Contentious issues

How evidence refutes motivational misconceptions

Conceptualizations of motivation are easily misunderstood because many of our beliefs concerning motivation are highly personalized and frequently formed through daily experience, resulting in learned patterns of behavior. Lacking evidentiary support, individual experience may lead to false conclusions and erroneous generalizations concerning the causes of behavior and the interrelations among motivational variables. This chapter introduces some of the more contentious issues endemic to motivational science, focusing on the distinction between beliefs and knowledge. Other interpretive ambiguities are discussed with the intention of providing clarity as to what inferences can or cannot be determined through the examination of motivation evidence. The chapter concludes with an overview of two popular motivation frameworks and describes specific approaches that promote and sustain optimal motivation in the self and others.

Keywords

Science misconceptions; beliefs; research methods flow; optimal motivation

As an impressionable child reared in New York City during the 1960s, I was affectionately known in the neighborhood as “BM.” Scatological connotations aside, my childhood moniker “big mouth” developed from a penchant to share my naive worldly views with anyone who would listen. For years, I truly believed, and regularly tried to convince my friends, that deliberately crossing your eyes would result in a permanent facial deformity. I held strong convictions about my esotropic belief, based upon repeatedly hearing this same proclamation almost daily from my stern-eyed mother. Little did I realize at the time that her fabricated science was merely a maternal manipulation, designed to quell my cross-eyed reactions to her repetitive requests. Like most children, I made little, if any, distinction between what was true and what I believed.

The influence of individual belief convictions is not endemic to loquacious toddlers. Many seemingly well-balanced, educated, and productive adults embrace beliefs supported only by conjecture and speculation. According to a 2005 poll of Americans, 61% believed in the existence of a physical hell, 24% reported that extraterrestrials have visited Earth, and 37% believed that houses can be haunted (www.gallup.com, Gallup, 2005). In a similar survey, participants reported experiencing at least one of the following: personally communicating with the dead (29%), visiting a fortuneteller or psychic (15%), and endorsing reincarnation (24%) (Pew Forum on Religion and Public Life, 2009). If you doubt these statistics represent the views of highly intelligent and educated people, think again. The frequency of future educators possessing pseudo-scientific beliefs in such topics as the Loch Ness Monster and Big Foot closely parallels that found in the general adult population. These proportions apply even to those individuals indicating an interest in teaching science, including a majority who reported that teaching evolution without discussing divine intervention was patently “false” (Losh & Nzekwe, 2010).

Principle #6—Motivational beliefs differ from motivational knowledge

The probability of ghosts or extraterrestrials appearing in your living room, classroom, or office is slim (except in Area 51, of course). However, it is highly likely you will encounter individuals with a variety of esoteric beliefs about learning and performance (tastefully referred to as “misconceptions” by psychologists). Motivational beliefs are defined as a set of propositions that are accepted as true by an individual, regardless of evidentiary support, and that influence the direction and intensity of effort toward a target. Examples of motivational beliefs include, but are not limited to, the assessment of your own intelligence; challenge you perceive in a specific task; degree of interest you have toward a topic; personal estimates of task value, utility, and importance; and presumed likelihood of successfully completing a task. Individual belief frameworks also consider how we are perceived by others and what criteria others use to assess and value our accomplishments.

The influence of beliefs on motivated behavior is pervasive, especially for teachers. Research affirms that teacher beliefs filter what information is taught to students, how knowledge is framed during a classroom discussion, and which teaching strategies are used by the instructor (Fives & Buehl, 2012). Pragmatically, teacher beliefs determine the goals teachers set for their learners, the effort and perseverance they invest in teaching, and the extent of cognitive engagement with subject matter (Bandura, 1997). Perhaps the most salient aspect of personal beliefs is the resistance to belief change (Vosniadou, 2001), even in the face of disconfirming scientific evidence (Dole & Sinatra, 1998).

Misconceptions develop as a result of learned experiences or observations, when few negative consequences are associated with holding a specific belief (Hynd & Guzzetti, 1993). Misconceptions about motivation become especially murky when the influence of personal emotion supersedes objectivity and individuals embrace ideas despite the availability of clear disconfirming evidence, resulting in what Shermer (2012) called false beliefs. Susceptible individuals fall into the emotional trap of wanting to advance convictions based on fact but, instead, rely on strong emotional connections to their championed cause. In the most egregious of circumstances, false beliefs turn into false enlightenment (Phillips & Burbules, 2000). In these situations, individuals become so fervently entwined with their skewed interpretations of reality that they begin to consciously and deliberately assert to others the apparent veracity of their contentions.

The annals of history are replete with examples of self-righteous beliefs. Consider Erik the Red sailing 1600 miles from Norway to a barren wasteland and naming it Greenland, or the miscalculating King George III of England who thought taxing the tea of American colonists would imbue loyalty to the mother country. Support of highly partisan beliefs continue to the present day, as evidenced by the unwavering patronage or utter contempt held by some Americans concerning the Affordable Care Act that overhauled the US health care system. Depending upon whom you ask, the law provided needed health coverage to over 7,000,000 previously uninsured Americans or raised the health insurance premiums by 18% to 81%, depending upon the age demographic (www.forbes.com, Forbes, 2013).

Misconceptions are a realistic source of contention for the motivational detective (MD) because the false enlightenment can be so pervasive that it results in accepted paradigms within the seemingly unbiased scientific community. French psychologists and early twentieth century thought-leaders Alfred Binet and Theodore Simon, creators of the first intelligence tests, were vocal proponents of scientific inquiry and gathering empirical evidence to make informed decisions. In their seminal work “Mentally Defective Children” they asserted, “Psychologists are studying the value of evidence, and are thinking out better methods of arriving at truth, in order to discover reforms which may be introduced into the organization of justice” (Binet & Simon, 1914, p. 2). The same pair proceeded to reach the preposterous conclusion that developmentally delayed children could be categorized into two main classes: “feeble-minded” and “ill balanced.” Their categorization scheme was based primarily upon behavioral observations, which led them to the appalling conclusion that “the more likeable the child is represented to be, the greater the amount of retardation one may safely attribute to him” (p. 20). Considering the longstanding influence of renowned scientists, such as Binet and Simon (for a marvelous exposition of belief misconceptions and their influence on human history see the classic work “Mismeasure of Man” by Steven J. Gould), it becomes prudent for MDs to distinguish between their own self-serving, socially constructed subjective views and the primary goal of science, which is to objectively pursue knowledge verification through scientific evidence (Shermer, 2002).

