Assignment 4, Part A: Individual Explanation of Rewards and Emotions
For EME 6646: Learning, Instructional Design, and Cognitive Neuroscience
By Richard Thripp
University of Central Florida
June 8, 2017

Moral Emotions

Using a task where participants passively viewed morally charged pictures (e.g., starving children and warfighting), Moll et al. (2002) found that while such images activated the amygdala, thalamus, and upper midbrain just like basic emotions, images evoking “moral emotions” additionally activated the orbital and medial regions of the prefrontal cortex, as well as the superior temporal sulcus region, both of which were previously known to be important for perception and social behavior. Moll et al. (2002) argue that these functional magnetic resonance imaging (fMRI) results indicate that humans automatically assign moral values to social events, and that this is an important function of human social behavior.

While traditionally, the prevailing paradigm was that moral judgments are guided by reason, neuroimaging evidence has shown us that emotion plays a vital role. For example, Greene, Sommerville, Nystrom, Darley, and Cohen (2001) tackled the issue by presenting fMRI-connected participants with a battery of moral dilemmas, with moral–personal (e.g., pushing a bystander off a bridge to stop a trolley that would kill five people), moral–impersonal (e.g., voting in favor of a referendum that would result in many deaths), and non-moral conditions (e.g., whether to stack coupons at the grocery store). Moral–personal dilemmas activated brain regions (i.e., medial frontal gyrus, posterior cingulate gyrus, and angular gyrus) that were significantly less active in the other conditions. Moreover, reaction time was higher in instances where a participant responded an action was “morally appropriate” (it was a dichotomous choice between this and “morally inappropriate”) when this was emotionally incongruent—for example, when participants said “appropriate” to sending the bystander to his or her death to stop the trolley from killing five people. The authors specifically compared this to the Stroop test, contending that this was a similar phenomenon in that it required extra processing time. Overall, emotions can help us understand why the majority will say it is acceptable to flip a switch that changes the direction of the trolley, killing a bystander to save five others, while a majority will say it is unacceptable to push the bystander in front of the trolley to stop it, even though the outcome is the same. Greene et al. (2001) say the latter is more emotionally salient. While the trolley problem is a philosophical paradox if considering only reason, adding emotion resolves it.

If moral dilemmas light up different parts of the brain, and if emotional salience is important to judging whether an issue is morally unacceptable, educators can use this to design instruction to engage moral emotions. For instance, the music industry has long argued that illegally downloading a song is no different than shoplifting the CD from Target. The former might be compared with flipping the trolley switch, while the latter is like pushing the bystander in front of the trolley—far fewer would shoplift than illegally download a song. Casting academic integrity in a similar light could help promote ethical and prosocial behaviors among students. Marketing research implies that most people are honest to a fault—they would not be grossly dishonest to get ahead, but if they can profit while continuing to believe they are righteous, they will do so (Mazar, Amir, & Ariely, 2008). In addition to promoting academic honesty, moral emotions can be evoked in instruction through vignettes, case studies, or interrogatories (e.g., “What would you do if you could save five people by harvesting the organs of a cerebrally dead 22-year-old who is an organ donor but whose family actively protests?”). Integrating these as both individual and group activities may be useful. Group activities invite going along with the group, so individually completion might precede group discussion. Sadly, while Walt Disney Studios appeals to our moral emotions and emotions of all forms in their motion pictures, instructors typically leave this engagement opportunity untapped.

