week at the early childhood stage (Ericsson et al., 1993; Macnamara et al., 2014).

Research supports the idea that practice can help elementary students overcome processing deficits (Merzenich et al., 1996).

Practice with phonemic awareness has been shown to have a positive effect on reading outcomes in elementary readers through 6th grade. Effects were not as strong for disabled readers as they were for at-risk and normally progressing readers. The effects of phonemic-awareness instruction were strongest when it was provided in earlier grades (especially pre-K and kindergarten), in small group settings, in shorter training programs and to English-speaking students (NICHD, 2000).

Research suggests that systematic phonics instruction helps students learning to read. The effects of systematic phonics instruction were greater when it was provided in earlier grades (especially kindergarten and 1st grade) (Dahlin, 2011; Gersten, Fuchs, Williams, & Baker, 2001; NICHD, 2000).

A meta-analysis of intervention strategies for students with learning disabilities found that phonics instruction positively affects development of vocabulary, although the results on comprehension were unclear (Swanson et al., 1999). (The mean age for the study participants was 11; 78% of the studies targeted 9-14 year age range.).

Research suggests that repeated and guided-repeated oral reading strategies have a positive effect on reading performance through grade four, especially for non-impaired readers (Dahlin, 2011; Gersten, et al., 2001; Rose et al., 1983).

Swanson et al.'s (1999) meta-analysis suggests that "the teaching of whole language without due attention to decoding places LD children at greater risk than the use of more traditional programs" (p. 254).

Vocabulary instruction (both explicit and implicit) has a positive effect on vocabulary acquisition. Research indicates that students should be exposed to vocabulary repeatedly and in multiple contexts (NICHD, 2000).

Research suggests that reading comprehension strategy instruction (e.g., cognitive and meta-cognitive strategies) has a positive effect on reading comprehension and memory in grades 3-6. The effects were greater when strategy instruction was more intensive and when students were instructed in multiple strategies (NICHD, 2000).

Research suggests that "drill-repetition-practice" instructional strategies are beneficial to students with learning disabilities across all age groups and across all subjects (Swanson et al., 1999).

Middle School

Middle school students may benefit from one to two hours of homework per night (Cooper, 2001; Campitelli & Gobet, 2011).

Research suggests that partial-task practice problems are more effective than whole-task practice problems for some 13 year-old math students. Partial-task practice lowered the processing demands on working memory for all students, but it only increased learning for students with low prior knowledge. Students with high prior knowledge benefited more from whole-task practice (Ayres, 2006).

Research suggests that when modeling problem-solving strategies, the use of worked examples is beneficial for middle school students' writing performance. As with Ayres' (2006) study, however, the positive effects were limited to low-performing students. The results suggested that "low-level" writers benefited from worked-example practice problems, while "high-level" writers benefited from "goal-free" practice problems.

Research suggests that the use of repeated and guided-repeated oral reading strategies positively affect students' reading performance. This is especially true for students with reading problems (NICHD, 2000).

Programs aimed at encouraging students to read more (e.g., self-selected, silent reading) yielded no significant positive effects on reading achievement (NICHD, 2000).

Swanson et al.'s (1999) meta-analysis suggests that "the teaching of whole language without due attention to decoding places LD children at greater risk than the use of more traditional programs" (p. 254).

Results that support the positive effects of vocabulary instruction (both explicit and implicit) on vocabulary acquisition indicate that students should be exposed to vocabulary repeatedly and in multiple contexts (NICHD, 2000).

Research suggests that reading-comprehension-strategy instruction (e.g., cognitive and meta-cognitive strategies) has a positive effect on reading comprehension and memory in grades 3-6. The effects were greater when strategy instruction was more intensive and when students were instructed on multiple strategies (NICHD, 2000).

Research suggests that "drill-repetition-practice" instructional strategies are beneficial to students with learning disabilities across all age groups and across all subjects (Swanson et al., 1999).

High School

High school students appear to benefit from at least two hours of homework per night, (Campitelli & Gobet, 2011; Cooper, 2001).

Students may benefit from more practice with the transfer of component skills than from practicing whole-task transfer (Pollock, Chandler, & Sweller, 2002). Examples worked out in advance for students were more effective for algebra problem solving transfer than were conventional problems for 9th and 11th grade students (Sweller & Cooper, 1985).

Repeated and guided-repeated oral reading strategies positively affect reading performance. This is especially true for high school students with reading problems (NICHD, 2000).

Programs aimed at encouraging students to read more (e.g., self-selected, silent reading) yielded no significant positive effects on reading achievement (NICHD, 2000).

Although research supports a positive effect of vocabulary instruction (both explicit and implicit) on vocabulary acquisition in grades 9-11, few studies have been conducted at that level. Results that do exist suggest that students should be exposed to vocabulary repeatedly and in multiple contexts (NICHD, 2000).

Research suggests that "drill-repetition-practice" instructional strategies are beneficial to students with learning disabilities across all age groups and across all subjects (Swanson et al., 1999).

Where can teachers get more information?

Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51(4), 355-368. http://dx.doi.org/10.1037/0003-066X.51.4.355

Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press. ISBN 0-309-07036-8

Carver, S. M. & Klahr, D. (Eds.). (2001). Cognition and instruction: Twenty-five years of progress. Mahwah, NJ: Erlbaum.

# data[].attributes.attachment_link.title
Practice for knowledge acquisition (not drill and kill)

# data[].attributes.attachment_link.description
It doesnt matter what subject you teach, differences in students performance are affected by how much they engage in deliberate practice. Researchers who have investigated expert and novice performance have uncovered important distinctions between deliberate practice and other activities, such as play or repetition. Deliberate practice is not the same as rote repetition.

# data[].attributes.body
Helpful chart

# data[].attributes.attachment_link.readable_text
Examples of Formative and Summative Assessments

Both forms of assessment can vary across several dimensions (Trumbull and Lash, 2013):

Informal / formal

Immediate / delayed feedback

Embedded in lesson plan / stand-alone

Spontaneous / planned

Individual / group

Verbal / nonverbal

Oral / written

Graded / ungraded

Open-ended response / closed/constrained response

Teacher initiated/controlled / student initiated/controlled

Teacher and student(s) / peers

Process-oriented / product-oriented

Brief / extended

Scaffolded (teacher supported) / independently performed

Recommendations

Formative Assessment Ideally, formative assessment strategies improve teaching and learning simultaneously. Instructors can help students grow as learners by actively encouraging them to self-assess their own skills and knowledge retention, and by giving clear instructions and feedback. Seven principles (adapted from Nicol and Macfarlane-Dick, 2007 with additions) can guide instructor strategies:

Keep clear criteria for what defines good performance - Instructors can explain criteria for A-F graded papers, and encourage student discussion and reflection about these criteria (this can be accomplished though office hours, rubrics, post-grade peer review, or exam / assignment wrappers). Instructors may also hold class-wide conversations on performance criteria at strategic moments throughout a term.

Encourage students' self-reflection - Instructors can ask students to utilize course criteria to evaluate their own or a peer's work, and to share what kinds of feedback they find most valuable. In addition, instructors can ask students to describe the qualities of their best work, either through writing or group discussion.

Give students detailed, actionable feedback - Instructors can consistently provide specific feedback tied to predefined criteria, with opportunities to revise or apply feedback before final submission. Feedback may be corrective and forward-looking, rather than just evaluative. Examples include comments on multiple paper drafts, criterion discussions during 1-on-1 conferences, and regular online quizzes.

Encourage teacher and peer dialogue around learning - Instructors can invite students to discuss the formative learning process together. This practice primarily revolves around mid-semester feedback and small group feedback sessions, where students reflect on the course and instructors respond to student concerns. Students can also identify examples of feedback comments they found useful and explain how they helped. A particularly useful strategy, instructors can invite students to discuss learning goals and assignment criteria, and weave student hopes into the syllabus.

Promote positive motivational beliefs and self-esteem - Students will be more motivated and engaged when they are assured that an instructor cares for their development. Instructors can allow for rewrites/resubmissions to signal that an assignment is designed to promote development of learning. These rewrites might utilize low-stakes assessments, or even automated online testing that is anonymous, and (if appropriate) allows for unlimited resubmissions.

Provide opportunities to close the gap between current and desired performance - Related to the above, instructors can improve student motivation and engagement by making visible any opportunities to close gaps between current and desired performance. Examples include opportunities for resubmission, specific action points for writing or task-based assignments, and sharing study or process strategies that an instructor would use in order to succeed.

Collect information which can be used to help shape teaching - Instructors can feel free to collect useful information from students in order to provide targeted feedback and instruction. Students can identify where they are having difficulties, either on an assignment or test, or in written submissions. This approach also promotes metacognition, as students are asked to think about their own learning. Poorvu Center staff can also perform a classroom observation or conduct a small group feedback session that can provide instructors with potential student struggles.

Instructors can find a variety of other formative assessment techniques through Angelo and Cross (1993), Classroom Assessment Techniques (list of techniques available here).