The distinction between truth and beliefs is, indeed, a slippery slope. Four frequently encountered scenarios will illustrate my point. Scenario one dictates that you have a belief and hypothesize it to be true. For example, if you stand outside in the rain, you would predict that you would get wet. Soggy evidence will likely confirm your prediction. Let’s call this scenario the “sure thing” because the physical evidence of wetness indisputably supports your rain hypothesis. Scenario two asserts that sometimes you embrace a personal truth that is, indeed, false. By example, the absurd theory advanced by Simon and Binet that likeability and retardation were related was based on a conclusion supported by belief, not evidence. Scenario three asserts that truth exists, but we are either preoccupied with alternative ideas or lack the necessary skills or understanding to recognize the truth. Scenario three develops primarily because our personal beliefs act as cognitive and emotional filters obscuring the truth, or secondarily when we are unable or unwilling to interpret available evidence (Chinn & Brewer, 1993). A great example of scenario three is the “discovery” of gravity by Galileo in the early seventeenth century. Galileo asserted that gravity accelerates all objects at the same rate and that differently weighted objects travel at the same speed when falling to the ground. This phenomenon was aptly named the “acceleration hypothesis”; however, gravity was an unrecognized truth before Galileo’s experimentation. Most of the medieval populace, like many current learners, possessed misconceptions about weight and inertia similar to the hackneyed debate about what falls to the ground faster, a pound of feathers or a pound of bricks (Chi, 2005; Lair & Cook, 2011). The delay in embracing the acceleration hypothesis clearly did not mean that the physical properties of matter were nonexistent; they were merely unrecognized. Finally, we will encounter scenario four, illustrated by early human attempts to emulate avian flight. There was no truth to affirm the belief that human flight was possible, and fortunately, most of the populace recognized that humans could not fly. Unfortunately, many would-be aviators needed hard evidence to modify their false beliefs. Going forward, I highlight the differences between our own motivational beliefs and scientific knowledge, as illustrated by scenario three, keeping in mind that many individuals will harbor profound beliefs despite available evidence to the contrary.

Pragmatically, untangling the impact of beliefs on the motivated action of a particular person can be a daunting task, even for the most seasoned scholars of motivational science. The dilemma of ardently embracing biased conceptions and harboring unwavering beliefs despite verified evidence is perhaps best illustrated by the ubiquitous media portrayal of the enigmatic financier Bernard “Bernie” Madoff. Arguably, no other person’s story in the annals of financial history is more contentious than that of Madoff. In 2008, confronted by voluminous amounts of indisputable incriminating evidence, Madoff confessed to masterminding the largest financial scheme in the history of mankind, which at the time federal prosecutors estimated defrauded investors out of $65 billion (Bandler & Varchaver, 2009). On June 9, 2009, he was sentenced to 150 years in a federal prison. However, are the predominantly villainous conceptions of Madoff based upon beliefs or sound, justified evidence? At face value, the story of Bernie Madoff seems like one of greed and corruption, with newspaper accounts of his sordid behavior resulting in the financial ruin of thousands of investors (Smith, 2010). However, there is more to know about Bernie that has not been accurately depicted in the popular media.

In reality, the federal investigation that produced objective and verified evidence revealed the surprising discovery that Madoff actually returned most, if not all, of the investment principal entrusted to him. Although the case details are inconsequential to the study of motivated behavior, fear of criminal implication convinced certain wealthy investors to return $9 billion to the case trustee that had been prematurely withdrawn in violation of trust agreements with Madoff. In total, $14 billion was recovered by the case trustee. Despite the inflated value of $65 billion of investor losses still reported in the press and on the Internet to this day, the official and allowed client losses were valued at $7 billion, as determined by the US Government Accountability Office, discounting the popular misconceived belief concerning the financial impact of Madoff’s transgressions (United States Government Accountability Office, 2012).

Ironically, the knowledge-or-belief dilemma was a motivating force in Madoff’s fateful decision to circumvent security laws and engage in risky economic transactions. In Madoff’s case, the false enlightenment that often accompanies entrenchment of unwarranted beliefs was instrumental in his professional demise. Madoff mistakenly harbored the false belief that demonstrating exceptional ability was more important than moral integrity or fiduciary responsibility. In Madoff’s case, generating massive returns on investments for clients and earning the perception of financial wizardry superseded rational decision making. Madoff indicated, “I allowed the greed of a few clients to head me down a path that while I knew was dangerous and wrong, my inherent insecurity and wish to always please overcame my realization of the risk I took to please others.” Commenting on his personal motivation, he added that his behaviors were “to both please my clients and demonstrate my financial ability” (B. Madoff, personal communication, September 25, 2014).

Motivational Leader—Bernard Madoff

Bernard (Bernie) Lawrence Madoff grew up in Laurelton, Queens, and like many of his peers aspired to the American dream of success and financial security. The only son of first-generation American parents, Bernie’s father managed a sporting goods business, while his mother tended to the home. After graduating from Far Rockaway High School in 1956, Bernie attended the University of Alabama for a year, before transferring to Hofstra University, where he graduated with a BA in Political Science. At the behest of his father, Bernie reluctantly attended Brooklyn Law School for a year, before taking the $500 he saved from his summer job as a life guard to start his own brokerage firm. Suddenly, the life of Bernie Madoff became far from ordinary.

Starting his own investment firm required meeting statutory and legal requirements, including approval from the US Securities and Exchange Commission (SEC). Bernie explained “at the time the SEC was so amazed that I had the nerve to start with so little capital that they required I undergo an interview with their staff” (B. Madoff, personal communication, August 14, 2014). Bernie agreed to the SEC interview and eventually grew his initial $500 investment into $900 million by 2008, all through legitimate investments and securities trading, completely separate and unrelated to his vilified arbitrage and investment advisory business reported by the press.

Bernie went from an obscure law school dropout to begin an illustrious 45-year march toward the pinnacle of professional and financial success, building massive wealth for himself, his family, and his celebrity clients. Along the way, the brilliant Bernie Madoff assumed leadership positions in the world’s largest and most respected organizations, serving in many senior executive roles and corporate advisory positions, including as Chairman of the NASDAQ stock exchange and member of the board of directors of the National Association of Security Dealers. Bernie was internationally recognized as an innovative financial genius, as evidenced by his appointments to regulatory and industry positions, including as the Chairman of the National Securities Clearing Corporation, Chairman of the Depository Trust Company, Vice Chairman of the Securities Industry Association, and member of the SEC advisory committee, with most appointments lasting for up to 10 years.

Although receiving minimal publicity, Bernie was highly philanthropic, donating time and millions of dollars to multiple organizations. He served on the boards of the NYC Ballet, Hofstra University, and the Yeshiva University, where he also served as Chairman of the Board for the Yeshiva School of Business. In addition, he was the founder of the Long Island and North Shore Hospital in New Hyde Park, NY, and, along with his wife Ruth, managed the Madoff Family Foundation, a charity organization recently valued at $19 million.

