All posts by Richard Thripp

UCF student in the Education Ph.D. program. 25-year-old photographer, writer, and pianist.

EME 6646 Assignment on Visual Working Memory Capacity, Cognitive Load Theory, and Hearing Range

Assignment 2: Explain Sense, Perception, Attention, and Control
For EME 6646: Learning, Instructional Design, and Cognitive Neuroscience
By Richard Thripp
University of Central Florida
May 28, 2017

Visual Working Memory Capacity

Visual working memory (VWM) capacity typically refers to the number of visual objects an individual can hold in short-term memory. Twenty years ago, Luck and Vogel (1997) found that VWM capacity was not tied to individual features of visual objects, but rather the objects themselves as an integrated whole. For example, we can remember the color, orientation, and shape (“conjunctions”) of four objects no less easily than if we were tasked with remembering only their color while orientation and shape were held fixed across all four objects. In instructional design, an implication is that we can reliably ask learners to remember several details about a small set of visual objects. For example, in designing an educational game where a player has to remember the characteristics of four keys that open various doors, the player could be required to remember the shape, size, color, and shininess of the required keys when tasked with selecting them out of a pool of thirty keys of which only four are correct. (Assume it is a matching game so that the keys must be remembered only briefly and thus long-term memory and short-term decay are side issues.) However, a game designer would produce nothing but frustration for gamers by changing this task to require remembering only the shapes of 16 different keys, even though in both cases, 16 features are presented. Loading several features onto a single object allows more information to be retained in VWM.

As an aside, individuals can hold about 3–4 objects in VWM, although there is a hot debate between researchers such as Luck and Vogel (2013) and Schneegans and Bays (2016) about whether a “slot” model for VWM capacity is more accurate, where additional items cannot be remembered even if sacrificing fidelity is an option (Luck and Vogel), or whether an analog model is more accurate, where fidelity may be sacrificed to load additional items into VWM (Bays). While the slot (“quantized”) model has a long history of experimental support, the analog (“continuous”) model has recently been gaining ground, in part due to neuroimaging advances.

Interestingly, VWM capacity differs significantly between individuals and may be stable and reliable (Xu, Adam, Fang, & Vogel, 2017). In other areas besides vision, individual differences in working memory are similarly important and are positively correlated to cognition and learning, due in part to aiding “planning, comprehension, reasoning, and problem solving” (Cowan, 2014, p. 217). Nonetheless, Cowan (2014) argues that although it may be impossible to increase learners’ working memory capacities, we can adjust our educational presentations accordingly for learners with less working memory capacity.

Cognitive Load Theory

Cognitive load theory (CLT) is arguably of fundamental importance to effective instructional design (Paas, Renkl, & Sweller, 2003). Whereas working memory alone would only allow us to deal with very simple problems, long-term memory is essential to learning complex knowledge and skills. Long-term memory contains schemas to organize information into frameworks that can be leveraged for automaticity and efficient use of working memory. For example, an accomplished sight-reading pianist can play a complex, unfamiliar piano score due to an iterative, years-long cycle of practice and schema-building that greatly reduces the intrinsic cognitive load for the task. However, the intrinsic cognitive load of this task would simply be overwhelming for a novice, regardless of whether instruction is delivered in an effective manner that minimizes extraneous cognitive load. Intrinsic cognitive load is irreducible to what is being learned, while extraneous cognitive load is introduced by ineffective instructional design. Finally, germane cognitive load is relevant to automation and acquiring schemas.

Extraneous and germane cognitive load can be influenced by the instructional designer. Avoiding situations where learners must divide their attention, such as between a presenter’s speech and words he or she is displaying on projected slides (the split-attention effect), is one example of reducing extraneous cognitive load (for many others, see Mayer & Moreno, 2003). Instructional designers may further aid learning by increasing germane cognitive load through strategies that increase learner motivation and effort (see “note” on p. 2 of Paas et al., 2003). For tasks with low intrinsic load, instruction may be inefficiently designed without noticeable consequences. However, harder tasks, particularly those with high levels of element interactivity, have high intrinsic load. Paas et al. (2003) give the example of image manipulation software, in which individual functions can be learned with low intrinsic cognitive load due to a low level of element interactivity, meaning that each function can be learned in isolation. However, putting this knowledge together to successfully edit a digital photograph has high intrinsic load, in part because of high element interactivity—the image-editing functions must be used in concert. However, novices are overwhelmed by intrinsic cognitive load if the functions are taught in concert—they must be taught in isolation. Other examples include piano performance (new pieces are learned hands-separate to reduce intrinsic load), learning to drive, et cetera.

