Hi everyone! NUS Neuroscience Student Interest Group is holding our first ever Undergraduate Research Symposium! Come join us to learn about neuroscience, as well as latest developments in research, in engaging talks by members of faculty as well as students here in NUS.
Please refer to the poster below for more details, and register your interest here!
I recently attended a seminar on ‘Applying Cognitive Science Principles to Promote Durable and Efficient Learning’ by Dr Sean Kang from the Dathmouth College Cognition and Learning Lab hosted by the NUS Department of Psychology. His talk focused on how optimizing test-taking and spaced learning could help improve memory for learned material and has inspired me to write this article, reviewing some of the findings he presented as well as other insights neuroscience research has provided that may be used to enhance learning and memory.
The Testing effect
For most students, tests and exams are just another hassle that stands between them and the holidays that everyone dreads and can’t wait to get over. Perhaps exams and tests elicit an unpleasant reaction in students because time and time again emphasis has been placed on performing well since the grades on such tests may determine placements in future classes, schools one is eligible to apply and even jobs one can apply to. Sadly, tests were actually initially introduced into the school curriculum to reinforce learning (Carrier & Pashler, 1992) and this true purpose has been eclipsed by the emphasis placed on scores and performing well as tests have evolved to be used as a tool to evaluate learning.
Research has shown that earlier testing will improve subsequent performance in a later test, a testing effect referred to as the benefit of retrieval practice (Carpenter, 2012; Rawson & Dunlosky, 2012). In fact, testing was found to render learned memories more robust to interference than mere reading of the material to be learnt (Tulving & Watkins, 1974; Szpunar, et al., 2008). Hence, it is a good practice for students to constantly test themselves while studying, as an adjunct to learning, to activate the same memory traces that were involved in encoding the memory the first time and allowing for re-consolidation. It was also found that a more demanding initial test (e.g. short answer questions which rely on recall memory instead of multiple choice questions which rely on recognition memory) led to better performances in a later test, regardless of whether it was short answer or multiple choice questions (K., et al., 2007). Most students would dread a tough mid-term exam and hopefully after reading this, they will learn to appreciate the fact that their lecturers are actually ‘helping’ them to perform better in the final exams by setting a tough mid-term paper!
The importance of feedback in learning
Studies have found that feedback with the correct answer following a test produced a significantly greater performance on a test administered a week later as compared to when no feedback was given (Pashler, et al., 2005), regardless of whether the initial answers were right or wrong (Butler, et al., 2007). Delayed feedback was found to produce beater retention, at least in the controlled setting of an experiment, than immediate feedback. Thus, students should make it a point to review their tests with their tutors.
Guessing answers during tests – does it affect learning?
Usually when you don’t know the answer to a question, the best option would be to guess at the answer, banking on the possibility of it being correct by chance, instead of leaving it blank and forfeiting the mark for the question. One might think that such guessing might interfere with later learning of the actual answer when feedback is given (retrograde interference). However, research has shown that such interference does not occur (Kang, et al., 2011). In fact, it was found that students learned the correct answer from feedback better when they were more confident of their earlier erroneous answers, as counter-intuitive as that might sound (Kulhavy, et al., 1976).
Research has shown that retrieval of learned information was better when keeping the context of retrieval (e.g. examination environment) similar to the context at encoding the memory (learning environment) (Godden & Baddeley, 1975; Smith, et al., 1978). Although students might not have the ability to modify the environment of the examination hall, they could study in a similar environment (e.g. a quiet library) to capitalize on this effect. Moreover, chewing on a particular flavor of sweet during learning and subsequent retrieval during exams might help with recall. Interestingly, research has also found that one’s general mood (Weingartner, et al., 1977), internal physiological state (Eich, et al., 1975; Miles & Hardman, 1998) or emotions (Lang, et al., 2001) and alcohol consumption (Lowe, 1982) could also affect context-dependent memory and subsequent recall.
Spaced vs Mass learning
Many of us might be guilty of cramming our lecture notes at the last minute just before the exams, thinking that keeping the content locked in our short term memory will allow us to recall the information during the test the next day. While it is true that such mass learning is superior to spaced learning in the short term retrieval of the memory, such memory traces are also more prone to decay and hence are, in the long term, forgotten more easily (Roediger & Karpicke, 2006). Thus, spaced learning is highly encouraged to maximize the lifespan of memory for learned items and has been shown to be more advantageous to mass learning in nearly all forms of learning (Dempster, 1996; Mammarella, et al., 2002; Cepada, et al., 2006).
However, in cases whereby you are just starting out on a topic, with little or no prior knowledge, mass learning is recommended to get that head start and spaced learning should be used to build up on that base knowledge in the time to come to maximize learning.
In the case of categorical learning, for example in learning to differentiate classes of organic compounds in organic chemistry, spaced learning with exposure to the different groups of compounds at the same time at each session is encouraged. By exposing one to the different classes of compounds at the same time (interleaving), the differences between them is emphasized and aids in better learning as opposed to exposing each compound one at the time (Kang & Pashler, 2012).
