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Harvard University Extension School, Cambridge, MA, United States

Faculty for Latin American Social Science Research, Quito, Ecuador

The learning sciences clarify how people learn best under which conditions and how human variability influences outcomes. Despite great advancements in some learning sciences over the past 30 years, there has been relatively little change in educational science, a sub-field of the learning sciences. To determine why knowledge from the learning sciences has not had a greater impact on educational policy, this study considered evidence from the learning sciences through a previously published systematic review of the literature followed by a Delphi panel of experts on the learning science ( Tokuhama-Espinosa, 2017). This was compared with a literature review of the trends in leadership decision-making models (data-based; context; distributed; transformative; goal-orientated, results-oriented) (Appendix A in Supplementary Material). This review found 30 current educational policies that contradict evidence from the learning sciences, suggesting a disconnect between educational science and other learning sciences. While there are some initiatives underway in a small number of educational leadership sectors to incorporate more learning science data into decision-making, it is not a norm. The current business-oriented model in educational policy design may explain this divide. The analysis of these results considers how switching from a business to a learning science model may result in different educational priorities. Such a vision offers a distinct and possibly more universally acceptable measure of "quality" education, detached from the immediate social and political goals and independent of the historical times in which they are taken. This paper suggests further research into this new learning sciences evidence-based framework on which educators base policy decisions.

Introduction

Educational Leadership

Public expectations of educational leaders have changed over the past 50 years ( Hallinger, 2011). Educational administration has given way to a broader personal role for educational leaders though it continues to maintain the universal objective of free and equal education for all ( Shields, 2017). This vision evolved in the United States from the 1960s and 1970s as schools sought out people with "courage, initiative and imagination," ( Broudy, 1962, p. 132) to the 1980s−2000s when the goal was to be caring ( Beck, 1994), moral ( Hodgkinson, 1991), and ethical ( Beck and Murphy, 1994). Starting in the early 2000s educational leadership added on elements of social justice ( Bogotch, 2002) and equity in leadership roles ( Grogan and Shakeshaft, 2010). Beginning at the turn of the twenty-first. century, technology ( Picciano, 2002) and innovation ( Jackson and Kelley, 2002) were added to the growing list of educational policy objectives, as was a new value in international comparisons of school leadership and decision-making (e.g., Darling-Hammond et al., 2007).

In a 2019 analysis of competency-based standards for school principals in Canada, Australia and the United States, Lambert and Bouchamma found that 85% of the measures that improved student success were similar or identical across countries, suggesting a shared vision of good leadership. These included 20 "professional behavioral standards," only three of which came directly from the learning sciences ("knowledge of pedagogy/programmes [sic], "learning practices and strategies" and "needs of the students"). Ten of the 20 standards came directly from business models (laws and policies; material and financial resources; human resource management; organizational climate; improvement and innovation; collaboration, shared leadership and empowerment; supervision; decisions and responsibilities; mission and vision formation; and data use and management). The last seven standards can be found in both the learning sciences and business models of educational leadership (balanced personal and professional life [self and others]; professional development [self and others]; technology [ICT]; communication; adaptation to change and feedback; community partnership and outreach initiatives; cultural diversity [inclusion]) ( Lambert and Bouchamma, 2019, p. 61). Other international comparisons similarly place educational leadership skills and behaviors into similar categories and suggest both a humanization of leaders in which personal investment into group dynamics is valued, as well a deeper reliance on data for decision-making (e.g., Amanchukwu et al., 2015).

Whereas, it was once thought that the role of principals and department heads was to manage ( Grace, 2005), effective leaders are now asked to inspire and transform ( Anderson, 2018). The current literature on educational leaders reflects their ever-growing areas of responsibility. On one end of the spectrum this spans from "instructional leadership" (e.g., Hallinger et al., 2015) in which school leaders are meant to drive better teaching initiatives (e.g., Smylie et al., 2016), social-emotional learning and wellness programs (e.g., Bryant et al., 2016), to the other end of the spectrum where they are also responsible for the design and maintenance of sustainable buildings (e.g., Veronese, 2012), and community outreach initiatives (e.g., Epstein, 2018).

To achieve this broad band of responsibilities special competencies are required. Some have researched how these competencies impact success through the effectiveness of different personality types or behaviors (e.g., Judge et al., 2002; Amanchukwu et al., 2015), or how theoretical models of leadership transpose onto school environments ( Wang et al., 2017; Gumus et al., 2018). Only a handful of studies have considered the broad range of responsibilities and the difficulties of prioritizing them (e.g., Cheong Cheng and Ming Tam, 1997; Scheerens, 2012). The challenges of being a good educational leader grows with every new competency, which continue to be added to the profile over time and as the role of schools expands. That is, what was valued 50 years remains valued today, but along with those competencies are newly added responsibilities.

The complex nature of educational leadership can make it tempting to go "back to the basics" of good administrative skills and core management concepts. It can be argued that managing numbers is easier than managing people ( Pfeffer and Veiga, 1999). Therefore, many find the decision of how to be a good educational leader easier when they lean toward the business and management side of their role than the educational side, meaning many schools are run more like businesses than places of learning ( Senge et al., 2012; Onorato, 2013). The business or economic model of quality education celebrates the achievement of cost-effective practices ( Levin and Belfield, 2015). As a public good that is almost always underfunded ( Ikpa, 2016), many educational leaders feel that their role is to stretch each dollar as far as possible, which in many cases means favoring decisions in which budget items that provide a high level of impact at low cost are favored. Standardized testing is an example of an attractive decision which promises a lot with a relatively low investment ( Dolton and Marcenaro-Gutierrez, 2016). Other researchers suggest, however, that education is not actually underfunded, but rather poorly managed ( James, 2015), adding to the pressure on educational leaders to spend wisely. An international comparative study found that there is a global trend is the use of "market terminology in educational settings" and "increased accountability and responsibility for school leaders," ( Townsend, 2011, p. 93). There are many, however, who also see the importance and potential gains of embracing more complex models of educational leadership.

Pont, for example ( Pont, 2013), describes both professional as well as behavioral standards of principals. In the professional standards he suggests five basic groupings: (a) prioritize guidance (e.g., mission, vision, innovation); (b) establish organizational conditions (e.g., material and financial resources); (c) develop self and others (e.g., professional development and human resource management); (d) exercise pedagogical management (e.g., use of relevant data, technology); (e) create harmony within the school (e.g., communication, organizational climate). In the behavioral standards, he suggests four groupings: (a) flexible management of change (e.g., adaptation to change and feedback); (b) communication; (c) values (e.g., decisions and responsibilities, cultural diversity); and (d) theory linked to practice (e.g., use of relevant data, results, and research). Pont's detailed work suggests that many educators are actively involved in trying to adapt to the growing list of responsibilities and several have managed to systematize the understanding of this range of tasks.

The Learning Sciences

The goal of the learning sciences is to, among other things, study how people learn best and under which conditions ( Sawyer, 2005a). Originally called the "science of learning" and changed to the "learning sciences" after the Cambridge Handbook of the Learning Sciences was revised in 2006, this field is really a collection of more than a dozen fields that share common foci on problems ( Sawyer, 2008). The learning sciences include a wide range of fields such as neuroscience, psychology and education ( Fischer et al., 2018), which grow independently but also collectively.

The new field of Mind, Brain, and Education science, founded in 2004 at the Harvard University School of Education, seeks to elevate teacher practice through greater professionalization ( Fischer et al., 2007). In part, this is done by helping educators see their roles are equally important in resolving school challenges as that of neuroscientists and psychologists. Mind, Brain, and Education science is a sub-field of the greater learning sciences and responds to challenges on topics of equal concern to neuroscientists, psychologists and educators. For example, "motivation," is studied by multiple learning sciences, but each with its own methods and units of analysis (neuroscientists study the brain, psychologist study the mind and educators study learners in classroom contexts). Mind, Brain, and Education science attempts to unify the collective knowledge on any one of these topics through translational research and/or unique methodologies. The logic of this approach is that it is more likely that a transdisciplinary vision of a problem in education will yield a better solution than a single discipline perspective ( Tokuhama-Espinosa, 2008).

There have been international recommendations, such as the OECD's expert panel on Pedagogical Knowledge and the Changing Nature of Teaching ( Guerriero, 2017), and counsel from academia pushing educational leaders to consider "How Can the Learning Sciences (Better) Impact Policy and Practice?" ( McKenney, 2018). Additionally, there have been two international Delphi expert panels which sought to gain consensus by learning scientists around the world about how educational leaders' work and teachers' new pedagogical knowledge should be shaped by a learning sciences knowledge base ( Tokuhama-Espinosa, 2008, 2017). These studies point to a consistent base of knowledge that should inform educational decisions and included a list of research, practice and policy goals that should guide educational decision-making processes ( Tokuhama-Espinosa, 2015, 2017). Only a small number of teacher colleges and policy makers, such as the Deans for Impact (2015), have actually incorporated learning sciences documentation into their decision-making practices, however. To better understand the slow uptake of information from the learning sciences into educational practice, this study sought to compare educational decision-making with evidence from the learning sciences to see where they differed and why.

Research Question

The primary research question of this study was How and to what extent do educational leaders take into consideration information from other learning sciences when making policy decisions?

Once answered, a new question emerged which was considered in the Conclusions: Can the learning sciences catalyze a change in educational policy priorities?

Materials and Methods

Materials

This study reports on the fourth of four stages of research, which is a comparative review of the literature from educational leadership and the learning sciences. The literature review specifically studied how decisions in favor of quality education are made from the educational leadership perspective as compared with the learning sciences perspective. The first stage of this research was a triangulated mixed-methods design including a grounded theory meta-analysis of the literature using open coding, followed by an international Delphi panel on Mind, Brain and Education science conducted by the author for her doctoral thesis ( Tokuhama-Espinosa, 2008). The second stage was conducted in 2015 which consisted of identifying 217 indicators that have been used to measure quality education over the past 40 years in 34 OECD nations ( Tokuhama-Espinosa, 2015) and how countries prioritized them based on individual choices including long- vs. short-term investments, budgetary restraints and political promises. This prioritization was used to identify key areas for educational leadership decision-making that were analyzed from both educational administrator perspectives and learning scientists' viewpoints. The third stage was a 10-year follow-up to the international Delphi panel on Mind, Brain, and Education science, proceeded by a meta-analysis of the literature 2007-2017 ( Tokuhama-Espinosa, 2017), the goal of which was to confirm learning science principles, tenets, neuromyths and research, practice and policy goals. This study along with the first Delphi in 2008 were used to establish the learning scientists' perspective. These background studies provided the platform upon which a comparison of the learning sciences could be made with educational leadership.

The literature review of this paper explores educational leadership research to determine areas of responsibility (Appendix A in Supplementary Material) based on key words used in meta-analyses over the past decade. Research was limited to studies for which there was free or open access or available through the Harvard Online Library (HOLLIS) and published between 2000 and 2019. This list was used to (a) appreciate the spectrum of responsibilities of educational leaders and (b) and compare it with the types of problems learning scientists research.

Methods

Backward Design

This literature review study applied Backward Design planning (objectives, evaluation, activities) ( Wiggins and McTighe, 1998) to compare and contrast the ways educational leaders and learning scientists articulate problems, frame research questions and identify objectives. One hypothesis was that heuristics was responsible for both actors' decision-making frameworks.

Backward Design or Understanding By Design from the book title ( Wiggins and McTighe, 1998) means beginning with the end in mind, which points to a clear path to success by marked stages of advancement. Backward Design is a planning mentality that was first introduced in the 1940s in business contexts ( Tyler, 1949) and since the late 1990s in educational models ( Wiggins and McTighe, 1998). In Backwards Design, users are prompted to first identify the objective. Second, they choose the evaluation method(s) that will best offer evidence of advancement toward that objective. Third and last, users are asked to select the activities which would most likely be able to generate the indicators for evaluation.

Wiggins and McTighe note that the most complicated aspect of Backwards Design is appropriately identifying the objective. It is for this reason that this study undertook a review of multiple literary sources to identify the collective agreement on objectives in education, rather than use a single perspective. Wiggins and McTighe note that identifying objectives individually can be deceptively illusive to educators. It is not uncommon to hear teachers declare an activity as an objective ("We will play a crossword puzzle" rather than "Students will improve their vocabulary"). Policy makers also mistakenly label global policy expectations as objectives ("Improve education") without understanding that the precision with which they can identify the objectives, the easier it will be to both measure and plan activities around it. It is for this reason that meta-analyses and systematic literature reviews were used to identify the objectives of learning scientists and objectives of educational leaders.

The reference to "backwards" is that many educators and educational leaders complete this planning process in the opposite order. That is, it is not uncommon to find that teachers and educational policy makers execute activities/policies and then evaluate them, sometimes without ever considering how closely the objective matched the activity or the evaluation tool.

In business, leaders set a target objective (for sales, or growth in market share, for example). They then choose indicators for success (e.g., earned income or brand affiliation testimonies). Lastly, they select the best activities to reach the objective(s) (e.g., investing in advertising, interviews with product users, and so on). In education, leaders do much the same. Educational leaders identify objectives (e.g., equitable timetabling for teachers or improved test scores), select an evaluation method (e.g., satisfaction rates of teachers and number of hours assigned per person or additional time for student test preparation), then choose activities that help them reach their goals (e.g., buy a timetabling software and review prior years' planning or devise test preparation activities, and so on).

Figure 1. Educational leadership requirements.

Figure 2. The learning sciences.

Figure 3. Backward design literature review parameters.

Determining the objective in Backwards design is complicated mainly due to the "framing" created by word choice ( Nelson, 2011) and influenced by heuristics ( Lockton et al., 2013). The precise articulation of objectives permits a clear and logical decision about the choice of evaluation tool and activities.

In summary, the current study consisted of a literature review of educational leaders' areas of responsibility based on publications between 2000 and 2019, which was compared with learning scientists' areas of responsibilities based on two Delphi panels on the learning sciences ( Tokuhama-Espinosa, 2008, 2017).

The evaluation of educational leader responsibilities was gauged by using the 217 indicators used to measure quality education by OECD member countries between 1975 and 2015 ( Tokuhama-Espinosa, 2015). The evaluation of the learning scientists' responsibilities was measured by using the second Delphi panel's stated research, practice and policy goals ( Tokuhama-Espinosa, 2017).

The results permitted the comparison and contrasting of the ways educational leaders and learning scientists approach the objective of quality education. This comparison identified 30 current educational policies that contradict evidence from neuroscience, psychology and other learning sciences, suggesting a disconnect between educational science and other learning sciences.

Theoretical Frameworks

This paper examined the contrasting views of educational leaders and learning scientists in their articulation and response to important topics in education including school start times, learning modalities, curriculum priorities, evaluation, teacher formation among others. To do so, two distinct theoretical frameworks were used: (a) Heck and Hallinger's Educational Leadership perspective; and (b) Sawyer's construct of the Learning Sciences, each of which is briefly described below.

Educational Leadership

First, today there is less agreement about the significant problems that scholars should address than in past years. Second, scholarly directions seem to be changing, as an increasing number of scholars are approaching educational leadership and management as a humanistic and moral endeavor rather than a scientific one. Third, although there are more diverse and robust methodological tools available for inquiry, programs of sustained empirical research are few in number. Fourth, a reluctance to evaluate the worth of contrasting conceptual and methodological approaches according to an accepted set of scholarly criteria leaves researchers, policy-makers and practitioners to fall back upon individual judgments of what is useful and valid knowledge. Finally, a lack of empirical rigor in the field continues to impact the development of a future generation of researchers (p. 229).

Based on Heck and Hallinger's work, this paper begins with five premises. First, there are diverse problems in education require leadership attention, and there is not always agreement on the priorities. Second, education is both a humanistic as well as scientific endeavor. Third, more diverse and robust methodological tools should be used in educational inquiry. Fourth, individual judgement is valid but should be informed by data. And fifth, empirical rigor should be improved in educational policy decisions.