For instructional designers, a primary consequence of CLT and related theoretical elements, distilled as the expertise reversal effect (Kalyuga, 2007), is that novices and experts cannot receive the same instruction. Instructing a novice on how to use Adobe Photoshop might best be accomplished one function at a time, but an intermediate or expert user may learn new techniques more effectively if editing an actual image, because intrinsic cognitive load has been subjugated by prior knowledge and the associated schemas and automations. Therefore, teaching an expert about Photoshop functions in a piecewise fashion would have too little intrinsic cognitive load, and therefore a holistic approach may be more effective, while the exact opposite might be true for a Photoshop novice. Hence, the expertise reversal effect.

Auditory Range

Human hearing typically operates in the range of 20–20,000 Hz (“hertz”), but men after Age 20 lose, on average, the ability to hear a hertz per day at the upper end of this range (Gray, n.d.). According to Gray, this means a 50-year-old likely cannot hear sounds over 10 kHz (note: one kHz is 1000 Hz). An amusing implication is that teenagers and young adults can use high-pitched ringtones on their phones to be alerted to phone calls or text messages without their teachers or parents knowing (Noise Help, n.d.). From trying the sample tones on the Neuroscience Online and Noise Help websites, I discovered I am able to hear tones at 15 kHz, but not at 17.5 kHz or 20 kHz, which may indicate that my hearing loss is already well underway. Another website ( contains more frequency choices. Here, I was able to hear the 15.8 kHz tone comfortably, the 16.7 kHz tone faintly, and could not hear the 17.7 kHz at all. If I set my text message ringtone to 16.7 kHz, I doubt I could reliably hear it, but would notice 15.8 kHz more readily.

The fundamental frequency of speech is typically within the 85–180 Hz range for males and 165–255 Hz range for females (Titze, 1994). This is the lowest frequency transmitted in the speech waveform. It appears curious, then, that the best range for hearing is around 3000–4000 Hz (Gray, n.d.). It may also surprise readers to learn that typical voice applications such as telephones and tele-conferencing software only transmits in the neighborhood of 300–3400 Hz, which completely excludes the fundamental frequencies of human speech! However, in actuality, human speech, like many sounds, encompasses a broad waveform with many overtones, which are tones higher than the fundamental frequency. Therefore, speech sounds natural, if somewhat tinny, with the fundamental frequencies omitted. Nevertheless, frequency restrictions, along with inferior visual cues and other factors, explain why it can be harder to understand a webinar or teleconference broadcast than a face-to-face (F2F) instructional engagement. Instructors and instructional designers should consider the modality of delivery—F2F instruction is more engaging of senses and perception, while distance audiovisual instruction has limitations with respect to auditory frequencies, visual depth, transmission latency, et cetera (Anderson, Beavers, VanDeGrift, & Videon, 2003). Thus, instructors in online or hybrid modalities may need to speak more slowly and clearly than in a purely F2F modality.


Anderson, R., Beavers, J., VanDeGrift, T., & Videon, F. (2003). Videoconferencing and presentation support for synchronous distance learning. Paper presented at the 33rd ASEE/IEEE Frontiers in Education Conference, Boulder, CO.

Cowan, N. (2014). Working memory underpins cognitive development, learning, and education. Educational Psychology Review, 26, 197–223.

Gray, L. (n.d.). Chapter 12: Auditory system: Structure and function. Neuroscience online: An electronic textbook for the neurosciences. Retrieved from

Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.

Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279–281.

Luck, S. J., & Vogel, E. K. (2013). Visual working memory capacity: From psychophysics and neurobiology to individual differences. Trends in Cognitive Sciences, 17, 391–400.

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.

Noise Help (n.d.). The teen buzz “ultrasonic” ringtones. Retrieved from

Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4.

Schneegans, S., & Bays, P. M. (2016). No fixed item limit in visuospatial working memory. Cortex, 83, 181–193.

Titze, I. R. (1994). Principles of voice production. Englewood Cliffs, NJ: Prentice Hall.