Although clearly Madoff was highly motivated for success, he was obviously influenced by deeply entrenched self-beliefs that manifested in questionable business practices. He followed misguided ethics according to the normative values and beliefs of most cultures. In retrospect, Madoff realized how his flawed thinking based upon faulty beliefs resulted in his pattern of corruption. His contrition continues to this day, as he told me, “Please understand that I am in no way suggesting the recovery of my client’s principal excuses the pain and suffering I have caused, nor does it eliminate the remorse that I suffer daily” (B. Madoff, personal communication, August 14, 2014). The intimate glimpse into Madoff’s motives is one stellar example of the power of self-beliefs (discussed throughout the text), conceptions which may or may not be based on sound and verifiable evidence.

Principle #7—Motivational evidence can only answer certain questions

Although the famous philosopher Karl Popper (1963) proclaimed that “there are no ultimate sources of knowledge” (p. 54), some sources of knowledge are, indeed, more reliable than others. First, knowledge can originate through individual experience; nevertheless, this type of knowledge is usually biased and limited in scope. Personal knowledge is filtered through the mind of the beholder, is frequently subjective, and cannot be duplicated by others under similar circumstances. Second, knowledge can be based upon cultural tradition; however, traditional knowledge is potentially flawed, as it is largely based upon collective experience and, in essence, resembles personal knowledge in that it lacks veracity of origin or replication. Third, knowledge can be authority based and predicated upon the power of the knowledge originator. Dictated knowledge is typically transient and self-serving to meet the needs of the entity initiating the knowledge, as evidenced by the goal of race annihilation at the whims of ruthless dictators throughout history. Fourth, the source of knowledge most relevant for the scientific study of motivation is described as “justified” knowledge. Justification implies that the knowledge will survive empirical scrutiny, it can be replicated under controlled conditions, and, if true, the knowledge can be analyzed and applied without bias or subjectivity.

Most definitions of knowledge include three important components: truth, belief, and evidence (Southerland, Sinatra, & Matthews, 2001). A prerequisite to verify truth is the ability to evaluate a belief rationally against an external and objective evidentiary standard. A personal belief, sometimes called a proposition, is the conviction that a set of rules, evaluations, or circumstances are assumed to be true (Southerland et al., 2001). Evidence is information gathered and evaluated objectively and systematically. The evidence can be replicated and verified while ruling out other plausible interpretations. If the evidence is subjective or cannot be externally verified, we describe the belief as unjustified (Sinatra & Seyranian, in press), lacking sufficient substance to classify the evidence as knowledge. If sufficient evidence is gathered, and other plausible explanations can be ruled out, we warrant the evidence as knowledge with explanatory power, otherwise known as a justified true belief (Gettier, 1963).

Sometimes providing learners with concrete evidence helps clarify the distinction between knowledge and beliefs. One important principle of cognition is the realization that humans have information processing limitations. Students usually acknowledge that there are restrictions to the amount of information they can hold in their heads at any one time; this restriction is known as working memory capacity. If you have ever tried to remember someone’s phone number while also being asked directions, you will quickly realize the limitations of your working memory. When I teach, I ask students to turn off their cell phones and refrain from texting during class. Invariably, some learners do not heed my warning and text anyway. Fortunately, my warning has sinister intentions and results in a fantastic teaching opportunity when someone texts in class. Typically, when caught texting, learners quickly affirm, contrary to most scientific evidence, that they have the unique ability to multi-task and usually swear they can text and pay attention simultaneously. The first question I ask after students make this resounding proclamation about multi-tasking is, “How do you know?” Typically, learners proceed to recount various examples of personal experience, indicating how they believe they can complete multiple tasks simultaneously. Multi-tasking, however, is one of many “urban legends” in education (Kirschner & van Merrienboer, 2013, p. 169) that is quickly dispelled by evidence. To make my point, I merely ask the student to recite the alphabet using a normal speaking pace and write the letters of the alphabet simultaneously. The outcome is usually unintelligible writing, or an alphabet recital that resembles a geriatric moose signaling its mate. This strategy usually does the trick and changes their beliefs about multi-tasking because learners fail at the task (go ahead, try it yourself, and then see if you can lick your elbow). Typically, the results from these amusing scenarios show that working memory is limited and that personal experience has limited explanatory power. This teaching scenario easily launches a discussion of what questions can be answered by scientific inquiry, while driving home the distinction between justified knowledge and beliefs.

Once future MDs realize the power of belief-bias, they are then able to successfully address the need for scientific inquiry as the source of knowledge. Although there is little consensus on the perfect process to conduct scientific inquiry (Shermer, 2002), most experimental regimens include making predictions, gathering evidence, analyzing data, and reaching plausible inferences about what the data means, while ruling out plausible rival hypotheses. By using systematic data collection and interpretation, knowledge is warranted, or certified, and defensible on methodological grounds, avoiding conclusions based upon perception, opinion, or conjecture. In other words, how knowledge is attained and disseminated will, in part, determine the validity of the knowledge.

The premise of fallibility dictates that knowledge is transient and always subject to revision (Phillips & Burbules, 2000). Justified knowledge must have the ability to be falsified by disconfirming evidence. Knowledge without contradiction has little explanatory power because it is stagnant. A stagnant theory cannot evolve by experimentation, nor can it be debunked. The pink elephant dilemma may convince you of the necessity of my fallibility contention. Most justified knowledge about elephants universally describes the animals as large, gray, sometimes tusked mammals with big floppy ears, equipped with a long proboscis designed to extract peanuts from small children, tourists, and the palms of zoologists. My elephant hypothesis suggests that pink polka dot elephants may exist but have yet to be discovered. My hypothesis is contrary to the justified knowledge that elephants only come in 50 shades of gray. Today, my outlandish idea is only an unjustified belief because no pink polka dot elephants have ever been observed. But is my hypothesis false? Does this mean no polka dot elephants exist? Surely, it is possible that one day we will discover pink polka dot elephants. Perhaps, our visually acuity is insufficient for us to see the polka dots that are already there. Thus, the theory of gray elephants can potentially be falsified and subsequently evolve. Today, we can rule out other plausible interpretations of the elephant data and reasonably infer that all elephants are, indeed, shades of gray, until an alternative hue of pachyderm is unearthed. Knowing that my theory is viable and testable, all the knowledge from the theory is warranted with explanatory power.

Now, we have illuminated the three foundational premises necessary to evaluate scientific inferences. Recapping, we should first verify the source of the knowledge; next, we must determine if the knowledge is justified or not; and finally, we should closely examine whether or not our inquiry can meet the falsification test. If these three criteria are met, we can confidently proceed and determine what questions can or cannot be answered by motivational science. Table 2.1 describes actual inquiries meeting the three principles of inquiry, along with some provocative situations that we shall encounter as we elaborate on the scientific evidence that explains motivation for learning and performance throughout the remainder of the book.