The Learning Sciences

The theoretical framework for the learning sciences is derived from Sawyer's work ( Sawyer, 2005a, b, 2008) as he has been one of the emergent field's best chronologists and has been featured as a main author in this field by the OECD and the Cambridge Handbook of the Learning Sciences. Sawyer suggests that

Learning sciences is an interdisciplinary field that studies teaching and learning. Learning scientists study learning in a variety of settings - not only the more formal learning of school classrooms, but also the more informal learning that takes place at home, on the job, and among peers. The goal of the learning sciences is to better understand the cognitive and social processes that result in the most effective learning, and to use this knowledge to redesign classrooms and other learning environments so that people learn more deeply and more effectively...This new science is called the learning sciences because it is an interdisciplinary science; the collaboration among these disciplines has resulted in new ideas, new methodologies, and new ways of thinking about learning [bold by author] ( Sawyer, 2008, p. 1).

A premise of this paper derived from Sawyer's work is on the role of researchers, teacher-practitioners and educational leaders as people who can leverage the new knowledge from the learning sciences to redesign learning.

Analysis

To analyze the comparison of the educational leadership objectives with learning science objectives, Tokuhama-Espinosa's decision-making process for the evaluation of quality education ( Tokuhama-Espinosa, 2015) was compared with the Delphi panel's 2017 delineation of goals for quality education ( Tokuhama-Espinosa, 2017).

The Evaluation of "Quality" in Education

A quote attributed to many people including Cameron (1963) is apropos in analyzing findings: "Not everything that counts can be counted, and not everything that can be counted counts." There is a temptation in education to measure what is easily measurable, rather than identifying what may be the most important ( Buros, 1977; Madaus and Stufflebeam, 1988; Coe and Fitz-Gibbon, 1998; Harvey and Newton, 2004; Ozga et al., 2011; Belfield and Brooks Bowden, 2019). This paper considers how educational decision-making often falls into the temptation of low-cost initiatives rather than framing decision-making around stated objectives using the learning sciences. This slant in favor of a business model of education is easily justified in the realm of economic planning, but becomes less defensible when framed in light of student learning objectives.

As Tokuhama-Espinosa found in a 2015 review of 40 years of literature on the evaluation of quality education, decision-makers "may resort to measuring easily available or less costly data, as opposed to appropriate data for their objectives" ( Tokuhama-Espinosa, 2015, p. 115). The 271 indicators used by OECD countries to measure quality indicators were divided into 11 categories, which were similar to the areas of responsibility mentioned by educational leaders: (a) Coverage; (b) Equality and Equity; (c) Retention, Completion and Staying Rates; (d) Standards; (e) Academic Achievement; (f) Teachers and Teaching; (g) Evaluation; (h) Finance; (i) Governance; (j) Family and Community; (k) Context (Culture, Legal, Demographic). Findings from the study showed that the "common or standard approach" of those responsible for educational quality was to identify quality education indicators by appraising "what is easily accessible, less costly, politically weighty, or recommended by large ...organizations," ( Tokuhama-Espinosa, 2015, p. 115) rather than to "find systematic, recurring reflective processes that respond to the many distinct aspects of indicator choice," (p. 115). In the case of educational decision-makers at the school level, pressure to use evaluation tools is compounded by market forces ( Burch, 2006) which sometime reveal a circular relationship between textbook publishers and testing companies ( Saltman, 2016) and may all but ignore indicators for student success that do not lend themselves to a multiple-choice test ( Duckworth et al., 2012). It is notable that educational leaders who prioritize student learning over economic thrift often have long-term payoffs at both the individual student level as well as at a macro level of regional, state or country benefits ( Hanushek and Woessmann, 2010, 2015).

Results

Two important findings were made. First, priorities in educational leadership are not the same as priorities in the learning sciences as determined by the way each group frames educational problems or decisions. Second, what is deemed important by society, educational leaders, parents and students related to education may vary and is not always easy to measure.

Problem Framing and Articulation

Educational leaders and learning scientists often see similar problems but articulate their research questions differently, leading to distinct ways of both measuring progress toward objectives as well as the choice of appropriate activities. For example, both learning scientists as well as educational leaders are concerned about the curriculum (e.g., Glatthorn et al., 2018; Tokuhama-Espinosa, 2019), the school calendar (e.g., Farbman et al., 2015; Finnie et al., 2018), and teacher training (e.g., Allen and Penuel, 2015; Darling-Hammond et al., 2017), but they approach these issues differently based on the words they use to state their objectives. The literature suggests at least 30 questions for which both educational leaders and learning scientists have conducted research and sought answers (see Table 1). Their approaches, however, were influenced by word choice and heuristics in how they framed the problems.

Table 1. How decisions in education would look if mind, brain, and education scientists ran schools.

Word Choice: Framing Questions to Identify Objectives

Table 1 explains 30 different topics in education broken down into six groupings: (a) time; (b) space; (c) content; (d) order; (e) the meaning of "success"; and (f) teachers and teaching. Each table identifies the general topic, followed by a question or problem statement. After this question or problem statement there are two columns, one each for educational leaders and learning scientists. Below each is a question based on actual research studies that may run through the mind of each coupled with its matching objective.

The ways problems are articulated are shaped by at least three factors: (a) vocabulary ( Dove, 2014), (b) heuristics and bias ( De Martino et al., 2006), and (c) the professional vocabulary of one's field of formation ( Loewenstein, 2014). Fairhurst points out that communication style and problem articulation are not merely surface decisions, but rather are deeply seeded in the ontological foundations of different types of thinkers. Because educational leaders and learning scientists frame problems differently, they see different objectives within each category. This results in different priorities in educational improvement initiative, which will be explored in the discussion.

Identifying Important Problems in Education: Measurable vs. Important

Measurable

A second interesting result was that the literature review revealed that the 217 indicators mentioned in the 2015 study by Tokuhama-Espinosa was an accumulative list of all of the indicators used over a 40-year period by 34 OECD member countries. This means it reported what had been used but did not factor in potential question or problem statements posed by the larger community nor those posed by learning scientists. This suggests a disconnect between educational leaders' decision-making priorities and what both the general public and learning scientists consider worthy of resources.

While the "Top 10 Issues in Education" ( Knowles, 2018; Chen, 2019; Rich, 2019) reported in the popular press are for the most part included in the indicators used by the OECD countries to measure quality-including hiring the right people to be teachers in the first place and supporting them to find professional success-some important indicators with a high potential to change educational outcomes were not measured by the OECD countries-such as how early bilingualism improved overall cognition or how later school day start times influenced learning outcomes.

The strong focus on measurable outcomes is rooted in data driven decision-making which prioritizes the quantitative (often short-term) aspects of quality education and often overlooks the qualitative (often long-term) aspects of quality education, which imbalances the evaluation of multiple indicators ( Schintler and Kulkarni, 2014). Cost is often a factor in determining measurement indicators; budget restraints may push educational leaders to opt for evaluation tools that that thrifty rather than thorough or important.

Important

A third example comes from early childhood education. It has been known for the past 60 years that high quality early childhood education has a lasting impact on student learning outcomes ( Pianta et al., 2016). Good early childhood programs also improve self-regulation skills ( Montroy et al., 2016), which have greater impact than innate intelligence on academic success ( Moffitt et al., 2011). It is also clear that strong early childhood education programs shape good nutritional habits ( Schwartz et al., 2011), sleep habits ( Cespedes et al., 2014), and social skills ( Jones et al., 2015), among others.

A fourth and final example comes from bilingual education. Early biliteracy skills have been shown to mitigate the effects of poverty ( Petitto and Dunbar, 2009), yet foreign language and bilingual programs are some of the first courses (along with physical education and the arts) to be cut from educational curricula to make room for more first language and math in order to respond to standardized tests ( Crocco and Costigan, 2007). It is ironic that the enhanced executive functions from early bilingualism ( Bialystok, 2018) which have a spillover effect in all other subject areas, is eliminated from some schools in order to try and improve learning which would naturally be enhanced by bilingual education.

The decision to choose indicators which are easily measurable over those that may be more important and potentially yield better educational outcomes will be explored in the Discussion section.

Discussion

This suggests that the way problems are framed influences perspective-taking changes primarily in one of two ways. Either the understanding and articulation of the problem are changed by the heuristics of the end user (leader or scientist), or the choice of available evaluation tools drives the objective and not the other way around. This implies that there are four possible reasons why educational science has not yet considered itself a learning science: (a) Problem articulation, (b) priorities from the learning sciences not being on educational leaders' agendas, (c) the need to nudge educational leaders into the learning sciences, and (d) no natural access to learning science information on the part of educators. Each will be explained in more detail below.

Why Don't Educational Leaders Think Like Learning Scientists?

The findings suggests that the indicators currently used to make educational decisions by school leaders may be incomplete as they ignore much of the data from the learning sciences. Given the gravity of the stakes-quality education-it is important educational leaders take advantage of all of the information possible to make the best decisions in all areas of responsibility. This means that educational leaders may improve outcomes if they think more like learning scientists. Evidence from the literature indicates that there are at least four contributors as to why educational leaders do not normally consider these high impact decisions. Each of these reasons is followed by a hypothetical solution.

Problem Articulation

Contribution 1

An initial hypothesis as to why educational leaders do not think like learning scientists suggests that the problem is not only systemic in terms of professional access, but that it may also be a problem of semantics. The first contribution is that educational leaders and learning scientists articulate problems differently. That is, when asked, what is the objective? In Backward Design thinking, they would phrase and focus questions and problems with the lenses of their professions, blinding them to different perspectives.

While the use and the steps in Backward Design are the same for business leaders as for educational leaders, business leaders are often facilitated in their objectives by the quantitative nature of problem conception. That is, business goals tend to be less "messy" as they broadly focus on "clean" numbers ( Mandinach and Jackson, 2012), whereas educational goals are complicated by multiple human variables. Educational leaders walk a fine line between the business objectives of efficiency, balanced budgets, satisfied workers, and so on, and educational objectives or quality teaching and learning and the human side of education ( Daly, 2012).

The professional schizophrenia between efficiency and expediency on the one side and caring human investment on the other also explains why some educational leaders confound variables in decision making. For example, learning scientists know that test scores do not measure intelligence but rather sub-elements of limited aspects of intelligence. The complexities of human intelligence cannot be measured in a single test, no matter how comprehensive ( Tokuhama-Espinosa, 2018). Ever since "intelligence" tests were conceived in the 1920s, the objective of improved "student learning outcomes" has used test scores, primarily in math and language, as a proxy for intelligence. Despite multiple detailed arguments to the contrary, the objective (learning) and the evaluation tool (multiple choice tests) do not align as Backward Design would suggest. However, it is very expedient to use a single tool such as a standardized test. Tests are also less costly, which explains why many educational leaders may be convinced of standardized test utility ( Gipps, 2011). Mistakes stemming from the articulation of the problem explain why educational leaders and learning scientists reach different conclusions about standardized testing and other key decisions in school settings.

Not on the Radar

Contribution 2

Second, it has been suggested that the lack of attention toward some of the information from the other learning sciences, such as neuroscientific data about school start times or social-anthropological data about school calendars, is because these are not recurrent or annual leadership decisions ( Robinson et al., 2008) and are simply not "on the agenda" or "the radar" due to time constraints ( Maule et al., 2000). That is, the general day-to-day demands and the wide spectrum of responsibilities of educational leaders does not permit them the time to add in additional decisions that are not regularly attended to.

This could potentially be resolved by calling attention to the importance of these topics and their relatively weighty impact on student learning outcomes, including significant effect sizes reported in recent work by Hattie and Yates (2013) and others. Despite decades of evidence, however, the National Academies of Sciences, Engineering and Medicine suggests in their consensus study report, How People Learn II (2018) that educational leaders may be aware, they just do not prioritize these findings. This leads to a third hypothesis which is at the heart of leadership.

The Need for a Nudge

Contribution 3

Most humans conform to the status quo unless otherwise nudged ( Thaler and Sunstein, 2009). Leaders tend to be the people who can do the nudging; however, this can go against some leaders' human nature. Some suggest that the lack of attention to these types of decisions is due to heuristics ( Busenitz and Barney, 1997; Hafenbrädl et al., 2016). Humans do not pay attention or question school start times, school calendars, early childhood education policies or bilingual education because they presume if there was an important decision to be made, "everyone" would be talking about doing it ( Kim and Gambino, 2016). It takes a truly bold leader to break from the pack and the risk-aversion that accompanies change. Most people are uncomfortable with "rocking the boat" or creating dissonance in the community, and therefore, safe inertia is prized over unstable change ( Hayes, 2018).

To compound this challenge, some educational leaders may know about the evidence from the learning sciences but either are unwilling or unable to communicate it to their constituencies or do not have a built-in context through which to facilitate this data. This means that educational inertia is both a sin of inactivity as well as inability. In the first case, the leader is culpable of taking the path of least resistance and ignoring the information. In the second, however, leaders may be willing to try and nudge for change, but they may either not be good enough communicators of the message, or they may be faced with a more articulate resistance. For example, many educational leaders report that their attempts to change school start times or academic calendars was met with push-back from the community who "resisted change." Rather than "power through" toward successful implementation, they bow to the pressure of a less informed community. Or, if they attempt to rebut the resistance and fail, it may be due to their lack of persuasive dialogue. Leadership requires perseverance as well as good communication ( Marques and Dhiman, 2016), characteristics not always present in administrator profiles.

No Natural Access to Information

Contribution 4

A final contribution as to why educational leaders do not think about high-impact decisions as seen by learning scientists is that they are simply unaware they exist. Many from the learning science community believe educational leaders do not access the information or data they need to make decisions ( Thomas et al., 2019), possibly because information from the learning sciences is not naturally shared with those in educational leadership ( Morris and Sah, 2016).

Information on the learning sciences has slowly become more available to educators, however. Access has improved over the past two decades with the emergence of more frequent high-quality opportunities for professional development such as the International Mind, Brain, and Education conference, the Learning & the Brain conference and educational leaders themselves who seek out professional advice and consulting from learning scientists. The Deans for Impact, a consortium of teacher colleges in the U.S., published a basic guidebook on the learning sciences and motivates educational leader participation in academic programs that bridge administration and knowledge from the learning sciences (2015). New online communities, such as Nature Partner Journal's Science of Learning community and the journals Learning Sciences, Science of Learning, Mind, Brain and Education, and others seek to merge knowledge from educational sciences and other learning sciences such as neuroscience and psychology, for better decision making.

The existence of such resources, however, does not guarantee that educational leaders use them. This means that educational leaders should be more pro-active in exploring the other learning sciences until a natural conduit of information to and from each of the learning sciences is facilitated.

What Would Happen if Educational Leaders Thought More Like Learning Scientists?

These reflections beg the final question: What would happen if educational leaders could be more conscious of their heuristics and how they articulate problems, and consequently attempt to view problems within schools from a learning science perspective?

There have been many shifts in the role of educational leaders over the past 50 years. None perhaps is as challenging as bridging knowledge across disciplines. The slow uptake of information produced in the learning sciences into schools is testament to the complexity of transdisciplinary thinking ( Fam et al., 2018). The mental reframing of problems to reflect a broader use of data from all of the learning sciences has the potential to significantly improve education and should, therefore, be considered by educational leaders.

This suggests that educational leaders must ask themselves whether they can see the forest for the trees. That is, what is the real objective of education? For many decades, the cost-effective model ( Levin and Belfield, 2015), which has now been fueled by the data-driven model ( Meyer et al., 2016), has steered leadership decisions in favor short-term economic indicators rather than long-term learning as the ultimate measure of "quality" and often without considering the roles of all actors involved ( Levin and Datnow, 2012). If education is meant to produce human cogs in a societal wheel of progress in an efficient way, then we may already have the perfect system. However, if formal education conducted in schools is meant to maximize the potential of all students to learn, then perhaps we should start thinking more like learning scientists.