Summative Assessment Because summative assessments are usually higher-stakes than formative assessments, it is especially important to ensure that the assessment aligns with the goals and expected outcomes of the instruction.

Use a Rubric or Table of Specifications - Instructors can use a rubric to lay out expected performance criteria for a range of grades. Rubrics will describe what an ideal assignment looks like, and "summarize" expected performance at the beginning of term, providing students with a trajectory and sense of completion.

Design Clear, Effective Questions - If designing essay questions, instructors can ensure that questions meet criteria while allowing students freedom to express their knowledge creatively and in ways that honor how they digested, constructed, or mastered meaning. Instructors can read about ways to design effective multiple choice questions.

Assess Comprehensiveness - Effective summative assessments provide an opportunity for students to consider the totality of a course's content, making broad connections, demonstrating synthesized skills, and exploring deeper concepts that drive or found a course's ideas and content.

Make Parameters Clear - When approaching a final assessment, instructors can ensure that parameters are well defined (length of assessment, depth of response, time and date, grading standards); knowledge assessed relates clearly to content covered in course; and students with disabilities are provided required space and support.

Consider Blind Grading - Instructors may wish to know whose work they grade, in order to provide feedback that speaks to a student's term-long trajectory. If instructors wish to provide truly unbiased summative assessment, they can also consider a variety of blind grading techniques.

Considerations for Online Assessments

Effectively implementing assessments in an online teaching environment can be particularly challenging. The Poorvu Center shares these recommendations.

References

Nicol, D.J. and Macfarlane-Dick, D. (2006) Formative assessment and self‐regulated learning: a model and seven principles of good feedback practice. Studies in Higher Education 31(2): 2-19.

Theall, M. and Franklin J.L. (2010). Assessing Teaching Practices and Effectiveness for Formative Purposes. In: A Guide to Faculty Development. KJ Gillespie and DL Robertson (Eds). Jossey Bass: San Francisco, CA.

Trumbull, E., & Lash, A. (2013). Understanding formative assessment: Insights from learning theory and measurement theory. San Francisco: WestEd.

# data[].attributes.attachment_link.description
Both forms of assessment can vary across several dimensions (Trumbull and Lash, 2013): Informal / formal Immediate / delayed feedback Embedded in lesson plan / stand-alone Spontaneous / planned Individual / group Verbal / nonverbal Oral / written Graded / ungraded Open-ended response / closed/constrained response Teacher initiated/controlled / student initiated/controlled Teacher and student(s) / peers Process-oriented / product-oriented Brief / extended Scaffolded (teacher supported) / independently performed Formative Assessment Ideally, formative assessment strategies improve teaching and learning simultaneously.

# data[].attributes.attachment_link.readable_text
There is no shortage of formative assessment strategies, techniques, and tools available to teachers who use formative instructional practice in their classrooms. Here is an extensive list of 75 digital tools, apps, and platforms that can help you and your students use formative assessment to elicit evidence of learning. We didn't just add any old tool to this list. Here are the criteria we used for those that made the cut:

Supports formative instructional strategies and ways to activate learners to be resources for themselves and peers

Is free or awful close to it (under $10 per year, where possible)

Allows both students and teachers to take the activator role when possible (sometimes teachers need to get things started)

Before you dig into the tools, I encourage you to spend some time thinking about exactly what you want to accomplish with your students. "How to pick the right digital tool: Start with your learning goal" by Erin Beard can help you wrap your head around goals and guide you in choosing the best tool for the task. "27 easy formative assessment strategies for gathering evidence of student learning" can help you decide what strategies work best for you and your students.

Record audio and video

Create quizzes, polls, and surveys

1. Crowdsignal Lets you create online polls, quizzes, and questions. Students can use smartphones, tablets, and computers to provide their answers, and information can be culled for reports.

2. Edulastic Allows you to make standards-aligned assessments and get instant feedback.

3. FreeOnlineSurveys Helps you create surveys, quizzes, forms, and polls.

4. Gimkit Lets you write real-time quizzes. And it was designed by a high school student!

5. Kahoot! A game-based classroom response system that lets you create quizzes using internet content.

6. MicroPoll Helps you create polls, embed them into websites, and analyze responses.

7. Naiku Lets you write quizzes students can answer using their mobile devices.

8. Obsurvey Designed to make surveys, polls, and questionnaires.

9. Poll Everywhere Lets you create a feedback poll or ask questions and see results in real time. Allows students to respond in various ways. With open-ended questions, you can capture data and spin up tag clouds to aggregate responses.

10. Poll Maker Offers unique features, like allowing multiple answers to one question.

11. ProProfs Helps you make quizzes, polls, and surveys.

12. Quia Lets you create games, quizzes, surveys, and more. Access a database of existing quizzes from other educators.

13. Quizalize Helps you create quizzes and homework.

14. Quizizz Guides you through designing quizzes and lets you include students in the quiz-writing process.

15. Quizlet Lets you make flashcards, tests, quizzes, and study games that are mobile friendly.

16. Survey Hero Designed to build questionnaires and surveys.

17. SurveyMonkey Helpful for online polls and surveys.

18. SurveyPlanet Also helpful for online polls and surveys.

19. Triventy Lets you create quizzes students take in real time using individual devices.

20. Yacapaca Helps you write and assign quizzes.

21. Zoho Survey Allows you to make mobile-friendly surveys and see results in real time.

Brainstorm, mind map, and collaborate

Present, engage, and inspire

Generate word or tag clouds

1. EdWordle Generates word clouds from any entered text to help aggregate responses and facilitate discussion. Word clouds are pictures composed of a cloud of smaller words that form a clue to the topic.

2. Tagxedo Allows you to examine student consensus and facilitate dialogues.

3. Wordables Helps you elicit evidence of learning or determine background knowledge about a topic.

4. WordArt Includes a feature that allows the user to make each word an active link to connect to websites, including YouTube.

Get real-time feedback

Foster family communication

1. Remind Lets you text students and stay in touch with families.

2. Seesaw Helps you improve family communication and makes formative assessment easy, while students can use the platform to document their learning.

3. Voxer Lets you send recordings so families can hear how their students are doing, students can chat about their work, and you can provide feedback.

Strengthen teacher-to-student or student-to-student communication

Keep the conversation going with live chats

1. Yo Teach A backchannel site great for keeping the conversation going with students.

2. Chatzy Supports live, online chats in a private setting.

Create and store documents or assignments

1. Google Forms A Google Drive app that allows you to create documents students can collaborate on in real time using smartphones, tablets, and laptops.

2. Piazza Lets you upload lectures, assignments, and homework; pose and respond to student questions; and poll students about class content. This tool is better suited for older students as it mimics post-secondary class instructional formats.

Learn more

There are several resources for learning more about formative assessment and responsive instruction strategies. Consider our formative practices workshops, where school and district teams can gain a better understanding of the role formative practice plays in instruction and the four foundational practices to use in the classroom. Or for a quick start, download our eBook "Making it work: How formative assessment can supercharge your practice."

Jump in, try new tools and methods, and have fun!

# data[].attributes.attachment_link.title
75 digital tools and apps teachers can use to support formative assessment in the classroom

# data[].attributes.attachment_link.description
We've compiled an extensive list of 75 digital tools that can help teachers use formative assessment to elicit evidence of student learning.

# data[].attributes.attachment_link.readable_text
If you peek into the office of Technology, Humans and Taste (THAT), an advertising firm in New York City, on a Friday afternoon, you might be puzzled to see employees and selected guests sitting on couches and clutching pencils. They're jotting down ideas on forms and hanging them on boards around the office as music plays against a backdrop of Big Apple traffic noise. Within an hour, dozens of cards are scattered across the walls.

THAT's unusual ideation process - called "THAT Creative Dim Sum" - is a cornerstone of the company's approach to innovation, says co-founder and chief creative officer Nathan Phillips. Collaboration is key, and messiness? An absolute must.

This might drive Marie Kondo disciples to distraction, but research suggests there are benefits to a cluttered workspace. A 2013 study by the University of Minnesota, for example, showed that participants in disorderly rooms were more creative than people in tidy ones. Experts say disorder stimulates creativity because physical artifacts can trigger people to draw connections between separate ideas - ones already in your head and seemingly unrelated - to generate novel solutions. It's a process known as "psychological bricolage," according to Jeffrey Sanchez-Burks, a professor of management and organizations at the University of Michigan.

There's a fundamental misconception that human brains are like an Excel spreadsheet, says Sanchez-Burks. The assumption is that everything we need to know is always kept top of mind to be accessed quickly, like sorting to a specific cell. But from a neurological standpoint, the brain stores memories through "associative networks," meaning that separate memories or experiences aren't organized together. People need nudges to combine ideas, which is exactly where the clutter comes in handy.

Moving through space among constantly changing scenery exposes the mind to diverse stimuli.

A tour of Sanchez-Burks' office reveals a Coachella poster, a skateboard carved by his grandfather, a Tibetan Buddhist figure and a bookshelf stacked with texts about design and travel. These objects evoke percolating memories that spark ideas he can apply to projects more so than a barren desk or blank notepad. Which is why it doesn't surprise him that innovators like Steve Jobs often came up with ideas while taking walks. Moving through space among constantly changing scenery exposes the mind to diverse stimuli, he says.