Xu, Z., Adam, K. C. S., Fang, X., & Vogel, E. K. (2017). The reliability and stability of visual working memory capacity. Behavior Research Methods. Advance online publication.

EME 6646 “Explain Brain Basics” Assignment

Assignment 1: Explain Brain Basics
For EME 6646: Learning, Instructional Design, and Cognitive Neuroscience
By Richard Thripp
University of Central Florida
May 21, 2017


Magenetoencephalography (MEG) is a new type of non-invasive brain scan that detects brain activity via the associated magnetic fields (MEG Community, 2010b; PBS, n.d.). Although strictly speaking it is not an “imaging” technique, it nevertheless provides time-sensitive data about the activity of groups of neurons, and can be combined with functional magnetic-resonance imaging for spatial information (Rees, 2011). MEG is very expensive—not only does one MEG device costs millions of dollars and weigh approximately eight tons (PBS, n.d.), but it must be placed in a room with carefully designed, comprehensive magnetic shielding. Magnetic fields emitted by the brain are so faint that the earth’s magnetic field itself is 100 million times more powerful (MEG Community, 2010b). Consequently, it is unsurprising that few MEG machines exist—in the entire state of Florida, the only MEG machine is at the Florida Hospital for Children in Orlando (Florida Hospital, n.d.; MEG Community, 2010a).

An MEG device principally includes a helmet with about 300 sensors that use superconducting coils cooled with liquid helium to –452° F. This array is able to detect signals from the brain to an accuracy of less than 1/1000 of a second, which was unheard of with prior technologies (MEG Community, 2010b). Thus, it can detect, in real time, both spontaneous brain activity and activity from an evoked response such as visual or auditory stimuli. MEG is valuable for both medical treatments (e.g., epilepsy; Florida Hospital, n.d.) and research (e.g., cognition; Freeman, Ahlfors, & Menon, 2009). On its own, it may provide more accurate “source localization” than electroencephalography (EEG), meaning that the source of brain activity can be isolated to within a general region of the brain (MEG Community, 2010b). However, while EEG has a much higher latency, it also has specific uses that make it complementary to MEG (Sharon, Hämäläinen, Tootell, Halgren, & Belliveau, 2007), and in fact, MEG, EEG, and fMRI can be used in concert to give a more accurate spatial and temporal depiction of brain activity, and perhaps even to determine the antecedents of cognition (Freeman et al., 2009), albeit with significant challenges and costs.

Security, Lie Detection, and Privacy

Rees (2011) explains that the desire for neuroimaging to allow humans to “detect covert mental states or deception” (p. 17) is strong. Despite the many problems and limitations associated with current techniques, a prevailing assumption that these will be overcome via technical means is apparent. While the polygraph is an unreliable approach to lie detection that relies on skin conductance rather than neuroimaging, neuroimaging techniques themselves are also quite susceptible to countermeasures—individuals may deceive such attempts at detecting deception with practice or training. While present attempts to deploy neuroimaging and related techniques for lie detection, predicting recidivism, and determining criminal intent are lacking in rigor and validity (Rees, 2011), the privacy implications of deploying such technologies to improve human–computer interactions are plainly evident (Fairclough, 2009). Data about neurophysiological states can be used to make computers more responsive and useful, but can also be leveraged to spy on or manipulate individual users, as well as to analyze users in aggregate without their consent. Therefore, Fairclough (2009) suggests that users should be given a great deal of control over the information collected, and should also be required to opt-in to such data collection with written consent.

How Much of the Brain Can One Develop Without?

Amazingly, anomalies in brain development can be compensated for by neuroplasticity, to the extent that such individuals may have a semblance of normalcy in adulthood. For example, Herkewitz (2014) summarizes the story of Michelle Mack, who was missing almost half of her brain at birth, yet graduated high school and is now in her 40s living a satisfying life. Another case described by Yu, Jiang, Sun, and Zhang (2015) involves a woman who has no cerebellum, and yet did not discover this until a hospital visit at Age 24. While according to her mother she could not speak intelligibly until Age 6 nor walk until Age 7, in her hospital visit she presented no signs of aphasia and only mild to moderately impaired speech, and she is married and gave birth to a daughter without incident. Finally, the case of Trevor Waltrip, a boy born with severe hydranencephaly whereby he developed with only a brainstem but no brain, is highly unusual because he lived to Age 12, although blind and unable to speak (Madden, 2014). Typically, children with this condition die shortly after birth. However, although there are many popular news articles with Waltrip’s story online (, it may be dubious because there appear to be no references to it in academic literature. Nevertheless, there are many other cases that demonstrate the brain’s plasticity particularly in childhood, but also to a less extreme degree in adulthood. Therefore, it has become clearly inaccurate to characterize the brain as a machine that can only deteriorate—the brain can also adapt to physical damage, and, of potentially greater importance, cognitive performance may be improved or regained through rehabilitation in a manner reminiscent of physical rehabilitation (Doidge, 2009).