Conclusions

This study centered around the research question, How and to what extent does educational science take into consideration information from other learning sciences when making policy decisions? It was determined that policy decisions by educational leaders are complex, broad in their scope and have been studied by many researchers over the years in multiple context and with varying lenses (e.g., Darling-Hammond et al., 2007). Aside from the recommendations from the Deans for Impact (2015), however, the literature review of this study did not reveal consistent or systematic use of data from other learning sciences such as neuroscience by educational leaders. This means that the answer to the original research question is that many educational leaders do not take into consideration much of the information available from the other learning sciences when they make policy decisions, despite its availability. This is due to numerous factors, including problem articulation (semantics, heuristics), not being on educational leaders' radars due to other priority issues which are more familiar, the need for a nudge into the unfamiliar territory of the learning sciences, and that education about the learning sciences is not a "natural" part of educational leaders' typical formation. This clearly points to an explanation as to why educational science has lagged behind other learning sciences, and creates an interesting invitation to do so now.

After considering the results, this study suggests a new research question: Can the learning sciences catalyze a change in educational policy priorities? Which is explained below.

Can the learning sciences catalyze a change in educational policy priorities?

Educational leaders and learning scientists both contribute to the improvement of educational objectives in society but do so from distinct perspectives due to their academic formations and accompanying heuristics. Educational science is a sub-field of the learning sciences, yet few educators consider themselves scientists in this respect. The results of this study suggest that if educational leaders thought more like other learning scientists, priorities in educational policy decisions would change to privilege the important quality indicators in education rather than easily measurable or less costly indicators of student learning outcomes.

Some promising examples have been seen in international schools which have the luxury of applying learning science findings into real school settings. Punahou School's educational leadership team in Hawaii, for example, has made school policies on daily start-times, physical space and ergonomics, transdisciplinary thinking and mastery learning goals, and the planning of social-emotional integration policies a priority (McCaren, personal conversation May 2018) thanks to their learning science decision-making. The International School in Geneva has modified school policies in hopes of ensuring all education contributes to a life-long love of learning by articulating the ways "school" and "work" can and should be integrated (Hawley and David, personal conversation, June 2028). The International School of Bangkok has recently tried "flipping" their leadership meetings (sending video summaries before actually meeting) to leverage time for deeper and more personal exchanges and to make policy decisions after taking advantage of the social aspects of learning (Simmers, personal conversation, October 2019). A recent meta-analysis of K-12 schools that have moved to year-round schooling show modest but positively improved results ( Fitzpatrick and Burns, 2019). Glen Whitman at the Center For Transformative Teaching has been training teacher leaders to understand the principles of learning science and to incorporate them into real classroom settings. His school, St. Andrew's Episcopal, assures that "100% of their teachers are trained in Mind, Brain, and Education science" before working with students. The Kamehameha Schools in Hawaii have also recently undergone a 2-year induction into the learning sciences and are now basing policy decisions on the learning sciences (Wong, personal conversation September 2019), including the decision to incorporate early bilingual education (Fields, personal conversation, October 2019).

While these examples are of privileged school systems, they nonetheless point to encouraging examples of better learning science decision making into educational leadership practice. Many of the low to no cost policy changes which rely on new teacher training for professional development possibilities have already been adopted by some teacher colleges ( Deans for Impact, 2015) and thousands more educational leaders in the public school systems have been trained in conference formats such as Learning & the Brain and Mind, Brain, and Education ( Kelleher and Whitman, 2018) and through programs like "BrainU" at the University of Minnesota ( Dubinsky et al., 2013).

To reach the stage where educators begin to think more like learning scientists, several barriers will have to be overcome. Most importantly, access and integration of data from the learning scientists needs to be shared in a more systematic way with educators. Once available, this data must be communicated well to all actors in formal education. This in turn is dependent on broadening educational leaders' perspectives to include the semantics and heuristics of learning scientists, a choice left solely to the leaders themselves. It is also important to remember that the integration of the learning sciences alone will not cure all of the ills facing education. To respond more agilely to the challenges in education, a broader look across not only the learning sciences is needed, but also visionary perspectives from social and economic sciences as well.

Additional research will help advance the discussion around educational leaders in the learning science context. First, more research should be conducted to directly query actors about their views. How do educational leaders feel about the utility of the information coming from the other learning sciences? Do educational leaders consider that data from the learning sciences has a role to play in their decision-making processes, and if so, what is it? Second, there should be additional research as to the specific heuristics and semantics used in the different learning sciences to better understand the historical evolution of communication challenges. Third, more research is needed to identify additional areas that are of mutual concern to educational leaders and to learning scientists. Fourth, it is recommended that collaborative research efforts by educational leaders and learning scientists be conducted in order to cross-pollinate ideas for problem resolution. Such steps can potentially launch a shift in educational leadership and decision-making that is informed by the learning sciences, which would benefit both individuals and their communities.

Data Availability Statement

All datasets generated for this study are included in the article/ Supplementary Material.

Author Contributions

TT-E design, applied, summarized, analyzed and wrote the study, and its findings.

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The author wishes to thank the following people for their time and energy toward the generation of this study, the lively debate and the on-going commitment to better educational practices. Thanks to the following for participating in the Delphi survey, offering to review findings, recommending panelists and/or for words of encouragement: Doris Alvarez, Eliot Berkman, Virginia Berninger, Jane Bernstein, Jeffrey Bowers, John T. Bruer, David Daniel, Bert DeSmedt, Stanislas Dehaene, Phil Fisher, Uta Frith, Howard Gardner, Walter Glannon, Ezequiel Gleichgerrcht, Usha Goswami, Ronald H. Grabner, Sonia Guerreiero, Mariale Hardinman, Takao Hensch, Paul Howard-Jones, Mary Helen Immordino-Yang, Jelle Jolles, Mary Layne Kalbfleisch, Johannes König, Iveta Kovalčíková, Abigail Larrison, Marilyn Leaske, Gigi Luk, Susan Magsamen, Pedro Maldonado, Richard Mayer, Bruce McCandliss, Renata Meneces, John Morrison, Eric Pakulak, Michael Posner, Sidarta Ribeiro, Vanessa Rodriguez, Todd Rose, Pankaj Sah, Daniel Schwartz, Jack Shonkoff, Beate Sodian, Elsbeth Stern, Michael Thomas, Nienke van Atteveldt, Judy Willis, and Patricia Wolfe. Thanks to Thomas Gorham, Julia Volkman, Juad Masters, Drew Nelson, Robert Murphy, and Curtis Kelly for their enthusiasm for this project and to Dee Rutgers for keeping me on a writing schedule.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2019.00136/full#supplementary-material

References

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The Learning Sciences Framework in Educational Leadership

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The learning sciences clarify how people learn best under which conditions and how human variability influences outcomes. Despite great advancements in some learning sciences over the past 30 years, there has been relatively little change in educational science, a sub-field of the learning sciences.

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Patricia Furstenberg, Writer of Historical Fiction, Children's Books - Travel via Stories

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The Six Principles That Move Mind, Brain, Education, MBE - Patricia Furstenberg, Writer of Historical Fiction, Children’s Books

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The Six Principles That Move Mind, Brain, Education, MBE, ITSI SA. Education, the limits of the human brain. What are the limits of the human brain? How do we learn best? How can we aid and support our children in reaching their full potential as students? Throughout the centuries, great teachers have been guided by

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Do you have much experience with Universal Design? Today we're thrilled to welcome a guest author who has not only implemented UDL on her own, but is assisting her colleagues in applying the principles in their classrooms as well. Tianhong Shi, instructional designer with Oregon State Extended Campus, joins WCET Frontiers today to tell us about her journey learning about UDL, applying the concepts in a variety of learning settings, and expanding the design across the OSU campus. Enjoy the read and enjoy your day,
Universal Design for Learning

Universal Design for Learning (UDL) is a practical tool for guiding course design to ensure that every learner succeeds, based on scientific insights into how humans learn (). There are three main principles of UDL:

1. multiple means of representations,

2. multiple means of engagement, and

3. multiple means of expressions.

UDL has roots in Inclusive pedagogy, which started around 1970s in the United States and as early as 1918 in the United Kingdom.

Why UDL?

With the multiple means of presentation, engagement and expressions, UDL strives to make content accessible to all learners, it stimulates interests and motivation for learning and it provides a pathway for every learner to succeed (; udlcenter.org).

By teaching students according to their individual needs, we make sure that every student succeeds and prepare them for the future (versus preparing for our own past by teaching them how we were taught) (Katie Novak,).

"You don't just get kids in the driver suit of learning; you get expert drivers once you use UDL" (Bill McGrath at UDL-IRN Summit 2018).

My Journey in UDL

In 2017, as part of the ID2ID program (a peer mentoring program for instructional designers organized by EDUCAUSE and Penn State University), my mentee Irene Knokh and I identified UDL as a learning topic. We read books such as Universal Design In Higher Education-Promising Practices, completed a free Canvas UDL training course (designed and taught by), browsed web resources (UDL at glance, ), and attended a webinar on Implementing UDL by Thomas Tobin.

Designing Courses Using UDL Principles: Removing Barriers

After intensive self-directed learning on UDL for several months, I started to promote UDL among online instructors at Oregon State University. My greatest gratitude goes to Oregon State University (OSU) online instructors Ted Paterson,, and Yvette Gibson who allowed me to share with them what I learned about UDL and started implementing the principles in their online courses in the Spring 2018 term. There are many applications of UDL design principles in some of OSU's Spring 2018 online courses.

During my meetings with Ted, Victor, and Yvette, we identified barriers and challenges to learning and designed courses to remove such barriers.

For Ted, the biggest challenge is the intensive reading and writing in managing ethics study. So, we added estimated average reading time for each reading assignment and created animated videos to explain complicated yet crucially important writing assignments.

For Victor, the challenge to his students was the abstractness of biophysics concepts in macromolecular structure study. To overcome this barrier, we designed simulation videos to explain the concepts and used graphics to illustrate other concepts. Students were assigned to use video, graphics, and texts to explain key concepts in meaningful ways.

For Yvette, the challenge was how to make learning meaningful and applicable in shrubland ecology study. We used learner-generated content as the main teaching strategy for the course and students were assigned to co-write portions of the textbook and create resources that will be published for public use upon instructor approval.

Examples of class activities in Ted's Managing Ethics course:

Text reading (Chapter 1 of textbook).

Listening to podcast (Ponzi Supernova podcast audio from Radio-lab).

Watching videos of instructor lectures.

Watching animated video explanation of a 7-page instruction for the term writing project.

Discussion forums: students post answers to prompts; students reply to peer classmates' posts.

Student developed Personal Ethical Action Plan (instructor provides feedback for students to incorporate prior to final due date).

Examples of class activities in Victor's Macromolecular Structure course and Yvette's Shrubland Ecology course:

Create a three-dimensional image.

Create a video to explain what "reciprocal space" means to the individual student.

Complete a literature search and review.

Write a letter to a relative to explain why the Fourier transform is so important to NMR spectroscopy.

Co-author part of course textbook.

UDL Implementation Success Tips

Promoting the implementation of UDL is bound to be a rough journey that is full of challenges. Why is it still worthwhile to do it? By adopting UDL principles, you will aid in a student's efforts to become expert learners.

Consider the following:

What challenges am I overcoming?

How could UDL add value to the learning design?

Implement one thing at a time (suggested by Thomas Tobin).

Consider UDL as operating system, not just as a framework (suggested by) in the days when you feel your UDL pioneering journey is becoming too rough: it is at work without you noticing it!

The College STAR program offers free access to UDL- Based teaching practices for faculty and staff members to implement in their college courses. College STAR also provides Incentive funding for faculty members to join Virtual Learning Communities and submit proposals which will be developed into online modules and case studies. Learn more about College STAR.

Here is a list of some tools that can help instructors implement UDL and several additional UDL resources for you to use and share.

Poll & Reflection Sharing

I would like to end this post with a simple poll (based on Katie Novak's presentation at UDL-IRN Summit 18) and a reflective question for us all to ponder:

"In your UDL journey for the next two years, what would you hope to be and how would you achieve that?"

Feel free to share your thoughts and ideas on this padlet wall.

Tianhong Shi
Instructional Designer
Extended Campus - Oregon State University
Tianhong.shi@oregonstate.edu
@tianhongshi

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Do you have much experience with Universal Design? Today we're thrilled to welcome a guest author who has not only implemented UDL on her own, but is assisting her colleagues in applying the principles in their classrooms as well. Tianhong Shi, instructional designer with Oregon State Extended Campus, joins WCET Frontiers today to tell us...

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TEDxEnola - Dr. Mariale Hardiman - The Brain-Targeted Teaching Model

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TEDxEnola - Dr. Mariale Hardiman - The Brain-Targeted Teaching Model

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Putting Neuroscience in the Classroom: How the Brain Changes As We Learn

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The more we know about how the developing brain changes as we learn, the better we can support young learners.

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National Center on Universal Design for Learning

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Cognition, Metacognition, and Epistemic Cognition: A Three-Level Model of Cognitive Processing on JSTOR

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A three-level model of cognitive processing to account for complex monitoring when individuals are faced with ill-structured problems, i.e., problems on which o...

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Smithsonian Science Education Center (SSEC)

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Good Thinking! — That’s so Meta(cognitive)!

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TrillEDU:  Culturally Responsive Pedagogy...  | Jeffrey Dessources | TEDxNewJerseyCityUniversity

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Carl Schoonover: How to look inside the brain

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Jay McTighe has a varied career in education. He served as director of the Maryland Assessment Consortium, a collaboration of school districts working to develop and share formative performance assessments and helped lead standards-based reforms at the Maryland State Department of Education. Prior to that, he helped lead Maryland's standards-based reforms, including the development of performance-based statewide assessments.

Well known for his work with thinking skills, McTighe has coordinated statewide efforts to develop instructional strategies, curriculum models, and assessment procedures for improving the quality of student thinking. He has extensive experience as a classroom teacher, resource specialist, program coordinator, and in professional development, as a regular speaker at national, state, and district conferences and workshops.

McTighe is an accomplished author, having coauthored more than a dozen books, including the award-winning and best-selling Understanding by Design® series with Grant Wiggins. He has written more than 50 articles and book chapters and has been published in leading journals, including Educational Leadership (ASCD) and Education Week.
UNDERSTANDING BY DESIGN® and UbD® are registered trademarks of Backward Design, LLC used under license.

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Upgrade Your Teaching: Understanding by Design Meets Neuroscience

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How can educators leverage neuroscience research about how the human brain learns? How can we use this information to improve curriculum, instruction, and assessment so our students achieve deep learn

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Too Much Stress Can Literally Shrink Your Brain, Experts Say

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A Brain Marker Pinpoints Vulnerability for Future Life Stress {Duke University Research}

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Check out 8:37

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There is growing evidence that simple, everyday changes to our lives can alter our brains - and we all have the power to control that to some extent, a process known as neuroplasticity.

How do we keep our brains healthy? And what can we do to help strengthen crucial connections and keep our minds younger in the process?

In these four films, science journalist Melissa Hogenboom looks at the most cutting-edge scientific research and has her own brain scanned and analysed, with intriguing results.

Part 1 of 4 looks how how meditation can rewire the brain.

A film by Melissa Hogenboom, Tom Heyden & Pierangelo Pirak

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There is growing evidence that simple, everyday changes to our lives can alter our brains.

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Memory is the ongoing process of information retention over time. Because it makes up the very framework through which we make sense of and take action within the present, its importance goes without saying. But how exactly does it work? And how can teachers apply a better understanding of its inner…

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Abstract

In the recent literature there has been considerable confusion about the three types of memory: long-term, short-term, and working memory. This chapter strives to reduce that confusion and makes up-to-date assessments of these types of memory. Long- and short-term memory could differ in two fundamental ways, with only short-term memory demonstrating (1) temporal decay and (2) chunk capacity limits. Both properties of short-term memory are still controversial but the current literature is rather encouraging regarding the existence of both decay and capacity limits. Working memory has been conceived and defined in three different, slightly discrepant ways: as short-term memory applied to cognitive tasks, as a multi-component system that holds and manipulates information in short-term memory, and as the use of attention to manage short-term memory. Regardless of the definition, there are some measures of memory in the short term that seem routine and do not correlate well with cognitive aptitudes and other measures (those usually identified with the term "working memory") that seem more attention demanding and do correlate well with these aptitudes. The evidence is evaluated and placed within a theoretical framework depicted in .