Table 2.1

Examples of what questions motivational science can and cannot answer

Inquiries motivational science CAN answer	Source	Inquiries motivational science CANNOT answer
Can we predict if mentally preoccupied people prefer cake or fruit?	Shiv and Fedorikhin (2002)	Does cake taste better than fruit?
Do monetary rewards lead to less effective work performance?	Ariely, Gneezy, Loewenstein, and Mazar (2005)	Do professional athletes prefer higher earnings or fame?
Can biological evidence substantiate evolution?	Geary (2008)	Were our distant relatives apes?
Do future teachers believe in zombies and ghosts?	Losh and Nzekwe (2010)	Are zombies and ghosts real?
Do people make better decisions when angry or calm?	Moons and Mackie (2007)	Are angry people more likely to commit suicide?
Can certain written words promote happiness in others?	Kloumann, Danforth, Harris, Bliss, and Dodds (2012)	Is this the best book about motivation ever written?

The answer to every question in the left-hand column of Table 2.1 is “yes.” Although you may debate the veracity of these findings based upon your own personal beliefs, each finding is supported by objective evidence warranting a justified knowledge conclusion. The studies cited all used rigorous scientific methods to rule out alternative explanations before reaching conclusions. Although the answers to all of the questions are affirmative, we should be cautious not to generalize these findings beyond the scope of the research question. The answers are warranted only for the specific situation described. The questions in the right-hand column of the table represent questions that cannot be answered through scientific inquiry. Answers to these questions are largely subjective (cake or fruit), unethical to investigate (anger and suicide), or impossible to measure using existing research methods (ancestry). Cognizant of the unique evaluation criteria needed to evaluate motivational research, we turn to the evidence and see precisely what practical inferences can be confidently made.

Principle #8—Motivation is related to learning and performance but causality is an uncertainty

Terrell Howard Bell, former Secretary of Education in the Ronald Reagan administration, had a clear vision about education. Bell claimed that there were only three things necessary to ensure effective education. The first was motivation, the second was motivation, and the third was motivation! However, can we reliably conclude that motivation is the cure for everything that ails education? Probably not (this is a research question methodologically impossible to answer), although thousands of studies have investigated the relationship between motivation and a variety of learning and performance variables.

Ideally, when investigating motivation, the MD seeks to determine the causality of behavior. Armed with knowledge of what factors result in specific behaviors, the investigator can determine appropriate strategies to mediate the undesirable behavior or sustain that which is desired. Although the ultimate goal is behavioral change, sometimes only behavioral consequences, not motives, are addressed. For example, one of the most frequent issues with which teachers wrestle in the classroom is academic procrastination (Katz, Eilot, & Nevo, 2013), where a resounding 75–90% of undergraduate college students are estimated to delay completing academic tasks, such as homework (Steel, 2007). Typical solutions used to address the homework problem include giving extra credit for timely completion of work, granting special privileges to homework completers, or perhaps overemphasizing the role of homework when determining course grades. However, none of these “solutions” actually addresses the reasons underlying academic procrastination, including questionable beliefs about learner competency, lack of interest in the subject, or the perception of a controlling teaching environment and loss of autonomy. Teachers addressing academic procrastination with incentives may successfully change behavior, but rewards do little to address why students fail to complete homework in the first place.

The homework dilemma brings to the forefront one of the most salient issues when interpreting scientific evidence: establishing a clear interpretive distinction between correlation and causality. The homework example suggested that certain factors cause procrastination, while teacher incentives, such as grades, are associated with reducing academic procrastination. My favorite example of the difference between causality and correlation is a variation on Stanovich’s claim that appliance ownership can influence birth control (Stanovich, 2013). As many people know, most single people only need a two-slice toaster because they live alone and must regulate their carbohydrate intake to maintain positive self-esteem. Four or six slice toasters are reserved for people with families, restaurants, and school cafeteria lunch ladies. Thus, there is a positive association between family size and type of toaster. However, I sincerely hope you don’t believe that smaller toasters reduce fertility! In fact, there are more plausible explanations for a large family size than the girth of your toaster. The proclivity for snap judgments creates vulnerability to accept potentially spurious interpretations of data, which happens when you falsely assume the influence of one factor on another. Wrongly attributing causality to a correlational relationship masks the true causal factor underlying the behavior of interest and creates a situation ripe for misinterpretation. Now upgrade your toaster, or at least read Stanovich’s exceptional book How to Think Straight about Psychology.

Due to a variety of methodological issues, the bulk of motivational research is correlational. This doesn’t mean that certain motivational variables cannot be identified as casual factors; instead, it means that the nature of motivational research is not conducive to experimental research that seeks to investigate causality. Motivation has been linked to literally dozens of variables, including some highly influential in learning and performance. Maehr and Meyer (1997) in their assessment of the state of motivational research at the time listed over two dozen factors related to adaptive motivation, including persistence, learning, achievement, creativity, effort invested in learning, positive emotions, school interest, and, of course, the quality of student knowledge. Not surprisingly, classrooms that comprise learners with high academic motivation have fewer classroom management issues, promote a stronger sense of learning community, and support a context of focused learner engagement (Ames, 1990; Perry, Turner, & Meyer, 2006).

More recent research broadly examines individual motivation and strategy differences among individuals. Studies frequently investigate the role of socioeconomic factors, culture, and other contextual and social influences on learning and performance outcomes. The common thread throughout these studies is understanding how interactivity among motivational variables influences optimal motivation for learning and performance. Perhaps, the most significant revelation is the idea of reciprocity between learning outcomes and sustaining personal beliefs about motivation (Schunk, Pintrich, & Meece, 2008). As students encounter success in learning, motivation to learn is enhanced. Students begin to believe in their own success and gain confidence that the strategies they use are influential in the learning process. When the learning strategies lead to success, students are motivated to continue using the effective strategy. Reciprocally, learner perception of content mastery leads to reaching academic goals, further improving motivation. The reciprocal relationship becomes a powerful cycle for the success of the student. Reciprocity can be equally devastating when students develop counterproductive beliefs and exhibit maladaptive motivation patterns when learning obstacles or failures are encountered. Although these findings imply causality, only under rare circumstances should we confidently conclude that one factor actually causes the other to happen.

Principle #9—Motivation is subordinate to character and personality

What’s so different about character, personality, and motivation? Speaking about character, the irreverent author Mark Twain believed that every person is like a moon, with a dark side shown to no one. As explained in Principle #5 (p. 12), Twain’s comments echo the introspective and sometimes opaque nature of how Homo sapiens portray themselves to the external world. Each of the above elements contribute to how we think, feel, and express ourselves, but distinct differences exist among the three constructs. “Character” and “personality” are frequently conflated, with the terms sometimes used interchangeably throughout the social psychology and personality literature. The primary distinction between character and personality revolves around qualitative judgments of the patterns of behaviors that define an individual. “Character” implies arbitrary standards and culturally specific evaluations indicating that some attributes are morally or socially preferred to others (Doris, 2002). Character is frequently studied by examining how people rationalize behavioral choices. For example, you see a parent smack a misbehaving child at the mall. As a bystander, should you intercede and admonish the outraged parent and say that his or her behavior is unacceptable? Does the parent have a reprehensible character because of how the child was disciplined? The parent’s decision and the evaluator’s moral code would provide insight into the person’s character. Unfortunately, the “theory” of character is problematic and easily falsified when individuals with seemingly “good” character display culturally unorthodox behavior. Examples of character gone haywire include the 2013 sexting fiasco of former democratic Congressman Anthony Weiner, who repeatedly sent lewd photos of himself to random women online, even after resigning from US Congress, or the outlandish conduct of beleaguered Toronto mayor Rob Ford, who openly glorified crack cocaine use to his constituents and the highly interested media.

