Higher Order Thinking and Adult Learning

Merriam, S., Baumgartner, L. (2020). Learning in Adulthood: A Comprehensive Guide (4th ed.). San Francisco: Jossey-Bass. ISBN: 9781119490487.

Chapter 15 Bran, Memory, and Cogni on

One of the predominant views about adult learning is that it is an internal mental process involving something happening inside our heads. When one considers that the human brain con nually processes all of the data that comes in through our five senses, our brain really is the control center for our learning. Thus a basic understanding of how the brain func ons, how memory “works” and how knowledge is structured would seem to enhance our efforts as adult educators in planning and facilita ng learning.

This chapter begins with a discussion of the basic mechanisms of the human brain. Cogni ve scien sts, primarily from the discipline of psychology, have had the longest history of research in this arena. What cogni ve scien sts do is “a empt to discover the mental func ons and processes that underlie observed behavior” (Bruer, 1997, p. 10). These mental func ons and processes include, but are not limited to, the study of how people receive, store, retrieve, transform, and transmit informa on. Neurobiologists, in contrast, “study the anatomy, physiology, and pathology of the nervous system” (Taylor, 1996, p. 301), including the brain and related systems. They are primarily interested in the structures of the brain and how the brain actually works, including its electrical and chemical systems. With more recent technological advances, like magne c resonance imaging (MRI) and positron emission tomography (PET), neurobiologists are genera ng actual pictures of how the brain operates.

Working from some brain “basics” we next explore how memory “works.” Although at this stage much of what we know about memory and how our brains func on is laboratory based, our ever expanding knowledge has enormous poten al for the study of adult learners and the way we prac ce our cra . Intertwined with the brain and memory is what we are calling cogni on or informa on processing. We present an overview of informa on processing and explore the concept of knowledge structures or schemas, the effect of prior knowledge and experience on learning, and cogni ve and learning styles. These three topics—the brain, memory, and informa on processing—are of course ghtly intertwined and grounded in the cogni ve sciences; however, a working knowledge of these topics has the poten al to enhance the research and prac ce of adult educators.

Neurobiology and the Brain

It is hard not to miss the latest developments in brain research. Stories about what researchers are finding about how our brain func ons abound in magazines, newspapers, and the Internet.

For example, you can read books about how the brains of men and women differ (Brizendine, 2010), you can learn that our brain has 400 miles of blood vessels and is 73% water (h ps://www.scilearn.com/blog/10-facts-about-your-wacky-brain), learn what foods are par cularly good for our brain (h ps://www.bbcgoodfood.com/howto/guide/10-foods-boost- your-brainpower), or watch a TED talk about what a brain “in love” is like (h ps://www.ted.com/talks/helen_fisher_studies_the_brain_in_love). On a more serious note, we are also learning more from scien sts who study the physical func ons of the brain and its related systems and how the brain relates to learning. For example, recent research has revealed that a par cular protein in the brain is responsible for conver ng short-term memories into long-term memories (h ps://www.medicalnewstoday.com/ar cles/320804.php). Viewing the devasta on of the memory and learning capacity of a person with advanced Alzheimer's disease or a massive stroke brings home to each of us the innate and yet almost mys cal ways in which the brain func ons.

Differing Views of the Brain

Restak (2000) observes that when we “speak of the mind—making up our mind, improving our mind, changing our mind—we are actually referring to ac vi es carried out by our brain” (p. 6). However, this assump on that the brain and the mind are one and the same has not always been the way that philosophers, religious scholars, and scien sts conceptualized the mind–body rela onship. The ancient Egyp ans, for example, saw the heart, not the brain, as the center of our thoughts and ac ons. This belief is reflected in their burial rites in which they preserved the heart as “the repository of the soul's earthly deeds…In contrast, the brain was extorted through the nose with an iron hook and thrown away” (Wickens, 2015, p. 1). Aristotle and a legacy of medieval thinkers shared a very similar belief in that they “a ributed all life forces to the heart and considered the brain to be nothing more than a cooling system for the blood” (Restak, 2000, p. 6).

In contrast, the Greek physician Hippocrates, considered the father of medicine, argued that the brain was the centrality of all thought. In his words, the brain was the

source of our pleasure, merriment, laughter and amusement, as of our grief, pain, anxiety, and tears….It is specifically the organ which enables us to think, see, and hear, and to dis nguish the ugly and the beau ful, the bad and the good, pleasant and unpleasant. (Hippocrates, as cited in Wickens, 2015, p. 15)

The meaning of the phrase “the brain is at the center of our thinking” has taken on many forms; involved many winding roads, detours, and blind alleys; and has become a con nuous journey,

joined by scholars from many disciplines. For example, scien sts and philosophers debated whether the actual brain ssues, which can be seen, or the fluid-filled cavi es, which lie deep within, are more relevant to the opera on of the brain; others believed that a person's character could be determined from bumps and other irregulari es of the skull (phrenology). In addi on, research focused on what parts of the brain are “in charge” of specific behavioral and cogni ve func ons remains a topic of interest among scholars (Taylor & Marienau, 2016).

The Structures and Func ons of the Brain

The way the brain is structured and func ons has captured, as noted earlier, the no ce of the general public, in addi on to study among the scien fic community. “For good reason, the brain is some mes hailed as the most complex object in the universe. It comprises a trillion cells, 100 billion of them neurons linked in networks that give rise to intelligence, crea vity, emo on, consciousness and memory” (Fischback, 1992, p. 51). Since the 1950s, the study of the structures and func ons of the brain, including how underlying electrical and chemical processes carry messages throughout the brain, has been dominated by neurobiologists, cogni ve psychologists, and physicians. Although much of this early work used animals as subjects, there have also been studies of people with a wide range of brain disorders. The studies on humans have relied primarily on what could be observed during brain surgery, the behavior and abili es of people with severe brain damage, or postmortem examina ons. With the advent of noninvasive imaging techniques, researchers are also now including healthy humans as subjects (see for example, Mehta, 2014).

Neuroimaging or brain imaging is the use of various techniques like PET and MRI that allow scien sts “to see into the brain,” with li le risk to humans, whether they are in good health or in need of medical care. With these technologies, researchers can actually “see” the effects of diseases, injuries, and even drugs and other chemicals. Further, and of great interest to educators, these techniques can show which brain areas are ac ve during different learning tasks, and what, if any, varia ons exist across individuals. They can also help researchers learn how a normally func oning brain works by showing which brain areas are ac ve during certain tasks and what kinds of varia ons exist among individuals. Although these newer scanning methods have shown that “while it's true that certain areas [of the brain] are specialized for specific purposes, this organ can only be understood as one highly complex and integrated func onal unit” (Restak, 2000, p. 25). For example, rather than each hemisphere of the brain opera ng separately, when the processing of informa on “within each hemisphere commences, the hemispheres rapidly send signals back and forth” (Robertson, 2005, p. 21). The result is a lot of “cross-talk” among the neurons rather than a direct response from one side or the other. “Connec ng the two cerebral hemispheres is a rope-like structure called the corpus callosum, which carries messages from one side of the brain to the other” (Restak, 2012, p. 24).