Keywords: attention, capacity of working memory, control of attention, decay of short-term memory, focus of attention, long-term memory, short-term memory, working memory

Historical roots of a basic scientific question

How many phases of a memory are there? In a naïve view of memory, it could be made all of one cloth. Some people have a good ability to capture facts and events in memory, whereas others have less such ability. Yet, long before there were true psychological laboratories, a more careful observation must have shown that there are separable aspects of memory. An elderly teacher might be seen relating old lessons as vividly as he ever did, and yet it might be evident that his ability to capture the names of new students, or to recall which student made what comment in an ongoing conversation, has diminished over the years.

The scientific study of memory is usually traced back to Hermann Ebbinghaus (1885/1913 translation), who examined his own acquisition and forgetting of new information in the form of series of nonsense syllables tested at various periods upto 31 days. Among many important observations, Ebbinghaus noticed that he often had a "first fleeting grasp ... of the series in moments of special concentration" (p. 33) but that this immediate memory did not ensure that the series had been memorized in a way that would allow its recall later on. Stable memorization sometimes required further repetitions of the series. Soon afterward, James (1890) proposed a distinction between primary memory, the small amount of information held as the trailing edge of the conscious present, and secondary memory, the vast body of knowledge stored over a lifetime. The primary memory of James is like the first fleeting grasp of Ebbinghaus.

The Industrial Revolution made some new demands on what James (1890) called primary memory. In the 1850s, telegraph operators had to remember and interpret rapid series of dots and dashes conveyed acoustically. In 1876, the telephone was invented. Three years later, operators in Lowell, Massachusetts started using telephone numbers for more than 200 subscribers so that substitute operators could be more easily trained if the town's four regular operators succumbed to a raging measles epidemic. This use of telephone numbers, complemented by a word prefix, of course spread. (The author's telephone number in 1957 was Whitehall 2-6742; the number is still assigned, albeit as a seven-digit number.) Even before the book by Ebbinghaus, Nipher (1878) reported on the serial position curve obtained among the digits in logarithms that he tried to recall. The nonsense syllables that Ebbinghaus had invented as a tool can be seen to have acquired more ecological validity in an industrial age with expanding information demands, perhaps highlighting the practical importance of primary memory in daily life. Primary memory seems taxed as one is asked to keep in mind aspects of an unfamiliar situation, such as names, places, things, and ideas that one has not encountered before.

Yet, the subjective experience of a difference between primary and secondary memory does not automatically guarantee that these types of memory separately contribute to the science of remembering. Researchers from a different perspective have long hoped that they could write a single equation, or a single set of principles at least, that would capture all of memory, from the very immediate to the very long-term. McGeoch (1932) illustrated that forgetting over time was not simply a matter of an inevitable decay of memory but rather of interference during the retention interval; one could find situations in which memory improved, rather than diminish, over time. From this perspective, one might view what appeared to be forgetting from primary memory as the profound effect of interference from other items on memory for any one item, with interference effects continuing forever but not totally destroying a given memory. This perspective has been maintained and developed over the years by a steady line of researchers believing in the unity of memory, including, among others, Melton (1963), Bjork and Whitten (1974), Wickelgren (1974), Crowder (1982, 1993), Glenberg and Swanson (1986), Brown et al. (2000), Nairne (2002), Neath and Surprenant (2003), and Lewandowsky et al. (2004).

Description of three kinds of memory

In this chapter I will assess the strength of evidence for three types of memory: long-term memory, short-term memory, and working memory. Long-term memory is a vast store of knowledge and a record of prior events, and it exists according to all theoretical views; it would be difficult to deny that each normal person has at his or her command a rich, although not flawless or complete, set of long-term memories.

Short-term memory is related to the primary memory of James (1890) and is a term that Broadbent (1958) and Atkinson and Shiffrin (1968) used in slightly different ways. Like Atkinson and Shiffrin, I take it to reflect faculties of the human mind that can hold a limited amount of information in a very accessible state temporarily. One difference between the term "short-term memory" and the term "primary memory" is that the latter might be considered to be more restricted. It is possible that not every temporarily accessible idea is, or even was, in conscious awareness. For example, by this conception, if you are speaking to a person with a foreign accent and inadvertently alter your speech to match the foreign speaker's accent, you are influenced by what was until that point an unconscious (and therefore uncontrollable) aspect of your short-term memory. One might relate short-term memory to a pattern of neural firing that represents a particular idea and one might consider the idea to be in short-term memory only when the firing pattern, or cell assembly, is active ( Hebb, 1949). The individual might or might not be aware of the idea during that period of activation.

Working memory is not completely distinct from short-term memory. It is a term that was used by Miller et al. (1960) to refer to memory as it is used to plan and carry out behavior. One relies on working memory to retain the partial results while solving an arithmetic problem without paper, to combine the premises in a lengthy rhetorical argument, or to bake a cake without making the unfortunate mistake of adding the same ingredient twice. (Your working memory would have been more heavily taxed while reading the previous sentence if I had saved the phrase "one relies on working memory" until the end of the sentence, which I did in within my first draft of that sentence; working memory thus affects good writing.) The term "working memory" became much more dominant in the field after Baddeley and Hitch (1974) demonstrated that a single module could not account for all kinds of temporary memory. Their thinking led to an influential model ( Baddeley, 1986) in which verbal-phonological and visual-spatial representations were held separately, and were managed and manipulated with the help of attention-related processes, termed the central executive. In the 1974 paper, this central executive possibly had its own memory that crossed domains of representation. By 1986, this general memory had been eliminated from the model, but it was added back again by Baddeley (2000) in the form of an episodic buffer. That seemed necessary to explain short-term memory of features that did not match the other stores (particularly semantic information in memory) and to explain cross-domain associations in working memory, such as the retention of links between names and faces. Because of the work of Baddeley et al. (1975), working memory is generally viewed as the combination of multiple components working together. Some even include in that bundle the heavy contribution of long-term memory, which reduces the working memory load by organizing and grouping information in working memory into a smaller number of units ( Miller, 1956; Ericsson and Kintsch, 1995). For example, the letter series IRSCIAFBI can be remembered much more easily as a series of acronyms for three federal agencies of the United States of America: the Internal Revenue Service (IRS), the Central Intelligence Agency (CIA), and the Federal Bureau of Investigation (FBI). However, that factor was not emphasized in the well-known model of Baddeley (1986).

What is clear from my definition is that working memory includes short-term memory and other processing mechanisms that help to make use of short-term memory. This definition is different from the one used by some other researchers (e.g., Engle, 2002), who would like to reserve the term working memory to refer only to the attention-related aspects of short-term memory. This, however, is not so much a debate about substance, but rather a slightly confusing discrepancy in the usage of terms.

One reason to pursue the term working memory is that measures of working memory have been found to correlate with intellectual aptitudes (and especially fluid intelligence) better than measures of short-term memory and, in fact, possibly better than measures of any other particular psychological process (e.g., Daneman and Carpenter, 1980; Kyllonen and Christal, 1990; Daneman and Merikle, 1996; Engle et al., 1999; Conway et al., 2005). It has been thought that this reflects the use of measures that incorporate not only storage but also processing, the notion being that both storage and processing have to be engaged concurrently to assess working memory capacity in a way that is related to cognitive aptitude. More recently, Engle et al. (1999) introduced the notion that aptitudes and working memory both depend on the ability to control attention, or to apply the control of attention to the management of both primary and secondary memory ( Unsworth and Engle, 2007). However, more research is needed on exactly what we learn from the high correlation between working memory and intellectual aptitudes, and this issue will be discussed further after the more basic issue of the short-term versus the long-term memory distinction is addressed.

Meanwhile, it may be helpful to summarize a theoretical framework ( Cowan, 1988, 1995, 1999, 2001, 2005) based on past research. This framework, illustrated in , helps to account for the relation between long-term, short-term, and working memory mechanisms and explains what I see as the relation between them. In this framework, short-term memory is derived from a temporarily activated subset of information in long-term memory. This activated subset may decay as a function of time unless it is refreshed, although the evidence for decay is still tentative at best. A subset of the activated information is the focus of attention, which appears to be limited in chunk capacity (how many separate items can be included at once). New associations between activated elements can form the focus of attention. Now the evidence related to this modeling framework will be discussed.

The short-term memory/long-term memory distinction

If there is a difference between short- and long-term memory stores, there are two possible ways in which these stores may differ: in duration, and in capacity. A duration difference means that items in short-term storage decay from this sort of storage as a function of time. A capacity difference means that there is a limit in how many items short-term storage can hold. If there is only a limit in capacity, a number of items smaller than the capacity limit could remain in short-term storage until they are replaced by other items. Both types of limit are controversial. Therefore, in order to assess the usefulness of the short-term storage concept, duration and capacity limits will be assessed in turn.

Duration limits

The concept of short-term memory limited by decay over time was present even at the beginning of cognitive psychology, for example in the work of Broadbent (1958). If decay were the only principle affecting performance in an immediate memory experiment, it would perhaps be easy to detect this decay. However, even in Broadbent's work contaminating variables were recognized. To assess decay one must take into account, or overcome, contaminating effects of rehearsal, long-term retrieval, and temporal distinctiveness, which will be discussed one at a time in conjunction with evidence for and against decay.

Overcoming contamination from rehearsal

According to various researchers there is a process whereby one imagines how the words on the list are pronounced without saying them aloud, a process called covert verbal rehearsal. With practice, this process comes to occur with a minimum of attention. Guttentag (1984) used a secondary task to show that rehearsal of a list to be recalled was effortful in young children, but not in adults. If, in a particular experimental procedure, no loss of short-term memory is observed, one can attribute that response pattern to rehearsal. Therefore, steps have been taken to eliminate rehearsal through a process termed articulatory suppression, in which a simple utterance such as the word " the" is repeatedly pronounced by the participant during part or all of the short-term memory task (e.g., Baddeley et al., 1975). There is still the possible objection that whatever utterance is used to suppress rehearsal unfortunately causes interference, which could be the true reason for memory loss over time instead of decay.

That problem of interference would appear moot in light of the findings of Lewandowsky et al. (2004). They presented lists of letters to be recalled and varied how long the participant was supposed to take to recall each item in the list. In some conditions, they added articulatory suppression to prevent rehearsal. Despite that suppression, they observed no difference in performance with the time between items in the response varying between 400 and 1600 ms (or between conditions in which the word "super" was pronounced one, two, or three times between consecutive items in the response). They found no evidence of memory decay.

A limitation of this finding, though, is that covert verbal rehearsal may not be the only type of rehearsal that participants can use. Perhaps there are types that are not prevented by articulatory suppression. In particular, Cowan (1992) suggested that the process of mentally attending to words or searching through the list, an attention-demanding process, could serve to reactivate items to be recalled in a manner similar to covert verbal rehearsal. The key difference is that it would not be expected that articulatory suppression would prevent that type of rehearsal. Instead, to prevent that type of rehearsal an attention-demanding task would have to be used.

Barrouillet et al. (2004, 2007) have results that do seem to suggest that there is another, more attention-demanding type of rehearsal. They have interposed materials between items to be recalled that require choices; they can be numbers to read aloud or multi-choice reaction times. It is found that these interfere with retention to an extent commensurate to the proportion of the inter-item interval used up attending to the distracting items. As the rate of the distracting items goes up, fewer of the to-be-recalled items are recalled. The notion is that when the distracting task does not require attention, the freed-up attention allows an attention- based rehearsal of the items to be recalled. When the interposed task is more automatic and does not require as much attention (e.g., an articulatory suppression task) there is much less effect of the rate of these interposed items.

Based on this logic, one could imagine a version of Lewandowsky's task in which not articulatory suppression but attention-demanding verbal stimuli are placed between items in the response, and in which the duration of this filled time between items in the response varies from trial to trial. The verbal, attention-demanding stimuli should prevent both attention-based rehearsal and articulation-based rehearsal. If there is decay, then performance should decline across serial positions more severely when longer filled intervals are placed between items in the response. Unfortunately, though, such results might be accounted for alternatively as the result of interference from the distracting stimuli, without the need to invoke decay.

What seems to be needed, then, is a procedure to prevent both articulation-based and attention-based rehearsal without introducing interference. Cowan and Aubuchon (in press) tried out one type of procedure that may accomplish this. They presented lists of seven printed digits in which the time between items varied within a list. In addition to some randomly timed filler lists, there were four critical trial types, in which the six inter-digit blank intervals were all short (0.5 s following each item) or all long (2 s following each item), or comprised three short and then three long intervals, or three long and then three short intervals. Moreover, there were two post-list response cues. According to one cue, the participant was to recall the list with the items in the presented order, but at any rate they wished. According to the other response cue, the list was to be recalled using the same timing in which it was presented. The expectation was that the need to remember the timing in the latter response condition would prevent rehearsal of either type. As a consequence, performance should be impaired on trials in which the first three response intervals are long because, on these trials, there is more time for forgetting of most of the list items. Just as predicted, there was a significant interaction between the response cue and the length of the first half of the response intervals. When participants were free to recall items at their own pace, performance was no better with a short first half ( M=.71) than with a long first half ( M=.74). The slight benefit of a long first half in that situation could occur because it allowed the list to be rehearsed early on in the response. In contrast, when the timing of recall had to match the timing of the list presentation, performance was better with a short first half ( M = .70) than with a long first half ( M = .67). This, then, suggests there could be decay in short-term memory.

Overcoming contamination from long-term retrieval

If there is more than one type of memory storage then there still is the problem of which store provided the information underlying a response. There is no guarantee that, just because a procedure is considered a test of short-term storage, the long-term store will not be used. For example, in a simple digit span task, a series of digits is presented and is to be repeated immediately afterward from memory. If that series turned out to be only slightly different from the participant's telephone number, the participant might be able to memorize the new number quickly and repeat it from long-term memory. The dual-store theories of memory allow this. Although Broadbent (1958) and Atkinson and Shiffrin (1968) drew their models of information processing as a series of boxes representing different memory stores, with long-term memory following short-term memory, these boxes do not imply that memory is exclusively in one box or another; they are better interpreted as the relative times of the first entry of information from a stimulus into one store and then the next. The question remains, then as to how one can determine if a response comes from short-term memory.

Waugh and Norman (1965) developed a mathematical model to accomplish this. The model operated with the assumption that long-term memory occurs for the entire list, including a plateau in the middle of the list. In contrast, by the time of recall, short-term memory is said to remain only at the end of the list. This model assumes that, for any particular serial position within a list, the likelihood of successful short-term storage (S) and long-term storage (L) are independent, so that the likelihood of recalling the item is S+L−SL.

A slightly different assumption is that short- and long-term stores are not independent but are used in a complementary fashion. The availability of short-term memory of an item may allow resources needed for long-term memorization to be shifted to elsewhere in the list. The data seem more consistent with that assumption. In several studies, lists to be recalled have been presented to patients with Korsakoff's amnesia and normal control participants ( Baddeley and Warrington, 1970; Carlesimo et al., 1995). These studies show that, in immediate recall, performance in amnesic individuals is preserved at the last few serial positions of the list. It is as if the performance in those serial positions is based mostly or entirely on short-term storage, and that there is no decrease in that kind of storage in the amnesic patients. In delayed recall, the amnesic patients show a deficit at all serial positions, as one would expect if short-term memory for the end of the list is lost as a function of a filled delay period ( Glanzer and Cunitz, 1966).