This doesn't mean being singularly focused can't boost productivity. Quite the opposite. In fact, psychologist Mihaly Csikszentmihalyi's research on psychological "flow" outlines a state in which people are so immersed in an activity that nothing else seems to matter - so much so that people don't note passing time. The activity itself is also important. Having emotionally evocative objects in sight is likely to be more helpful to an author with writer's block, for example, than to an accountant focused on finance reports.

But Sanchez-Burks pushes back a bit on the notion that clutter only benefits creative workers. Rarely does an employee execute 100 percent of their time without any problem-solving, he says. Because different tasks call for different stimuli, this balance should be framed instead in terms of the percentage of time people spend on each. When Phillips' employees are producing pitch decks in specific formats, he says it can be productive to "crawl into a cave." Inspired creativity, on the other hand, calls for collaboration.

And while people have personal preferences for messiness, Sanchez-Burks says neither neat freaks nor their messy counterparts are hard-wired. Perfectionists, too, benefit by breaking from orderly habits when situations demand different thinking. What's more, there's an unlikely overlap between decluttering proponents like Marie Kondo and his exact rationale for keep clutter. Kondo is all about only keeping objects that spark joy - which is not too far from Sanchez-Burks' explanation that objects that prompt ideas can enhance productivity.

Phillips, for one, thinks of his creative process like doing laundry - and he notes that a mess isn't always dirty. "If you keep your apartment really clean, you can always find what you're looking for," he says. But if you're getting dressed for a party and have to sort through a pile of laundry, he says, you might discover that Whitesnake T-shirt you lost 10 years ago ... and realize it's just the thing you need.

# data[].attributes.attachment_link.title
Slobs, Rejoice: Why You Shouldn't Clean Your Messy Desk

# data[].attributes.attachment_link.description
If you peek into the office of Technology, Humans and Taste (THAT), an advertising firm in New York City, on a Friday afternoon, you might be puzzled to see employees and selected guests sitting on couches and clutching pencils.

# data[].attributes.body
Decoding the contents and strength of imagery before volitional engagement | Scientific Reports - www.nature.com

# data[].attributes.attachment_link.readable_text
Abstract

Is it possible to predict the freely chosen content of voluntary imagery from prior neural signals? Here we show that the content and strength of future voluntary imagery can be decoded from activity patterns in visual and frontal areas well before participants engage in voluntary imagery. Participants freely chose which of two images to imagine. Using functional magnetic resonance (fMRI) and multi-voxel pattern analysis, we decoded imagery content as far as 11 seconds before the voluntary decision, in visual, frontal and subcortical areas. Decoding in visual areas in addition to perception-imagery generalization suggested that predictive patterns correspond to visual representations. Importantly, activity patterns in the primary visual cortex (V1) from before the decision, predicted future imagery vividness. Our results suggest that the contents and strength of mental imagery are influenced by sensory-like neural representations that emerge spontaneously before volition.

Introduction

A large amount of psychology and, more recently, neuroscience has been dedicated to examining the origins, dynamics and categories of thoughts. Sometimes, thoughts feel spontaneous and even surprising; while other times they feel effortful, controlled and goal oriented. When we decide to think about something, how much of that thought is biased by pre-existent neural activity? Mental imagery, a sensory thought, can be triggered voluntarily or involuntarily4. However, how much of the content and strength of our mental images we actually control when we voluntarily generate imagery remains unknown. For example, individuals with post-traumatic stress disorder (PTSD) report a complete lack of control of both the content and strength of their mental imagery5. While evidence suggests that imagery strength varies both between and within individuals in the normal population5,6. Previous research has shown that prefrontal activity can predict future decisions7,8,9,10, and nonconscious sensory activity11, and that mental images can be decoded from early visual cortex12,13. However, it remains unknown whether nonconscious sensory activity influences what we think and how strongly we think it.

To investigate the origins of the content and strength of voluntary imagery, we crafted a thought-based mental imagery decision task, in which individuals could freely decide what to imagine, while we recorded brain activation using functional magnetic resonance imaging (fMRI). We used multi-voxel pattern analysis (MVPA, see Materials and Methods for details) to decode information contained in spatial patterns of brain activation recorded using fMRI. Additionally, in an independent control experiment, we estimated the temporal reliability of the reported onset of thoughts, as it has been criticized in previous paradigms17. Using a design exploiting the known effect of imagery priming on subsequent binocular rivalry as a function of time18, we show that participants' reports of thought onsets were indeed reliable within the temporal resolution of fMRI.

Models of determinants of decision making postulate that executive areas in the prefrontal cortex would trigger selection processes leading to future choices. In addition to the executive areas involvement in future visual thoughts, we aimed to test whether predictive information could also be decoded from visual areas, as previous results have shown that visual imagery recruits visual areas12,13. To test this, we used both searchlight and visual (from V1 to V4) regions-of-interest (ROI) decoding. We also sought to determine the representational content of the predictive signals: is predictive information, to some extent, similar to perceptual visual representations? To assess this, we perceptually presented gratings outside of attention to participants in separate runs. Functional brain images from the perceptual blocks were then used to train classifiers, which were subsequently tested on imagery blocks both before and after the decision. This so called perception-imagery generalization cross decoding was thus used to show common informational content between visual perceptual representations and predictive signals. Finally, we tested whether the subjective strength of visual imagery could be decoded from information in visual areas before reported volition. Such an involvement of visual areas in the future strength of visual imagery would provide further evidence that sensory areas also play an important role in the phenomenology of future thoughts.

Using this paradigm, we found that activity patterns were predictive of mental imagery content as far back as 11 seconds before the voluntary decision of what to imagine -in visual, frontal and subcortical areas. Importantly, predictive patterns in the primary visual cortex (V1) and the lateral prefrontal cortex were similar to perceptual representations elicited by unattended images. We show that the subjective strength (vividness) of future mental imagery can be predicted from activation patterns contained in the primary visual cortex (V1) before a decision is made. Our results suggest that the contents and strength of mental imagery are influenced by sensory-like neural representations that emerge spontaneously before volition. These results are important as they point to a role of visual areas in the pre-volitional processes leading to visual thought production, thus shedding light on the mechanisms of intrusive mental imagery in conditions such as PTSD, as well as the origins of normal mental imagery.

Results

Free decision visual imagery paradigm

Our paradigm consisted of a mental decision leading to the formation of a visual mental image. In every trial, participants had to choose to imagine one of two possible different colored and oriented gratings while we recorded brain blood-oxygen-level dependent (BOLD) using fMRI (Fig. 1, see Materials and Methods for details). After the start of the trial, participants had a maximum of 20 seconds to freely decide which pattern to think of. As soon as they felt they had made the decision, they pressed a button (always the same button for both gratings) with the right hand, thus starting 10 seconds of imagery generation. During this time, participants imagined the chosen grating as vividly as they could. Subsequently, they were prompted with two questions: "what did you imagine?" and "how vivid was it", to which they answered by pressing different buttons (Fig. 1). On average, participants took 5.48 s (±0.15 SEM) to decide which grating to imagine, while the average trial time was 31.18 s (see Fig. S1 and Materials and Methods for details). Each trial included a blank period of 10 s at the end to avoid spillover effects from one trial to the next20,21. Participants chose to imagine each grating with similar probabilities (50.44% versus 49.56% for vertical and horizontal respectively, Shannon entropy = 0.997, with a switch probability of 58.59% ±2.81 SEM, see Materials and Methods for detailed behavioral results).

Figure 1

fMRI task paradigm. Participants had to freely choose between two predefined gratings (horizontal green/vertical red or vertical green/horizontal red, counterbalanced across participants). Each trial started with the prompt: "take your time to choose - press right button" for 2 seconds. While the decision was made, a screen containing a fixation point inside a rectangle was shown. This period is referred as "pre-imagery time" and was limited to 20 seconds. Participants were instructed to press a button with the right hand as soon they decided which grating to imagine (always the same button independently of the chosen grating). During the imagery period (10 seconds), participants imagined the chosen grating as vividly as possible. At the end of the imagery period, a question appeared on the screen: "what did you imagine? - Left for vertical green/red - Right for horizontal red/green" (depending on the pre-assigned gratings for the participant). After pressing the relevant button to answer, a second question appeared: "how vivid was it? -1 (low) to 4 (high)", to which participants answered using one of 4 buttons. After each trial there was a blank interval of 10 seconds where we instructed the participants to relax and not to think about the gratings nor subsequent decisions. Gray hand drawings represent multiple possible button responses, while black drawing represents a unique button choice.