Doidge, N. (2009). The brain: How it can change, develop and improve [Video file]. Retrieved from

Fairclough, S. H. (2009). Fundamentals of physiological computing. Interacting With Computers, 21, 133–145.

Florida Hospital. (n.d.). MEG: Advanced neuroimaging at Florida Hospital for Children. Retrieved from

Freeman, W. J., Ahlfors, S. P., & Menon, V. (2009). Combining fMRI with EEG and MEG in order to relate patterns of brain activity to cognition. International Journal of Psychophysiology, 73, 43–52.

Herkewitz, W. (2014). How much of the brain can a person do without? Retrieved from

Madden, N. (2014, September). Keithville boy born without brain dies at 12. Retrieved from

MEG Community. (2010a). Groups and jobs page. Retrieved from

MEG Community. (2010b). What is MEG? Retrieved from

PBS. (n.d.). Scanning the brain: Magenetoencephalography. Retrieved from

Rees, G. (2011, January). The scope and limits of neural imaging. In C. Blakemore et al. (Eds.), Brain Waves Module 1: Neuroscience, society, and policy (pp. 5–18).

Sharon, D., Hämäläinen, M. S., Tootell, R. B. H., Halgren, E., & Belliveau, J. W. (2007). The advantage of combining MEG and EEG: comparison to fMRI in focally stimulated visual cortex. NeuroImage, 36, 1225–1235.

Yu, F., Jiang, Q.-J., Sun., X.-Y., & Zhang, R.-W. (2015). Letter to the editor: A new case of complete primary cerebellar agenesis: Clinical and imaging findings in a living patient. Brain, 138(6), 1–5.

Rebuttal to Northwestern Mutual’s 2017 Planning & Progress Financial Literacy Study

The 2017 Planning & Progress Study by the Northwestern Mutual Life Insurance Company (NWM) has a press release titled Americans Besieged by Debt: 4 in 10 Spend Up to 50% of Monthly Income on Debt Payments. The inaccuracy and uselessness of this statistic is astonishing, particularly for an in-house press release.

NWM Screenshot 01

Firstly, the statistic should be 4 in 10 among those who reported having debt. According to NWM’s 2017 Debt Dilemma presentation, they surveyed 2117 U.S. adults, plus an oversample of 632 Millennials, for a total of 2749 respondents. NWM’s slides are inconsistent—Slides 3 and 6 say “those with some debt” constituted 1086 respondents, while Slides 4–5 say 1597 respondents. I think the 1086 figure likely included the 2117 adults, while the 1597 figure likely included the 2749 adults (including the oversample), which would indicate that 511 of 632 oversampled Millennials (80.9%) reported having some debt, compared to 1086 of 2117 from the general sample (51.3%). This seems reasonable, given that Millenials are more likely to have student loan debts.

Even if we include the oversample, 1597 of 2749 respondents is only 58.1%. On Slide 4, NWM says that “more than 4 in 10 Americans with debt (45%) spend up to half of their monthly income on debt repayment.” Therefore, among all Americans, this figure should be .581 × .45 = .261 or 26.1%—only 2.6 in 10 Americans report spending up to half their monthly income on debt payments, not 4 in 10 as NWM incorrectly claims. Confusingly, the question asked respondents to exclude their primary home mortgage, yet includes a “mortgage is my only debt” choice, which 15% selected. Adding to the confusion, on Slide 3 it says the “amount of debt” question excluded mortgages, even though the question prompt is “how much do you estimate your debt to be?” without mentioning mortgages. Who knows what is going on here? Can such a survey even be cited when NWM keeps changing their story and refuses to provide the actual questions or raw data?