Overcoming contamination from temporal distinctiveness

Last, it has been argued that the loss of memory over time is not necessarily the result of decay. Instead, it can be caused by temporal distinctiveness in retrieval. This kind of theory assumes that the temporal context of an item serves as a retrieval cue for that item, even in free recall. An item separated in time from all other items is relatively distinctive and easy to recall, whereas an item that is relatively close to other items is more difficult to recall because it shares their temporal cues to retrieval. Shortly after a list is presented the most recent items are the most distinct temporally (much like the distinctness of a telephone pole you are practically touching compared to poles extending further down the road). Across a retention interval, the relative distinctiveness of the most recent items decreases (much like standing far away from even the last pole in a series).

Although there are data that can be interpreted according to distinctiveness, there also are what look like dissociations between the effects of distinctiveness and a genuine short-term memory effect. One can see this, for example, in the classic procedure of Peterson and Peterson (1959) in which letter trigrams are to be recalled immediately or only after a distracting task, counting backward from a starting number by three, for a period lasting up to 18 s. Peterson and Peterson found severe memory loss for the letter trigram as the filled delay was increased. However, subsequently, sceptics argued that the memory loss occurred because the temporal distinctiveness of the current letter trigram diminished as the filled delay increased. In particular, this delay effect was said to occur because of the increase across test delays in the proactive interference from previous trials. On the first few trials, the delay does not matter ( Keppel and Underwood, 1962) and no detrimental effect of delay is observed if delays of 5, 10, 15, and 20 s are tested in separate trial blocks ( Turvey et al., 1970; Greene, 1996).

Yet, there may be a true decay effect at shorter test intervals. Baddeley and Scott (1971) set up a trailer in a shopping mall so that they could test a large number of participants for one trial each, so as to avoid proactive interference. They found an effect of the test delay within the first 5 s but not at longer delays. Still, it seems that the concept of decay is not yet on very firm ground and warrants further study. It may be that decay actually reflects not a gradual degradation of the quality of the short-term memory trace, but a sudden collapse at a point that varies from trial to trial. With a control for temporal distinctiveness, Cowan et al. (1997a) found what could be a sudden collapse in the representation of memory for a tone with delays of between 5 and 10 s.

Chunk capacity limits

The concept of capacity limits was raised several times in the history of cognitive psychology. Miller (1956) famously discussed the "magical number seven plus or minus two" as a constant in short-term processing, including list recall, absolute judgment, and numerical estimation experiments. However, his autobiographical essay ( Miller, 1989) indicates that he was never very serious about the number seven; it was a rhetorical device that he used to tie together the otherwise unrelated strands of his research for a talk. Although it is true that memory span is approximately seven items in adults, there is no guarantee that each item is a separate entity. Perhaps the most important point of Miller's (1956) article was that multiple items can be combined into a larger, meaningful unit. Later studies suggested that the limit in capacity is more typically only three or four units ( Broadbent, 1975; Cowan, 2001). That conclusion was based on an attempt to take into account strategies that often increase the efficiency of use of a limited capacity, or that allow the maintenance of additional information separate from that limited capacity. To understand these methods of discussing capacity limits I will again mention three types of contamination. These come from chunking and the use of long-term memory, from rehearsal, and from non-capacity-limited types of storage.

Overcoming contamination from chunking and the use of long-term memory

A participant's response in an immediate-memory task depends on how the information to be recalled is grouped to form multi-item chunks ( Miller, 1956). Because it is not usually clear what chunks have been used in recall, it is not clear how many chunks can be retained and whether the number is truly fixed. Broadbent (1975) proposed some situations in which multi-item chunk formation was not a factor, and suggested on the basis of results from such procedures that the true capacity limit is three items (each serving as a single-item chunk). For example, although memory span is often about seven items, errors are made with seven-item lists and the error-free limit is typically three items. When people must recall items from a category in long-term memory, such as the states of the United States, they do so in spurts of about three items on average. It is as if the bucket of short-term memory is filled from the well of long-term memory and must be emptied before it is refilled. Cowan (2001) noted other such situations in which multi-item chunks cannot be formed. For example, in running memory span, a long list of items is presented with an unpredictable endpoint, making grouping impossible. When the list ends, the participant is to recall a certain number of items from the end of the list. Typically, people can recall three or four items from the end of the list, although the exact number depends on task demands ( Bunting et al., 2006). Individuals differ in capacity, which ranges from about two to six items in adults (and fewer in children), and the individual capacity limit is a strong correlate of cognitive aptitude.

Another way to take into account the role of multi-item chunk formation is to set up the task in a manner that allows chunks to be observed. Tulving and Patkau (1962) studied free recall of word lists with various levels of structure, ranging from random words to well-formed English sentences, with several different levels of coherence in between. A chunk was defined as a series of words reproduced by the participant in the same order in which the words had been presented. It was estimated that, in all conditions, participants recalled an average of four to six chunks. Cowan et al. (2004) tried to refine that method by testing serial recall of eight-word lists, which were composed of four pairs of words that previously had been associated with various levels of learning (0, 1, 2, or 4 prior word-word pairings). Each word used in the list was presented an equal number of times (four, except in a non-studied control condition) but what varied was how many of those presentations were as singletons and how many were as a consistent pairing. The number of paired prior exposures was held constant across the four pairs in a list. A mathematical model was used to estimate the proportion of recalled pairs that could be attributed to the learned association (i.e., to a two-word chunk) as opposed to separate recall of the two words in a pair. This model suggested that the capacity limit was about 3.5 chunks in every learning condition, but that the ratio of two-word chunks to one-word chunks increased as a function of the number of prior exposures to the pairs in the list.

Overcoming contamination from rehearsal

The issue of rehearsal is not entirely separate from the issue of chunk formation. In the traditional concept of rehearsal (e.g., Baddeley, 1986), one imagines that the items are covertly articulated in the presented order at an even pace. However, another possibility is that rehearsal involves the use of articulatory processes in order to put the items into groups. In fact, Cowan et al. (2006a) asked participants in a digit span experiment how they carried out the task and by far the most common answer among adults was that they grouped the items; participants rarely mentioned saying the items to themselves. Yet, it is clear that suppressing rehearsal affects performance.

Presumably, the situations in which items cannot be rehearsed are for the most part the same as the situations in which items cannot be grouped. For example, Cowan et al. (2005) relied on a running memory span procedure in which the items were presented at the rapid rate of 4 per second. At that rate, it is impossible to rehearse the items as they are presented. Instead, the task is probably accomplished by retaining a passive store (sensory or phonological memory) and then transferring the last few items from that store into a more attention-related store at the time of recall. In fact, with a fast presentation rate in running span, instructions to rehearse the items is detrimental, not helpful, to performance ( Hockey, 1973). Another example is memory for lists that were ignored at the time of their presentation ( Cowan et al., 1999). In these cases, the capacity limit is close to the three or four items suggested by Broadbent (1975) and Cowan (2001).

It is still quite possible that there is a speech-based short-term storage mechanism that is by and large independent of the chunk-based mechanism. In terms of the popular model of Baddeley (2000), the former is the phonological loop and the latter, the episodic buffer. In terms of Cowan (1988, 1995, 1999, 2005), the former is part of activated memory, which may have a time limit due to decay, and the latter is the focus of attention, which is assumed to have a chunk capacity limit.

Chen and Cowan (2005) showed that the time limit and chunk capacity limit in short-term memory are separate. They repeated the procedure of Cowan et al. (2004) in which pairs of words sometimes were presented in a training session preceding the list recall test. They combined lists composed of pairs as in that study. Now, however, both free and serial recall tasks were used, and the length of list varied. For long lists and free recall, the chunk capacity limit governed the recall. For example, lists of six well-learned pairs were recalled as well as lists of six unpaired singletons (i.e., were recalled at similar proportions of words correct). For shorter lists and serial recall strictly scored, the time limit instead governed the recall. For example, lists of four well-learned pairs were not recalled nearly as well as lists of four unpaired singletons, but only as well as lists of eight unpaired singletons. For intermediate conditions it appeared as if chunk capacity limits and time limits operate together to govern recall. Perhaps the capacity-limited mechanism holds items and the rehearsal mechanism preserves some serial order memory for those held items. The exact way in which these limits work together is not yet clear.

Overcoming contamination from non-capacity-limited types of storage

It is difficult to demonstrate a true capacity limit that is related to attention if, as I believe, there are other types of short-term memory mechanisms that complicate the results. A general capacity should include chunks of information of all sorts: for example, information derived from both acoustic and visual stimuli, and from both verbal and nonverbal stimuli. If this is the case, there should be cross-interference between one type of memory load and another. However, the literature often has shown that there is much more interference between similar types of memoranda, such as two visual arrays of objects or two acoustically presented word lists, than there is between two dissimilar types, such as one visual array and one verbal list. Cocchini et al. (2002) suggested that there is little or no interference between dissimilar lists. If so, that would appear to provide an argument against the presence of a general, cross-domain, short-term memory store.

Morey and Cowan (2004, 2005) questioned this conclusion. They presented a visual array of colored spots to be compared to a second array that matched the first or differed from it in one spot's color. Before the first array or just after it, participants sometimes heard a list of digits that was then to be recited between the two arrays. In a low-load condition, the list was their own seven-digit telephone number whereas, in a high-load condition, it was a random seven-digit number. Only the latter condition interfered with array-comparison performance, and then only if the list was to be recited aloud between the arrays. This suggests that retrieving seven random digits in a way that also engages rehearsal processes relies upon some type of short-term memory mechanism that also is needed for the visual arrays. That shared mechanism may be the focus of attention, with its capacity limit. Apparently, though, if the list was maintained silently rather than being recited aloud, this silent maintenance occurred without much use of the common, attention-based storage mechanism, so visual array performance was not much affected.

The types of short-term memory whose contribution to recall may obscure the capacity limit can include any types of activated memory that fall outside of the focus of attention. In the modeling framework depicted in , this can include sensory memory features as well as semantic features. Sperling (1960) famously illustrated the difference between unlimited sensory memory and capacity-limited categorical memory. If an array of characters was followed by a partial report cue shortly after the array, most of the characters in the cued row could be recalled. If the cue was delayed about 1 s, most of the sensory information had decayed and performance was limited to about four characters, regardless of the size of the array. Based on this study, the four-character limit could be seen as either a limit in the capacity of short-term memory or a limit in the rate with which information could be transferred from sensory memory into a categorical form before it decayed. However, Darwin et al. (1972) carried out an analogous auditory experiment and found a limit of about four items even though the observed decay period for sensory memory was about 4s. Given the striking differences between Sperling and Darwin et al. in the time period available for the transfer of information to a categorical form, the common four-item limit is best viewed as a capacity limit rather than a rate limit.

Saults and Cowan (2007) tested this conceptual framework in a series of experiments in which arrays were presented in two modalities at once or, in another procedure, one after the other. A visual array of colored spots was supplemented by an array of spoken digits occurring in four separate loudspeakers, each one consistently assigned to a different voice to ease perception. On some trials, participants knew that they were responsible for both modalities at once whereas, in other trials, participants knew that they were responsible for only the visual or only the acoustic stimuli. They received a probe array that was the same as the previous array (or the same as one modality in that previous array) or differed from the previous array in the identity of one stimulus. The task was to determine if there was a change. The use of cross-modality, capacity-limited storage predicts a particular pattern of results. It predicts that performance on either modality should be diminished in the dual-modality condition compared to the unimodal conditions, due to strain on the cross-modality store. That is how the results turned out. Moreover, if the cross-modality, capacity-limited store were the only type of storage used, then the sum of visual and auditory capacities in the dual-modality condition should be no greater than the larger of the two unimodal capacities (which happened to be the visual capacity). The reason is that the limited-capacity store would hold the same number of units no matter whether they were all from one modality or were from two modalities combined. That prediction was confirmed, but only if there was a post-perceptual mask in both modalities at once following the array to be remembered. The post-perceptual mask included a multicolored spot at each visual object location and a sound composed of all possible digits overlaid, from each loudspeaker. It was presented long enough after the arrays to be recalled that their perception would have been complete (e.g., 1 s afterward; cf. Vogel et al., 2006). Presumably, the mask was capable of overwriting various types of sensory-specific features in activated memory, leaving behind only the more generic, categorical information present in the focus of attention, which presumably is protected from masking interference by the attention process. The limit of the focus of attention was again shown to be between three and four items, for either unimodal visual or bimodal stimuli.

Even without using masking stimuli, it may be possible to find a phase of the short-term memory process that is general across domains. Cowan and Morey (2007) presented two stimulus sets to be recalled (or, in control conditions, only one set). The two stimulus sets could include two spoken lists of digits, two spatial arrays of colored spots, or one of each, in either order. Following this presentation, a cue indicated that the participant would be responsible for only the first array, only the second array, or both arrays. Three seconds followed before a probe. The effect of memory load could be compared in two ways. Performance on those trials in which two sets of stimuli were presented and both were cued for retention could be compared either to trials in which only one set was presented, or it could be compared to trials in which both sets were presented but the cue later indicated that only one set had to be retained. The part of working memory preceding the cue showed modality-specific dual-task effects: encoding a stimulus set of one type was hurt more by also encoding another set if both sets were in the same modality. However, the retention of information following the cue showed dual-task effects that were not modality-specific. When two sets had been presented, retaining both of them was detrimental compared to retaining only one set (as specified by the post-stimulus retention cue to retain one versus both sets), and this dual-task effect was similar in magnitude no matter whether the sets were in the same or different modalities. After the initial encoding, working memory storage across several seconds thus may occur abstractly, in the focus of attention.

Other evidence for a separate short-term storage

Last, there is other evidence that does not directly support either temporal decay or a capacity limit specifically, but implies that one or the other of these limits exist. Bjork and Whitten (1974) and Tzeng (1973) made temporal distinctiveness arguments on the basis of what is called continual distractor list recall, in which a recency effect persists even when the list is followed by a distracter-filled delay before recall. The filled delay should have destroyed short-term memory but the recency effect occurs anyway, provided that the items in the list also are separated by distracter-filled delays to increase their distinctiveness from one another. In favor of short-term storage, though, other studies have shown dissociations between what is found in ordinary immediate recall versus continual distractor recall (e.g., word length effects reversed in continual distractor recall: Cowan et al., 1997b; proactive interference at the most recent list positions in continual distractor recall only: Craik & Birtwistle, 1971; Davelaar et al., 2005).

There is also additional neuroimaging evidence for short-term storage. Talmi et al. (2005) found that recognition of earlier portions of a list, but not the last few items, activated areas within the hippocampal system that is generally associated with long-term memory retrieval. This is consistent with the finding, mentioned earlier, that memory for the last few list items is spared in Korsakoff's amnesia ( Baddeley and Warrington, 1970; Carlesimo et al., 1995). In these studies, the part of the recency effect based on short-term memory could reflect a short amount of time between presentation and recall of the last few items, or it could reflect the absence of interference between presentation and recall of the last few items. Thus, we can say that short-term memory exists, but often without great clarity as to whether the limit is a time limit or a chunk capacity limit.

The short-term memory/working memory distinction

The distinction between short-term memory and working memory is clouded in a bit of confusion but that is largely the result of different investigators using different definitions. Miller et al. (1960) used the term "working memory" to refer to temporary memory from a functional standpoint, so from their point of view there is no clear distinction between short-term and working memory. Baddeley and Hitch (1974) were fairly consistent with this definition but overlaid some descriptions on the terms that distinguished them. They thought of short-term memory as the unitary holding place as described by, for example, Atkinson and Shiffrin (1968). When they realized that the evidence actually was consistent with a multi-component system that could not be reduced to a unitary short-term store, they used the term working memory to describe that entire system. Cowan (1988) maintained a multi-component view, like Baddeley and Hitch, but without a commitment to precisely their components; instead, the basic subdivisions of working memory were said to be the short-term storage components (activated memory along with the focus of attention within it, shown in ) and central executive processes that manipulate stored information. By Cowan's account, Baddeley's (1986) phonological loop and visuospatial sketchpad would be viewed as just two of many aspects of activated memory, which are susceptible to interference to a degree that depends upon the similarity between features of the activated and interfering information sources. Baddeley's (2000) episodic buffer is possibly the same as the information saved in Cowan's focus of attention, or at least is a closely similar concept.