These newer forms of technology have also allowed us to gain a clearer picture of the architecture of the brain. One of the most striking structures of the brain are the large, seemingly symmetric cerebral hemispheres that sit on a central core or base. The le hemisphere (LH) is analy cal, verbal, linguis c, and sequen al whereas the right hemisphere (RH) is more holis c, imagina ve, pictorial, and spa al. The corpus callosum allows the two hemispheres to collaborate on many tasks. Taylor and Marienau (2016) explain the strengths of the two hemispheres as well as how they work together:

The LH concentrates on words, logic, and reduc ve reasoning; limited and defined variables; probabilis c analysis; and objec vity, which distances the knower from the known; it sees the body it belongs to as a compila on of its parts. The RH concentrates on images, feelings, sensa ons, and induc ve reasoning; it is more a uned to the big picture and contextual analysis, is connected to its whole self as embodied, and makes meaning that connects the knower and the known. Unless disease or injury has damaged the brain, neither hemisphere works in isola on; They constantly communicate through a thick bundle of nerve fibers (the corpus callosum). (p.70)

In a moving tes mony to the importance of both hemispheres of the brain, Jill Taylor, a brain scien st, published a book, My Stroke of Insight, about her suffering a stroke on the le side of her brain leaving her unable to walk, talk, read, or write. It took her nearly a decade but she “retaught” her brain and once again lives a normal life. However, she came to value her right brain strengths, wri ng that “I shi ed from the doing-consciousness of my le brain to the being-consciousness of my right brain…I stopped thinking in language and shi ed to taking new pictures of what was going on in the present moment” (p. 71). As she recovered, she wondered how she could retain her “newfound sense of connec on with the universe” and what she “would have to sacrifice in order to recover the skills of her le mind” (Taylor, 2008, p. 132).

Other structures of the brain that have generated interest in recent years are those connected to emo on.

Emo on was largely neglected by neuroscience during most of the twen eth century, but it is now the focus of intense scru ny, and not a moment too soon considering its importance in human lives. The neurobiological underpinning of the emo ons has begun to be elucidated and it has become clear that the brain handles different emo ons with the help of different components. (Damasio, 2000, p. 14)

These components [structures] include the amygdala, the ventral and medial prefrontal cortex, and the hypothalamus. This “discovery offers important clues in the inves ga on of diseases such as depression and mania” (p. 16). In addi on, Damasio argues that gaining further knowledge about what the interac ons are among these parts of the brain underscores “the degree to which emo on and feeling are inextricably interwoven with the mechanisms that ensure the maintenance of life” (p. 16). Indeed, emo ons are internal responses to triggers in the environment, whereas feelings are our responses to those triggers (for an update on the neurobiology of emo ons, see Esperidião-Antonio et al., 2017).

Pert (1999), although in agreement with Damasio's basic assump ons, has challenged researchers on the idea that the brain is the only part of us that can gather, process, and share informa on related to emo ons. Based on her findings that chemical substances and their receptors are found in the body's nerves of all kinds, she argues that emo ons could be stored and mediated by other parts of the body. This discovery is important for apprecia ng how memories are stored not only in the brain but in a psychosoma c network that “can extend out to the very surface of our skin” (Pert, 1999, p. 143). This recogni on that emo on and memory are clearly linked—whether these func ons are based primarily in the brain or throughout our bodies—could have enormous implica ons for how we understand learning in adulthood.

Currently, there appears to be quite a bit of research on the interconnectedness of the brain, memory, emo ons, and learning including a journal, Cogni on and Emo on, devoted to the topic. Pichora-Fuller, Dupuis, and Smith (2016), for example, explored the effects of vocal emo on on memory in younger and older adults, and Storbec and Maswood (2016) discovered that happiness increases verbal and spa al working memory capacity whereas sadness does not. In a recent review of the research on the influences of emo on on learning and memory using neuroimaging techniques, the authors conclude that “substan al evidence has established that emo onal events are remembered more clearly, accurately and for longer periods of me then are neutral events” (Tyng, Amin, Saad, & Malik, 2017, para. 38).

Three other structures—the hypothalamus, the pituitary gland, and the pineal gland—control bodily func ons, such as temperature regula on, hormone produc on, and sleep and wakefulness. In addi on, two other crucial structures form the central core or base of the brain. These are the medulla, which contributes to the control of central life func ons (including respira on, circula on, and diges on), and the cerebellum, which coordinates movement and plays a vital role in the processing of informa on (Restak, 2012). All of these deeper structures of the brain, depending on their opera onal effec veness, could affect how adults learn.

Currently there appears to be a mix of researchers from various fields interested in studying the brain and its rela onship to emo ons, memory, and learning. At the heart of cogni ve neuroscience, for example, “lies the fundamental ques on of knowledge and its representa on

by the brain—characterized not inappropriately by William James (1842–1910) as ‘the most mysterious thing in the world’ (James, Vol. 1, 216)” (Albright & Neville, 1999, p. li.). Thus, rather than relying on either neurobiology or cogni ve sciences, the promise of connec ng what we know about how the brain func ons and learning comes primarily from the merger of the two sciences. Bruer (1997) has used the metaphor of the bridge to illustrate this point. On the one hand, we have a “well-established bridge” of knowledge about learning from the cogni ve sciences. On the other hand, we have a newer bridge between cogni ve psychology and neuroscience. “This newer bridge is allowing us to see how mental func on maps onto brain structures,” providing “useful insights for educators about instruc on and educa onal prac ce” (p. 4). Currently, neuroscience is “a broad, interdisciplinary field that spans many disciplines, including medicine, psychology, biology, chemistry, physics, computer science, mathema cs, economics, educa on, ethics, and law” (Rekart, 2015, Item 97176622).

Though cogni ve neurosciences have amassed an amazing amount of informa on about the structures and func ons of the brain, some wonder if, as Albright and Neville (1999) observe, “[T]he applica ons of new experimental techniques [in this case, more sophis cated, noninvasive imaging techniques] have o en raised more ques ons than they have answered. But such are the expansion pains of a thriving science” (p. lvii). Breakthroughs that have been made as a result of research in the cogni ve neurosciences have been applied to prac ce and in par cular adult learning (see for instance, Taylor and Marienau, 2016). In another applica on of this knowledge base, Shreeve (2005) tells the fascina ng story of how, before removing a cancerous tumor from a young woman, the physicians and a neuropsychologist needed to find the exact “address” for Corina's language abili es. In doing so they had a good chance of being able to retain her language func ons, which are a vital dimension of the learning process. However, this search is not an easy one because “every person's brain is as unique as their face” (p. 9). In this case the surgery was a marvelous success, and as the main surgeon observed: “Corina's brain is the most beau ful object that exists, for it allows her to perceive beauty, have a self, and know about existence in the first place” (p. 8).

