Mnemonics – Outmoded Evergreens?

Cognitive training can be roughly divided into strategy and core, or domain-general training.  Von Bastian and Oberauer (2013) based their classification of training approaches on transfer mechanisms, enhancing either memory efficiency or memory capacity. Strategy training is usually defined as targeting task-specific strategies that might help trainees to improve memory performance, most often resembling different mnemonic techniques. This transfer yields to increased memory efficiency providing the possibility for acquiring knowledge and skills such as strategy usage, chunk learning, and automation of basic processes and in that way decreasing the time needed to move the focus of attention between single items.

Theory

Both approaches, strategy as well as core training, target working memory. The difference between them is in the specific mechanisms they assume intelligence and working memory share. This aspect to a great extent shapes the training tasks that they advocate.

We discussed core training in detail in our previous blog about the n-back, as well as in our forthcoming book Increasing Intelligence, therefore only a brief recap will be presented here. Core training involves the repetition of demanding working memory (WM) tasks that target domain-general WM mechanisms assumed to overlap with the same cognitive resources needed in complex problem solving. Furthermore, it was suggested that repetitive cognitive demands that exceed resource limits also induce changes in brain regions related to working memory performance (Taya et al., 2015). More simplified, working memory and intelligence share a common capacity constraint, thus by increasing WM capacity, intelligence should also increase (Buschkuehl and Jaeggi, 2010). Although it is usually not explicitly stated that the assumed intelligence – WM shared component is capacity. This is in line with Cowan’s (Cowan et al. 2005) and Colom’s (Colom et al., 2008) research findings that short term storage largely accounts for the relation between intelligence and working memory.

While the emphasize of core training is to minimize the possibility for the development of task specific strategies, in contrast, the goal of strategic training is to develop specific strategies that would allow better working memory performance.

Engle (Engle et al., 1999) suggested that executive functioning, especially control of attention over interference and conflict, is the central process contributing to the shared variance with intelligence. Their idea has been further developed suggesting that when subjects are confronted with a complex working memory test where they have to solve mathematical equations between the presentation of the to-be-remembered items, they have to displace the items from primary to secondary memory. The same happens for long lists in a memory span task (more than 4 items). The relation with intelligence derives from these two processes, the ability to retain information in primary memory and to recover it from secondary memory (Unsworth and Engle, 2007).  Even more extreme is the position that only the ability to retrieve information from secondary memory contributes to the shared variance between working memory and intelligence (Mogle et al., 2008). However, this finding could not be replicated (Shelton et al., 2010). Yet another process identified as crucial for the intelligence-WM overlap was relational integration, defined as the ability of building new relations between elements and thereby creating structural representations (Oberauer et al., 2008).

Thus it is not surprising that in a recent paper Engle suggested that because core programs showed no lasting far transfer effects, we should turn to lower-cost learning-strategy training programs. They concluded: “Working-memory training as currently implemented does not work. One hundred years of research on basic memory phenomena has discovered many procedures that do!” (McCabe et al., 2016, p. 190).

Types of strategy training

In a more recent review by Dunlasky et al. (2013), ten strategies were proposed: 