Self-Regulation of BOLD Signals and Monetary Rewards

Recently, Sepulveda et al. (2016) combined measurement via real-time FMRI neurofeedback (NF) with instructing participants to increase their blood-oxygen-level dependent (BOLD) signals (i.e., self-regulation of brain physiology), in a between-groups study with four groups (n = 20 with five per group) which received either NF only (a.k.a. contingent feedback), NF and motor-imagery training, NF and monetary reward, and NF + motor-imagery training + monetary reward. The BOLD signal is a proxy for “volitional control of supplementary motor area” (Sepulveda et al., 2016, p. 3155)—this ability can improve “planning and execution of motor activity” (p. 3155), and may be important to self-regulation, learning, academic success, et cetera. Interestingly, while all groups were successful at up-regulating their BOLD signals, monetary reward resulted in the greatest increase, while motor-imagery training did not even result in a statistically significant enhancement. That is to say, the participants who were evidently the most motivated to increase their BOLD signals were the ones who received NF and an on-screen dollar amount where the amount increased in proportion to their real-time increase in BOLD signal. While the authors were careful to note that monetary rewards—which are by definition an extrinsic motivator—lose their effectiveness over time and thus should be used as an initial motivator that is withdrawn over time (hopefully giving way to intrinsic motivation), their discussion does not mention that this neuroimaging evidence may be important to the use of monetary rewards for academic and organizational success.

Monetary Rewards May Be Ineffective in Academic Settings

In contrast to Sepulveda et al. (2016), Mizuno et al. (2008) found that while learning motivated by monetary rewards activated the putamen bilaterally much like self-reported level of motivation learning, the intensity of activity (measured via fMRI BOLD signals) increased with higher levels of motivation for learning, but not with increased monetary rewards. This may suggest that, at least in an academic context, greater monetary rewards do not increase motivation. While it did not employ neuroimaging, a study of 300 middle schoolers by Springer, Rosenquist, and Swain (2015) may be relevant. They offered either no incentive, $100, or a “certificate of recognition signed by the district superintendent” (p. 453) to students who attended tutoring regularly. While the preceding fMRI research may lead us to believe that the monetary incentive would have been effective, in fact it had no significant differences from the control group, while the certificate of recognition was a highly effective motivator. Therefore, for academic motivation, financial rewards may be inferior to other forms of extrinsic motivation (e.g., a certificate), or to intrinsic motivation. Nevertheless, they may be a useful tool for the unimaginative instructor, particularly in contexts where a grading scheme cannot be implemented (e.g., some forms of organizational training). For a more typical academic setting, grades and “extra” credit opportunities (which, ironically, are available even to students who achieve far less than 100% on their work) may basically take the place of what would have been monetary rewards in another setting.

References

Greene, J. D., Sommerville, R. B., Nystrom, L. E., Darley, J. M., & Cohen, J. D. (2001). An fMRI investigation of emotional engagement in moral judgment. Science, 293, 2105–2108. http://doi.org/10.1126/science.1062872

Mazar, N., Amir, O., & Ariely, D. (2008). The dishonesty of honest people: A theory of self-concept maintenance. Journal of Marketing Research, 45, 633–644. http://doi.org/10.1509/jmkr.45.6.633

Mizuno, K., Tanaka, M., Ishii, A., Tanabe, H. C., Onoe, H., Sadato, N., & Watanabe, Y. (2008). The neural basis of academic achievement motivation. NeuroImage, 42, 369–378. http://doi.org/10.1016/j.neuroimage.2008.04.253

Moll, J., de Oliveira-Souza, R., Eslinger, P. J., Bramati, I. E., Mourão-Miranda, J., Andreiuolo, P. A., & Pessoa, L. (2002). The neural correlates of moral sensitivity: A functional magnetic resonance imaging investigation of basic and moral emotions. Journal of Neuroscience, 22, 2730–2736. http://doi.org/10.1016/j.bandc.2016.07.007

Sepulveda, P., Sitaram, R., Rana, M., Montalba, C., Tejos, C., & Ruiz, S. (2016). How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI. Human Brain Mapping, 37, 3153–3171. http://doi.org/10.1002/hbm.23228

Springer, M. G., Rosenquist, B. A., & Swain, W. A. (2015). Monetary and nonmonetary student incentives for tutoring services: A randomized controlled trial. Journal of Research on Educational Effectiveness, 8, 453–474. http://doi.org/10.1080/19345747.2015.1017679

Assignment 3, Part A: Individual Explanation of Learning and Memory
For EME 6646: Learning, Instructional Design, and Cognitive Neuroscience
By Richard Thripp
University of Central Florida
June 2, 2017