The Brain and Learning in Adulthood

Connec ng what we know about the brain and related systems to learning in adulthood is at best a set of working hypotheses. Although books and ar cles linking the brain with learning (see, for example, the Forbes list of “must read” brain books (DiSalvo, 2017) or “7 books that will train your brain to overachieve” (James, 2017)) and some educators have tried to make very direct correla ons by developing what they term brain-based learning programs (for example, Sprenger, 2010), there is s ll something of a gap between the theore cal and empirical knowledge of how the brain and related systems work, and prac cal applica ons of that knowledge.

Two main methods have surfaced related to how educators have applied this knowledge to their prac ce. The first is the applica ons some educators have made based on supposed factual knowledge about the brain. As Bruer (1997, p. 4) observes, even neuroscien sts,

while interested in how their research might find applica on outside the laboratory and clinic, are more guarded in their claims. O en they are puzzled by the neuroscien fic results educators choose to cite, by the interpreta ons educators give those results, and by the conclusions educators draw from them. (p. 4)

For example, one of these applica ons of brain research to learning in adulthood has been educators designing programs that purport to increase the func ons of the le or right brain. By the end of these programs, sponsors claim that par cipants will have fully developed their untapped poten al of the right or le brain. Although, indeed, there is localiza on of func ons that different parts of the brain support (such as the le brain favoring logic while the right brain houses our crea ve and ar s c abili es), more current research suggests there are no defini ve maps that provide guides to which of the many structures of the brain controls what. Rather, what has been hypothesized is that it is the interac ons among various structures that may be the key to understanding how our brain func ons (Jensen, 2008; Taylor & Marienau, 2016).

The second prac ce is the linking of specific instruc onal techniques to the knowledge we have of the brain. Jensen (2008) makes the case that “brain-based educa on is learning in accordance with the way the brain is naturally designed to learn” (p.4). He suggests that “by using what we know about the brain we can make be er decisions and reach more learners….it is learning with the brain in mind” (p. 4). Tate (2012, 2016) proposes specific “learning strategies” that can be used with adult learners that engage the brain such as games, experiments, drawings, and brainstorming.

In summary, research into the link between cogni ve neuroscience and learning in adulthood offers some promising discoveries and new direc ons that may well have direct links to adult learning. A sampling of some of these intriguing ideas and ques ons are summarized as follows:

Could our increasingly fast-paced and technology-saturated lives have the poten al to limit our capacity in our informa on processing systems to the point that they will create bo lenecks in our ability to process informa on? (Bormann, 2018). Marois (2005) suggests three areas in which our capaci es as learners could be nega vely affected by technology: conscious visual percep on, short-term memory, and ac on and decision making. Restak (2012) wonders how communica on technology might be “scrambling our brains” resul ng in “thinking in new and different ways” (p. 190).

Are there actual differences in male and female brains that affect both what and how we learn (Baron-Cohen, 2005; Brizendine, 2010)? If so, how might taking these differences into considera on change our current prac ces as educators?

Will brain injury no longer be considered hopeless based on plas city or neuroplas city (the brain's ability to modify its connec ons or rewire itself; see, for example, Taylor, 2009)? If so, what are the roles of adult educators in assis ng these adults to become the normal learners they once were?

Do the techniques that Buddhists have employed for over twenty-five hundred years to guide their mental state away from destruc ve emo ons and toward a more compassionate, peaceful state of being change how the mind works (Wright, 2017)? If so, how can using these techniques change our way of thinking and being as educators of adults?

Are there interdependent rela onships that exist between emo on and reason, exhibited in how the brain func ons, that are crucial components of how adults learn? If so, what are the effects of these rela onships on learning, and how can adult educators best facilitate these influences?

Memory

Fear of memory loss is a common concern of people as they age. Parents o en observe how much more easily their children can remember such simple things as telephone numbers and computer access codes, whereas many older adults seem to remember childhood events vividly but some mes have difficulty remembering the names of people they just met. These observa ons and images foster the idea that memory loss is a normal result of aging and thus is something we all must accept. Are these percep ons of memory loss accurate, and if so, what effects do they have on learning in adulthood? O en, memory func ons are equated with learning or are seen as one of the primary mental processes associated with learning. If adults do suffer major changes, especially decline, in their memory func ons, it follows that the learning process may also be impaired. To understand how memory can be affected by the aging process, we first need to examine the nature of memory, how memories form and how they are retrieved.

Since the 1960s, human memory has been studied primarily from the informa on processing approach. The mind was visualized as a computer, with informa on being entered, stored, and then retrieved as needed. Conceptualizing where people store or file what they learn, termed the structural aspect of memory, was the first major focus of study from this perspec ve. Three categories have been tradi onally used to describe the different structures of memory: sensory memory, short-term memory, and long-term memory. More recently there has been a movement away from dividing up the structure of memory in such a defini ve manner. This

change in thinking has stemmed primarily from the study of working memory, and our growing knowledge of how the brain func ons. Working memory has been conceptualized in three different ways: as part of long-term memory, as part of or the same as short-term memory, or as the mediator between sensory memory and either long- or short-term memory (Anderson, 2015; Ormrod, 2016). For the purposes of our discussion, we examine human memory within the framework of sensory, working, and long-term memory. What is important to keep in mind when discussing memory as a process is that the different forms of memory do not exist in specific “places” in the brain but rather are metaphors for each of the main processing components. As Taylor and Marienau (2016) point out,

Memory is neither a discrete thing, nor does it occupy a par cular place in the brain; it is a process, not merely a subject of retrieval. By the me we become aware of, or inten onally bring to mind, that which we call a memory, the brain has been busily associa ng various prior and current memory traces located in far-flung neural networks. (p. 47)

The process of forming a memory begins with an experience that enters our consciousness through our senses of vision, hearing, and/or touch. These images, sounds, and/or vibra ons, our “sensory memory” has a very brief storage me of only milliseconds before it either enters our working memory system or is lost. Working memory, or what some label short-term memory, entails “the ac ve and simultaneous processing and storing of informa on” (Hoyer & Roodin, 2003, p. 277). Hoyer and Roodin compare working memory to a desktop:

During the course of a day, new pieces of informa on (memos, reports, work requests, and maybe empty pizza boxes) constantly accumulate on an individual's desk. The individual has to determine: (1) which informa on is the most important, (2) which pieces of informa on require further processing, (3) which processing strategy to use, and (4) which pieces of informa on are clu ering up the desktop and should either be discarded or stored. Working memory tasks require individuals to simultaneously select, coordinate, and process incoming informa on. (pp. 277–278)

The storage capacity of working memory is es mated to be from 5 to 30 seconds. Long-term memory, however, has an enormous capacity for storage and is that part of the memory structure that retains informa on for long periods of me. Long-term memory stores declara ve knowledge or “how things are, were or will be” and procedural knowledge or “how to do things” (Ormrod, 2016, p. 194). Long-term memory has been conceptualized as the most complicated component of the memory system, and therefore has received the most a en on in the research literature.