Decoding sanity checks

We first verified the suitability of our decoding approach to classify the contents of visual perception and imagery. We used SVM classifiers trained and tested (in a cross-validation scheme) on 10 s of perception or imagery data and classified the perceptual or imagined stimuli (red/green horizontal/vertical gratings) in visual areas from V1 to V4 (see Materials and Methods for details). Fig. S2 shows the results of this sanity check. We found above chance decoding accuracy for perception (91.7, 91.7, 91.7 and 71.4%; one-tailed t-test p = 3.1·10 −8, 1.2·10 −9, 7·10 −11 and 1.5·10 −3; from V1 to V4) and imagery (66.9, 67, 69.1 and 63.7%; p = 8·10 −4, 1.2·10 −3, 1·10 −4 and 8·10 −3). These results are comparable to previous results on decoding perception and imagery and thus validate our decoding approach.

Searchlight decoding results

To investigate which brain areas contained information about the contents of imagery, we employed a searchlight decoding analysis on fMRI data from the whole brain16. We used two sources of information to decode the contents of imagery: neural activation patterns within the imagery condition (imagery decoding) and patterns from unattended perceptual stimuli to decode imagery data (perception-imagery generalization cross-decoding). For the imagery decoding, we trained and tested classifiers using the imagery data. In the imagery-perception generalization analysis we trained the classifiers using data from the perception scans and tested on imagery data. The latter allowed us to explore shared information between perception and imagery, without the effects of attention (see Materials and Methods for details & behavioral attention task during perception).

We defined the areas bearing information about the contents of imagery as those revealing above chance decoding accuracy at any point in time during a 28 s time window around the decision (cluster definition threshold p < 0.001, cluster threshold p < 0.05, see Materials and Methods for details). Under this selection criterion, above chance decoding at any point in time is trivial and not relevant for our question. Rather, the purpose of this analysis is investigating the temporal dynamics of the imagery-content information. Specifically, we were interested to test whether any area contained information about the contents of imagery before the decision. In this respect, our analysis is bias-free regarding the temporal position of the information, as we considered many time-points before and after the decision (7 points each).

Using the above explained analysis, we found a network of four areas: frontal, occipital, thalamus and pons (Fig. 2, central panels, see Table S1 for cluster locations in MNI coordinates). We then examined the information-content time course in these areas from −13 to +13 seconds from the reported imagery decision (time = 0). As expected, time-resolved (2 s) decoding yielded lower (but statistically significant) accuracies than averaging over longer periods (see Fig. S2 for comparison), presumably due to its lower signal-to-noise ratio. Importantly, in the context of neuroscience research, decoding accuracy scores are not the most relevant output of classification, but rather their statistical significance is25. Time-resolved classification in the imagery condition reached above chance decoding accuracy up to 11 seconds before reported imagery onset in occipital and thalamus while significant classification was reached at −9 seconds in the pons (Fig. 2; black solid points with inner white circle, p < 0.05, one-sample, one-tailed t-test, controlled for FWER p < 0.05, permutation test, see methods for details).

Figure 2

Searchlight decoding of the contents of imagery. Using searchlight decoding, we investigated which regions contained information about the contents of mental imagery (see Materials and Methods for details). We defined these regions as those showing above chance accuracy at any point in time (Gaussian random field correction for multiple comparisons, see Materials and Methods for details). We found 4 such regions (central panels): occipital (O), frontal (F), thalamus (T) and pons (P). Then, we investigated the temporal dynamics of each one of these regions (lateral plots), from −13 to +13 seconds from the voluntary imagery onset (time = 0). We decoded imagery contents using the information from imagery runs (imagery, black line) and using information from perception (perception-imagery generalization, grey line). For the imagery decoding (black line), all four regions showed significant above-chance accuracy both before and after imagery onset, indicating that information from imagery was predictive of the chosen grating before (up to −11 seconds) and after the imagery onset. On the other hand, the perception-imagery generalization (grey line) showed significant above-chance decoding before the onset of imagery only in occipital and frontal areas, indicating that perceptual-like information was predictive of the chosen grating before the imagery onset only in cortical areas and after the imagery onset in both cortical and subcortical areas. Numbers on upper-right slices' corners indicate MNI coordinates. Error bars represent SEM across participants. Full circles represent above chance decoding (p < 0.05, one-sample t-test against chance: 50%). White points inside full circles represent time courses where the number of significant points was significantly above chance level after correction for family-wise error rate (p < 0.05, permutation test, see Fig. S3 for details).

The perception-imagery generalization decoding showed significant above chance accuracy as early as −9 seconds before the onset of imagery in occipital areas (although these results did not survived the control for FWER) and −3 seconds in frontal areas (Fig. 2; grey solid points with inner white circle, p < 0.05, one-sample, one-tailed t-test, controlled for FWER p = 0.003, permutation test), indicating that pre-volitional predictive information shares properties with perception in frontal areas. In subcortical areas, above-chance generalization decoding accuracy was only observed after the onset of imagery (+1 and +11 seconds in the thalamus and the pons respectively) and was not significant after controlling for FWER. Importantly, during the perceptual scans visual attention was diverted by a demanding fixation task (see Materials and Methods), hence such generalization should not be due to high-level volitional or attentional mechanisms. Interestingly, decoding accuracy in occipital areas during the imagery period was lower than expected (see for example26). Previous studies have shown that prior decisions can impair subsequent cognitive tasks27. Therefore, the cognitive load for the decision element of our task could impair imagery, which is consistent with the results of a behavioral control experiment showing that cued imagery (no-decision) was stronger than decision followed by imagery (Fig. 3B,C).

Figure 3

Behavioral experiment: testing the accuracy of imagery onset reports. We tested perceptual priming and subjective imagery vividness a function of imagery time as a means to verify the accuracy of reporting the imagery onset. ( A) Paradigm. Free decision and cued trials were pseudo-randomized. Perceptual priming was measured as a function of imagery time (3.3, 6.7 and 10 s), as the dominance bias on binocular rivalry. ( B) Perceptual priming. Imagery time significantly increased perceptual priming on the free decision and cued conditions (ANOVA, F = 7.15, p = 0.002), and priming in the free decision condition was significantly lower than in the cued condition (ANOVA, F = 5.77, p = 0.021), thus ruling out that participants were reporting the imagery onset after starting imagining. ( C) Imagery vividness. Imagery time also significantly increased subjective imagery vividness on the free decision and cued conditions (ANOVA, F = 18.49, p < 10 −5). Stars show significant differences between the first two time points, thus setting a lower bound of temporal resolution on this behavioral task. These results show that the reported onset of imagery is reliable relative to the temporal resolution of fMRI. Error bars show ±SEM. Black and gray lines present free and cued conditions, * and ** represent p < 0.05 and p < 0.01, two-sample t-test.

Behavioral imagery onset reliability experiment

We ran an independent behavioral experiment outside the scanner to test whether participants might have begun imagining before they reported having done so, which could explain early above chance classification. We utilized a method that exploits binocular rivalry to objectively measure imagery strength18,28 as a function of time in a free decision and a cued condition (Fig. 3). We reasoned that if participants were reporting the onset of imagery a few seconds late, this would be detected as an increase in rivalry 'priming' compared to a condition where the onset of imagery is controlled by the experimenter, as such priming is known to be dependent on time18. Figure 3B shows the effects of imagery time on sensory priming for both conditions. Imagery time showed a significant effect on priming for free decision and cued conditions (ANOVA, F = 7.15, p = 0.002, Fig. 3B), thus confirming the effect of imagery time on priming. Priming for the free decision condition was significantly lower than in the cued condition (ANOVA, F = 5.77, p = 0.021), indicating that participants did not start imagining before they reported doing so (which would have resulted in the opposite pattern) and also suggesting that sensory priming is somehow disrupted by the decision task, perhaps due to cognitive load, analogous to what has been shown in other cognitive tasks27. Importantly, significant differences in priming between 3.33 and 6.67 seconds of imagery time were found for the free decision and cued conditions (one-tailed t-test, p < 0.05), indicating that this behavioral task can resolve differences in priming spaced by 3.33 seconds, at least for these two first time points, thus providing a lower bound of temporal resolution of the accuracy of the reported imagery onset which is comparable to that of fMRI.

Figure 3C shows the effects of imagery time on subjective imagery vividness. Imagery time showed also a significant effect on vividness for free decision and cued conditions (ANOVA, F = 18.49, p < 10 −5, Fig. 3C). However, differences between free decision and cued conditions were not significant (ANOVA, F = 2.42, p = 0.127). Again, significant differences in vividness between 3.33 and 6.67 seconds of imagery time were found for the free decision and cued conditions (one-tailed t-test, p < 0.01). While a similar pattern of results could arguably be explained by subjects starting to imagine the opposite target before they reported it, or imagining the two possible targets alternatively, these outcomes are not consistent with our fMRI results. This control largely overcomes one of the major limitations to prior free-choice paradigms, as it enables us to measure precision of thought-choice reporting17.

Searchlight decoding control analyses

We employed a permutation test to check whether the decoding distributions contained any bias, in which case above chance decoding would be overestimated and the use of standard parametric statistical tests would be invalid29 (see Materials and Methods for details). Permutation tests yielded similar results to those using parametric tests (Fig. S4), and, importantly, decoding accuracy distributions under the null hypothesis showed no bias, thus validating the use of standard parametric statistical tests (Table S2).