There has been some shift in the definition or description of working memory along with a shift in the explanation of why the newer working memory tasks correlate with intelligence and aptitude measures so much more highly than do simple, traditional, short-term memory tasks such as serial recall. Daneman and Carpenter (1980) had assumed that what is critical is to use working memory tasks that include both storage and processing components, so as to engage all of the parts of working memory as described, for example, by Baddeley and Hitch (1974). Instead, Engle et al. (1999) and Kane et al. (2001) proposed that what is critical is whether the working memory task is challenging in terms of the control of attention. For example, Kane et al. found that working memory span storage-and-processing tasks correlates well with the ability to inhibit the natural tendency to look toward a suddenly appearing stimulus and instead to look the other way, the antisaccade task. Similarly, Conway et al. (2001) found that individuals scoring high on storage-and-processing tests of working memory notice their names in a channel to be ignored in dichotic listening much less often than low-span individuals; the high-span individuals apparently are better able to make their primary task performance less vulnerable to distraction, but this comes at the expense of being a bit oblivious to irrelevant aspects of their surroundings. In response to such research, Engle and colleagues sometimes used the term working memory to refer only to the processes related to controlling attention. By doing so, their definition of working memory seems at odds with previous definitions but that new definition allows the simple statement that working memory correlates highly with aptitudes, whereas short-term memory (redefined to include only the non-attention-related aspects of memory storage) does not correlate so highly with aptitudes.

Cowan et al. (2006b), while adhering to the more traditional definition of working memory, made an assertion about working memory similar to that of Engle and colleagues, but a bit more complex. They proposed, on the basis of some developmental and correlational evidence, that multiple functions of attention are relevant to individual differences in aptitudes. The control of attention is relevant, but there is an independent contribution from the number of items that can be held in attention, or its scope. According to this view, what may be necessary for a working memory procedure to correlate well with cognitive aptitudes is that the task must prevent covert verbal rehearsal so that the participant must rely on more attention-demanding processing and/or storage to carry out the task. Cowan et al. (2005) found that the task can be much simpler than the storage-and-processing procedures. For example, in a version of the running memory span test, digits are presented very quickly and the series stops at an unpredictable point, after which the participant is to recall as many items as possible from the end of the list. Rehearsal is impossible and, when the list ends, information presumably must be retrieved from activated sensory or phonological features into the focus of attention. This type of task correlated with aptitudes, as did several other measures of the scope of attention ( Cowan et al., 2005, 2006b). In children too young to use covert verbal rehearsal (unlike older children and adults), even a simple digit span task served as an excellent correlate with aptitudes.

Other research verifies this idea that a working memory test will correlate well with cognitive aptitudes to the extent that it requires that attention be used for storage and/or processing. Gavens and Barrouillet (2004) carried out a developmental study in which they controlled the difficulty and duration of a processing task that came between items to be recalled. There still was a developmental difference in span, which they attributed to the development of a basic capacity, which could reflect a developmental increase in the scope of attention (cf. Cowan et al., 2005). Lépine et al. (2005) showed that what was important for a storage-and-processing type of span task to correlate well with aptitudes is for the processing component of the task (in this case, reading letters aloud) to occur quickly enough to prevent various types of rehearsal to sneak in between (see also Conlin et al., 2005).

Several papers have pitted storage and processing (perhaps the scope versus control of attention?) against one another to see which is more important in accounting for individual differences. Vogel et al. (2005) used a visual array task modified for use with a component of event-related potentials that indicates storage in visual working memory, termed contralateral delay activity (CDA). This activity was found to depend not only on the number of relevant objects in the display (e.g., red bars at varying angles to be remembered), but sometimes also on the number of irrelevant objects to be ignored (e.g., blue bars). For high-span individuals, the CDA for two relevant objects was found to be similar whether or not there also were two irrelevant objects in the display. However, for low-span individuals, the CDA for two relevant objects combined with two irrelevant objects was similar to the CDA for displays with four relevant objects alone, as if the irrelevant objects could not be excluded from working memory. One limitation of the study is that the separation of participants into high versus low span was based on the CDA also, and the task used to measure the CDA inevitably required selective attention (to one half of the display) on every trial, whether or not it included objects of an irrelevant color.

Gold et al. (2006) investigated similar issues in a behavioral design, and testing the difference between schizophrenic patients and normal control participants. Each trial started with a cue to attend to one part of the display at the expense of another (e.g., bars of one relevant color but not another, irrelevant color). The probe display was a set that was cued for relevance on most trials (in some experiments, 75%) whereas, occasionally, the probe display was a set that was not cued. This allowed a separate measure of the control of attention (the advantage for cued items over uncued items) and the storage capacity of working memory (the mean number of items recalled from each array, adding across cued and uncued sets). Unlike the initial expectations, the clear result was that the difference between groups was in the capacity, not in the control of attention. It would be interesting to know whether the same type of result could be obtained for high versus low span normal individuals, or whether that comparison instead would show a control-of-attention difference between these groups as Vogel et al. (2005) must predict. Friedman et al. (2006) found that not all central executive functions correlated with aptitudes; updating working memory did, but inhibition and shifting of attention did not. On the other hand, recall that Cowan et al. (2006b) did find was that a control-of-attention task was related to aptitudes.

In sum, the question of whether short-term memory and working memory are different may be a matter of semantics. There are clearly differences between simple serial recall tasks that do not correlate very well with aptitude tests in adults, and other tasks requiring memory and processing, or memory without the possibility of rehearsal, that correlate much better with aptitudes. Whether to use the term working memory for the latter set of tasks, or whether to reserve that term for the entire system of short-term memory preservation and manipulation, is a matter of taste. The more important, substantive question may be why some tasks correlate with aptitude much better than others.

Conclusion

The distinction between long-term and short-term memory depends on whether it can be demonstrated that there are properties specific to short-term memory; the main candidates include temporal decay and a chunk capacity limit. The question of decay is still pretty much open to debate, whereas there is growing support for a chunk capacity limit. These limits were discussed in a framework shown in .

The distinction between short-term memory and working memory is one that depends on the definition that one accepts. Nevertheless, the substantive question is why some tests of memory over the short term serve as some of the best correlates of cognitive aptitudes, whereas others do not. The answer seems to point to the importance of an attentional system used both for processing and for storage. The efficiency of this system and its use in working memory seem to differ substantially across individuals (e.g., Conway et al., 2002; Kane et al., 2004; Cowan et al., 2005, 2006b), as well as improving with development in childhood ( Cowan et al., 2005, 2006b) and declining in old age ( Naveh-Benjamin et al., 2007; Stoltzfus et al., 1996; Cowan et al., 2006c).

Acknowledgment

This work was completed with the assistance of NIH Grant R01 HD-21338.

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What are the differences between long-term, short-term, and working memory?

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In the recent literature there has been considerable confusion about the three types of memory: long-term, short-term, and working memory. This chapter strives to reduce that confusion and makes up-to-date assessments of these types of memory. Long- and short-term memory could differ in two fundamental ways, with only short-term memory demonstrating (1) temporal decay and (2) chunk capacity limits.

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Center for Teaching and Learning | Wiley Education Services

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Principles of Multimedia Learning - Center for Teaching and Learning | Wiley Education Services

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Multimedia materials are everywhere now. Check out these tips for how to harness the power of multimedia learning in your online classroom.

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Disclaimer

The simplified model presented here is just one of many ways to categorize types of memory. There are other models too, and some of them use slightly different definitions for some of the terms used here.

This model highlights just a few of the key brain regions involved in different memory functions. Since no part of the brain exists in isolation, most types of memory include more areas than the ones listed here. The big idea to take away is that different areas of the brain have different functions.

If you wish to delve more deeply into the types of memory or the brain anatomy involved, we encourage you to explore the references linked at the bottom of the page.

When we learn, our brain takes in information about the world that causes us to change our behavior in some way.

For example, a young baby doesn't understand that fire can hurt them. But thanks to learning, if they touch a candle flame even one time, they will probably not do it again. The information that feeds into the brain-candle, flame, hot, pain-is physically recorded in the connections between cells. And those connections influence our future behavior-don't touch!

As the brain takes in any information and stores it, there are physical changes to the connections between brain cells. And those connections influence our behavior.

Neurons that fire together wire together

The brain is an enormous interconnected network of specialized cells called neurons and supporting cells called glia. To transmit information in the brain, neurons pass signals to one another. This passing of signals can cause physical changes to the connections between cells.

When we learn a skill (like riding a bike) or new information (like multiplication tables), we are building brain connections. The more we use those brain connections, the stronger they get. It's the reason "practice makes perfect."

In the activity above, you can read about how injuries to certain brain areas cause certain memory functions to stop working. But just as important are the connections between them. If two brain areas can no longer communicate, then learning and memory can break down.

Like trails through the wilderness, brain connections get stronger the more they're used

Learning is a process that relies on long-term memory systems in the brain. People who study learning typically divide it into 4 steps: attention, storage, consolidation, and retrieval. These steps dovetail with the Types of Memory described above, and they provide a slightly different framework for thinking about what happens when one or more of these steps go wrong.

Attention - Our sensory systems are constantly bombarded with information, yet most of it fades before we are even aware of it. Attention is like a filter that lets important information into the brain while keeping the irrelevant information out. Without attention, we would remember very little.

Encoding - As sensory information comes in, the brain processes it. Strings of sounds become words, flashes of light and color become images. The brain begins connecting this new information with what it already knows about the world. Some information that is encoded will fade, and some will stick around.

Storage - Long-term storage of information requires new brain connections. The brain has its own way of rehearsing experiences (there's evidence that this is what dreaming is all about), which helps strengthen brain connections. But we can also consciously repeat information or skills to help the brain form strong memories.

Retrieval - Retrieval is the process of accessing stored memories. We use it when we recall a fact or reimagine an experience. But we also use it when we take in new information. It's part of what helps us connect new information to old.

Even when memory systems function normally, memory is imperfect. To learn more about the fallibility of memory, visit The Seven Sins of Memory.

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The simplified model presented here is just one of many ways to categorize types of memory. There are other models too, and some of them use slightly different definitions for some of the terms used here. This model highlights just a few of the key brain regions involved in different memory functions.

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Catharine Young: How memories form and how we lose them

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Think back to a really vivid memory. Got it? Now try to remember what you had for lunch three weeks ago. That second memory probably isn't as strong- but why not? Why do we remember some things, and not others? And why do memories eventually fade?

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MOOC EDSCI1x | Video 1: Cognitive Load | Student Learning Strategies

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Learning Outcomes

Learning outcomes are what you want your students to learn, either as a result of a specific lesson or on the grander, more general scale of the entire course. Learning outcomes target knowledge, skills, or attitudes for change. Bloom's taxonomy specifically targets these by seeking to increase knowledge (cognitive domain), develop skills (psychomotor domain), or develop emotional aptitude or balance (affective domain). Learning outcomes might be identified by someone outside the teacher, such as state-wide or departmental standards. The taxonomy provides a basis for developing sub-goals and assessment methodology to meet these goals. It is important to note that learning outcomes are goals, and are not the activities performed to achieve those goals. Outcomes can be categorized into broad, global outcomes that may take many years to achieve and provide direction for education, educational goals that guide curriculum development over the weeks or months it takes to complete a specific course, and instructional goals that narrowly focus on the daily activities, experiences, and exercises used in a specific lesson plan.

Objective Builder Tool

For applications specific to a course, the Center for Distributed Learning has developed an objective builder tool to help craft the language for measurable learning outcomes.

Applications of the Taxonomy

Taxonomies are developed to provide a framework for organizing a continuum along an underlying structure. For example, languages may be classified as Romantic, Germanic, etc. based on their underlying grammatical structure and origin. Bloom's taxonomy primarily provides instructors with a focus for developing their course learning outcomes. There are a number of reasons why a teacher would want to use Bloom's taxonomy. Initially, it can be used to increase one's understanding of the educational process. Teachers can see and understand complex cognitive development and how lower-level skills build into higher-order thinking (e.g., recalling facts and comprehending previous problems allows a student to apply their experience to similar problems). Using this understanding facilitates the prioritizing of material and can steer the organization of lessons to maximize class time. For example, lower-level skills (e.g., memorizing factual knowledge) can be developed before higher-level skills are introduced (e.g., analysis of relationships). Current educators frequently face a confusing array of standards and curriculum requirements. Bloom's taxonomy offers a guiding framework for breaking these criteria down into accessible chunks which can be used to direct day-to-day lesson plans and can be easily compared to their own goals for the class. Just as different levels require different instructional delivery methods, they also require different assessment methods. Bloom's taxonomy can be used as a checklist to ensure that all levels of a domain have been assessed and align assessment methods with the appropriate lessons and methodologies. In this way, the taxonomy also makes it easier for you to maintain consistency between assessment methods, content, and instructional materials and identify weak areas.

Update to Bloom's Taxonomy (2001)

Originally, Bloom's taxonomy was one-dimensional with an exclusive focus on the knowledge domain. The current updated version developed by Anderson and Krathwohl (2001) reorganizes, and highlights the interactions between, two dimensions: cognitive processes and knowledge content. Anderson and Krathwohl identify two reasons for updating the original handbook. They emphasize a refocusing of educational outcomes back to the original handbook, which was ahead of its time and can still offer assistance to modern educators and to incorporate new findings in psychology and education into the framework. In their revision, cognitive processes are presented as verbs and the knowledge content are presented as nouns. Along with exchanging the levels of Evaluation and Synthesis (which they rename to Creation), Anderson and Krathwohl redefine the knowledge dimension to include four types:

Factual Knowledge: Basic elements of a discipline that a student must know and be able to work with to solve problems including basic terminology and specific details and elements.

Conceptual Knowledge: Interrelationships between basic factual knowledge that demonstrate how elements work together, for example, classifications and categories, principles and generalizations, and theories, models, and structures.

Procedural Knowledge: How something is done including the methods of inquiry, skills, algorithms, techniques, and methods needed to investigate, apply, or analyze information.

Metacognitive Knowledge: Awareness and knowledge of one's own cognition including strategies for learning, contextual and conditional knowledge about cognitive tasks, and self-knowledge.

References:
Bloom, B. S. (1969). Taxonomy of educational objectives: The classification of educational goals : Handbook I, Cognitive domain. New York: McKay.
Anderson, L. W., Krathwohl, D. R., & Bloom, B. S. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. New York: Longman.

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IntroductionBloom's taxonomy was developed to provide a common language for teachers to discuss and exchange learning and assessment methods. Specific learning outcomes can be derived from the taxonomy, though it is most commonly used to assess learning on a variety of cognitive levels. The table below defines each cognitive level...

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Learn how the brain can rewire itself after an injury.

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Abstract

Prior researchers have shown that the brain has a remarkable ability for adapting to environmental changes. The positive effects of such neural plasticity include enhanced functioning in specific cognitive domains and shifts in cortical representation following naturally occurring cases of sensory deprivation; however, maladaptive changes in brain function and development owing to early developmental adversity and stress have also been well documented. Researchers examining enriched rearing environments in animals have revealed the potential for inducing positive brain plasticity effects and have helped to popularize methods for training the brain to reverse early brain deficits or to boost normal cognitive functioning. In this paper, two classes of empirically based methods of brain training in children are reviewed and critiqued: laboratory-based, mental process training paradigms and ecological interventions based upon neurocognitive conceptual models. Given the susceptibility of executive function disruption, special attention is paid to training programs that emphasize executive function enhancement. In addition, a third approach to brain training, aimed at tapping into compensatory processes, is postulated. Study results showing the effectiveness of this strategy in the field of neurorehabilitation and in terms of naturally occurring compensatory processing in human aging lend credence to the potential of this approach.