NWM Screenshot 02

Next, I turn to the elephant in the room—something that is blatantly obvious, at least to me. HOW is “up to half of their monthly income” in ANY way a useful statistic?! This only tells us they do not spend more than half their monthly income on debt payments, which is almost worthless. It would be like saying “4 in 10 Americans consume up to half their calories from donuts,” meaning that they consume anywhere from zero to 50% of their calories from donuts. The NWM statistic does not mean that 55% of Americans with debt spend more than half their monthly income on debt payments (which, if true, would be astonishing and much more meaningful). In fact, in addition to “mortgage is my only debt” (15%), “not sure” (21%) is also an option.

The egregious statistical illiteracy of NWM’s PR department is evident, as is their lack of consultation with whomever at NWM concocted this study, although NWM’s slides are also, at times, bewildering. An interesting and relevant statement would have been “among Americans with debts, 18% reported spending more than half their monthly income on debt payments.” But, “up to half” is sophomoric.

As a general trend, note also that NWM proffers only descriptive statistics rather than inferential statistics. My recent poster presentation, Relationships Between Financial Capability and Education Attainment: An Analysis of Survey Data From the 2015 National Financial Capability Study (NFCS), used inferential statistics to compare knowledge of personal finance with degree attainment. The FINRA Investor Education Foundation is government-funded and is tasked specifically with conducting surveys and statistical analyses, unlike NWM. Nevertheless, FINRA’s 2015 annual report, like NWM’s reports, is devoid of inferential statistics. This is sad. The NFCS provides detailed statistical files, so it’s tempting to argue that such analyses will come out on their own, from unaffiliated researchers. However, too often, this simply does not happen, even though there are many interesting relationships to be examined. In the NWM studies, for instance, running inferential procedures to compare the oversample of Millennials with the general population would empower us to say that Millennials are significantly different along dimensions such as debt burdens, student loans, et cetera, and provide effect sizes to boot. Surprisingly, NWM’s 2017 Planning and Progress Study provided only two reports (the Debt Dilemma and the Financial States of America), unlike past years (e.g., 2016) which had many more reports (eight in 2016), and neither of the 2017 reports include even descriptive statistics on the oversample of Millennials. Why bother collecting the data, then? Well, we know the reason. To market life insurance.

FINRA and NWM should both employ more statisticians, so they can provide insightful and detailed inferential analyses, among other useful statistics. This would greatly increase the value of their surveys to the public and to researchers, including researchers who are capable of performing the analyses but for whom it would only provide tangential value (e.g., supporting evidence for an argument in their manuscript).

NWM Screenshot 03

To complete my rebuttal, I should analyze the rest of NWM’s 2017 Planning and Progress data. Their Financial States presentation is brief, and involves only perceptions. Unlike NFCS, there are no questions that actually measure content-knowledge. The usefulness of asking respondents questions such as “my long-term savings strategy has a mix of high and low risk investments” is dubious. This is the same sample of which 21% does not even know how much debt they have. How do we know whether they consider high-risk investments penny stocks and low-risk investments to be their Bank of America 0.03% APY savings account? Their “mix” of high- and low-risk “investments” could be totally stupid. Without explicitly defining our terminology, and ideally being able to correlate responses with questions measuring financial knowledge or competence, it is difficult to draw inferences from attitudinal questions like the preceding, or questions like “the ‘American dream’ is still attainable for most Americans.” How do you define the American dream? Does it involve emitting an ungodly amount of carbon dioxide and destroying the earth? Perhaps “the ‘human existential nightmare’ is still attainable for most Americans” might be a more accurate question.

NWM Screenshot 04

I found the above figure enlightening. These responses are among the 1086 respondents with “some” debt (evidently excluding the oversample of Millennials; see my discussion earlier). Granted, this question asks “which of the following best describes your strategy for managing your debt,” and much of what is listed are non-strategies. However, the option for “pay all bills monthly/on time” is present, and was selected by only 3% of those with debts. This is horrible. “I pay as much as I can on each of my debts each month” is not a strategy, yet 35% picked it. If you pay as much as you can, how do you know you will even be able to make the minimum payments next month? What do you do for unexpected expenses? Probably payday loans, given the deplorable state of American’s financial expertise. Where is the foresight? “I pay what I can when I can” is equally bad and also a non-strategy—at least 53% of respondents endorsed non-strategies. On the other hand, while not ideal, making minimum payments each month, or focusing on high-interest debts while making at least the minimum payments on others, are strategies. Doing so protects your credit from delinquency and allows you to avail of technical tricks like credit card balance transfers (BT) to mitigate high-interest debts. You can’t get a BT offer on a new credit card if you can’t get approved because of late payments or collections on your credit report.