We also conducted a control analysis to test whether the searchlight results could be explained by any spillover from the previous trial. We trained the classifiers on the previous trial (N-1 training) and tested on the subsequent trial (trial N). If there was spill over from the previous trial, this analysis should show similar or higher decoding accuracy in the pre-imagery period. We found no significant above chance classification for any of the regions, thus ruling out the possibility that these results are explained by any spill over (Fig. S5).

Visual regions-of-interest (ROI) decoding

Results from the searchlight analysis were inconclusive regarding whether the predictive information before the decision share similarities with visual perception, as only frontal areas exhibited robust perception-imagery generalization decoding (Fig. 2). To test whether predictive information can be found in visual areas, we conducted a time-resolved decoding analysis only in visual regions-of-interest (ROI) from V1 to V4 defined by an independent functional experiment (see Materials and Methods for details). We reasoned that if we find information that predicts the imagery decision in perception-devoted visual areas this would be a strong argument in favor of perceptual predictive information.

The imagery ROI decoding analysis revealed similar temporal dynamics to the searchlight approach, showing earliest above-chance decoding accuracy −11 seconds from the reported imagery decision in the primary visual cortex, V1 (Fig. 4A). In the imagery decoding, all visual ROIs showed above chance decoding accuracy before imagery onset at different time points (small points, Fig. 4A, p < 0.05, one-sample, one-tailed t-test against chance: 50%), however only V1 and V4 were consistent across time points (from −11 to −5 and to −5 to 15 seconds, Fig. 4A outline circles, p < 0.05, one-sample, one-tailed t-test, controlled for FWER p < 0.05, permutation test). The early (−11s) predictive information in primary visual cortex suggest that predictive signals would correspond, at least partly, to visual representations.

Figure 4

Decoding the contents of imagery in visual regions-of-interest (ROI). We examined the contents of imagery in visual areas using a ROI approach. Visual areas from V1 to V4 were functionally defined and restricted to the foveal representation (see Materials and Methods for details). ( A) Imagery decoding. We found above-chance decoding accuracy for imagery decoding both before (from −11 seconds) and after imagery onset. Different visual ROI showed significant above-chance decoding accuracy at different time points, while V1 ROI was the most consistent across time points. ( B) Perception-imagery generalization. The cross-decoding generalization analysis showed consistent above chance decoding accuracy only in V3. Error bars represent SEM across participants. Full points represent above chance decoding (p < 0.05, one-sample t-test against chance: 50%). Outline circles represent time courses where the number of significant points was significantly above chance level after correction for family-wise error rate (p < 0.05, permutation test, see Fig. S3 for details).

The perception-imagery generalization showed more modest effects with above chance decoding accuracy in V3 just 3 s before imagery onset (Fig. 4B outline circles, p < 0.05, one-sample t-test against chance: 50%, controlled for FWER p < 0.003, permutation test). The overall low perception-imagery generalization decoding accuracy after imagery onset suggests that the analysis might not be effectively capturing the representational commonalities between perception and imagery as reported previously30,31. This discrepancy with previous results could be due to experimental noise or to a lack of representational similarity between perception and imagery. To distinguish between these two alternatives, we performed a new analysis seeking more sensitivity by abandoning the time-resolved analysis as described in the next section.

Predictive information in visual areas shares properties with perceptual information

While imagery decoding in visual areas suggests that predictive information is perceptual in nature, it does not rule out other possibilities, such as attentional effects. In particular, the time-resolved generalization analysis failed at showing strong decoding before the decision in visual areas (Fig. 4A). This can be due to a number of factors such as differences in the neural representations between imagery and perception, as well as the differences in signal-to-noise ratio between these conditions, which could lead to poor classification performance. We thus tested whether abandoning the time-resolved analysis would produce more conclusive results by increasing the signal-to-noise ratio. To achieve more sensitivity, we trained classifiers on perception runs and tested them on the imagery before-decision period (−10 to 0 s) and the after-decision period (0 to 10 s), thus effectively pooling the data across time points for the imagery condition as opposed to analyzing each point separately (as it was done in the time-resolved analysis, Fig. 4B). This analysis showed modest but significant decoding before the decision in V1, and after the decision in V3 (Fig. 5, solid points, p < 0.05, one-sample, one-tailed t-test against chance: 50%). This result thus supports the idea that predictive information is at least partly perceptual in nature and that the predictive perceptual representations would be housed in the primary visual cortex.

Figure 5

Predictive signals in visual areas share information with perceptual representations before and after the decision. In order to test whether predictive information in visual areas shared properties with perceptual representations in visual areas, we conducted generalization decoding by training on perception and testing on imagery data, during a period before (−10 to 0 s) and after the decision (0 to 10 s). Error bars represent SEM across participants. Significant decoding was found in V1 and V3 before and after the decision, respectively (p < 0.05, one-sample t-test against chance: 50%).

Visual areas ROI decoding control analyses

We also conducted a number of control tests on the ROI decoding results to ascertain the validity of our results. Permutation tests on the ROI decoding yielded similar results (Fig. S6). We controlled for whether these results could be accounted by any spill over from previous trials by again conducting an N-1 analysis. This analysis did not show any above chance accuracy before the imagery onset, but we found a significant time point at t = 5 s in V4 for the imagery condition (Fig. S7).

Imagery decoding as a function of reported vividness

Next, we investigated the effect of subjective imagery vividness on decoding accuracy for imagery. We divided the trials into low- and high-vividness (mean split, see Materials and Methods for details). As expected, the decoding accuracy for imagery content was higher in high-vividness trials, but surprisingly, the strongest differences were observed before the onset of imagery (Fig. S8A). The generalization analysis showed the same trend. We found above chance decoding only in high vividness trials (Fig. S8B), suggesting that in more vivid imagery trials, shared representations between perception and imagery would emerge more readily before volition. This result suggests that the subjective strength of future imagery is associated with better predictive power in visual areas.

Decoding future imagery vividness from pre-imagery responses

Finally, we reasoned that if prior, pre-imagery sensory representations in early visual cortex do indeed dictate the strength of subsequent visual imagery, then the pre-imagery data should predict the reported vividness from the subsequent imagery period. Accordingly, we tested exactly this, we attempted to decode the subjective strength of imagery (i.e. vividness) by using only the fMRI data from before the imagery period (Fig. 6). Decoding accuracy was significantly above chance in V1 (62.2%, p = 0.0035, one-sample, one-tailed t-test against chance: 50%), but not in other visual ROIs (p > 0.05, Fig. 6), indicating that information contained in V1 predicted future subjective imagery strength. This result shows that the predictive information in primary visual cortex not only has an influence on the contents of future imagery, but also impacts the subjective quality of the future visual thought.

Figure 6

Pre-imagery activation patterns in the primary visual cortex (V1) predict the strength of subsequent visual thoughts. We used pre-imagery data (from −10 to 0 s from the voluntary imagery onset) to decode subsequent imagery vividness (high vs low, see text for details). Information in V1 from before the imagery decision predicted how vivid the future imagery will be. Error bars represent SEM across participants. Full point represents above chance decoding (p = 0.0035, one-sample t-test against chance: 50%).

Discussion

We found that neural activation patterns were predictive of the contents of voluntary visual imagery as far as −11 seconds before the choice of what to imagine. These results suggest that the contents of future visual imagery can be biased by current or prior neural representations.

While previous interpretations have assigned predictive signals an unconscious origin7,9,32,33, we remain agnostic as to whether predictive signals were accompanied by awareness or not. We acknowledge the inherent limitations of most paradigms at capturing the state of awareness of the participants before their decision (see for example17). We have nonetheless gone to great lengths to overcome these limitations by developing a behavioral test aimed at probing the accuracy of imagery onset reports (Fig. 3). While this independent experiment suggested that participants were not imagining the gratings before the reported onset, the experiment does not completely exclude the possibility that participants engaged in imagery before the reported onset while in the scanner.

Our results show predictive patterns in occipital, frontal and subcortical areas (Fig. 2). While previous results highlight the role of frontal areas carrying information about subsequent decisions; to the best of our knowledge, predictive signals in visual and subcortical areas have not been reported. Interestingly, recent results have shown that brainstem centers can be a source of variability in the form of biases in perceptual decisions due to arousal mechanisms34. A similar mechanism could support the involvement of subcortical regions in the bias of future visual imagery.

Predictive signals in visual areas have perceptual properties

Are the predictive signals low-level visual representations or more abstract signals? Our results suggest that the predictive signals share properties with perceptual representations. Two pieces of evidence support this interpretation. First, predictive signals were found in visual areas V1, V3 and V4, which are devoted to visual processing, thus suggesting that predictive information has perceptual properties (Fig. 4). Secondly, and more conclusively, using brain signals elicited by unattended perceptual gratings, we were able to classify the contents of imagery before the decision (Fig. 5). The result of this generalization decoding analysis shows that predictive information in V1 shares similarities with perception, thus suggesting that these signals correspond, at least partly, to visual representations.