Our understanding of long-term memory has moved from viewing it as one monolithic system “to one that is less hierarchical, involving several different kinds of memory, each playing a

significant role” (Taylor, 1997b, p. 263). Most of the research on long-term memory has involved explicit (or declara ve) memory, which is “the term used to describe knowledge that we can consciously recall” (Anderson, 2015, p. 367) and can be expressed in language. “This form of memory is more sensi ve and prone to interference, but it is also invaluable, providing the ability for personal autobiography and cultural evolu on” (Taylor, 1997b, p. 263). Implicit (or nondeclara ve) memory, in contrast, are memories that we are not conscious we have. Although these memories are developed unconsciously and thus form a hidden world we know li le about, “people are influenced by [these types of memories] without any awareness they are remembering” (Schacter, 2013 p. 161). Classic examples of implicit memories are riding a bike, using a computer keyboard, and the “acquisi on of rules o en found in grammar [involving categorical knowledge]. Grammar is a par cularly good example of implicit memory, where people have acquired abstract rules, but are unable to ar culate what guides their speech and wri ng” (Taylor, 1997b, p. 264).

How we handle informa on is integrally related to the cogni ve processes involved in memory. Usually the memory process is divided into three phases (Anderson, 2015; Ormrod, 2016; Schacter, 2013). The encoding or acquisi on phase is the ini al process in which the informa on is entered into the system. Filing this material for future use is termed the storage or reten on phase. The final phase, retrieval, describes how we get material out of storage when needed. Two of the most common methods of retrieval are recall, or bringing forth “to-be-remembered” informa on, and recogni on, which involves choosing from a group of possible answers. As we well remember from our school tes ng days of essay versus mul ple-choice exams, recalling the “correct” answer is considered to be more difficult than recognizing the correct answer among other possibili es presented. Research has demonstrated that as we get older, we have more problems encoding and retrieving memories; the actual reten on or storage of our memories remains fairly constant, however.

Memory from the informa on processing perspec ve works as follows: Informa on from our environment is registered within sensory memory through our visual, auditory, and tac le senses. Material is then selec vely transferred or encoded into working memory. The control system of selec ve a en on determines what is important enough to be moved into working memory. There is considerable flexibility with what can be done with the informa on in working memory. It

can be used as a cue to retrieve other informa on from long-term memory, it can be elaborated, it can be used to form images, it is used in thinking, it can be structured to be placed in long-term or secondary memory, or if nothing is done with it, it can be discarded. (Di Vesta, 1987, p. 211)

Because the func ons of working memory are complex and its me and capacity are limited, two major control processes are used to sort and file the data: chunking and automa za on. Chunking essen ally is organizing the informa on in groups or pa erns (a phone number in three chunks: 351-555-2119, for example), and automa za on allows for a chunk of informa on to become so familiar that a person can handle it without recall thinking (Ormrod, 2016). The material structured in working memory for long-term memory is then encoded into that memory bank for permanent storage. Because individuals organize informa on received in different ways, a ending to different cues, and associa ng similar pieces of informa on together, what is stored is not likely to be exactly what was received. Hence, individuals can see the same event and remember things differently (Ormrod, 2016). This type of processing is some mes referred to as deep processing versus the shallow processing done at the working memory level. The informa on is then retrieved as needed from this long-term storage.

Memory and Aging

A great deal of research from the informa on processing framework has been conducted on the topic of memory and aging. The general consensus from that work is that certain memory func ons do decline with age. Nevertheless, a number of authors have cau oned that because of methodological considera ons and the variables being studied, this work must be interpreted with care. The great majority of it has focused on comparing young adults (usually college students) with older adults by using cross-sec onal designs. These two factors combined make it difficult to generalize across age groups because of subject and cohort bias. Subject bias comes from using people in a study who do not necessarily represent the general popula on (such as college students versus the broad popula on of young adults). Cohort bias or effect “is any difference between groups of adults of varying ages that is due not to any matura on or developmental process, but simply to the fact that the different age groups have grown up under different historical and cultural circumstances” (Bee & Bjorklund, 2004, p. 10). In addi on, although the focus of the research is memory and aging, some of the authors of these memory and aging studies do not define older adult, not even in terms of age ranges (Naveh- Benjamin, Hussain, Guez, & Bar-On, 2003; Rodgers & Fisk, 2001). Rodgers and Fisk, for example, provide a very thorough review and cri que of the literature on understanding how age may affect the role of a en on in older adults. However, except for one of many studies included in their review, they neither describe nor cri que the study par cipants. Moreover, most of this research has been conducted primarily in laboratory se ngs using memory tasks and ac vi es, such as repea ng back nonsense words and lists of random numbers. The primary cri cism leveled against this type of research on memory is that these tasks and skills are generally ar ficial and taken out of the context of everyday life. A response to this cri cism in recent years has been to design “ecologically valid” research that takes into account the everyday learning demands of adults (Anderson, 2015; Hoyer & Roodin, 2003; Langer, 1997; Rodgers & Fisk, 2001).

Park and Fes ni (2017) in their review of memory and aging research over the past 50 years concur, a ribu ng progress in understanding memory and aging to neuroimaging and interven on studies. They applaud the growing interest in “applied methods for improving memory across the adult life span. Considerable a en on has been given to interven on techniques (i.e., cogni ve training, life style adjustments) to boost cogni ve func on and delay the onset of memory decline” (p. 86). With these limita ons in mind, we offer a summary of this research on memory in adulthood.

Sensory and Working Memory

In general, few clearly defined changes have been found in sensory memory as people age. Because there are fairly major changes with age in both vision and hearing, one would expect to see these changes reflected in sensory memory. If you do not hear someone's name in an introduc on, for example, there is no way it can be registered for recall later. However, it is o en difficult with tes ng procedures to dis nguish between age-related physiological decline in the senses themselves, especially hearing, and actual decrements in the process of sensory memory.

Working memory, in contrast, is more problema c as we age, especially when we need to store the informa on in the memory while performing a computa on (Bjorklund, 2016). Bjorklund suggests three reasons for a decline in working memory. One possibility is that older adults “don't have the mental energy or a en onal resources that younger people do” (p. 105). A second possibility is that older adults do not employ the same strategies for dealing with working memory tasks as do younger people. The third commonly cited reason for this decline in working memory is that older adults appear to process materials more slowly, especially ones that are more complex in nature (Bjorklund, 2016). One of the explana ons for this slowing of the processing of informa on seems to be the “older adults' capacity to simultaneously perform a cogni ve task while trying to remember some of the informa on for a later memory task” (Smith, 1996, p. 241). In other words, it appears to be more difficult for older adults to both respond immediately to whatever s mulus triggered working memory and at the same me store per nent informa on in long-term memory. Finally, older adults are less likely to even a empt to deal with “irrelevant and confusing informa on” (Bjorklund, 2016, p. 106).

Long-Term Memory

As with working memory, age deficits are also more commonly found in long-term memory. Three major differences have surfaced in long-term memory for older versus younger learners: changes in the encoding or acquisi on of material, the retrieval of informa on, and the speed of processing. Few changes have been noted in the storage or reten on capacity of long-term memory over the life span.