Personality, a more frequently researched construct than character, was defined by Cattell (1950) as “that which permits a prediction of what a person will do in a given situation” (p. 2). However, the prediction business is quite risky because of the lack of constancy in behaviors both within and between individuals. The same people react differently in similar situations, different people respond similarly in similar situations, and different people react differently to same event. The question then becomes what accounts for the differences within and between individuals, and why is behavior so inconsistent? Corr, DeYoung, and McNaughton (2013) appeased this contentious reality by suggesting that “in order to answer this why question, we must discover what drives people’s actions and reactions” (p. 158), leading us back on the seemingly circular path to motivation as an explanation of behavior.

The most widely accepted and empirically supported personality framework is the five-factor model, aptly named the “Big Five” (John, Naumann, & Soto, 2008). The model distinguishes five expansive dimensions of personality, primarily measured through observation and self-report inventories: Openness (O), Conscientiousness (C), Extraversion (E), Agreeableness (A), and Neuroticism (N). Each factor represents a series of traits, some of which are correlated with academic motivation, most notably social interaction among learners (Zeidner, 2009). The classroom and the boardroom provide robust environments to observe the expression of personality traits, as individuals frequently work in teams, necessitating regular interpersonal communication. Logically, individuals high in (A) and (C) typically demonstrate attributes necessary for project success, including willingness to agree, control of personal impulses, more regulated and predictable affect, and a pronounced helping orientation toward others (John et al., 2008). Table 2.2 lists definitions and behaviors associated with the “Big Five” personality dimensions.

Table 2.2

Definition and explication of the “Big Five”

Factor	Extraversion	Agreeableness	Conscientiousness	Neuroticism	Openness
Verbal labels	Energy	Altruism	Constraint	Negative	Originality
	Enthusiasm	Affection	Control of impulse	Emotionality	Open-mindedness
	Enthusiasm	Affection	Control of impulse	Nervousness	Open-mindedness
Conceptual definition	Implies an energetic approach toward the social and material world and includes such traits as sociability, activity, assertiveness, and positive emotionality.	Contrasts a prosocial and communal orientation toward others with antagonism and includes such traits as altruism, tender-mindedness, trust, and modesty.	Describes socially prescribed impulse control that facilitates task- and goal-directed behaviors, such as thinking before acting; delaying gratification; following norms and rules; and planning, organizing, and prioritizing tasks.	Contrasts emotional stability and even-temperedness with negative emotionality, such as feeling anxious, nervous, sad, and tense.	Describes the breadth, depth, originality, and complexity of an individual’s mental and experiential life.
Positive behavioral examples	Approach strangers at a party and introduce myself; take the lead in organizing a project; keep quiet when I disagree with others.	Emphasize the good qualities of other people when I talk about them; lend things to people I know; console a friend who is upset.	Arrive early or on time for appointments. Study hard in order to get the highest grade in class; double-check a term paper for typing and spelling errors.	Accept the good and the bad in my life without complaining or bragging. Get upset when somebody is angry with me. Take it easy and relax.	Take the time to learn something for the joy of learning; watch documentaries on TV; come up with novel setups for my living space; look for stimulating activities that break up the routine.

Reprinted with permission of John, Naumann, & Soto from Paradigm shift to the integrative Big Five trait taxonomy: History, measurement, and conceptual issue. In O. P. John, R. W. Robins & L. A. Pervin (Eds.), Handbook of Personality: Theory and Research, (pp. 114–158). © 2008 Guilford Press. Reprinted with permission of The Guilford Press.

Perhaps the most relevant contribution of personality theory to the study of motivation is the enacted bias of individuals high in (E) who consistently display behaviors indicative of active engagement. The (E) personality exemplifies energy, enthusiasm, and sociability, all qualities found to predict academic and vocational success. However, important developmental trajectories (see Chapter 4) are noted in the evolution of (E), suggesting an inverse relationship between age and extraversion—that is, as people age they become more introverted. The decrease in extraversion is largely a function of the highly competitive environment students encounter in middle school and beyond (Zeidner, 2009), conditions that are frequently perpetuated in the workplace through performance incentive systems. The expressive inclinations described above are important because of their relation to achievement but minimally explain the nature of individual motives and provide little causal explanation for demonstrated behaviors (Corr et al., 2013).

Personality and motivation are both exemplified by a series of behavioral patterns. Motivation is subordinate to personality, more situational, transient, and just one of many influences on personality. The description of the “Big Five” dimensions as traits suggests a generalized tendency for individuals to exhibit behaviors that are consistent and predictable. However traits, like motives, can either be enduring or transient depending on the person (see Principle #4, p. 11). For example, students typically confident in their mathematics ability and accustomed to solving problems accurately without restrictions may develop temporary performance anxiety when forced to solve problems under time constraints (Hoffman, 2010). Conversely, mathematics anxiety can be a static and stable trait of the individual, experienced across a broad variety of environments (Miller & Bischel, 2004) and less amenable to intervention (Chen, Gully, Whiteman, & Kilcullen, 2006). Regardless, even the most anxious individual will exude an aura of contentment and confidence under optimal conditions. Stability suggests a personality connotation, whereas transience implies motive. In either case, we must acknowledge that traits, like motives, will vary according to the context of observation (Cloninger, 2009) and that neither can be predicted with complete accuracy.

Principle #10—Motivation is the responsibility of leaders and can be taught

Considering the conundrum in deciphering the difference between beliefs and knowledge, combined with the uncertainty as to what causes motivation and personality, you might be a wee bit skeptical that “motivation” can actually be taught. The prevalence of university motivation courses, combined with a walk down the self-help aisle of any bookstore minimally attests to at least a broad interest in the topic of motivation. A random search of the world’s largest marketplace, Amazon.com, during January 2015 using the keyword “motivation” revealed an astounding 191,490 books or products in the motivation category! Perhaps the more relevant question, with a potentially more elusive answer, is how to teach motivation. Is it realistic for us to believe that we can change the deeply entrenched beliefs that guide behavioral decisions? The answer to the question is a resounding “yes,” as responsibility for explicitly teaching motivation falls to the classroom teacher (Alderman, 2004), the aspiring business leader (Cummings, 2014), and the athletic coach (Berinato, 2013).