As for the specific features coded by the predictive sensory-like representations, it is unclear whether they correspond to orientation, color or to the conjunction of both. This question can however be answered by future experiments on perception-imagery generalization cross-decoding by using perceptual stimuli in the form of greyscale oriented gratings and solid color patches, while imagining the same colored oriented gratings as in the current study. Such a design would be able to distinguish the specific feature content of these representations: differences in decoding accuracy between color patch-imagery and achromatic gratings-imagery generalization should shed light on which features are coded by the pre-volitional signals.

Timing of the predictive signals and possible confounds

The finding of predictive signals up to 11 seconds before the decision can seem surprisingly early. However, early predictive signals have been detected using similar techniques in previous studies on motor decisions (up to 10 and 7 seconds before decision7,9) and also on abstract decisions (up to 4 seconds10). Crucially, questions have been raised about whether the decoding of such signals can correspond to neural activity elicited by the preceding trial. The N-1 trial shifting or "spillover" analysis performed in our study (Figs S5 and S7), is an accepted way to control for this issue19,35. The spillover analysis tests the hypothesis that if there is a temporal carry-over of information from one trial to the next, predictive signals should be best accounted by shifting the label of the current trial to the trial before (see Materials and Method for details). Results of the spillover control analysis showed that the predictive signals in our study are not explained by the previous trial, thus dismissing spillover effects as an explanation of our data (Figs S5 and S7).

Another relevant issue is that the sequential dependencies might have an impact on the classifier itself. In other words, any deviation from randomness in the choice across trials (captured for example by the entropy value or the probability of switch) could be potentially exploited by the classifier. Previous studies have shown that classifiers trained only on behavioral responses can perform as well as or better than classification on neural responses20,36. While sequential dependencies have been argued to be negligible on previous experiments21, this issue is difficult to dismiss without independent experiments. While, in our experiment, the probabilities of choosing vertical or horizontal were very similar (50.44% and 49.56%, Shannon entropy = 0.997) the probability of switching gratings from one trial to the next deviated from chance (58.59%). Therefore, by taking the results from the imagery decoding alone, we cannot rule out that sequential dependencies could have influenced the classification, as the classifier would have reached 58.59% decoding accuracy just by predicting that the decision on next trial would be switched from the previous one. Crucially, our independent perception-imagery generalization decoding analysis does not suffer from sequential dependencies issues as classifiers were trained exclusively on perception trials (presented in a 15s-on/15s-off block design) and tested on imagery trials. The perception-imagery generalization decoding confirmed predictive signals before the decision from (Figs 5 and S8B), thus indicating that our results are not explained by sequential dependencies in the participants' choices.

Predictive information in the primary visual cortex (V1) impacts the subjective strength of future imagery

Interestingly, information contained in the primary visual cortex (V1) predicted the subjective strength of visual imagery (Fig. 6). This suggests that the phenomenology of future mental images is supported by patterns of activations in the primary visual cortex that are present before the onset of voluntary imagery. This result again, links information contained in visual areas with the subjective properties of future voluntary imagery.

Choice prediction can be explained by decisions relying on spontaneously generated representations

In previous experiments applying MVPA to study decision processes, predictive information about choices has been interpreted as evidence for nonconscious decision making. Thus, it could be possible to interpret our results as the imagery decision being made (at least partly) non-consciously, supporting the idea that subjective sensation of making the decision emerges after the decision is already made7,9,32,33.

An alternative hypothesis is that these results reflect decisional mechanisms that rely on spontaneously generated visual representations present before the decision. Since the goal of the task was to randomly choose and imagine a grating as vividly as possible, one strategy might be to choose the pattern that is spontaneously more strongly represented. In other words, spontaneous grating representations might stochastically fluctuate in strength while remaining weak compared to voluntary imagery. Thus, prior to the decision, one grating representation might dominate, hence being more prone to decisional thought-selection. An analogous interpretation has been advanced to explain the buildup of neural activity prior to self-initiated movements, aka readiness potential37.

Interestingly, it has been recently shown that self-initiated movements can be aborted even after the onset of predictive neural signals38, suggesting that the decision can be somewhat dissociated from predictive neural signals. Therefore, our results can be explained by a conscious choice that relies on weak neural representations during the decision production; perhaps analogous to blindsight39, subliminal priming studies40,41 or nonconscious decisional accumulation42. Such a mechanism is intriguing in light of theories of mental imagery and thought generation that propose involuntary thought intrusion as both an everyday event, and, in extreme cases, a component of mental disorders like PTSD43,44.

In summary, we think that the best way to explain our results is not in terms of unconscious decision processes (as it has been advanced previously in the literature), but rather by a process in which a decision (which could be conscious) is informed by weak sensory representations.

Concluding remarks and future directions

Our current study can be seen as the first to capture the possible origins and contents of involuntary thoughts and how they progress into or bias subsequent voluntary imagery. This is compatible with the finding that the most prominent differences between low and high vividness trials are seen for the pre-imagery period in visual areas, especially the primary visual cortex, which can be interpreted as when one of the patterns is more strongly represented it will induce a more vivid subsequent volitional mental image. This is in line with reports showing that imagery vividness depends on the relative overlap of the patterns of activation elicited by visual perception and imagery45. Our results expand that finding by showing that the vividness of future visual thoughts is predicted by information stored in the primary visual cortex.

It is up to future research to reveal whether representations biasing subsequent voluntary imagery are genuinely non-conscious or not. This will not only shed light on age-old questions of volition, but also provide a clear mechanism for pathological intrusive thoughts common across multiple mental disorders.

Material and Methods

Participants

Experimental procedures were approved by the University of New South Wales Human Research Ethics Committee (HREC#: HC12030). All methods in this study were performed in accordance with the guidelines and regulations from the Australian National Statement on Ethical Conduct in Human Research ( https://www.nhmrc.gov.au/guidelines-publications/e72). All participants gave informed consent to participate in the experiment. For the fMRI experiment, we tested 14 participants (9 females, aged 29.1 ± 1.1 years old, mean ± SEM). We selected the sample size based on both estimations of effect sizes and the number of participants used in previous studies employing decoding to track brain signals predictive of subsequent decisions. Previous works tested from 8 to 14 participants, we thus used the participant's number upper bound in order to maximize the reliability of the results. We performed power analyses to corroborate that this number of participants was adequate to achieve a power of at least 0.8. Based on effect size estimations using G*Power 346. Soon at al. study on the pre volitional determinants of decision making9 tested 14 participants achieving a power of 0.812 in the time resolved decoding analysis while Bannert and Bartels study on perception-imagery cross-decoding generalization tested 830. Post hoc effect size analysis revealed that they would have needed to test 12 participants to achieve a power of 0.8. For the behavioral free decision and cued imagery priming task, we invited all the previous 14 participants to be part in this psychophysics experiment. Only 8 participants (4 females, aged 29.3 ± 0.5 years old), were able to come back to complete this new experiment.

fMRI free decision visual imagery task

We instructed participants to choose between two predefined gratings (horizontal green/vertical red or vertical green/horizontal red, counterbalanced across participants), which were previously familiar to the participants through prior training sessions. We asked the participants to refrain from following preconceived decision schemes. In the scanner, participants were provided with two dual-button boxes, one held in each hand. Each trial started with a prompt reading: "take your time to choose - press right button" for 2 seconds (Fig. 1). After this, a screen containing a fixation point was shown while the decision as to what to think of was made. This period is referred as "pre-imagery time" and was limited to 20 seconds. Participants were instructed to settle their mind before deciding. Participants pressed a button with the right hand as soon as they decided which grating to imagine. Participants reported that in some trials they felt in control of their decision, whereas in other trials one of the gratings just "popped-out" in their mind. Importantly, participants were instructed to press the button as soon as possible when they reached the decision or a grating appeared in their mind. After pressing the button, the fixation point became brighter for 100 ms indicating the participants that the imagery onset time was recorded. During the imagery period (10 seconds), participants were instructed to imagine the chosen pattern as vividly as possible, trying, if possible, to project it onto the screen. At the end of the imagery period, a question appeared on the screen: "what did you imagine? - Left for vertical green/red - Right for horizontal red/green" (depending on the pre-assigned patterns for the participant). After giving the answer, a second question appeared: "how vivid was it? - 1 (low) to 4 (high)" to which participants answered using 4 different buttons. After each trial, there was a blank interval of 10 seconds where we instructed the participants to just relax and try not to think about the gratings nor any subsequent decisions. Systematic post-experiment interviews revealed that some participants (n = 4) could not help thinking about gratings in some trials during the inter trial interval. They reported different strategies to avoid these thoughts such as ignoring them, replacing them for another image/thought, or choosing the other grating when the decision came. The remaining participants (n = 10) reported not having any thoughts or mental images about gratings during the rest period. We tested if the effects we found could be explained by the former group of participants who could not refrain from thinking about gratings. We thus performed the analysis using only data from the participants who did not think/imagine gratings outside the imagery period (n = 10). Fig. S10 shows the results of this control. Results are comparable to those shown in Fig. 2, thus ruling out the possibility that that the effects we report were driven by the 4 participants who had thoughts about gratings in the rest period. We delivered the task in runs of 5 minutes during which the participants completed as many trials as possible. Participants chose to imagine horizontal and vertical gratings with a similar probability (50.44% versus 49.56% for vertical and horizontal gratings respectively, mean Shannon entropy = 0.997 ± 0.001 SEM) and showed an average probability of switching gratings from one trial to the next of 58.59% ±2.81 SEM. Participants completed in average 7.07 runs each, with each run containing an average of 9.2 trials.