The ques on that o en surfaces in reviewing the process related to long-term memory is whether it is more difficult for adults as they age to get informa on into the system (to encode it) or to get it out (to retrieve it). The response to this ques on appears to be both. It is not yet clear which part of the process creates more difficulty (Bjorklund, 2016; Ormrod, 2016). Encoding problems are most o en associated with the organiza on of informa on. Specifically, older adults appear to be less efficient at organizing new material. Possible explana ons of why organiza on is a problem relate to the amount and type of prior knowledge they already possess. Although it is clear that the more we can relate new informa on to already stored informa on, the be er we will remember it, it may also be that new informa on may be distorted to fit one's prior beliefs. Ormrod (2016) explains:

If people think that new informa on is clearly wrong within the context of what they believe about the world, they may ignore the informa on altogether. Alterna vely, they may distort the informa on to be consistent with their ‘knowledge’ and as a result learn something quite different from what they saw, heard or read. (p. 222)

In other words, this type of informa on may never enter long-term memory because it is incompa ble with what the person already knows.

On the retrieval side, changes are most o en noted in the recall versus recogni on of informa on. In tests of recall, for example, major differences have been demonstrated for older and younger people, whereas in recogni on ac vi es, the differences are small or nonexistent, although the retrieval me may be slower. Memory training, and “external memory aids” such as “making lists, wri ng notes, placing items-to-be-remembered in obvious places, and using voice mail, mers, and handheld audio recorders” improve older adults' recall (Bjorklund, 2016, p. 110). In fact, older adults who made lists performed as well at recalling items as younger adults (Burack & Lackman, 1996 as cited in Bjorklund, 2016). Another aspect of retrieval that is o en taken as a given is that older persons can retrieve “ancient memories” be er than younger people, along with the accompanying myth that older people can clearly remember events in their distant past but have trouble recalling recent events. Rather, it appears that this reversal of memory strengths—remote memories are stronger than recent memories—may be a natural phenomenon that occurs at all ages, not just with older people. Further, we all

possess varying amounts of knowledge in selected domains of work, sports, hobbies, music, and other areas. Access to such knowledge is unaffected by aging. Individuals maintain their ability to use well-learned knowledge, strategies, and skills throughout middle age and into old age (Rybash, Hoyer, & Roodin, 1986). Tests of factual knowledge (e.g., vocabulary or news events) typically show no decline from young adulthood to old age (Hoyer & Touron, 2002). (Hoyer & Roodin, 2003, p. 295)

In summary, in rela on to long-term memory it appears that older adults may not acquire or retrieve informa on as well as do younger adults nor organize informa on as effec vely. However, this line of research may have limited generalizability because of the research designs, the subjects, the memory ac vi es tested, and the separa on of the research from the real world of the adult learner.

Memory in Context

In response to some of the cri cisms of memory research just cited, a different approach has been taken by placing memory tasks in the context of everyday adult lives, called func onal memory by some researchers. This strand of research, which has been termed ecological validity, has received li le a en on, primarily because it is affected by so many different variables and is s ll considered controversial by some researchers. The term ecological validity assumes that the tasks being studied are meaningful to the person and accurately reflect real- life adult experiences. These studies use a variety of memory tests, from “memory for text” formats, which include reviews of sentences, paragraphs, and stories versus single words and symbols, to memory skills for everyday ac vi es, such as keeping appointments and remembering what items to buy at the grocery store (Anderson, 2005; Knopf, 1995; Ormrod, 2016). These studies also address some of the other concerns voiced by scholars of the contextual approach, such as the person's needs and mo va on, the specificity of the task, and situa onal variables. Other factors that might affect differences in memory and cogni on are a person's health status (e.g., chronic diseases and medica ons for those diseases), level of formal educa on, intellectual ac vity (e.g., par cipa ng in clubs, traveling, reading books, taking classes), and physical exercise (Bjorklund, 2016).

Overall, the assump on underlying the research on memory is that memory capacity and skills form one of the keys to how adults learn. Formal memory training, the most structured approach to building memory skills, has been shown to be useful in helping older adults cope with memory deficits (Bjorklund, 2016; Hoyer & Roodin, 2003). This training has most o en focused on the teaching of encoding strategies, such as rehearsing informa on or using mnemonics. Adult educators have in fact suggested ways to integrate training in memory skills into formal learning programs for adults such as providing both verbal and wri en cues (for example, advance organizers and overheads) when introducing new material to learners; using mnemonics and rehearsal strategies; and giving opportuni es to apply the new material as soon a er the presenta on as possible. Adults learning on their own may also find it helpful to use memory aids in their learning ac vi es. These can come in many forms, from structured checklists for learning a new skill to personal note taking on items of interest.

Cogni ve psychologists, in addi on to their work on memory and aging, have provided us with a number of other important concepts related to learning in adulthood. Three of those

concepts—informa on processing, the role of prior knowledge and experience, and learning and cogni ve styles—are discussed next in the chapter.

Informa on Processing

Informa on processing involves both the acquisi on of knowledge, discussed in the sec on on human memory, and the actual structure of that knowledge (Anderson, 2015). In this perspec ve, considerable importance is placed on prior knowledge as well as on new knowledge being accumulated. Because it is assumed that most adults have a greater store of prior knowledge than children, understanding the role that this knowledge plays in learning is cri cal. In thinking through the possible connec ons of prior knowledge to learning in adulthood, the concept of schemas provides a useful framework.

Schemas “represent categorical knowledge according to a slot structure in which slots are a ributes that members of a category possess” (Anderson, 2015, p. 112). “People o en form schemas about events as well as objects; such event schemas are o en called scripts. For example, what things usually happen when people go to a doctor's office?” (Ormrod, 2016, p. 298). These schemas, which may be embedded within other schemas or may stand alone, are filled with descrip ve materials and are seen as the building blocks of the cogni ve process. Schemas are not just passive storehouses of experience, however; they are also ac ve processes whose primary func on is to facilitate the use of knowledge.

We all carry around with us our own individualized set of schemas that reflect both our experiences and our worldview. Therefore, as adult learners, each of us comes to a learning situa on with a somewhat different configura on of knowledge and how it can be used. For example, some par cipants in a workshop on diversity in the workplace may bring to that experience firm beliefs that achieving diversity is a worthwhile goal based on their posi ve experiences with women and people of color. Others may not believe in the principle of diversity at all and view it as an easy way for “some people” to get hired. And s ll others may be downright angry, believing they have either been discriminated against or passed over for a promo on because they were of the “wrong color” or gender. Therefore, each learner in the workshop not only comes with different schema sets but also departs having learned very different things—even though all were exposed to basically the same material.