1. Elaborative interrogation: Generating an explanation why an explicitly stated fact or concept is true. In other words, the strategy requests the trainee to ask: Why? This prompts the learners to generate explanations. The strategy was rated as having a moderate utility.
2. Self-explanation: Explaining how new information is related to known information, or explaining steps taken during problem solving. The strategy was rated as having a moderate utility.
3. Summarization: Students are prompted to write summaries of the to-be-learned text thereby identifying the main points while excluding unimportant material. The strategy was rated as having a low utility.
4. Highlighting/underlining is probably one of the most often used strategies by students. Marked or underlined are potentially important portions of text while reading. The strategy does not tend to show beneficial effects on learning and was rated as having a low utility.
5. Keyword mnemonic is a technique developed for learning foreign language vocabulary. The student is prompted to relate the word to-be learned in foreign language with one in mother tongue that sounds similar. For instance the French word “la dent” (tooth) with the English “dentist”. The strategy was rated as having a low utility.
6. Imagery is one of the most researched strategies. Students are asked to mentally imagine the to-be learned word or content using simple and clear mental images. It is assumed that in utilizing multiple modalities for encoding, the number of routes for retrieval are enhanced (e.g., Paivio's 1986 dual-coding theory). The strategy was rated as having a low utility.
7. Rereading is yet another technique frequently used by students during self-regulated learning. The strategy was rated as having a low utility.
8. Practice testing can involve several forms such as recall of target information with flashcards, completing practice problems given at the end of chapters, or completing practice tests included in the electronic materials or via E-learning platforms. The strategy was rated as having a high utility.
9. Distributed practice means that studying is distributed over time (either within a single study session or across sessions), which is opposite to the most often used strategy by students, namely to study only immediately prior to a test and falsely believing that this cramming strategy is effective. From a theoretical viewpoint the cramming strategy may actually interfere with the learned material providing the student with a false impression of knowing the newly learned material. Another aspect coming from neuroscience is memory consolidation. Distributed practice was rated as having a high utility.
10. Interleaved practice is the opposite of blocking study, in which all of the content from one subtopic is studied before the student moves on to the next set of materials. It is a holistic approach in which students alternate their practice with different kinds of problems. The strategy was rated as having a moderate utility.

In addition to the 10 strategies proposed by Dunlasky et al. (2013), Ritchey and Nokes-Malach (2015) suggested the strategy of analogical comparison for science learning. The student is exposed to multiple examples that share the same general principles but have different superficial features. The extracted principle is then applied to novel problems. The strategy is assumed to enhance the relations within and between problem domains, thus it should influence also far transfer effects. However, the main problem is that students seldom find the common principle when surface characteristics differ, even when provided with a hint.

Another group of strategies can be categorized as mnemonics, which are either verbal or visual or the combination of both. The common underlying principle of mnemonic techniques is to impose a chunking scheme on material and by that making the material meaningful to the learner (McCabe et al., 2016). The most often used mnemonic techniques are firstletter (e.g., acronyms, acrostics), keywords (see point 5. of the list provided by Dunlasky et al., 2013), the pegword method, the method of loci, and the use of songs, rhymes, and stories. In addition, Gross et al. (2012) in their meta-analysis included:

1. Categorization which involves recognizing and grouping items by conceptual relations.
2. Associations - linking items with respect to time, environment, or specific characteristics, combining them with images, senses, words, and sentences.
3. The method of loci is a visuo-spatial mnemonic technique that pairs location (known places, such as body parts, or landmarks on the way to work) with to-be-remembered items.
4. External memory aids are environmental cues such as lists, reminder notes, calendars, and messages to one’s self.

Near and far transfer effects

Compared to the number of studies on core training effects, there is much less recent research dealing with strategy training. The beneficial effect of strategic training has been mainly investigated in older populations (Gross et al., 2012; Lustig et al., 2009; Rebok et al., 2007; Verhaeghen et al., 1992). In a review paper by Verhaeghen et al. (1992), effect sizes for mnemonics such as loci (d=.80), name-face (d=.83), peg-word (d=.62), imagery (d=.14), and organization (d=.85) were reported. Rebok et al. (2007) stressed that multiple mnemonic approaches in comparison to single ones lead to more successful transfer to everyday memory tasks. Lustig et al. (2009, p. 505) analyzing 11 mnemonic strategy training studies concluded: “…these studies often found large and long-lasting effects on the trained task, but only limited evidence for transfer.”

In a more recent meta-analysis by Gross et al. (2012), in which 35 studies were reviewed, an overall effect size of 0.31 was reported. It was further shown that training gains were not related to strategy type, average age of participants, session length, or type of control condition. With respect to far/near transfer effects the meta-analysis did not provide any information, because the different measures of pre- posttest were collapsed into a single one of memory performance. However, in explaining the rather low effect sizes obtained, the authors pointed out that probably the main reason was the heterogeneity of memory tasks used in pre- posttests in the studies.