The first step in the process of teaching motivation is modeling interest and enthusiasm toward the topic you teach. Sadly, I have seen dozens of training professionals and teachers lacking even basic enthusiasm during instruction. Hallmark behaviors of negative models are those exerting obligatory effort or taking a compliant approach to instruction. Comments, such as, “We need to discuss evolutionary science today because it’s required curriculum, but I don’t really believe in evolution,” or “I don’t agree with this new policy, but we have to follow it anyway” will quickly alienate learners and cultivate passive engagement and behavioral apathy due to the learners’ perception of the educator’s lack of interest in or low evaluation of the content.

Brophy (2004), specifically addressing classroom learning, suggested that the starting place for stimulating motivation for learning is to capitalize on the existing motivational dispositions of learners. The term dispositions refers to the insights, skills, and values that influence behavior. Brophy advocated that through modeling and clear communication of expectations, along with specific and detailed content feedback, academic motivation can be accelerated because the teacher can make personal connections with the learner. Brophy also made an important distinction concerning how motivation is taught. One can teach specific conceptual skills and strategies directly, but values and learner insights should be consciously addressed as well.

Borrowing strategies from the literature on conceptual change, discussed in detail in Chapter 12 (p. 345), we know that changing beliefs is challenging and potentially frustrating for the person promoting change. Even when learners express openness to change and espouse a willingness to modify beliefs, change may be fleeting and shallow (Dole & Sinatra, 1998). Realistically, we should operate under the primary premise that students and scientists alike will blatantly reject data inconsistent with their current ideologies (Chinn & Brewer, 1993) and expect that old conceptions will not easily die but, instead, gradually fade away. Mere presentation of anomalous evidence that challenges existing beliefs is insufficient because data may be challenged on the grounds of inaccuracy (bad measurement or interpretive practices), the source of origination (some sources are more credible that others), or applicability (e.g., you are correct, but your belief doesn’t apply to me in “this” situation). Although customized strategies to teach specific aspects of motivated behavior are outlined in Chapter 12, changing beliefs can and should first be addressed in a more general way. The road to belief enlightenment starts with an explicit focus on understanding how learners view the creation and application of their own knowledge. But first, are you conscious of your own beliefs? Do you recognize the origin and pattern of knowledge building? What models do you use to evaluate and subsequently certify your own knowledge? Polonius, the mythical counselor in Shakespeare’s Hamlet, sternly warned Laertes (Hamlet’s nemesis and eventual killer), “To thine own self be true,” stressing the importance of self-awareness of motives. Being true begins with the informed and objective approach of analyzing our own beliefs before we can ever expect to understand and potentially influence the beliefs of others.

Principle #11—Theoretically, motivated behavior operates on a continuum

Analyzing our own beliefs starts with having a theory. The purpose of a theory is to serve as a baseline or benchmark of established knowledge providing researchers and MDs with a platform to test the validity of new knowledge. As new evidence is amassed and empirically tested, the theory will either be supported or refuted by the data. Theories that are most useful to consumers of motivation research are those that offer viable explanations of motivated behavior that can be applied across contexts, cultures, and people. Since personal motivation is a fluid, situational, and malleable collection of actions and strategies, a theory should offer a comprehensive lens that explains both the antecedents and the consequences of actions across a wide spectrum of exhibited behavior. Two theories highly useful to explain motivated behavior are the Theory of Knowledge Acquisition proposed by Reynolds, Sinatra, and Jetton (1996) and Organismic Integration Theory conceived by Ryan and Deci (2000). Both views examine the breadth of motivated behavior and describe observations along a continuum, emphasizing that behavior progressively evolves based upon contextual influences and the degree of individual self-regulation and reflection.

The continuum crafted by Reynolds et al. (1996) was not intended to explain learner motivation but, instead, explains how knowledge and beliefs are acquired and represented in the mind. Although we are not interested in learning theory per se, the reciprocal linkages between learning and motivation described earlier can be symptomatic of underlying motivations as to why and how learners seek knowledge; this will contribute to a clearer understanding of observed performance. The continuum posited by Reynolds et al. (1996) serves as an adaptive model to explain potential links between motivation and the process of knowledge acquisition. Five orientations are described, classified along the dichotomy of “centeredness” suggesting that ultimately knowledge can be either “experienced” or “created.” On one end of the spectrum, individuals are highly polarized in their views of the world, believing that knowledge is absolute in the way it is constructed. In the simplest form, knowledge is merely a series of stimulus–response bonds that result in uniform behavior. In other words, one thing consistently leads to another. Individuals that display this learning orientation are merely a function of what they have encountered in life, demonstrating little ability to consider divergent views or accept alternate conceptions of knowledge. This polarized experience-centered perspective discounts the influence of social motives and the ability of individuals to creatively problem-solve and reason when encountering unorthodox or unfamiliar situations. From a motivational perspective, embracing the experience-centered perspective can often mean limited interest, or even potential contempt, for diversity of thought.

The opposing end of the Reynolds et al. (1996) knowledge continuum is described as mind centered, with individuals applying meaning to the world through active integration of purposeful cognitive representations formed through social experience. Individuals with a mind-centered orientation also represent knowledge acquisition as a function of environmental interaction, but unlike the experience-centered individual, the mind-centered individual adapts and changes conceptions situationally. Those with a mind-centered orientation are strongly influenced by the culture of their existence, showing less rigidity in thought and action. Mind-centered individuals exercise more volition in their worldly endeavors, demonstrate less passivity, and do not readily accepting the status quo. Like the experience-centered person, beliefs are equally as prominent; however, the caliber of beliefs of the mind-centered individual shows a greater openness and willingness to influence the world, as opposed to the acceptance orientation of the experience-centered individual.

The second continuum, proposed by Ryan and Deci (2000), is rooted in one of the most prominent orientations to explain motivated behavior, Self-Determination Theory (SDT). The theory, discussed in detail in Chapter 6 (p. 139), emphasizes that individuals have three prominent needs that must be satisfied for optimal motivation: competence, autonomy, and relatedness. Competence implies that individuals are energized through self-assessments and self-reflections of their personal capabilities and are confident in their own knowledge and abilities. In order to meet the need of competence, individuals must also perceive the ability to exercise free will, or autonomy, in order to demonstrate their competence. Demonstrating autonomy allows free expression of behaviors as a means for the individual to feel self-determined and not controlled by the context of their efforts. Relatedness is the tendency to seek external validation or recognition from others as the person exhibits competence by exercising autonomy. Feeling self-determined and successful in executing actions is, in part, influenced by the ability to gather social support for personal effort. Ryan and Deci caution, as outlined in Principle #7 (p. 26), that SDT is not causal but, instead, describes the conditions and circumstances necessary to develop and sustain self-determination.