In categorizing schema types, two kinds of knowledge are most o en dis nguished: declara ve knowledge and procedural knowledge. Ormrod (2016) describes declara ve knowledge as “the nature of how things are, were, or will be” (p. 250). Declara ve knowledge helps us interpret the world around us and “recall past events” (p. 250). Procedural knowledge, by contrast, “is knowledge of how to perform various tasks” (Anderson, 2015, p. 370). We may be able to describe two or three different models for instruc on (declara ve knowledge), for example, but

when we try to put these models into ac on (procedural knowledge), we may fail miserably. Because the ques on is open whether learning facts or knowing how to perform comes first, the scenario just described could also be reversed: a person may be an excellent instructor and yet have no specific knowledge of instruc onal models.

Educators, however, understand most learning in adulthood goes far beyond the simple memoriza on of facts. The expecta on is that adults will be able to put those facts to good use in their everyday lives, whether as workers, parents, spouses, friends, and so on. Therefore, the processes of adjus ng and restructuring of informa on, as well as accommoda ng both declara ve and procedural knowledge, become vital in adult learning. The general processes of problem solving and cri cal thinking are good examples of the importance of these constructs. Specifically, in most problem-solving situa ons, we are trying to fit new ideas (declara ve knowledge) and ways of ac ng (procedural knowledge) into earlier pa erns of thinking and doing (our current schemas). If we are unable to change our earlier thought pa erns (that is, fine-tune or restructure them), our chances of being able to frame and act on problems from a different perspec ve are remote, if not impossible.

Cogni ve scien sts also cite the importance of metacogni on, defined as “people's awareness and understandings of their own thinking and learning processes, as well as their regula on of those processes to enhance their learning and memory” (Ormrod, 2016, p. 363). A related term, metamemory, refers to the self-ra ngs of memory performance, or the self-appraisal or self- monitoring of memory. “Some studies have found that older persons' metamemory is mostly accurate, whereas other studies have found that older adults exaggerate their memory failures (Hertzog & Hultsch, 2000)” (Hoyer & Roodin, 2003, p. 274). Researchers have speculated, though, that the discrepancies between people's opinion of their memory performance and their actual ability may be largely due to older adults assuming memory loss or failure as they age.

Prior Knowledge and Experience

Many writers, as discussed in Chapter 8, have spoken about the importance of acknowledging adults' prior knowledge and experience as integral to the learning process. In exploring the role of prior knowledge and experience in learning, two ideas are important: the amount of prior knowledge and experience and the nature of that knowledge and experience.

In terms of the amount of prior knowledge and experience one possesses, the difference between those who know a great deal about what they are doing (experts) and those who know very li le (novices) is key. A person can be an expert in a variety of areas, from growing tomatoes to skiing. According to Sternberg and Horvath (1995, p. 10), “Perhaps the most fundamental difference between experts and novices is that experts bring more knowledge to

solving problems … and do so more effec vely than novices.” In addi on, experts are able to solve problems faster and in a more economical way, have stronger self-monitoring skills, and are able to view and solve problems at a deeper level than novices (Ferry & Ross-Gordon, 1998; Gill, 2015; Tennant & Pogson, 2002). Similarly, Anderson (2015) has observed that experts “learn to perceive problems in ways that enable more effec ve problem-solving procedures to apply” (p. 221). Further, “exper se in different domains requires the adop on of those approaches that will be successful for those par cular domains” (p. 221). And “as people become more expert in a domain, they develop a be er ability to store problem informa on in long-term memory and to retrieve it” (p. 227). Finally, “no one develops exper se without a great deal of hard work … [and] the difference between rela ve novices and rela ve experts increases as we look at more difficult problems” (p. 210).

As Anderson (2015) and others have pointed out, being an expert is related to certain domains or subject ma er areas. Educators have o en observed that being an expert in one area does not necessarily translate into being an expert in another, no ma er what the learner's mo va on or background. Perhaps because of the recogni on that competence or exper se is related to learning, there now exists a growing database of studies examining the development of exper se in areas as varied as sports, health professions, performing arts, management, and so on. A recent issue of the journal Intelligence, for example, is devoted to the topic “Acquiring Exper se: Ability, Prac ce, and Other Influences” (De erman, 2014). In another interes ng review of exper se, Hallam (2010) focused on changes in the brain as exper se develops as well as how transi ons (between learning environments, for instance) and other factors affect the development of exper se. One of her observa ons is that “in their field, experts have superior short- and long-term memory” (p. 4), but that experts can also be overconfident and inflexible, “unable to assess the future performance of novices” which can be a “serious problem for teachers” (p. 6).

Therefore, in helping adults connect their current experience to their prior knowledge and experience, we need to be knowledgeable about the amount of prior knowledge they possess in a par cular area and design our learning ac vi es accordingly. For example, in teaching a group of expert instructors of adults, it probably does li le good to outline just one instruc onal model, even when this model is the newest and supposedly the most complete model. They will probably think of every possible excep on as to why this model will not work with all of their learners. It would make more sense to ask these instructors to look at alterna ve models, including this new model, and then have them consider which of these models or parts of these models have worked best for them in what type of situa ons. By following this plan, the par cipants' level of exper se would be acknowledged, they would be asked to think more deeply about the many situa ons they have faced in teaching, and they would need to use their problem-solving abili es related to their prior knowledge and experience as instructors.

It would be helpful, in addi on, to know how the transi on between being a novice and being an expert takes place in order to facilitate learning from prior knowledge and experience. To this end, Anderson (2015), Lajoie (2003), Pillay and McCrindle (2005), and Sternberg and Horvath (1995), among others, have provided comprehensive descrip ons of the development of exper se that are useful in designing learning ac vi es to assist adults in moving along the con nuum from novice to expert. Although there are differences among these authors' portrayals of exper se, there are also a number of commonali es in their descrip ons of what cons tute its main dimensions. Using primarily the framework of expert knowledge in the professions (for example, teaching, veterinary medicine) these scholars agree that experts:

Require extensive knowledge in one or more specific domains (content areas).

Recognize the importance of the sociocultural context of their work, including, as applicable, their own professions.

Are challenged by complex and novel situa ons and problems.

Process complex informa on quickly.

Arrive faster at more crea ve and accurate solu ons.

Addi onal research is needed to dis ll further the main dimensions of exper se, which would be helpful to educators in planning programs that would assist novices not to become just more experienced novices, but indeed experts in areas that are significant to who they are and also significant to their fields of prac ce.

In an example of what such a program might encompass, Lajoie (2003) has iden fied two different approaches to fostering exper se development. The first, dynamic assessment, is “defined as a moment-by-moment assessment of learners during problem solving so that feedback can be provided in the context of the ac vity” (p. 22). This approach, framed in the concept of situated cogni on, was explored in more depth earlier in Chapter 8. Second, Lajoie advocates “making the exper se trajectory visible to learners through models of exper se, feedback, or examples that promote the ac ve transfer of knowledge and self-monitoring.” This requires openness on the part of experts to share what they know, rather than having novices “learn the ropes” by trial and error, although that might be part of the learning process. For example, in some professions many of the experts do not have the me to share their exper se with novice learners on any meaningful level, nor are the organiza ons they work for willing to change the workday to allow for that me. Unfortunately, there are also experts in every field who are unwilling to pass along their “trade secrets” because doing so might erode their power and dominant posi ons in their organiza ons.