Behavioral imagery onset reliability experiment

Since the self-report of the onset of decisions has been criticized due to its unreliability and unknown variance17, we developed a novel independent psychophysics experiment to test its reliability. We objectively measured imagery strength as a function of time for a subset of the participants from the fMRI experiment. Importantly, the results of this experiment revealed that the reported onsets of decisions are indeed reliable relative to the temporal resolution of the fMRI (Fig. 3).

We employed two conditions: free decision (freely chosen imagined stimulus and imagery onset) and cued (i.e., imposed imagined stimuli and imagery onset), see Fig. 3A for a schematic of the paradigm. We used binocular rivalry priming as a means to objectively measure sensory imagery strength. When imagining one of the two competing rivalry stimuli prior to a binocular rivalry presentation, rivalry perception is biased towards the imagined stimulus, with greater levels of priming as the imagery time increases18; see18,28 for discussion of why this is an objective measure of imagery strength and not visual attention, binocular rivalry control or response bias. We asked participants to imagine one of the rivalry gratings for different durations and then measured rivalry priming as a function of the different imagery durations (Fig. 3B). We reasoned that if participants reported the onset of imagery a few seconds after they actually started imagining, this would be detected as an increase in priming compared to the condition where the onset of imagery is controlled by the experimenter. Thus, in the free decision condition, participants had to freely choose to imagine one of the two predefined gratings (horizontal green/vertical red or vertical green/horizontal red, counterbalanced across participants). In the cued condition, participants were presented with a cue indicating which grating to imagine, thus imposing the onset of imagery as well as which grating needed to be imagined. Each trial started with the instruction "press spacebar to start the trial" (Fig. 3A). Then, either the instruction "CHOOSE" or a cue indicating which grating to imagine (i.e., "horizontal red") was presented for 1 second. In the free decision condition, the imagery time started after the participant chose the grating to imagine, which they indicated by pressing a key on the computer keyboard (Fig. 3A). For the cued imagery condition, the imagery time started right after the cue was gone (i.e., no decision time). We tested 3 imagery times (3.33, 6.67 and 10 seconds). After the imagery time, a high pitch sound was delivered (200 ms) and both gratings were presented through red/green stereo glasses at fixation for 700 ms. Then, participants had to report which grating was dominant (i.e., horizontal red, vertical green or mixed if no grating was dominant), by pressing different keys. After this, they had to answer which grating they imagined (for both free decision and cued trials). Participants then rated their imagery vividness from 1 (low) to 4 (high) by pressing one of the 4 buttons in their response boxes. Free decision and cued trials as well as imagery times were pseudo-randomized within a block of 30 trials. We added catch trials (20%) in which the gratings were physically fused and equally dominant to control the reliability of self-report18,49. We tested 120 trials for each free decision and cued imagery conditions (40 trials per time point), plus 48 catch trials evenly divided among time points.

Raw priming values were calculated as the number of congruent dominant gratings in binocular rivalry (e.g., imagined vertical led to vertical dominant in binocular rivalry) divided by the total number of trials excluding mixed dominance binocular (piecemeal), for each time point and condition independently. Raw vividness values were calculated as the average per time point and condition excluding mixed perception trials. Priming and vividness were normalized as z-score within participants and across time-points and conditions to account for baseline differences across participants, but otherwise conserving relative differences amongst conditions and time-points. Rivalry dominance self-report reliability was verified with fake rivalry catch trials, where gratings were physically fused and equally dominant, which were reported as mixed above chance level (83.8%, p = 0.002, one-sample t-test against baseline). Priming and vividness z-scores were subjected to a one-way ANOVA to detect main the effects of conditions. We also performed post-hoc two-sample t-tests to verify that priming and vividness scores differed significantly between time points (Fig. 3).

We tested this independent behavioral experiment on 8 participants from the fMRI experiment (all 14 original participants were invited but only 8 were able to come back), who had extensive experience as subjects in psychophysics experiments. We further sought to test if these results would generalize to completely inexperienced participants who did not participate in the fMRI experiment (N = 10). We did not, however, find a significant increase of priming or vividness as a function of time as for results on Fig. S11, suggesting that this is a highly demanding task and experience in psychophysics might be important to perform the task properly (i.e., being able to hold the mental image for the duration of the imagery time).

Functional and structural MRI parameters

Scans were performed at the Neuroscience Research Australia (NeuRA) facility, Sydney, Australia, in a Philips 3T Achieva TX MRI scanner using a 32-channel head coil. Structural images were acquired using turbo field echo (TFE) sequence consisting in 256 T1-weighted sagittal slices covering the whole brain (flip angle 8 deg, matrix size = 256 × 256, voxel size = 1 mm isotropic). Functional T2*-weighted images were acquired using echo planar imaging (EPI) sequence, with 31 slices (flip angle = 90 deg, matrix size = 240 × 240, voxel size = 3 mm isotropic, TR = 2000ms, TE = 40 ms).

fMRI perception condition

We presented counter-phase flickering gratings at 4.167 Hz (70% contrast, ~0.5 degrees of visual angle per cycle). They were presented at their respective predefined colors and orientations (horizontal green/vertical red or vertical green/horizontal red). The gratings were convolved with a Gaussian-like 2D kernel to obtain smooth-edged circular gratings. Gratings were presented inside a rectangle (the same that was used in the imagery task, Fig. 1) and a fixation point was drawn at the center (as for the imagery task). Within a run of 3 minutes, we presented the flickering patterns in a block manner, interleaved with fixation periods (15 seconds each). Importantly, an attention task was performed consisting of detecting a change in fixation point brightness (+70% for 200 ms). Fixation changes were allocated randomly during a run, from 1 to 4 instances. Participants were instructed to press any of the 4 buttons as soon as they detected the changes. Participants showed high performance in the detection task (d-prime = 3.33 ± 0.13 SEM).

Functional mapping of retinotopic visual areas

To functionally determine the boundaries of visual areas from V1 to V4 independently for each participant, we used the phase-encoding method50,51. Double wedges containing dynamic colored patterns cycled through 10 rotations in 10 min (retinotopic stimulation frequency = 0.033 Hz). To ensure deployment of attention to the stimulus during the mapping, participants performed a detection task: pressing a button upon seeing a gray dot anywhere on the wedges.

Experimental procedures

We performed the 3 experiments in a single scanning session lasting about 1.5 h. Stimuli were delivered using an 18" MRI-compatible LCD screen (Philips ERD-2, 60 Hz refresh rate) located at the end of the bore. All stimuli were delivered and responses gathered employing the Psychtoolbox 352,53 for MATLAB (The MathWorks Inc., Natick, MA, USA) using in-house scripts. Participants' heads were restrained using foam pads and adhesive tape. Each session followed the same structure: first the structural scanning followed by the retinotopic mapping. Then the perception task was alternated with the imagery task until completing 3 runs of the perception task. Then the imagery task was repeated until completing 7 or 8 (depending on the participant) runs in total. Pauses were assigned in between the runs. The 4 first volumes of each functional runs were discarded to account for the equilibrium magnetization time and each functional run started with 10 seconds of fixation.

Phase-encoded retinotopic mapping analysis

Functional MRI retinotopic mapping data were analyzed using the Fast-Fourier Transform (FFT) in MATLAB. The FFT was applied voxel-wise across time points. The complex output of the FFT contained both the amplitude and phase information of sinusoidal components of the BOLD signal. Phase information at the frequency of stimulation (0.033 Hz) was then extracted, using its amplitude as threshold (≥2 SNR) and overlaid them on each participant's cortical surface reconstruction obtained using Freesurfer54,55. We manually delineated boundaries between retinotopic areas on the flattened surface around the occipital pole by identifying voxels showing phase reversals in the polar angle map, representing the horizontal and vertical visual meridians. In all participants, we clearly defined five distinct visual areas: V1, V2, V3d, V3v and V4; throughout this paper, we merge V3d and V3v and label them as V3. All four retinotopic labels were then defined as the intersection with the perceptual blocks (grating > fixation, p < 0.001, FDR corrected) thus restricting the ROI to the foveal representation of each visual area.