The continuum proposed by Ryan and Deci is designed to illustrate the incremental and regulatory nature of motivated behavior, which outlines the distinction between individuals who are self-motivated in comparison to those who rely on external influences as the reason for their actions. The crux of a long line of confirming research suggests that individuals judiciously expend energy toward a goal because of two reasons: (1) the internal value of the goal or (2) external coercion and incentives. The regulatory nature of motivation indicates that contingent upon the underlying reason for selecting and approaching a task, individuals will demonstrate varying levels of commitment, intensity, interest, and strategies in pursuit of a goal. Ideally, individuals who pursue tasks based totally upon personal choice will be highly committed to reaching their goal and persistent in overcoming obstacles. Conversely, those individuals who select and value a task only because it is required, expected, or rewarded will display behaviors ranging from apathy to drudgery. Task completion has marginal meaning and is generally perceived as unavoidable to accomplish other more satisfying objectives.

The paradigms introduced above illustrate the range of motives and the spectrum of beliefs that can undermine motivated behavior. The explanations provided by each perspective are well supported by warranted evidence with explanatory power. However, these views are by no means universal, exclusive, or as simplistic as described. We can infer from these primary principles that individuals use specific frameworks or mental orientations that determine how they approach tasks and navigate the world. Each framework will be accompanied by a repertoire of complementary behavioral strategies designed to meet specific objectives. The motives described and the strategies deemed effective will change based upon numerous social and contextual factors. The variability suggests, at least idealistically, that for each task, there is an ideal set of motivations and corresponding strategies. If true, then perhaps the goal of all teachers, leaders, and coaches should be to orchestrate social and contextual conditions that promote optimal motivation. But is there such a thing?

Principle #12—Optimal motivation is obtainable

I have a proposition for you. I am going to hypothetically give you $300,000,000, provided you complete one simple task; all you have to do is spend $30,000,000 in 30 days. There is a caveat, however: You may tell no one that you inherited your fortune, and you cannot waste any money or show any assets from your spending spree. This formidable challenge, which appears easy at first glance, is actually the premise of a 1985 movie called Brewster’s Millions, starring John Candy and Richard Pryor. Brewster is totally absorbed in his quest to diligently spend the money and will stop at nothing to reach his goal despite the many humorous obstacles he encounters on his lavish spending spree.

From a motivational perspective, Brewster exemplified the type of drive, focus, and determination that we yearn for in school and at work. Brewster demonstrated “optimal performance,” a state in which an individual blocks out external distractions and dedicates all available attentional, cognitive, and motivational resources to the task at hand. Distractions can be a huge obstacle, shifting our attention from a task, even when we are highly motivated to complete the task. Distractions that interfere with optimal performance may include environmental factors, such audience noise or uncomfortable temperatures; physical factors, such as poor health or lack of sleep; and emotional and cognitive instigators, such as anxiety, worry, doubt, and other feelings of inferiority. All of these might interfere with an unwavering focus on the task. In the face of cognitive and affective intrusions, performance can suffer dramatically, unless the individual deliberately and consciously recognizes the source of the disruption and uses a series of strategies to offset the interference.

Sometimes known as being “in the zone,” the phenomenon of peak performance has been scrupulously investigated across disciplines to identify under which conditions individuals are able to achieve results that demonstrate “the superior use of human potential” (Privette, 1981, p. 51). Perhaps there is no better example of being in the zone than the 38-point game-winning performance of Michael Jordan in the 1997 NBA finals. Jordan was battling food poisoning, nearing collapse, and had been confined to bed rest during the 48 hours before the game. Doctors insisted it was impossible for Jordan to play due to severe dehydration and weight loss. Despite their warnings, Jordan emerged from his bed with a high fever, just 2 hours before start time. Playing 44 out of 48 possible minutes, he led his team to a last-minute victory on a miraculous fade away three-point jump shot. After making the game-winning score, he collapsed from physical and mental exhaustion into the arms of teammate Scottie Pippin. Jordan enhanced his legendary status and, in turn, provided us with a great benchmark of determination, demonstrating the influence of motivation on performance.

Substantial debate prevails across disciplines as to the definition, components, and contextual conditions necessary for optimal performance, especially when identifying which motivational components, if any, drive episodes of superior functioning (Hallett, 2011). The Jordan example illustrates the phenomenon known as “peak performance” that occurs when an individual accesses, selects, and flawlessly executes skills needed to complete a task on demand and while under high-stakes pressure to deliver results (Hallett & Hoffman, 2014). Performance tasks in the workplace, such as consulting, selling, and presenting, are tasks that might require peak performance to ensure success, while high-stakes classroom testing best represents the type of condition that necessitates optimization in the classroom. Fictitious and athletic performances are intriguing examples but provide little substance as to the precise skills or environment needed to cultivate optimal motivation.

Research in psychology and education focuses on a variety of contextual, affective, and cognitive factors that contribute to “optimal experience,” “flow” (Csikszentmihalyi, 1997), or “cognitive and motivational efficiency” (Hoffman & Schraw, 2010). Collectively, the terms represent a series of prerequisite attributes, goals, emotions, and conditions needed if individuals wish to maximize performance while regulating their cognitive and motivational resources in a quest to achieve desired results. Flow theory maintains that during the course of everyday living, individuals will encounter a variety of tasks and events conducive to achieving “optimal experience.” The quality of the experience is measured by factors that include the degree of motivation, concentration, creativity, satisfaction, and relaxation that individuals report when completing a task (Csikszentmihalyi & LeFevre, 1989). One criterion necessary to achieve optimal experience is to have suitable opportunities to act upon and use one’s abilities. The experience includes an intentional focus of cognitive energy toward a task that has sufficient challenge for the individual, based upon his or her existing skill set. Tasks that are too difficult will impede “flow” because overly challenging tasks have the potential to induce anxiety, worry, or doubt concerning the ability to complete the task. Overly easy tasks will hinder flow because individuals may become bored or apathetic toward a task that has no perceived potential for challenge or excitement. Task feedback is an important component in the quest to maintain flow; that is, the individual must have a perception of success in order to maintain flow. Feedback includes the internal generation of positive feelings while performing the task but can also include external markers, such as making progress in completing a puzzle or realizing you have answered a test question correctly. In all cases, the flow experience requires the ability to set clear task goals, approach a task with skills commensurate to the challenge, and an inherent task mechanism that enables the participant to calibrate his or her task effectiveness.

If the conditional aspects of flow are met, the individual becomes fully engaged in task completion, blocking out any motivational or cognitive intrusions. Individuals experiencing flow report a loss of time sensation. The flow experience is exemplified by a sense of deliberate and focused consciousness, thereby insulating the individual from negative ruminations and distractions. The flow experience only occurs during absolute focus, a state so intense that the individual does not recognize typical emotions, such as feeling happy or content. If the individual recognizes and evaluates her own emotions during a task, flow has been interrupted because the individual has shifted from a task focus to a personal and evaluative focus. Flow experiences are often reported by individuals engrossed in video games or competitive events because individuals target and regulate their thoughts and efforts only toward completing successive steps that result in progress toward the ultimate goal. However, it is not what we do that influences the ability to achieve flow, but, rather, it depends on how we approach and complete the intended target of our attention (Csikszentmihalyi, 1997).