Long-Term Potentiation

In their 2014 literature review, Granger and Nicoll lament that long-term potentiation (LTP) has never been precisely defined. They explain that “the broadest definition is a long-lasting enhancement in synaptic strength following a brief high-frequency stimulation” (p. 1), which might be summarized as “neurons wire together if they fire together” (Löwel & Singer, 1992, p. 211). While the debate over whether long-term potentiation occurs pre- or postsynaptically continues, it is perhaps tangential to the educational ramifications. Fundamentally, the discovery of long-term potentiation in the 1970s was critical to our understanding of the mammalian brain (Teyler & DiScenna, 1987), showing us that the brain is not a static object, but that, even in the short term, neural pathways can be strengthened by mental exercise, not unlike physical exercise. This synaptic plasticity is important for many types of declarative memory (Byrne, n.d.), which, for educators, suggests that an important component of learning is repeated activation of the relevant synapses. Consequently, practice of the to-be-acquired skill should be integrated early and throughout a program of study. For example, given our knowledge of LTP, it would be inappropriate to structure a course on driving with 15 weeks of reading a textbook followed by one week of application behind the wheel. In the realm of teacher education, LTP might be accounted for by integrating field experiences early, which may also narrow the theory–practice gap (Coffey, 2010), rather than inefficiently delaying such fieldwork for a senior-year internship.

Learning Strategies

The strategy used during learning is a determinant of what type(s) of memory systems are engaged, and subsequently, the degree of success (Squire, 2004). This was discovered, in part, by inhibiting the hippocampus in rats, which aided them in navigating a maze tailored for non-declarative memory by solidifying the supremacy of the caudate nucleus. In fact, hippocampal lesions allowed the rats to perform better at this task! In humans, one can imagine a renegade researcher invasively inhibiting regions of the anterior cingulate and dorsolateral prefrontal cortices in an attempt to improve human performance on the Stroop test. More practically, educators can attempt to evoke efficient learning strategies for the materials at hand. For example, trying to learn a habit while also trying to memorize the requisite steps can result in failure on both counts (Squire, 2004). Therefore, educators might specifically instruct learners to focus on repetition in one trial and memorization in another. For example, if the task is executing a mathematical operation with a series of sub-steps, a textbook author could encourage habit learning by providing several problems with a reference sheet containing the sub-steps in view. Then, to encourage memorization, the reference sheet could be subsequently confined to a separate sheet requiring a page turn, with the learner being explicitly directed to test his or her memorization of the requisite steps.

Memory Consolidation and the Essentiality of Sleep

While it is easily observed via EEG oscillations that normal human sleep consists of 90-minute cycles including rapid eye-movement sleep (REM) and four stages of non-REM sleep, how memory consolidation occurs during sleep is less clear (Stickgold, 2005). However, that it has occurred is abundantly clear because certain tasks such as finger-tapping, rotation adaption, and visual texture discrimination have been experimentally shown to be enhanced subsequent to sleep, but when tested after 4 to 12 hours of wakefulness without sleep, the enhancement does not occur. Moreover, for some such tasks, improvement occurs even more when re-tested 72 hours later rather than 24, suggesting that additional nights of sleep further enhance these forms of procedural learning. Stickgold’s (2005) literature review goes on to summarize what he dubs as “converging evidence” (p. 1276) at the molecular, cellular, and higher levels, showing that sleep helps cell membranes, myelin, and cortical neuronal responsiveness. Further, at least in the Zebra finch, songs rehearsed during wakefulness appear to continue to be rehearsed in sleep, based on detection of similar “patterns of neuronal excitation” (Stickgold, 2005, p. 1277). Therefore, strong evidence exists that several forms of procedural learning are consolidated via sleep, although the evidence for declarative memory is less conclusive.