In conclusion, while I agree with NWM’s conclusion that Americans are a financial basket case, their methodology is idiotic and their claims are blatant statistical misrepresentations. To cap it off, NWM’s infographic below claims that Americans spend 40% of their monthly income on leisure… without mentioning that the question asked respondents to exclude spending on “basic necessities” including housing, food, and transportation! Clearly, NWM is more interested in giving bombastic, just-plain-wrong talking points to the media, rather than an accurate representation of their survey data, which actually is not even in need of embellishment.

NWM Screenshot 05

Educational Attainment and Financial Literacy Questions from the National Financial Capability Study, 2009–2015

Here are my comments and an overview of the questions on the National Financial Capability Study (NFCS) about educational attainment and financial literacy, as they have changed during the three iterations (“waves”) of the survey (2009, 2012, and 2015). The NFCS is a survey that is administered nationally (by the FINRA Investor Education Foundation) to approximately 500 participants per U.S. state (about 27,000 per iteration total, due to large states or certain ethnicities being over-sampled) every three years. It began in 2009, so there have only been three iterations so far. While the raw data is not nationally representative—obviously, sampling 500 people from Alaska and 500 people from Florida grossly over-represents Alaska by proportion of population—the datasets include weighting variables to account for this at the national, state, and census-area levels. It is disappointing to see that the NFCS only began oversampling highly populous states in the latest iteration (2015), and only did so for New York, Texas, Illinois, and California (1000 respondents instead of 500), but this may be due to decisions about the NFCS being surprisingly political.

It is disappointing to see the lack of depth in the educational attainment question in the 2009 and 2012 surveys. Only in the latest version (2015) were options for Associate’s and Bachelor’s degrees added, while the vague “college graduate” was removed. However, now we have no option for trade school or certificate graduates. Moreover, making comparisons between the surveys is difficult. We can combine the two high-school graduate options in the 2012 and 2015 iterations to compare them to the single 2009 option, but it is somewhat tenuous to compare “some college” (2009 and 2012) to “some college, no degree” (2015), to consolidate “Associate’s degree” and “Bachelor’s degree” (2015) to compare them to “college graduate” (2009 and 2012), or to compare “post graduate education” (2009 to 2012) to “post graduate degree” (2015). These changes between survey iterations are not necessarily trivial. Indeed, the “tracking dataset” provided by FINRA, which includes respondents from all three survey iterations but only questions that are included in all three iterations, omits educational attainment due to the lack of consistency.

For the other questions about actual and perceived financial literacy, which is also known as financial capability (the two terms are fairly synonymous but “financial literacy” is the more popular term, despite FINRA and the post-2011 Obama administration relabeling it “financial capability”), the response options (not shown below but can be seen at the NFCS website or the Washington Post) remained identical between survey iterations. As someone interested in investigating the relationship between educational attainment and financial literacy (perceived and actual), it is disappointing to see “do you think financial education should be taught in schools?” being only included in the 2012 iteration, and to see “how strongly do you agree or disagree with the following statements? – I REGULARLY KEEP UP WITH ECONOMIC AND FINANCIAL NEWS” only included in the 2009 iteration. However, it is understandable that the survey cannot be overly long, and perhaps these questions were judged to be unimportant.

Special note on the question: “Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?” —— The response options are “more than $102,” “exactly $102,” “less than $102,” and “don’t know.” This wording is very easy. In fact, it may have been more interesting to make the options centered around $110 rather than $102, in which case the question would be about understanding exponentiation (compound interest) rather than simple addition. Nevertheless, in every iteration, 25 to 27% of respondents got this question wrong! And, as with every question, those with higher educational attainment did better. However, for some questions, such as the one on interest rates and bond prices, even postgraduates did shockingly bad—only 46% of postgraduates in the 2015 iteration correctly answered “they will fall,” and overall, a mere 28% of respondents answered correctly, with 38% answering “don’t know.” Although some of the interpretations are spurious and I disagree with using pie charts to represent such data, this blog post by “the Weakonomist,” regarding the 2012 iteration, shows how terrible the public’s financial literacy is. Of course, on the question where respondents are asked to assess their financial knowledge on a 1–7 scale, most think they are geniuses… pretty sad.