In a recent study by Li et al. (2016), pure memory strategy training (method of loci and face-name mnemonic) was compared with a combined cognitive training approach that consisted of executive function training (updating and switching training) and memory strategy training. The results showed that pure memory strategy training significantly improved (in comparison to a no-contact control group) performance on memory tasks (mainly trained ones, but also on a composite score including untrained memory tasks). In this group, no effects were observed on executive function tasks.

The study by Carretti et al. (2007) was the only one that reported a far transfer effect of strategy training on working memory. The authors used imagery training prompting the trainees to create an image for each word and to interrelate the images they constructed. The pre- posttest tasks were an immediate list recall task asking subjects to recall 15 words presented at a rate of 2s per word (near transfer task). The second task was a categorization working memory span task, similar to complex memory span tasks such as the reading span task. Instead of sentences, lists of words (3-6 in each series) were read, asking subjects to tap their hand on the table whenever they heard an animal noun and to remember the last word in each series. At the end of the series, the participants had to recall the last word of each list in serial order (far transfer task). The authors concluded: “The results of the current paper strongly confirmed the positive effect of strategic training for enhancing the performance in a working memory task and in immediate recall both for the younger and older experimental group” (Carretti et al., 2007, p. 317). In our opinion the conclusion of a far transfer does not seem justified because the training and the criterion task (categorization working memory span task) were rather similar. It is interesting that in a follow up study the same authors used a core training approach. In this study the training involved just the categorization working memory span task used in their first study as the far transfer task (Borella et al., 2010). The core training showed near and far transfer effects on WM and intelligence.

This short review has demonstrated that there is little evidence that strategy or mnemonic training could influence intelligence. The only exception is analogical comparison strategy training. However, as pointed out by Ritchey and Nokes-Malach (2015), students seldom succeed to identify the analogy when surface problem characteristics differ.

In one of our earlier studies we tried to analyze the relationship between analogical reasoning and intelligence (Jaušovec, 1993). For that purpose 4 experiments were conducted. In the first experiment, highly intelligent and average intelligent undergraduates were presented with a story analogy before an attempt to solve the insight radiation problem. Intelligent individuals did not produce significantly more dispersion solutions (enhanced by the analogy) than average ones. In experiment 2, the same students as in the first experiment solved 6 problems while thinking aloud. Intelligent individuals used significantly more analogies (most often association analogies, but sometimes transformation analogies) than the average intelligent group of students. In the third experiment, students were trained in strategies used in problem solving (e.g., means end, analogical reasoning). They solved 3 problems before and after training, while thinking aloud. Training affected problem solving performance but not analogical reasoning. In the last experiment students were given training geared more toward improving analogical reasoning abilities. Effects of this training were less impressive than effects of training in other strategies.

In conclusion at the moment it does not seem that strategy training is the right choice in the endeavor to increase intelligence or to further elucidate its understanding.

References

   Borella, E., Carretti, B., Riboldi, F., & De Beni, R. (2010). Working memory training in older adults: Evidence of transfer and maintenance effects. Psychology and Aging, 25(4), 767–778. https://doi.org/10.1037/a0020683

   Buschkuehl, M., & Jaeggi, S. M. (2010). Improving intelligence: A literature review. Swiss Medical Weekly, 140(19-20), 266–272.

   Carretti, B., Borella, E., & De Beni, R. (2007). Does Strategic Memory Training Improve the Working Memory Performance of Younger and Older Adults? Experimental Psychology, 54(4), 311–320. https://doi.org/10.1027/1618-3169.54.4.311

   Colom, R., Abad, F. J., Quiroga, M. Á., Shih, P. C., & Flores-Mendoza, C. (2008). Working memory and intelligence are highly related constructs, but why? Intelligence, 36(6), 584–606. http://doi.org/10.1016/j.intell.2008.01.002

   Cowan, N., Elliott, E. M., Scott Saults, J., Morey, C. C., Mattox, S., Hismjatullina, A., & Conway, A. R. A. (2005). On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology, 51(1), 42–100. http://doi.org/10.1016/j.cogpsych.2004.12.001

   Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266

   Engle, R.W., Tuholski, S.W., Laughlin, J.E., & Conway, A.R.A. (1999). Working memory, shortterm memory and general fluid intelligence: A latent variable approach. Journal of ExperimentalPsychology:General,128, 309–331.