By employing a method called “experience sampling” Csikszentmihalyi and LeFevre (1989), used beepers and asked individuals at random times during the day to indicate exactly what activity they were doing and to numerically rate how they felt about the activity at the exact moment of sampling. Individuals more frequently reported experiencing the factors associated with flow when they were working (54%), in comparison with those participating in leisure activities (17%), with similar results observed across individuals in managerial, clerical, and blue-collar jobs. Motivation, measured by responses to the question, “Do you wish you had been doing something else?” (p. 817) was more closely related to leisure activities than work. However, in contrast to expectations, individuals reported a diminished role for motivation during flow experiences. Surprisingly, the most uniformly positive experiences were reported when individuals were driving. Csikszentmihalyi suggested that leisure activities (but not driving) provide less challenge, and thus, individuals are not afforded the ability to invest serious mental effort in leisure, which results in a lower probability of performance optimization. Although individuals are highly motivated to participate in leisure, work is often perceived as more challenging and potentially satisfying and, thus, more conducive to episodes of optimal experience.

Efficiency theory uses a similar conceptual scheme as flow to suggest that there are certain conditions needed to maximize cognition and motivation. Unlike flow theory, efficiency theory places a greater emphasis on the quantitative input of effort and what factors may potentially impede optimal performance. Efficiency is described as “increases in the rate, amount, or conceptual clarity of knowledge, versus costs, such as cognitive effort, needed to attain knowledge” (Hoffman, 2012, p. 133). Although most efficiency research targets learning outcomes, several contributory variables have motivational implications, such as the amount of mental effort an individual invests in completing a task, beliefs concerning the probability of achieving certain task outcomes, and an evaluation of the task concerning which strategies learners are willing and able to use to achieve desired outcomes.

Efficiency theory suggests that optimization of motivation is crucial to cognitive efficiency, overall learning, and problem solving because each task has a unique set of parameters that warrant a complementary and ideal skill set to complete the task efficiently (Hoffman & Schraw, 2010). For example, individuals overly confident in task success may deliberately withhold effort toward completing a task based upon their perception that the task is easy. The poor calibration of the skills and the associated strategies needed for task success may inadvertently result in diminished task success or ultimate task failure (Vancouver & Kendall, 2006). A common, yet unfortunate, representation of this phenomenon is texting while driving. Many individuals believe that they can efficiently drive and text without consequence and, as such, consciously divert their attention away from the road to their devices. In 2009, there were 5,870 reported incidences of deaths due to distracted driving (Wilson & Stimpson, 2010), perhaps suggesting that for certain tasks, the drivers were fatally wrong, and there is, indeed, an ideal set of skills, beliefs, and motivations that result in optimal task performance.

Although we can debate whether humans are realistically capable of achieving optimal motivation or performance for a given task or learning goal, we can easily subscribe to the premise that minimally optimal motivation and subsequent performance can serve as a benchmark by which we can compare actual behavior. In fact, some disciplines, such as economics (Sanfey, Loewenstien, McClure, & Cohen, 2006) and health care (Jacobs, Smith, & Street, 2006), determine performance optimization by calculating efficiency as the deviation between an expected standard and observed results. The deviation method allows individuals to calibrate how closely their observed performance compares with their performance potential. James (1890) was likely the first scholar to consider calculating personal efficiency, although he did not label the behavior according to the current terminology. James postulated that the maintenance and development of self-esteem were determined, in part, by how individuals assess and evaluate themselves according to their own expectations or standards. James used an equation to determine the ratio of self-esteem to expectation. A higher observed ratio was indicative of higher self-esteem and feelings of task accomplishment, thought to contribute to setting more aggressive future goals. Low ratios had the opposite effect. When individuals believed that accomplishments fell short of expectations, feelings of shame and doubt could potentially develop based upon perceptions of personal failure.

Although James’ insight did not consider the role of social comparison or cultural factors on goal setting and motivation (see Chapter 6, p. 139), his approach was an important first step in understanding how learners purposely regulate and evaluate their own performances based on upon past results. Appropriate evaluation of outcomes and an understanding of how to regulate effort are critical factors required to realize optimal performance. Improperly timed assessments are intrusive and usurp precious cognitive resources that otherwise might be dedicated toward task completion. During evaluation, motivation can be affected and potentially redirected. Sometimes, the reflection positively promotes added persistence through an examination of the strategies used to reach learning or performance goals. Alternatively, if unfavorable or untimely assessments prevail, the evaluation can derail a learner’s goal progress. In the short term, negative perceptions of task success may quickly lead to reductions in effort or enhanced task anxiety, leading further to frustration and the impossibility of achieving flow while minimizing efficiency. Even worse, in the long term, learners who are unwilling or unable to effectively evaluate and regulate their own motivation can make emotionally charged academic decisions, negatively influencing future task choices, effort, and strategies used (Wolters, 2003). The beliefs learners harbor about learning and performance may change, sometimes for the better but potentially for the worse. Ultimately, like many aspects of motivation, beliefs about the self may determine when or if an individual can ever reach the “high-water mark” (Shepherd, 1966 p. 28) and experience the bacchanalia of flow and optimal motivation.

Chapter summary/conclusions

The ability to achieve and cultivate instances of optimal motivation begins with the MD possessing a clear understanding of the distinction between knowledge and beliefs. Conscious effort must be devoted toward untangling those beliefs that are supported by evidence as opposed to those that are personal representations of individual reality. Relying upon motivational science for answers requires that the MD acknowledge the difference between evidence that reliably leads to causal conclusions and that which only illustrates the relationship between two or more factors. Once individuals have a clear conception of how to interpret evidence, they can strive toward meeting the more meaningful goal of situationally determining what factors influence motivation, with the understanding that not all inquiries can be resolved through motivational science.

Acknowledging the distinctions among character, personality, and motivation provides the MD with a way to understand which aspects of the individual are amenable to intervention. Changing belief conceptions is, indeed, a formidable challenge that starts by understanding where individuals stand on the conceptual continuum of motivation. Such factors as the degree of individual mind-centeredness and an understanding of the source of motives provide promise to leverage the resistant, but fluid and malleable, nature of motivation beliefs. Although debates persist concerning the effectiveness of actually teaching motivation, through modeling and the judicious introduction of goal and strategy choices, MDs can actively influence how learners view themselves, as well as when and how they can influence the motivation of others. The nirvana of determining optimal motivation according to task has yet to be empirically confirmed, but we do know that peak performance is realistically obtained, as illustrated by the story of Michael Jordon and from the Experience Sampling Method used to determine flow.