Keywords: plasticity, training, intervention, developmental cognitive neuroscience

To what extent are children's brains pliable and trainable? Moreover, what are the most effective techniques for training and shaping the brains of children to achieve positive and prevent negative outcomes? What are the advantages and disadvantages of laboratory-based versus ecologically grounded, family- and community-based approaches? Should we think of these approaches as alternatives to traditional psychotherapeutic techniques or as complementary approaches? At what developmental points are such programs most beneficially employed, and is there a point past which such programs are not likely to be effective? Are all brain systems equally pliable? If not, which show the greatest degree of plasticity in response to intervention? Lastly, are there circumstances under which we should be concerned about potential iatrogenic effects of intervention or training programs?

Although the answers to these questions are complex and, in many cases, unresolved, interest in children's brain plasticity and interventions that promote plasticity appears to be widespread and rapidly growing. Such interest exists among neuroscientists focused on understanding the basic science of brain development, developmental psychologists focused on the emergence of key competencies necessary for healthy adjustment over time, child psychologists and other clinicians focused on understanding and treating psychological disorders, prevention scientists and educators focused on designing effective programs for reducing risks and promoting resiliency in high risk populations, and policy makers focused on allocating funding/resources for such programs. In addition, the media and general public appear to be intrigued by this subject, as indicated by the frequency with which the results of neuroscientific investigations of plasticity published in professional journals are being covered in the popular press. Furthermore, the burgeoning array of commercial products aimed at "brain fitness," from online tutorials to computer software packages to object-based games now available to the general public marks an important penetration of brain science into the consumer marketplace. Put simply, this is an important and much-debated topic.

In this article, we explore the state of the science in this area, with the goal of stimulating dialogue on the topic of training the brain in children, paying particular attention to how research in this area can inform current policy and shape prevention and intervention programs for high-risk children and their families. We focus on efforts to train cognitive control (commonly referred to as executive functioning [EF]), reviewing evidence from the two approaches employed in this area among children with attention deficit hyperactivity disorder (ADHD) and children who manifest neurodevelopmental difficulties as a result of exposure to early stressful environments: (a) laboratory-based studies that directly train specific neurocognitive processes and (b) ecologically based interventions informed from a neurobiological perspective. We concentrate on studies that provide instructional training, rather than rote, unguided learning via repetition only. We then speculate on a possible third route to brain training in children that is grounded in neurorehabilitation and adult aging research: utilization of compensatory neural processes. Finally, we consider the future directions for this work.

Human Neural Development and Plasticity

Much of the current state of knowledge about neural plasticity in children is embedded in the science of brain development. Until the advent of modern neuroscience, conventional wisdom held that brain development was largely complete relatively early in life, perhaps owing to appreciable anatomy: by early childhood, the human brain has reached approximately 90% of its adult weight and changes very little in size after age 5 ( Durston et al., 2001; Reiss, Abrams, Singer, Ross, & Denckla, 1996). However, the results from histological postmortem studies on humans and nonhuman primates and from in vivo imaging studies have provided strong evidence that human brain development is far from complete by early childhood. In fact, dynamic and continuing changes in brain architecture occur throughout the course of development. For example, in humans, the ratio of gray matter (unmyelinated neurons) to white matter (myelinated neurons) changes dramatically from birth through adulthood, particularly in the cerebral cortex. Grey matter density follows a nonlinear trend of initial growth during early childhood, with a subsequent decrease in density during adolescence and young adulthood. Further, regional differences exist such that primary motor and sensory areas tend mature the earliest in development, with higher level association and multimodal areas (such as the dorsolateral prefrontal cortex and the superior temporal gyrus) reaching adult levels the latest ( Giedd, 2005; Gogtay et al., 2004; Riess et al., 1996). White matter volume conversely follows a steady linear increase throughout childhood and up to adulthood ( Giedd et al., 1999; Gogtay et al., 2004).

The inverse relationship seen between decreases in grey matter and increases in white matter has been postulated to reflect both synaptic pruning (loss of grey matter) and increased myelination (the formation of glial support cells); this pattern is thought to be due to a combination of loss in redundant or unused connections and the strengthening of relevant connections based on environmental input and experience ( Huttenlocher, 1990). Functionally, these dynamic changes in brain architecture most likely reflect increased neural efficiency and faster network connections that parallel the behavioral changes observed during development.

As with physical development, brain development is genetically programmed. Although there are individual differences in the exact timing of this programming, there is a great degree of invariance in the sequence of maturation of particular brain regions for typically developing individuals. For example, auditory and visual regions mature early, language later, and higher-order cognitive functions later still. However, the maturing brain is also strongly influenced by experience in the prenatal, childhood, and adolescent developmental periods; further, there is increasing evidence that brain development continues throughout adult life. This has led to the characterization of the extent to which the environment affects neural development in terms of the idea that experience shapes the architecture of the developing brain ( National Scientific Council on the Developing Child, 2007). Indeed, it is within this experiential sculpting process that the plasticity of the developing brain might best be understood.

Many of the early scientific demonstrations of neural plasticity highlighted the changes in the brain induced from deprivation of normal, experience-expectant stimulus input. For example, Wiesel and Hubel (1963) demonstrated the effect of profound sensory deprivation on the development of the visual cortex. In these experiments, kittens underwent monocular deprivation (i.e., one eye sewn shut) during a critical period of visual development. Normally, this interval (age 4 weeks to 3 months) corresponds to a period of preprogrammed ocular dominance column formation, in which alternating bands form in the primary visual cortex that are preferentially sensitive to input from either eye. In the case of monocular deprivation, however, profound visual impairment was seen after reopening the deprived eye. However, these effects were not seen if the deprivation occurred later (i.e., age 3-4 months; Hubel & Wiesel, 1970). At the physiological level, the ocular dominance columns representing the deprived eye failed to develop and appeared to be replaced by the columns representing the nondeprived eye. This suggests that cortical areas are able to represent the winner of competitive interaction between environmental inputs received, at least during certain critical periods, rather than preprogrammed representation only ( LeVay, Wiesel, & Hubel, 1980; Wiesel & Hubel, 1963). The results from these studies provided evidence of the profound ability of the brain to alter so-called hard-wired connections as a result of subsequent environmental experiences, demonstrating for the first time the remarkable plasticity of the brain.

Much of the early evidence for neural plasticity came from sensory deprivation experiments using animal models, but parallel studies of plasticity in humans could not employ such methods for obvious reasons. Instead, scientists have relied on experiments of nature. An indication of the profound malleability of the human brain is given in the following examples. Neville and Lawson (1987) showed that congenitally deaf individuals exhibit an early attention effect compared to participants with normal hearing when tracking motion presented in the peripheral visual field. Similarly, Bavelier et al. (2000) conducted a neuroimaging study that revealed greater activity in the motion sensitive portion of visual cortex (the middle temporal cortex) by congenitally deaf rather than by hearing participants when tracking motion. Similar results have been obtained with congenitally blind individuals in response to auditory stimuli ( Röder et al., 1999). Individuals who are deaf have also been shown to have superior tactile ability compared to nondeprived control samples; further, the primary visual cortex has been shown to be activated when blind participants perform tactile discrimination tasks such as reading Braille ( Levänen & Hamdorf, 2001). Thus, these results demonstrate cross-modal plasticity. That is, the blind or deaf participants showed enhanced use of brain regions typically associated with the deprived sense, suggesting that the cortical areas involved in a particular sensory modality, if unused, may be recruited by other neighboring cortical areas ( Pascual-Leone, Amedi, Fregni, & Merabet, 2005).

Negative Consequences of Neural Malleability

Neural plasticity facilitates healthy development across a vast continuum of rearing conditions and might help to account for resiliency even when children experience nonoptimal parenting or conditions of social and economic adversity. However, adaptive neural plasticity might also represent vulnerability under certain circumstances. In particular, there is increasing evidence that exposure to stress at levels that overwhelm the organism's ability to manage that stress may negatively affect brain development ( P. A. Fisher & Gunnar, 2010).

For example, there is over half a century of research evidence involving animal models that stressful rearing environments are associated with changes in key neural regulatory systems ( Levine, 2005). Evidence from parallel studies in human populations has documented similar effects. Specifically, research findings involving children reared in institutions in developing countries, which offer extremely neglectful early care, have shown long-term alterations in brain development ( Pollak et al., 2010). Converging evidence has been obtained in studies of maltreated children ( Cicchetti, Rogosch, Gunnar, & Toth, 2010) and foster children ( P. A. Fisher, Gunnar, Dozier, Bruce, & Pears, 2006). Notably, stressful experiences do not have to be extreme to alter the course of brain development. Shonkoff, Boyce, and McEwen (2009) noted that one pathway for stress effects over time might be cumulative in nature (i.e., allostatic load) and that chronic exposure to moderate stressors might result in changes in the developing brain. In a review of the literature in this area, P. A. Fisher and Gunnar (2010) noted that the timing (especially in the first 24 months), duration, and severity of stress might need to be considered when examining the association between early adversity and alterations in brain development.

Intervention Efforts to Promote Neural Plasticity

If negative life events such as early stress can alter and remodel brain development, then is the converse also true? Is it possible to leverage neural plasticity to promote healthy development and to remediate the effects of early stress via intervention efforts? Early evidence along these lines came from investigations of the effects of rearing rodents in enriched environments following exposure to early stress. In these studies, researchers have documented neuroanatomical changes after exposure to enriched environments, including increased brain weight and size, increased dendritic branching and length, changes in synaptic size and number, and behavioral improvements on long-term spatial memory tasks such as the Morris water maze and the T-maze (see van Praag, Kempermann, & Gage, 2000; Nithianantharajah & Hannan, 2006). The two key components of an enriched environment seem to be complexity and novelty ( Sale, Berardi, & Maffei, 2009).

Important questions arise, however, when attempting to apply these findings to humans. Certainly, the animal studies provide proof-of-concept that the brain is malleable in response to environmental intervention and that improved outcomes are possible following early stress. However, there is considerable cross-species variation in the rate of development. Moreover, the architecture of the human brain is vastly more complex than that of rodents, and the neural systems in which changes might be most desired in humans involve higher-order cognitive processing. Thus, the limits, approaches, and effectiveness of interventions to promote positive neural plasticity in humans are uncertain.

It is worth highlighting the growing body of convergent animal and human literature that demonstrates beneficial changes in brain morphology and cognition after physical exercise. The findings from animal studies in rodents, for example, have consistently shown increased neurogenesis-the formation of new neurons in the adult-following induced or voluntary exercise. Behavioral effects such as improved acquisition and retention of spatial memory have also been reliably observed in rodents after exercise (see van Praag, 2008); it should be noted, however, that there is insufficient evidence that the observed performance improvements result from exercise-induced neurogenesis ( Hillman, Erickson, & Kramer, 2008; van Praag, 2008). Comparable effects of exercise on cognition have been found in human studies; in a recent review, Hillman et al. (2010) highlighted moderate but positive overall effects of physical activity on a range of child cognitive abilities, including academic performance. Similar findings in aging populations have shown beneficial relationships between exercise and various cognitive domains, particularly executive control ( Colcombe & Kramer, 2003). Recent evidence indicates exercise might also contribute to the continued plasticity of brain structures in old age. Erickson et al. ( 2011), for example, showed a gain in anterior hippocampus volume in a group of seniors who participated in a year-long exercise regimen and a comparable loss in volume in those who only practiced stretching and toning. Critically, these changes in volume were positively related to changes in fitness level, blood levels of brain-derived neurotrophic factor (a putative mediator of neurogenesis), and improvements in a spatial memory task. Additionally, the results from functional neuroimaging studies have shown activation differences between physically fit individuals and their less-fit peers in key control-related cortical areas (e.g., the dorsal lateral PFC and anterior cingulate, areas implicated in the allocation of attention and conflict detection, respectively). Further, advancement in the field will no doubt be aided by the recent development of an in vivo imaging marker (cerebral blood volume) of exercise-induced neurogenesis in humans ( Pereira et al., 2007). This literature is highlighted to illustrate an example of the profound neural and behavioral remodeling that can arise after accompanying environmental enrichment. Similar cortical plasticity is thought to following intense intervention or cognitive training regiments.

To capitalize on the adaptability of the brain, some of the earliest cognitive training researchers incorporated principles of cortical plasticity and basic neuroscience into their designs (e.g., the importance of competitive processes in driving change in neural networks). For example, basic research findings have implicated language-based learning impairments in children as a deficit in the temporal dynamics of auditory processing, so researchers devised a training program designed to improve basic perceptual processing (specifically, the spatiotemporal aspects of sounds) in young children diagnosed with language-based learning impairments. This program successfully improved speech discrimination and language comprehension abilities ( Tallal et al., 1996). Similar plasticity-based training programs have proven effective at enhancing memory performance in older adults and verbal memory in schizophrenic patients ( M. Fisher, Holland, Merzenich, & Finogradov, 2009; Mahncke et al., 2006).

Existing EF Training Paradigms

In this section, we review the major findings from brain and cognitive training and intervention studies to shed light on questions about the potential and limits of training the brain. In particular, we highlight training programs and findings focused on EF, the cognitive processes that allow for the flexible selection of behavior based on internal goals or rules (e.g., Koechlin & Summerfield, 2007; Zelazo, Müller, Frye, & Marcovitch, 2003). These processes include related but separate components such as updating representations in working memory, shifting between mental representations (cognitive flexibility), and inhibiting competing, prepotent representations or responses ( Huizinga, Dolan, & van der Molen, 2006; Miyake, Friedman, Emerson, Witzki, & Howerter, 2000). The anatomical basis of EF has been intricately linked to the prefrontal cortex (PFC) in humans ( Miller & Cohen, 2001). The development of EF follows a protracted course, with EF components first emerging in infancy, exhibiting pronounced changes occurring during the preschool period, with reaching full competency in adolescence or young adulthood ( Carlson, 2005; Huizinga et al., 2006; Zelazo, Carlson, & Kesek, 2008). We chose to focus on EF for a number of reasons. First, EF deficits are one of the chief effects manifested in relation to early adversity. For example, researchers working with foster children ( Lewis, Dozier, Ackerman, & Sepulveda-Kozakowski, 2007; Pears, Kim, & Fisher, 2008) and low-income children ( Hackman & Farah, 2009) have found poorer performance on EF tasks in these high-risk samples. Second, the development of EF is clearly important to healthy adjustment in the context of family, school, peers, and community. Proficient EF capabilities play a critical role in a multitude of other domains, including theory of mind ( Carlson & Moses, 2001), self-regulation ( Blair, 2002), and long-term memory retrieval ( Levy & Anderson, 2002). Additionally, EF deficits appear to be at the core of developmental psychopathologies such as autism ( Ozonoff, 1995), ADHD ( Pennington & Ozonoff, 1996), and substance abuse disorders ( Giancola & Tarter, 1999). Thus, EF might be particularly susceptible to disruption yet amendable via training.

The current EF training methods generally involve two approaches: laboratory-based training and neurobiologically informed ecological interventions. Improvements on psychosocial, behavioral, and/or physiological measures have been demonstrated with both strategies, and each strategy has specific advantages, limitations, and implicit assumptions.

Laboratory-Based Training

Laboratory-based training approaches often involve repeated performance, typically computer-based, on speeded-choice tasks. These approaches tend to target a particular cognitive domain (often labeled a specific process or direct intervention) rather than a domain-general approach aimed at improving overall psychosocial well-being, reducing behavioral problems, or reducing clinical symptomatology.