Cogni ve Style and Learning Style

Another important aspect of cogni on related to learning in adulthood is the no on of cogni ve style. Cogni ve styles are characterized as consistencies in informa on processing that develop in concert with underlying personality traits. They are reflected in “how individuals typically receive and process informa on” (Joughin, 1992, p. 4) and encompass the ways people see and make sense of their world and a end to different parts of their environment. Some people tend to look at problems from a global perspec ve, whereas others are more interested in taking in the detail (Flannery, 1993). The la er types, which Flannery labels analy cal informa on processors, want informa on in a step-by-step manner and tend to perceive informa on in an abstract and objec ve manner. In contrast, “the global learners process informa on in a simultaneous manner. The ideas or experiences are seen all at once, not in any observable order” (p. 16). In addi on, global learners perceive informa on in a concrete and subjec ve manner.

A number of cogni ve-style dimensions, including the concepts of global and analy cal processing styles, have been iden fied through research (Cassidy, 2004; Joughin, 1992; Messick, 1996). The outstanding feature of these varying dimensions is their tendency to be bipolar. In contras ng people's cogni ve styles, we tend to label people as being at either end of the con nuum, and for the most part, cogni ve styles are considered rela vely stable.

Although a great deal of research has been conducted on cogni ve styles, much of it has been done with children or college students and “no style has led to clear implica ons with respect to adult learning” (Joughin, 1992, p. 4). Therefore, it is s ll unclear how this work may relate to helping adults learn more effec vely. Hiemstra and Sisco (1990) have conjectured that knowledge about cogni ve styles might assist instructors in predic ng how learners are “likely to form typical learning tasks such as remembering, selec ng, comparing, focusing, reflec ng, and analyzing” informa on (p. 241). In addi on, Flannery (1993) has asserted that “teaching, texts and structures can be adapted to teach to different” cogni ve styles (p. 19).

A related yet somewhat different phenomenon is the concept of learning style. The literature describing cogni ve and learning style is rather confusing; some authors use the two terms interchangeably, others view cogni ve style as the more encompassing term, and s ll others see learning style as the more inclusive term. In a study underscoring the lack of clarity in differen a ng between cogni ve style and learning style, Armstrong, Peterson, and Rayner (2012) conducted a Delphi study with 65 researchers who are members of the European Learning Styles Informa on Network (ELSIN). In sor ng through a “bewildering array of defini ons and a prolifera on of terms and concepts” (abstract, p. 449) and a er three rounds of clarifying defini ons, the par cipants defined cogni ve styles as “people's preferred way of processing (perceiving, organizing, and analyzing) informa on using cogni ve brain-based mechanisms and structures. They are assumed to be rela vely stable and possibly innate” (p.

453). Learning styles, on the other hand, are “individuals' preferred ways of responding (cogni vely and behaviorally) to learning tasks” and might “change depending on the environment or context” (p. 453).

Clearly there is no common defini on of learning style nor is there a unified theory on which this work is based (Armstrong, Peterson, & Rayner, 2012; Cassidy, 2004). Learning style “a empts to explain learning varia on between individuals in the way they approach learning tasks” (Toye, 1989, pp. 226–227). More specifically, Cranton (2005, p. 362) defines learning styles as “preferences for certain condi ons or ways of learning, where learning means the development of meaning, values, skills, and strategies.” Although this defini on and other parallel defini ons of learning style are quite similar to cogni ve style, it appears that the real difference between these two concepts lies in the emphasis placed by learning style researchers on the prac cal learning situa on versus the more general no on of how people perceive, organize, and process informa on. Therefore, those who study learning style usually place the emphasis on both the learner and the learning environment. Desmedt and Valcke (2004, p. 459) concur, summarizing the differences as follows:

Most cogni ve style models are developed in laboratory or clinical se ngs to explain individual differences in cogni ve processing, and they are applied in various fields. The recurrent features of the concept seem to be stability, pervasiveness, bipolarity and a strong interdependence with personality.

The learning style models are developed and used in various educa onal contexts to explain and accommodate individual differences in learning. Learning styles are generally defined as rela vely stable and consistent. It is however acknowledged that the characteris cs of the learning environment and learning experiences influence their development.

Cranton (2005, p. 362) has iden fied “at least six approaches to learning style in the adult educa on literature: (a) experience, (b) social interac on, (c) personality, (d) mul ple intelligences and emo onal intelligence, (e) percep ons, and (f) condi ons or needs.” The experience, personality, and the percep on preferences approaches have received the most a en on in the literature in adult educa on, as well as the various learning style instruments that are associated with each of these approaches. The experience approach addresses the issue that “learners have different styles or preferences when it comes to making meaning out of and learning from experiences” (pp. 362–363). Kolb's Learning Style Inventory, now in its fi h version and probably the most o en used instrument to assess learning styles in adult educa on, classifies learning styles into four different preferences in one's approach to learning: Concrete Experience (CE), Reflec ve Observa on (RO), Abstract Conceptualiza on (AC), and Ac ve Experimenta on (AE). (see Kolb, 1984, and Kolb & Kolb, 2005, for a more complete

descrip on of each style). The personality approach is a more encompassing way of assessing learning style in that it gives a broader and more in-depth picture of individual learners. The Myers-Briggs Type Indicator (Myers, 1985, as cited in Cranton, 2005) is the most o en used measure to assess learning styles based on psychological type preferences. Learners' visual, auditory, and kinesthe c learning preferences are the central focus of the percep ons approach to determining learning styles. O en prac oners think that this approach cons tutes what they mean by learning styles.

It is also important to acknowledge that learning styles may be in part culturally based. Anderson (1988, p. 4), for example, asserts that “it would seem feasible that different ethnic groups, with different cultural histories, different adap ve approaches to reality, and different socializa on prac ces, would differ concerning their respec ve learning styles.” He goes on to observe that “there is no such thing as one style being ‘be er than another,’ although in our country [the United States] the Euro-American style is projected by most ins tu ons as the one which is most valued” (p. 6). Anderson characterizes the Euro-American style as primarily field- independent, analy c, and nonaffec ve, which to him reflects primarily male and acculturated minority views. In contrast, he views a non-Western style (meaning such groups as Na ve Americans, African Americans, and many Euro-American females) as field dependent, rela onal and holis c, and affec ve. Bell's (1994) research with African Americans confirms some of Anderson's thinking on learning styles. Bell's findings support “a holis c African American learning style … which consistently reflects a rela onal style…. The rela onal style has been defined as a preference for a whole-to-parts (rather than parts-to-whole) analysis of informa on, a perceptual vigilance for person social cues over object cues, and a preference for contextually ‘rich’ over contextually ‘sterile’ (abstract) learning/problem-solving structures” (p. 57).