   Gross, A. L., Parisi, J. M., Spira, A. P., Kueider, A. M., Ko, J. Y., Saczynski, J. S., … Rebok, G. W. (2012). Memory training interventions for older adults: A meta-analysis. Aging & Mental Health, 16(6), 722–734. https://doi.org/10.1080/13607863.2012.667783

   Jaušovec, N. (1993). The influence of ability on analogical transfer. Ricerche di Psicologia, Vol 17(2), 37-50.

   Li, B., Zhu, X., Hou, J., Chen, T., Wang, P., & Li, J. (2016). Combined Cognitive Training vs. Memory Strategy Training in Healthy Older Adults. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.00834

   Lustig, C., Shah, P., Seidler, R., & Reuter-Lorenz, P. A. (2009). Aging, Training, and the Brain: A Review and Future Directions. Neuropsychology Review, 19(4), 504–522. https://doi.org/10.1007/s11065-009-9119-9

   McCabe, J. A., Redick, T. S., & Engle, R. W. (2016). Brain-Training Pessimism, but Applied-Memory Optimism. Psychological Science in the Public Interest, 17(3), 187–191. https://doi.org/10.1177/1529100616664716

   Mogle, J. A., Lovett, B. J., Stawski, R. S., & Sliwinski, M. J. (2008). What’s so special about working memory? An examination of the relationships among working memory, secondary memory, and fluid intelligence. Psychological Science, 19, 1071–1077.

   Oberauer, K., Süβ, H.-M., Wilhelm, O., & Wittmann, W. W. (2008). Which working memory functions predict intelligence? Intelligence, 36(6), 641–652. https://doi.org/10.1016/j.intell.2008.01.007

   Paivio, A. (1986). Mental representations: A dual coding approach. Oxford, England: Oxford University Press.

   Rebok, G. W., Carlson, M. C., & Langbaurn, J. B. S. (2007). Training and maintaining memory abilities in healthy older adults: traditional and novel approaches. Journals of Gerontology Series B-Psychological Sciences and Social Sciences, 62, 53–61.

   Richey, J. E., & Nokes-Malach, T. J. (2015). Comparing Four Instructional Techniques for Promoting Robust Knowledge. Educational Psychology Review, 27(1), 181–218. https://doi.org/10.1007/s10648-014-9268-0

   Shelton, J. T., Elliott, E. M., Matthews, R. A., Hill, B. D., & Gouvier, W. D. (2010). The relationships of working memory, secondary memory, and general fluid intelligence: Working memory is special. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(3), 813–820. http://doi.org/10.1037/a0019046

   Taya, F., Sun, Y., Babiloni, F., Thakor, N., & Bezerianos, A. (2015). Brain enhancement through cognitive training: a new insight from brain connectome. Frontiers in Systems Neuroscience, 9. http://doi.org/10.3389/fnsys.2015.00044

   Unsworth, N., and Engle, R. W. (2007). The nature of individual differences in working memory capacity: Active maintenance in primary memory and controlled search from secondary memory. Psychological Review, 114, 104−132.

   Verhaeghen P, Marcoen A, Goossens L. Improving memory performance in the aged through mnemonic training: A meta-analytic study. Psychology and Aging. 1992; 7:242–251. [PubMed: 1535198]

   von Bastian, C. C., & Oberauer, K. (2013). Effects and mechanisms of working memory training: a review. Psychological Research, 78(6), 803–820. https://doi.org/10.1007/s00426-013-0524-6