Improving working memory capacity, for example, has been the goal of several recent training studies in typically developing and developmentally delayed child populations. Targeting working memory ability, especially in children, is a naturally attractive goal given the relationship between individual differences on measures of working memory and fluid intelligence in adults ( Engle & Kane, 2004) in addition to the relationship seen between working memory scores and scholastic performance in children ( Gathercole, Brown, & Pickering, 2003). Early researchers investigating training on working memory, however, found marginal improvements at best in working memory capacity ( Kristofferson, 1972) or highly task specific improvements ( Ericsson, Chase, & Faloon, 1980). However, perhaps owing to the recent development of more precise intervention techniques and/or measurement methods, promising results have begun to emerge. The results of these studies, as well as those targeting other EF domains, are summarized in . (Direct comparison of effect sizes from different designs and statistics in - is cautioned [ Morris & DeShon, 2002]. See Supplementary Material for procedures and formulas for calculating effect sizes, additional demographics, and additional details on the studies listed in -.) As shown in , laboratory-based training intervention methods excel at enhancing specific neurobehavioral processes (or highly related processes) of interest, and some produce broader transfer of training effects. Further, the rigorous methodological considerations in these studies limit nuisance or confounding variables. Given the specificity of the processes targeted for improvement, the design and objectives of these studies are driven by relatively precise neurobehavioral theoretical bases.

The laboratory nature of the training in these studies inevitably raises questions of ecological/external validity and the generalizability of improvements. At present, there is only limited evidence that training on computerized, laboratory-based tests of specific cognitive abilities generalizes to real-world situations. Understanding the degree to which cognitive improvement from such training applications enhances daily cognitive functioning is clearly needed. Similarly, little is known of the long-term effects of these training paradigms. Retest improvements have been shown over a matter of months in several studies ( Holmes, Gathercole, & Dunning, 2009; Klingberg et al., 2005), but understanding the long-term efficacy of these methods is clearly needed to move beyond the proof-of-concept phase.

Moreover, questions remain as to how effective such training is for the extreme ends of the impaired EF spectrum. Although some researchers have investigated training effects with developmentally delayed populations such as children with ADHD (see ), little is known about the effectiveness of such training methods in populations demonstrating more severe behavioral, cognitive, or emotional deficits.

Neurobiologically Informed Ecological Interventions

Numerous researchers conducting more traditional contextually based school and family-centered prevention and intervention studies have demonstrated evidence of brain training via changes in behavioral and psychosocial functioning. Such ecological approaches acknowledge the importance of understanding the neural underpinnings of behavior, especially non-normative behavior, as a critical factor in designing and implementing prevention and intervention paradigms. In discussing the rationale for these approaches, Blair (2002) argued that the extent to which children successfully navigate the transition to primary school depends on a set of social-emotional skills that are based in the EF neural substrates. These skills include self-regulation, effortful control, and working memory. Ecological interventions that target these domains typically integrate high rates of employing EF-based skills in school or family settings. Several of these interventions have resulted in impressive findings (see ).

Ecological intervention approaches have a number of advantages over the laboratory-based training methods: in particular, real-world applicability. These approaches can be relatively easily implemented and require no special equipment. Moreover, because they are conducted in real-world settings, the skills learned might be more likely to generalize to other, similar real-world contexts. In addition, these approaches appear to be effective with multiproblem children who might have fairly large deficits in the targeted intervention areas.

Despite these potential advantages, these approaches tend to be more intensive, longer-lasting, and more costly than the laboratory-based training methods. In addition, inasmuch as many of these interventions consist of multiple components, it can be challenging to deconstruct and distill the effective component(s). Similarly, these approaches are typically less specific with regard to the targeted brain systems. Although neural and biological indices of functioning are acquired, precise theories of how the interventions affect the underlying neural circuitry are often less well articulated. Isolating particular neurocognitive systems affected by such interventions is difficult in the context of these studies in part because of their domain-general, multimodal natures. As such, one goal of future ecological interventions targeting EF is to more adequately identify the targeted neurocognitive systems.

A Promising New Approach

Given the limitations of the aforementioned approaches, we propose a third possible approach to brain training in this area: strengthening compensatory processes. Rather than being aimed at only restoring neurocognitive functions that are not operating optimally, this method is aimed at leveraging other neural systems to accomplish complementary cognitive and behavioral outcomes. As is shown below, this is not a novel concept in of itself; however, to our knowledge, this type of approach has not been employed as a therapeutic intervention for remedying EF and other neurocognitive deficits in children.

Numerous neurorehabilitation therapies for patients with traumatic brain injuries are aimed at improving cognitive functioning by using strategic training techniques to offset rather than restitute lost functionality. In these approaches, the underlying neural systems supporting a given function are generally assumed to be damaged beyond repair. These approaches thus target dormant, complementary neural systems (i.e., redundant pathways) unaffected by the brain insult or wholly different brain systems capable of carrying out functions formerly handled by damaged brain regions. The conceptual models driving these approaches involve rerouting the neural circuitry so individuals learn new methods of handling old problems or goals. Generally, these compensatory approaches target specific cognitive deficits incurred after brain damage such as attentional, memorial, or visual abilities (see ).

Table 3

Other examples of compensatory processing are more spontaneous in nature. Recruitment of right hemisphere areas, homologous to left hemisphere language centers, has been shown to aid individuals with aphasia in recovering language function. It has been suggested that these right hemisphere activations reflect the recruitment of additional processes to support language recovery rather than the restoration of language functions per se (see Raymer et al., 2008). Similarly, neuroimaging researchers conducting aging studies have shown overactivation patterns (i.e., more diffuse activity) in the brains of senior compared to young adult participants during complex cognitive tasks. This distributed pattern in seniors is often seen as activity in homologous regions in the prefrontal cortex to the unilateral activity seen in young adults and positive correlations between activation and performance have been reported. Such overactivation patterns have been demonstrated even when performance levels between groups were matched. The claim that increased activation is necessary for improved performance is supported by a pair of transcranial magnetic stimulation (TMS) studies. In one study, older adults performed more poorly when deactivating TMS was applied to either prefrontal hemisphere during a recognition memory task (older adults normally show bilateral activation in this task), whereas younger adults were impaired when deactivating TMS was applied to only one hemisphere ( Rossi et al., 2004). Conversely, in a subgroup of low performing seniors, activating TMS increased task performance. Moreover, the fMRI activation patterns in this low-performing group were more unilateral before and more bilateral after the activating TMS ( Solé-Padullés et al., 2006), which is consistent with improved performance. Taken together, this evidence supports the compensation account of overactivation: increased activation reflects neural processes that aid older adults during task performance (see ; see Reuter-Lorenz & Lustig, 2005). However, bilateral activity during performance on inhibitory tasks has also been linked to poorer performance in older adults, suggesting that overactivation patterns might not universally indicate the presence of compensatory processing ( Colcombe, Kramer, Erickson, & Scalf, 2005).

As is shown in and , compensatory or strategic processes can be effective remedies for ameliorating lost or degraded cognitive functioning. As such, this form of brain training should be further explored as a potential model to consider for application with vulnerable populations. One such compensatory strategy, broadly defined, might involve teaching self-verbalization strategies (i.e., using one's inner voice to stay on task) to children with attention or inhibitory control deficits. This is based on the Vygotskian hypothesis that inner language helps to guide action, particularly during development, and on recent empirical evidence supporting the role of self-verbalization in the performance of demanding EF situations like task switching ( Bryck & Mayr, 2005; Emerson & Miyake, 2003).

Valuable lessons can be gleaned from the aforementioned compensatory rehabilitation techniques when addressing severe early adversity (e.g., childhood physical and emotional trauma and neglect), which can greatly impact neurodevelopment. The results from neuroscience research are increasingly demonstrating the existence of redundant and complementary neural systems, making it plausible that similar compensatory systems could be tapped in children who have endured similar alterations in neural functioning. Much work is needed to tailor these training programs to meet the specific needs and impairments of vulnerable childhood populations and to design clinical trial examinations of the effectiveness of compensatory training in young and/or at-risk populations. Despite this, the compensatory processes brain training approach is worth exploring, especially for individuals who are less responsive to more traditional intervention approaches.

Discussion

The findings from research on neural plasticity provide the impetus for interventions designed to reverse the effects of early adverse environments on child brain development, particularly regarding EF. Although experimental evaluations of the impact of interventions on EF neural plasticity are relatively new, the research findings to date are promising. Laboratory-based training and neurobiologically informed ecological interventions have been shown to be efficaciousness and have great promise for improving outcomes for high-risk children. Moreover, enhanced outcomes may be obtained by hybridizing these two approaches.

Compensatory processes brain training has not yet been applied to prevention and intervention programs for high-risk children despite its analogs in neural rehabilitation research. Such methods might prove to be efficacious on their own or as supplementary components appended to emerging methods with documented efficacy. Clearly, the structure and components involved in such approaches need to be specified and evaluated.

In addition to the development of novel intervention methods, there is a need for parallel development of innovative measurement methodologies. For example, examining fMRI activation on tasks known to recruit EF neural systems before and after the application of strategy training could be applied as a useful tool in determining whether, where, and how much compensatory processing has occurred as a result of the training. Similarly, employing measures of connectivity among regions of interest, both in terms of resting-state and task-related functional connectivity, is likely to be highly promising (see Fair et al., 2007). The hypothesis that compensatory mechanisms can be recruited to overcome early deficits in children with early adversity is an exciting and potentially highly rewarding avenue that necessitates exploration.

Limitations and Assumptions of Current Methods

There are consistent limitations seen across the methods of brain training reviewed here. Foremost among these is that a training regimen designed for a particular group or population can have limited success in dissimilar populations. The most straightforward solution is to tailor interventions or training protocols to the population of interest. Similarly, within any population, a large degree of variation in the deficits observed is likely, and there are inherent challenges in targeting particular skills/functions that might only benefit a subset of the targeted population.

Additionally, much of the work to date has been on preschool- and kindergarten-aged children. Given the considerable behavioral and neural changes throughout the course of development, more work is needed to understand whether interventions of this nature (especially after services have ceased) are efficacious throughout development. The existing evidence to this end is limited. More data on the long-term persistence of these effects is needed to determine whether booster sessions would be appropriate. The efficacy of such booster sessions would need to be evaluated as well.

When considering the intended beneficial effects of these approaches for high-risk children, it is also critical to examine the implicit assumptions made by each methodology. Laboratory-based approaches are generally aimed at improving functioning within one cognitive domain. Such research findings suggest that improvements in a given domain can mediate real-world improvements in classroom behavior/achievement or reduce psychopathology symptoms, the implicit assumptions being that strengthening a specific cognitive process via laboratory training allows for more efficient use of this pathway when called upon in ecological settings and that the given neural pathway has been strengthened. The problem with these assumptions is that few of the laboratory-based training studies have tested the potential positive effects of training outside of the laboratory, though some exceptions exist: Klingberg et al. (2002; 2005); Holmes et al. (2009); Shalev, Tsal, and Mevorach (2007); and Stevens, Fanning, Coch, Sanders, and Neville (2008). Future training studies in children should include pre/post measures of academic achievement and teacher reports of classroom behavior to allow for an assessment of the potential broad effects training might induce in ecologically valid contexts.

The assumption that neural processing is somehow strengthened after training has been examined previously. For example, Olesen, Westerberg, and Klingberg (2004) found increased prefrontal activity resulting from working memory training in adults. Additionally, Rueda, Rothbart, McCandliss, Saccomanno, and Posner (2005) and Stevens et al. (2008) demonstrated evidence of enhanced neural processing after training via changes in event-related potential (ERP) attentional components. However, more evidence is needed from neuroimaging studies to delineate the mechanisms behind these observed changes; for example, it is unclear whether training results in stronger connectivity between regions, the recruitment of more diffuse neural areas, and more available cognitive resources or whether neural processing in a given pathway becomes more efficient after training. Collecting a full range of neural indices such as resting state connectivity patterns, EEG power and source localization analyses, fMRI activation, and regional connectivity analyses before and after training paradigms are implemented could help answer such questions.

It might appear that neurobiologically based ecological interventions make opposing assumptions compared to laboratory-based training interventions because the generality of the former typically prevents defining the precise systems affected. For example, although EF deficits are thought to contribute heavily to classroom behavioral regulation problems and poorer academic performance, many interventions lack appropriate or well-defined markers of these deficits or their potential for improvement. Incorporating a working memory task known to activate the EF systems targeted by many of these interventions, for example, would allow for a pre/post estimation of EF change. Such a marker-particularly if it is a neural index-would support the claim that these systems are enhanced or might show that these systems are affected differently after the intervention than in normal populations (e.g., through the use of compensatory pathways to achieve the same result). Regardless, such knowledge is critical for developing efficacious brain training techniques.

A recent study, Mackey et al. (in press; see ) took a unique approach to training by incorporating aspects of both laboratory based and ecologically based intervention methods. They selectively chose computerized and noncomputerized games that required relational integration, the simultaneous processing of multiple relations between stimuli. This ability is thought to be integral to fluid reasoning ability, which is a strong predictor of school performance. In this study, the children (aged 7-9; low SES) participated in an 8-week session of either fluid reasoning training or processing speed training in a classroom setting where games were played individually and in groups. The children in the reasoning group showed significant improvement in the number of matrix reasoning problems, a measure of fluid intelligence, completed after training. This large effect is particularly impressive given the wide-held belief that fluid reasoning is a static trait that is not amendable to training (c.f., Jaeggi, Buschkuehl, Jonides, & Perrig [2008] for a similar training effect on fluid reasoning in adults). These results provide a first step towards integrating ecologically valid approaches with rigorous laboratory-tested methodologies to achieve promising changes in mental abilities.

Summary

Training the brain, specifically in at-risk populations, is a difficult undertaking with many factors to consider, including the program type for a given population, the skills or abilities to target, and the program cost and duration. Given the difficulties and the limitations involved in effective brain training, we advocate for a more collaborative effort. Continued advancements in neuroscience will allow greater insight into the neural processes underlying learning and training, particularly in brains having undergone damage, insult, or abnormal development. Such advances will continue to inform intervention, prevention, and training efforts as to the specific deficits affected and the particular brain systems to target in differing populations. Understanding the neural mechanisms affected will also advance our understanding of the most malleable brain systems for training or intervention. Conversely, intervention science will continue to make advancements regarding the best means of implementing training and applications for real-world contexts. Regarding effective policy, the most critical factor will undoubtedly be the ability of neuroscientists and intervention scientists to listen, communicate, and collaborate with each other.

Acknowledgments

This research was supported by the following grants: MH059780 and MH078105, NIMH, U.S. PHS; HD045894, NICHD, U.S. PHS; DA021424 and NIDA, U.S. PHS; and R324A080026, Institute of Education Science, U.S. Department of Education. The authors thank Matthew Rabel for manuscript editing.

Footnotes

¹It should be noted that many of these products lack a solid neuroscience framework upon which they are based; further, empirical evidence supporting the efficacy of some of these programs has recently been called into question ( Owen et al., 2010)

² Exercise is just one way neurogenesis can be promoted; other factors implicated in regulating new cell growth in adults include exposure to enriched environments and exposure to hippocampal-dependent learning (e.g., spatial navigation tasks).

³ Several of the studies reviewed here target the training of attention. On the surface, attention training might not seem to fit into the EF category; however, various types of attention have been hypothesized, including executive attention, and the studies reviewed here contain components that can be categorized as training this type of attention and/or control over the allocation of attention, thereby broadly fitting the EF construct.

Contributor Information

Richard L. Bryck, University of Oregon and Oregon Social Learning Center.

Philip A. Fisher, University of Oregon and Oregon Social Learning Center.

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It focuses on the assets students bring to the classroom rather than what students can't do.

It raises expectations and makes learning relevant for all students.

Culturally responsive teaching is a research-based approach to teaching. It connects students' cultures, languages, and life experiences with what they learn in school. These connections help students access rigorous curriculum and develop higher-level academic skills.

Our brains are wired to make connections. It's easier for our brains to learn and store information when we have a hook to hang it on. That hook is background knowledge. Students bring this knowledge to the classroom every day.

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Culturally responsive teaching values and reflects the assets of all students. By doing that, it raises academic expectations for all learners. It also sends the message that multiculturalism is an asset.

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