Notably, when the items being learned are similar and the differences between them are very subtle, for example when learning to distinguish different composers by being exposed to classical music composed by them, learning of the categories is better when music from the same composer is played in the same block so that the similarities can be abstracted better to form a concept of that composer’s style of composing (blocking). This blocked presentation in spaced learning is found to be superior to interleaving in this context when the items being learned are only subtly different.
Another finding is that spaced learning at increasing/ expanding intervals ( e.g. day 1, day 3, day 7, day 13) is slightly superior to spaced learning at equal intervals (e.g. day 1, day 3, day 5, day 7) as memory was more available and efficiently retrieved and available for longer periods (Landauer & Bjork, 1978; Kang, et al., 2014). Thus, students might find it useful to review study materials at increasing intervals.
Sleep and Memory
Besides the topics discussed above, sleep has also been demonstrated to have beneficial effects on learning and memory by re-organising existing memories to accommodate the learning of new information (Stickgold & Walker, 2007). Sleeping after learning has been found to aid in recall of memory (Gais, et al., 2006). Specifically, N-REM (non-rapid eye movement) phase of sleep was found to be essential for strengthening declarative (memory that can be verbalised such as semantic memory for knowledge and facts and episodic memory for life events) and procedural (skills-related) memories (Gais & Born, 2004). A study found that sleep deprivation for one night led to poor performance on cognitive tasks the following day and that this deficit could not be overcome even after 2 nights of adequate sleep (Stickgold, et al., 2000). Thus, pulling an all-nighter just before the exams might not be a good idea as you may be doing more harm than good.
Having read up more on this topic for the purpose of this brief review, I for one, am looking forward to my upcoming tests!
Butler, A. C., Karpicke, J. D. & Roediger, H. L., 2007. The Effect of Type and Timing of Feedback on Learning from Multiple-Choice Tests. Journal of Experimental Psychology: Applied, Volume 13, pp. 273-281.
Carpenter, S. K., 2012. Testing Enhances the Transfer of Learning. Current Directions in Psychological Science, Volume 21, pp. 279-283.
Carrier, M. & Pashler, H., 1992. The Influence of Retrieval in Retention. Memory and Cognition , Volume 20, pp. 633-642.
Cepada, N. J. et al., 2006. Distributed Practice in Verbal Recall Tasks: A Review and Quantitative Synthesis. Psychological Bulletin, Volume 132, pp. 354-380.
Dempster, F. N., 1996. Distributing and Managing the Conditions of Encoding and Practice. In: E. L. Bjork & R. A. Bjork, eds. Handbook of Perception and Cognition: Memory. San Diego, CA: Academic Press, pp. 317-344.
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Kang, S. H. K. et al., 2011. Does Incorrect Guessing Impair Fact Learning?. Journal of Educational Psychology, 103(1), pp. 48-59.
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Lang, A. J., Craske, M. G., Brown, M. & Ghaneian, A., 2001. Fear-Related State Dependent Memory. Cognition and Emotion, 15(5), pp. 695-703.
Lowe, G., 1982. Alcohol-Induced State-Dependent Learning: Differentiating Stimulus and Storage Hypotheses. Current Psychology, 2(1), pp. 215-222.
Mammarella, N., Russo, R. & Avons, S. E., 2002. Spacing Effects in Cued-Memory Tasks for Unfamiliar Faces and Nonwords. Memory and Cognition, 30(8), pp. 1238-1251.
Miles, C. & Hardman, E., 1998. State-Dependent Memory Produced by Aerobic Exercise. Ergonomics, 41(1), pp. 20-28.
Pashler, H., Cepeda, N. J., Wixted, J. T. & Rohrer, D., 2005. When Does Feedback Facilitate Learning of Words?. Journal of Experimental Psychology: Learning Memory, and Cognition, Volume 31, pp. 3-8.
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On the 22nd of February 2016, members of NUS Neuroscience Student Interest group attended a workshop on Neuroimaging hosted by A*STAR-NUS Clinical Imaging Research Centre (CIRC).
The Workshop started with an introductory lecture by Dr John Totman, Head of Imaging Operations at CIRC. He recounted the history behind various imaging techniques such as Ultrasound, X-Ray, CT, PET and MRI, and gave a brief overview on the science behind these techniques, their limitations and common uses, for example, in the field of nuclear medicine.
One of the most common imaging techniques, ultrasound makes use of the Doppler Effect. Ultrasound waves are emitted from the probe, which then detects the reflection of those waves off anatomical structures to construct an image of the structure; similar to echolocation used by dolphins and bats. Ultrasound is not commonly used in neuroimaging because sound waves cannot penetrate the skull.