Collec vism versus individualism and high-context versus low-context cultures have been found to affect learning style differences in several studies (Barry & Egan, 2018; Lemke-Westco & Johnson, 2013; Wang & Greenwood, 2015). Speece (2012) examined learning style, culture, and delivery mode in online distance educa on and found that asynchronous online modes were most congruent with students from low-context cultures. He recommends mul ple modes of instruc on to accommodate the learning style preferences of students from both low- and high- context cultures. Joy and Kolb (2009) found moderate cultural preferences in a mul cultural study of Kolb's Learning Inventory. As learning style inventories are primarily Euro-American in their orienta on, researchers “need to ques on the usefulness of conduc ng cross-cultural comparisons using assessment strategies based on Western conceptualiza ons of learning style” (Cranton, 2005, p. 365).

Despite the lack of uniform agreement about which elements cons tute a learning style, it seems apparent that learning-style inventories, unlike most cogni ve-style instruments, have proved useful in helping learners and instructors alike become aware of their personal learning styles and their strengths and weaknesses as learners and teachers. What must be remembered in using these instruments, however, is that each inventory measures different things, depending on how the instrument's author has defined learning style. In using the variety of learning-style inventories available, it is important that learners understand how the author(s) of the instrument has conceptualized learning style. It is also important to remember that “learning style instruments are best used as tools to create awareness that learners differ and as star ng points for individual learners' con nued inves ga on of themselves as learners” (Hiemstra & Sisco, 1990, p. 240). Those who use learning style instruments regularly as part of their educa on and training must impress upon their learners that their learning styles are not the only, nor necessarily “the best way” for them learn. In addi on, they also need to dispel the myth that these styles are “fixed” and change very li le.

Careful use of learning-style inventories, especially in making programming decisions about learners, is par cularly crucial. James and Blank (1993, p. 55) have observed that “although various authors claim strong reliability and validity for their instruments, a solid research base for many of these claims does not exist.” Their analysis is confirmed by Cassidy's (2004) review of more than 20 learning style measures. For each instrument Cassidy presents the model or theory upon which it is based, a descrip on of the measurement itself, and comments about the instrument and research conducted on the instrument. He concludes that “what is necessary is further empirical work to provide evidence to assess the validity of many of the proposed models” (p. 440). Coffield, Moseley, Hall, and Ecclestone (2004, as cited in Della Porta, 2006), using similar procedures as Cassidy (2004), found that just three instruments, the Allison and Hayes Cogni ve Style Index, Apter's Mo va onal Style Profile, and Vermunt's Inventory of Learning Styles, came close to demonstra ng “both internal consistency and test-retest reliability and construct and predic ve validity” (Coffield et al., 2004, p. 56, as cited in Della Porta, 2006). Della Porta went on to observe that “most surprising is the fact that some of the most widely used instruments, including the Myers-Briggs' Type Indicator and Kolb's Learning Style Inventory did not meet the minimum criteria for a psychometric instrument” (p. 10). Most reviews of the research on learning styles offer li le support for their use. For example, Cuevas (2015) found that although learning style instruc on enjoys “broad acceptance in prac ce…the majority of research evidence suggests that it has no benefit to student learning” (p. 308). In a recent review of the research on learning styles and adult learners, Barry and Egan (2018) found “an absence of rigorous research findings” in support of the use of learning style instruments and concluded that “while learning styles assessments can be useful for the purpose of reflec on on strengths and weaknesses, they should play a limited role in educa onal choices”

(abstract, p. 31). Kirschner (2017) is even more adamant about not using learning style inventories as evidenced in his ar cle “Stop Propaga ng the Learning Styles Myth.”

Sternberg (1994a, 1996a) has proposed the term thinking styles, which seems very similar, if not iden cal, to learning styles. Sternberg (1994a) defines a thinking style as “a preferred way of using one's abili es. It is not in itself an ability but rather a preference. Hence, various styles are not good or bad” (p. 36). Although Sternberg has described his theory of thinking styles primarily in the context of children, and more specifically childhood educa on, some components of his theory might also be useful in understanding the thinking pa erns of adults. His work on thinking styles is grounded in 10 general characteris cs of styles, such as “styles can vary across tasks and situa ons, people differ in strengths of stylis c preferences, styles are socialized, and styles can vary across the life span—they are not fixed” (Sternberg, 1996a, pp. 349–350). He uses the concept of mental self-government, pa erned a er the kind of governments and government branches that exist worldwide, to describe his theory of thinking styles: “According to this theory, people can be understood in terms of the func ons, forms, levels, scope, and leanings of government” (p. 351). Sternberg (1994a, p. 39) emphasizes the importance of taking into account people's thinking styles in designing learning programs and cau ons that most instructors are best at teaching people “who match their own styles of thinking and learning … and tend to overes mate the extent to which their students share their own styles.”

In summary, scholars studying learning from a cogni ve perspec ve have added a great deal to our knowledge about learning in adulthood. Some of the major contribu ons described thus far in this chapter are our understanding of memory and how aging may affect memory processes, how our knowledge is organized into schemas, what effect prior knowledge and experience have on learning, and the concepts of cogni ve and learning style.

Summary

The internal workings of the learning process have fascinated scien sts for decades. Researchers from the cogni ve sciences have the longest history of research in this important arena, and more recently scholars from the neurobiological sciences are offering new hypotheses about how the brain and related systems are involved in learning. Perhaps the most exci ng new arena of study, with the greatest poten al for expanding our knowledge base of the internal processes of learning, are the combined efforts of cogni ve scien sts and neuroscien sts working together to address how and where learning happens in the brain.

We described how the brain has been viewed differently throughout the ages and discussed how the brain is structured and organized and informa on is exchanged inside the structures of the brain. Cogni ve neuroscience, a new field that bridges the gap between the cogni ve

sciences and neurobiology, has provided some fascina ng descrip ons of how the brain is organized and func ons. Especially with the newer imaging techniques, such CT, PET, and func onal MRI scans, we can catch glimpses of how our brains are structured and operate during differing types of learning episodes. Direct connec ons between what we see and have learned about the brain and learning interven ons are yet to come.

From a descrip on of the brain as a kind of a central processing unit, we moved into a discussion of memory, and how informa on is processed through sensory memory to short- term memory, and into long-term memory storage. Of great interest to adult educators, of course, is how aging affects memory. Research suggests that there are some apparent losses in both working and long-term memory as people age. How this loss affects the everyday learning ac vi es of adults is s ll an unanswered ques on, although we know that some older adults take a longer me to process complex informa on.

Other important aspects of cogni on reviewed in this chapter are the effect of prior knowledge and experience on learning, and cogni ve and learning-style theories. The concept of schemas has provided a useful framework for thinking about both the forms of knowledge (declara ve and procedural) adults have accumulated over me and how that knowledge is transformed and used. In exploring the effects of prior knowledge and experience on learning, the concepts of novice and expert learners were also addressed. The differences between cogni ve and learning styles were discussed as well, with the resul ng observa on that learning styles seem to be a more useful concept. The learning-style inventories, although many have ques onable reliability and validity from a research standpoint, appear to have been informa ve in helping both learners and instructors gain some basic understanding of their strengths and weaknesses as learners and instructors.

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