What was the last year of education that you completed? [2009 codes]

  1. Did not complete high school
  2. High school graduate
  3. Some college
  4. College graduate
  5. Post graduate education

What was the last year of education that you completed? [2012 codes]

  1. Did not complete high school
  2. High school graduate – regular high school diploma
  3. High school graduate – GED or alternative credential
  4. Some college
  5. College graduate
  6. Post graduate education

What was the highest level of education that you completed? [2015 codes]

  1. Did not complete high school
  2. High school graduate – regular high school diploma
  3. High school graduate – GED or alternative credential
  4. Some college, no degree
  5. Associate’s degree
  6. Bachelor’s degree
  7. Post graduate degree


2009, 2012, 2015: How strongly do you agree or disagree with the following statements? – I AM GOOD AT DEALING WITH DAY-TO-DAY FINANCIAL MATTERS, SUCH AS CHECKING ACCOUNTS, CREDIT AND DEBIT CARDS, AND TRACKING EXPENSES.

2009, 2012, 2015: How strongly do you agree or disagree with the following statements? – I AM PRETTY GOOD AT MATH.

2009 only: How strongly do you agree or disagree with the following statements? – I REGULARLY KEEP UP WITH ECONOMIC AND FINANCIAL NEWS.

2009, 2012, 2015: On a scale from 1 to 7, where 1 means very low and 7 means very high, how would you assess your overall financial knowledge?


2009, 2012, 2015: Suppose you had $100 in a savings account and the interest rate was 2% per year. After 5 years, how much do you think you would have in the account if you left the money to grow?

2009, 2012, 2015: Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per year. After 1 year, how much would you be able to buy with the money in this account?

2009, 2012, 2015: If interest rates rise, what will typically happen to bond prices?

2015 only: Suppose you owe $1,000 on a loan and the interest rate you are charged is 20% per year compounded annually. If you didn’t pay anything off, at this interest rate, how many years would it take for the amount you owe to double?

2009, 2012, 2015: A 15-year mortgage typically requires higher monthly payments than a 30-year mortgage, but the total interest paid over the life of the loan will be less.

2009, 2012, 2015: Buying a single company’s stock usually provides a safer return than a stock mutual fund.


2012 only: Do you think financial education should be taught in schools?

Thoughts on Cognitive Load and the Modality Effect; Self-Regulation and Mindsets

I wrote the following discussion replies for an assignment in IDS 6504: Adult Learning, instructed by Dr. Kay Allen. The first reply is about cognitive load theory and the modality effect; the second is about self-regulation and mindsets.

IDS 6504 Assignment 6: Replies to Others
Richard Thripp
University of Central Florida
March 17, 2017


Richard Thripp, responding to [redacted]

Question: What are strategies that can be implemented to reduce cognitive load?

General Comment: Reducing extraneous cognitive load, that is, cognitive load unrelated to the instructional materials themselves, is a worthy goal. Two of your references might be characterized as the modality effect—that presenting information both visually and auditorily can reduce cognitive load as compared to using only one modality.

Supplement: When considering cognitive load and the modality effect, one should also look at whether the instruction at hand is system-paced or self-paced. Classroom lecturing, such as the Lewis (2016) article you cited, is a classic example of system-paced instruction, because the learner cannot decouple the auditory portion of the presentation from the visual portion—these two modalities are temporally linked. This is good. In fact, Ginnas’s (2005) meta-analysis found a strong presence of the modality effect for system-paced instruction, but a weaker presence when instruction is self-paced. In self-paced instruction, the learner consumes instructional materials in one modality while having the option of referring to materials in other modalities. An example is a textbook or learning modules with graphics and text, supplemented by an audio or video clip to be accessed separately. The modality effect may be so bad for self-paced instruction that it may even be worse than presenting instruction in one modality, at least according to a study by Tabbers, Martens, and van Merriënboer (2004). This implies that temporal contiguity is essential. Therefore, instructional designers may want to be cautious about providing text-based modules with multimedia supplements. In fact, if we accept the argument of Tabbers et al. (2004), it may be better to force students to watch a video where the temporal contiguity of multimodal information is preserved (i.e., learners hear the audio that accompanies relevant text at the right time, rather than minutes or hours after reading the text in the learning module or textbook), at least with respect to cognitive load theory and the modality effect.