Another commonly used technique, X-ray works on the principle that structures in the body attenuate x-rays that are projected on one side of the body such that the rays emerging from the other side expose a sheet of film to produce an image reflecting the body structures. However, the images produced are two-dimensional and thus may miss out certain structural details. As a result, multiple images at different sections of the body are required for a more accurate representation of the body structures.
A variant of X-ray imaging is Computerized Tomography (CT). It uses a rotating x-ray emitter and detector to provide a 3D reconstruction of anatomical structures. It is commonly used in neuroimaging to provide the structural information that aids in medical practices, such as detecting tumours and aneurysms. The grey and white matter of the brain can be distinguished clearly on CT images.
Another technique, Positron Emission Tomography (PET) requires an ingestible tracer consisting of glucose in conjugation with radioactive fluorine, which is prepared in the expensive cyclotron reactor by accelerating and colliding sub-atomic particles. Other positron-emitting radionuclides such as oxygen might also be used depending on how long the effect is required to last. When the radioactive substance decays, positrons are released which collide with the electrons released by the PET scanner, producing two gamma rays which scatter in exactly opposite directions. The PET scanner detects these two gamma rays and computes the average distance at which the rays originated to indicate the location of the anatomical structure. PET can be used for functional as well as anatomical imaging. The principle behind anatomical imaging is that cancer cells take up glucose rapidly, but fludeoxyglucose (FDG) cannot be easily metabolised and thus accumulate in these cells and can be detected.
SPECT, which stands for Single-photon Emission Computerized Tomography, is a combination of PET and CT. PET provides metabolic information while CT provides structural anatomical landmarks such as bones to give relevance to the metabolic activity reported by PET scans.
After Dr Totman’s lecture, Ms Caroline Wong, a Research Officer at CIRC, introduced us to Functional Magnetic Resonance Imaging (fMRI), which is the most commonly employed imaging tool for functional studies in neuroscience research. fMRI provides both structural and functional information of the brain. The basic unit of fMRI images is the voxel, which is a 3D pixel with modifiable size and length. Smaller the voxel, greater is the time required to acquire it, but more detailed the image would be, which is analogous to having thinner slices: many voxels make up a slice, and many slices make up the volume.
The science behind MRI is that protons have different spins in random directions, which align when placed in a magnetic field. When the MRI scanner emits radio frequency pulses, the protons are tilted out of alignment from each other. When the radio frequency pulses stop, the protons lose energy and return to their baseline aligned state, thereby releasing electromagnetic waves that are detected by the scanner.
In fMRI practice, active areas of the brain receive more oxygenated blood flow due to the dilation of local cerebral blood vessels. The protons in oxygenated haemoglobin and deoxygenated haemoglobin have different rates at which they return to their baseline aligned state. Moreover, oxygenated blood is diamagnetic, does not distort the surrounding magnetic field and thus there is no signal loss. In contrast, deoxygenated blood is paramagnetic, distorts the surrounding magnetic field and thus there is signal loss. These differences are used to highlight brain areas that are active during a task, since active areas are marked by a higher level of oxygenated blood flow. A more detailed relation is displayed by Figure 2, the hemodynamic response function profile.
In Figure 2, the initial dip is due to blood oxygen taken in by active brain areas from surrounding blood vessels. The rapid rise is due to overcompensation of blood flow due to dilation of blood vessels, which usually lasts for around 4-8 secs, but the exact time period depends on the brain area. The post-stimulus undershoot is due to elastic recoil of expanded blood vessels. Meanwhile, voxel colour changes with the time course of the curve.
Typical fMRI task designs include the block design, which consists of periods of rest between periods of activity; the slow event-related (ER) design, which has been phased out as it is inefficient; the rapid counterbalanced ER design, which is the fastest; and the mixed design, which consists of both block and ER. One difficulty with fMRI is that it requires minimal movement of non-task related areas of the body since movements may create “noises” that confound the results. Another difficulty is that brain functions are not localised to particular areas; meanwhile, the same area may be involved in many different functions. Notably, synchronisation between multiple brain areas that process the same cognitive properties have been seen to be out of synchrony in patients with neuropsychological disorders such as schizophrenia and bipolar disorder. Thus, careful interpretation of neuroimaging results is necessary.
After the presentations, we were invited to visit the fMRI laboratory, and witnessed several interesting phenomena such as the strong force of attraction between metal objects and the fMRI scanner, and the fact that conductors would fall due to gravity when suspended, albeit at a slower than expected rate in the fMRI scanner chamber due to electromagnetic induction. Towards the end of the workshop, Ms Caroline Wong also indicated that Dr Qiu Anqi from NUS Computational Functional Anatomy Lab was looking for research assistants. Details can be found in this link: http://www.bioeng.nus.edu.sg/cfa/.
Within the two-hour session, the workshop provided us with an essential foundation for appreciating the various neuroimaging techniques used in neuroscience research and clinical practice. We would like to thank the Clinical Imaging Research Centre (CIRC) for accommodating our group and conducting this workshop.