More recently, Tononi and Cirelli (2014) have argued, with molecular, electrophysiological, and structural evidence, for the synaptic homeostasis hypothesis (SHY). This basically says that “sleep is the price the brain pays for plasticity” (Tononi & Cirelli, 2014, p. 12). The essentiality of sleep is supported by neuroscience and behavioral evidence, yet this advice is often not followed by students nor educators and other professionals. Educators might encourage learners to get adequate sleep by directly exposing them to neuroscience research on sleep’s importance and by discourage cramming or “all-nighters” with adequate instructional scaffolding (e.g., setting draft and format review deadlines prior to a final submission deadline). Finally, researchers have recognized (e.g., Piffer, Ponzi, Sapienza, Zingales, & Maestripieri, 2014) that some humans are geared toward “morningness” (i.e., “early birds”) while others have a propensity toward “eveningness” (i.e., “night owls”).

As an individual with a lifelong propensity toward eveningness, I am baffled by the culture in America and elsewhere that favors early birds while mocking and ridiculing night owls for their purported laziness. The machinations of society are organized to confer privilege upon early birds—such as requiring children to go to school early in the morning, businesses and government offices that open early in the morning, and a majority of employment opportunities requiring us to wake up shortly after sunrise. Now that most people have electricity, if we are to facilitate learning, memory consolidation, and human performance in general, why not provide parallel structures and opportunities for night owls? Surely, would it not relieve congestion on Orlando’s roads if instead of a majority of workers working in the neighborhood of 9 a.m. to 5 p.m., if workplaces could be staffed in the evening and overnight so that unused nighttime road capacity might be leveraged? What about tweens and teenagers who would be better served if school was from noon to 7 p.m. rather than requiring them to rise before dawn and be tired all day? In higher education, afternoon and night course offerings can cater to night owls while not necessarily being punitive toward early birds. To further improve memory consolidation, housing developments and apartment complexes might be constructed with soundproofing and futuristic windows that use amorphous metal oxides to become completely opaque at the flip of a switch (Llordés, Garcia, Gazquez, & Milliron, 2013), facilitating daytime quiet and darkness for improved sleep, memory consolidation, and learning.

References

Byrne, J. H. (n.d.). Chapter 7: Learning and memory. Neuroscience online: An electronic textbook for the neurosciences. Retrieved from http://neuroscience.uth.tmc.edu/s4/chapter07.html

Coffey, H. (2010). “They taught me”: The benefits of early community-based field experiences in teacher education. Teaching and Teacher Education, 26, 335–342. http://doi.org/10.1016/j.tate.2009.09.014

Granger, A. J., & Nicoll, R. A. (2014). Expression mechanisms underlying long-term potentiation: A postsynaptic view, 10 years on. Philosophical Transactions of the Royal Society, 369(1633), 1–6. http://doi.org/10.1098/rstb.2013.0136

Llordés, A., Garcia, G., Gazquez, J., & Milliron, D. J. (2013). Tunable near-infrared and visible-light transmittance in nanocrystal-in-glass composites. Nature, 500, 323–326. http://doi.org/10.1038/nature12398

Löwel, S., & Singer, W. (1992). Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity. Science, 255, 209–212. http://doi.org/10.1126/science.1372754

Piffer, D., Ponzi, D., Sapienza, P., Zingales, L., & Maestripieri, D. (2014). Morningness–eveningness and intelligence among high-achieving US students: Night owls have higher GMAT scores than early morning types in a top-ranked MBA program. Intelligence, 47, 107–112. http://doi.org/10.1016/j.intell.2014.09.009

Squire, L. R. (2004). Memory systems of the brain: A brief history and current perspective. Neurobiology of Learning and Memory, 82, 171–177. http://doi.org/10.1016/j.nlm.2004.06.005

Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature, 437, 1272–1278. http://doi.org/10.1038/nature04286

Teyler, T. J., & DiScenna, P. (1987). Long-term potentiation. Annual Review of Neuroscience, 10, 131–161. http://doi.org/10.1146/annurev.ne.10.030187.001023

Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: From synaptic and cellular homeostasis to memory consolidation and integration. Neuron, 81, 12–34. http://doi.org/10.1016/j.neuron.2013.12.025