While I have not mentioned the cueing effect, it may be important to the modality effect if cues are linked across modes (e.g., a narrator telling the learner to look at a particular portion of a diagram). However, the cueing effect, quite often, is seen purely in the visual modality, such as highlighting or otherwise visually drawing attention to an area of a figure, graph, table, diagram, or block of text.

As an added comment, what Dr. Allen does in this course with real-time learning sessions is a great example of using system-paced instruction to leverage the modality effect. She does not read from the slides, but auditorily elaborates on the points on the slides with different words. She does not offer the slides for download, nor a text transcript of the spoken portion of the presentation. Ironically, not offering these supplements may actually be preferable to offering them; even learners who miss the real-time session must review a video-recording of it, which ensures that temporal contiguity of the instructional modalities is preserved. If slides and transcripts were offered, learners availing themselves of them would become self-paced with respect to instructional modality, which can have deleterious, or at least sub-optimal, results (Tabbers et al., 2004).


Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15, 313–331.

Tabbers, H. K., Martens, R. L., & van Merriënboer, J. J. G. (2004). Multimedia instructions and cognitive load theory: Effects of modality and cueing. British Journal of Educational Psychology, 74, 71–81.


Richard Thripp, responding to [redacted]

Question: How can instructors of adult language-learners address the issue of learners’ self-regulation so they may better manage their learning?

General Comment: Self-regulation is multi-faceted. Explaining the research on self-regulation to learners may be beneficial. Influencing learners’ mindsets is another worthy avenue. The instruction or assessment goal at hand is a factor in whether self-regulation should be prioritized or deferred.

Supplement: In their blockbuster literature review and position piece, Muraven and Baumeister (2000) contend that self-regulation is like a muscle—it is finite, can be easily depleted, and yet may also be strengthened by being frequently exercised. Explaining this to learners may improve their understanding of self-regulation and perhaps reduce inappropriate self-blame. Moreover, learners’ personal situations and an educator’s present goals are important. During instruction and formative assessment, encouraging self-regulation among learners may be beneficial. However, allowing learners to exhibit self-regulation by making all assignments and assessments due on the last day of the semester may have profoundly negative results for learners who fail to self-regulate; instead, staggered deadlines can reduce learners’ self-regulatory burdens. Further, educators and institutions arguably should reduce the need for self-regulation among learners who are going through transitions or already have a lot of self-regulatory burdens. For instance, the self-regulation required of doctoral candidates may be foreign and overwhelming, which is a contributory factor toward the undesirable outcome of doctoral attrition (Bair & Haworth, 1999). In response, universities might mandate format reviews, committee meetings, and draft deadlines to reduce doctoral candidates’ reliance on self-regulation.

Another important factor is mindset—whether the learner has a growth mindset (incremental theory of intelligence), meaning they believe they can improve their abilities with effort, or a fixed mindset (entity theory of intelligence), meaning they believe their abilities in a particular domain, or in general, cannot be increased through effort (Thripp, 2016). In an extensive meta-analysis, Burnette, O’Boyle, VanEpps, Pollack, and Finkel (2013) found that having a growth mindset predicted superior self-regulation. Growth mindset can be easily taught through brief instructional modules advocating the brain’s plasticity and potential for growth (Paunesku et al., 2015). Such interventions may have collateral benefits to self-regulation. Efforts should be made by educators to demystify important concepts, such as mindsets and self-regulation, among their learners. Then, learners may achieve metacognitive awareness, becoming empowered to recognize and adjust for their human limitations as a step toward truly taking control of their educations.


Bair, C. R., & Haworth, J. G. (1999, November). Doctoral student attrition and persistence: A meta-synthesis of research. Paper presented at the meeting of the Association for the Study of Higher Education, San Antonio, TX.

Burnette, J. L., O’Boyle, E. H., VanEpps, E. M., Pollack, J. M., & Finkel, E. J. (2013). Mindsets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139, 655–701.

Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources: Does self-control resemble a muscle? Psychological Bulletin, 126, 247–259.

Paunesku, D., Walton, G. M., Romero, C., Smith, E. N., Yeager, D. S., & Dweck, C. S. (2015). Mind-set interventions are a scalable treatment for academic underachievement. Psychological Science, 26, 784–793.

Thripp, R. X. (2016, April 21). The implications of mindsets for learning and instruction. Retrieved from