Functional MRI signal processing

All data were analyzed using SPM12 (Statistical Parametric Mapping; Wellcome Trust Centre for Neuroimaging, London, UK). We realigned functional images to the first functional volume and high-pass filtered (128 seconds) to remove low-frequency drifts in the signal, with no additional spatial smoothing. To estimate the hemodynamic response function (HRF), we generated regressors for each grating (horizontal green/vertical red or vertical green/horizontal red) for each run and experiment (perception and imagery) independently. We used finite-impulse response (FIR) as the basis function. This basis function makes no assumptions about the shape of the HRF which is important for the analysis of the free decision imagery data9. We employed a 14th order FIR basis function encompassing 28 seconds from −13 to +13 seconds from the imagery onset, thus obtaining 14 bins representing each TR. For the perception condition, we employed a 1 st order FIR basis function from the onset of each perceptual block to its end (15 seconds). We also employed 1 st order FIR basis functions for the sanity check imagery decoding (from 0 to 10 s, Fig. S2) and the before-after decision perception-imagery generalization (−10 to 0 and 0 to 10 from imagery decision, Fig. 5). For the vividness analysis, we split the trials into low-vividness (ratings 1 and 2) and high-vividness (ratings 3 and 4), we then obtained the regressors for both gratings as explained above.

Multi-voxel pattern analysis (MVPA)

We used a well-established decoding approach to extract information related to each grating contained in the pattern of activation across voxels of a given participant (in their "native" anatomical space) using the decoding toolbox (TDT)56. Using a leave-one-run out cross-validation scheme, we trained a L2-norm regularized linear supporting vector machine (SVM, as implemented in LIBSVM) on beta values using all but one run and then tested on the remaining one. No additional scaling (normalization) was performed on the data as beta values represent a scaled version of the data relative to the run mean. Training and testing was repeated until all runs were used as test and then averaged the results across validations (7 or 8-fold, depending on the participant). We performed leave-one-run out cross validation for every temporal bin independently.

We also employed cross-classification to generalize information between the perception and the imagery tasks in the "perception-imagery generalization".

For the perception-imagery cross-classification, we trained on the ensemble of the perception runs and tested on the ensemble of the imagery runs. In each perception run, green and red gratings were shown pseudorandomly in 6 blocks of 15 s each. Perceptual blocks (15 s) were convolved with a 1 st order FIR filter, yielding regressors for red and green perceptual gratings, as explained in the previous section. Imagery trials were pre-processed exactly as in the imagery decoding, yielding time-resolved (2 s) or block (10 s) regressors (see previous section for details). Thus, classifiers trained on the perceptual runs (e.g., perceptual vertical-green vs perceptual horizontal-red) were tested on the imagery data (e.g., imagined vertical-green vs imagined horizontal-red). Accuracy was calculated as in the imagery decoding (e.g., percentage of vertical-green vs horizontal-red decoding accuracy), except for that the training-testing procedure was performed only once (i.e., all perceptual data was used to train and all imagery data was used to test the classifiers), since it is not necessary to use cross-validation in such cross-classification schemes as the training and testing data are different and independent (as opposed to the imagery decoding condition where a fraction of the data was used for training and another for testing).

We employed 2 different decoding approaches: searchlight and region-of-interest (ROI). We used a spherical searchlight of 3 voxels of radius and obtained volumes in which a value of decoding accuracy was assigned to each voxel. We normalized the decoding accuracy volumes into the MNI space and applied a spatial smoothing of 8 mm FWHM, which has been found to be optimal in order to account for anatomical idiosyncrasies across participants57. We then performed a one-tail one-sample t-test against 50% (chance level) across participants for every voxel. We corrected for multiple comparisons using cluster-extent based thresholding employing Gaussian Random Field theory58,59, as implemented in FSL60. We used a primary threshold of p < 0.001 at the voxel level, as recommended in previous studies61, and a cluster level threshold of p < 0.05 in every time point volume independently. Importantly, these thresholds have been shown to be valid within the nominal false positive ratios62.

ROI decoding was used to test information content in visual areas specifically. We defined the boundaries of visual areas from V1 to V4 which volumes were used as ROI. Note that because visual ROI were defined on the cortical surface (see phase-encoded retinotopic analysis for details), only gray-matter containing voxels were considered, as opposed to the searchlight approach which also considers non-gray matter containing voxels, potentially explaining differences on sensitivity between these approaches.

We tested if there was a difference in the average BOLD response between stimuli (i.e., univariate difference). We did not find any significant differences (p > 0.05, uncorrected) in the average BOLD response (Fig. S9), thus ruling-out the possibility that the results would be explained by differences in the average level of activity across conditions.

Permutation test

In order to validate the use of standard parametric statistics, we performed a permutation test and thus empirically determined the distribution of decoding accuracies under the null hypothesis63. Previous reports have highlighted the possibility of obtaining skewed decoding distributions, which would invalidate the use of standard parametric statistical tests29. We thus randomly shuffled the labels (i.e., horizontal red/vertical green) among trials and within blocks (i.e., number of red/green imagined trials was conserved within a run but trial labels were shuffled) for each participant and condition (imagery and generalization) to generate empirical data under the null hypothesis. After reshuffling the labels, we generated regressors for each stimulus and performed decoding following the same procedure described in the previous paragraph. We repeated this procedure 1000 times and obtained the empirical distribution under the null hypothesis. At each iteration, the second level analysis (across participants) consisted of averaging the results across participants (exactly as performed on the original data), from which we obtained confidence intervals for each decoding time point and area (Figs S4 and S6) using the percentile method63. Our results show that the decoding null hypothesis followed a normal distribution (Table S2) and importantly, significant results using permutation test confidence intervals were comparable to the results using standard parametric tests (compare significant points on Figs 2 and 3 with Figs S4 and S6). This analysis thus validates the use of standard statistical tests to test significance on our dataset.

Across time-points family-wise error rate (FWER) control

We estimated the probability of obtaining an n number of significantly above-chance decoding time points (p < 0.05, one tailed t-test) under the null hypothesis. To do this, we employed the data from the null distribution obtained with the permutation test (randomly shuffled labels, 1000 iterations; see previous paragraph for details). Fig. S3 shows the result of such analysis. Insets show the family-wise error rate for the empirically observed number above-chance decoding time points for each area.

Spillover effect (N-1) decoding control

We conducted a control analysis to directly test whether the searchlight results could be explained by any spill over from the previous trial, as performed in a previous study (Soon et al.19). To do this, we shifted the labels by one trial (N-1). Briefly, the rationale behind this control is the following: if there was spill over from the previous trial, this analysis should show higher decoding accuracy in the pre-imagery period as effects from the previous trial would spillover over the next trial (for a comprehensive explanation of the rationale please refer to Soon et al.19). All the decoding details were otherwise identical to what is described in the section "Multi-voxel pattern analysis (MVPA)" except for that the first trial of each run was not considered as there was no N-1 trial in that case. Analogously, for the perception-imagery generalization, training was performed on perception data and tested on imagery trials labeled as N-1.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

1. 4.
Pearson, J. & Westbrook, F. Phantom perception: voluntary and involuntary nonretinal vision. Trends Cogn. Sci. 1-7, https://doi.org/10.1016/j.tics.2015.03.004 (2015).

2. 7.

3. 13.
Naselaris, T., Olman, C. A., Stansbury, D. E., Ugurbil, K. & Gallant, J. L. A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes. Neuroimage, https://doi.org/10.1016/j.neuroimage.2014.10.018 (2015).

4. 34.

5. 63.
Good, P. Permutation, Parametric and Bootstrap Tests of Hypotheses., https://doi.org/10.1007/b138696 (Springer-Verlag, 2005).

Download references

Acknowledgements

We would like to thank Johanna Bergmann for her input in the experimental design, useful comments and help with participant testing. Eugene Kwok for his help in the behavioral testing. Collin Clifford, Damien Mannion and Kiley Seymour for useful comments. This research was supported by Australian NHMRC grants GNT1046198 and GNT1085404 and JP was supported by a Career Development Fellowship GNT1049596 and ARC discovery projects DP140101560 and DP160103299.

Author information

Affiliations

School of Psychology, The University of New South Wales, Sydney, Australia

Roger Koenig-Robert

& Joel Pearson

Authors

1. Search for Roger Koenig-Robert in:

2. Search for Joel Pearson in:

Contributions

All authors developed the study concept and design. Testing, data collection, and data analysis were performed by R.K. Data interpretation was done by all authors. All authors wrote and approved the final version of the manuscript for submission.

Competing Interests

The authors declare no competing interests.

Corresponding author

Correspondence to Roger Koenig-Robert.

Supplementary information

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

# data[].attributes.attachment_link.title
Decoding the contents and strength of imagery before volitional engagement

# data[].attributes.attachment_link.description
Is it possible to predict the freely chosen content of voluntary imagery from prior neural signals? Here we show that the content and strength of future voluntary imagery can be decoded from activity patterns in visual and frontal areas well before participants engage in voluntary imagery.

# data[].attributes.body
Neuroscientists can read brain activity to predict decisions 11 seconds before people act - Quartz

# data[].attributes.attachment_link.title
Neuroscientists can read brain activity to predict decisions 11 seconds before people act - Quartz

# data[].attributes.attachment_link.description
Free will, from a neuroscience perspective, can look like quite quaint. In a study published this week in the journal Scientific Reports, researchers in Australia were able to predict basic choices participants made 11 seconds before they consciously declared their decisions. In the study, 14 participants-each placed in an fMRI machine-were shown two patterns, one...