3. FACTORS AFFECTING COGNITIVE LOAD
Although most research on Cognitive Load is intended to improve teaching and learning—specifically, the effective transmission of information—for me, understanding CLT allows me to do the opposite to deliberately break down information processing in order to collapse working memory (WM) and thereby sink wandering thoughts 😊. So, beyond just understanding the components of WM load, I will also list specific factors that influence cognitive load. I continue to base this on the work of Sweller et al. (2011). The overarching concept in cognitive load is Element Interactivity—the degree to which informational units (chunks, bundles, arrays, layers)[1] interact with each other.
A. Under Intrinsic Load (IL):
(1) Task Difficulty—This depends on:
(1.1) Number of information units or the length of the sequence to be processed. (e.g., 2 + 2 is easy, but 2 + 3 + 14 + 29 is harder.)
(1.2) Complexity of the problem. (e.g., 12 + 35 + 24 is simpler than 12 × 35 × 24. The complexity increases if we reverse the numbers or change the combinations—e.g., 21 × 53 × 42 or 15 × 34 × 22, which involve more complex pairings.)
(1.3) Number of steps needed for computation. (e.g., 3! is simpler than (3! + 5) ÷ 7 – 2.)
(1.4) Interdependence between steps (e.g., 23 + 45 involves at least three substeps: (i) 3 + 5 = 8, (ii) 20 + 40 = 60, (iii) 60 + 8 = 68. Step 3 depends on Steps 1 and 2. For a more complex example like 1463 + 7624, there are at least five steps, and the final one depends on the previous four. Or take 24 + 56 + 76 + 31—the number of backtracking steps depends on our method of addition. If we let k represent the number of steps to backtrack for information, the larger k is, the more blurred the memory of those elements becomes, and the greater the chance of forgetting. This is the Progressive Memory Rule[2] (how memory naturally fades). The harder we try to recall (Regressive Memory), the heavier the load and the higher the pressure.
(1.5) Specificity of the problem’s requirements. For example, with the numbers 25 and 76, the task might simply be to add them (easy), or to manipulate them to arrive at a total of 108—e.g., 25 + 76 = 101, then 101 + 2 + 5 = 108. Or the task could be goal-free (no specific instruction—free to add, subtract, multiply, etc.).
(2) Depth of understanding across steps. For example, if we're just doing 2 × 3 = 6, the load is light. But if we want to understand why it equals 6—like 2 × 3 = 2 + 2 + 2 = 3 + 3 = 6—the WM load increases. The more we intentionally try to break down each step and understand it deeply, the higher the intrinsic load and the greater the pressure on WM.
B. Under Extraneous Load (EL):
(1) Time pressure. If a problem must be solved within a fixed time, that requirement can place enormous stress on WM. Even something as simple as giving ourselves only a few seconds to look at and memorize a long string of numbers before closing our eyes and recalling it can create strong load.
(2) How the information is presented. Research often explores modality—i.e., auditory and/or visual channels (Dual Modality). For example:
(2.1) Font changes. Instead of “2 + 4”, we could present it as “2 + IV,” “II + 4,” or “2 + 4” in different styles.
(2.2) Type changes. Make the task harder by replacing "2 + 4" with "II + [square image]", or "2 + d (if a =1, b = 2, etc.), or "2 + (. . . . _)" (Morse code for 4). Since the square, the letter d, and the Morse code are not numerical in themselves, WM has to split attention: one part processes the recognizable number (2), while the other converts the non-numerical input into a number before doing the math.[3]
(2.3) Format/style changes. We could show the number “2” in bold, but make “4” blink, rotate, or fade in and out. Or flip the screen orientation. These distortions make mental visualization and recall harder during practice.
(2.4) Audio-visual combinations. In my own math training, I haven’t felt the need to use sound, and I’ve never tried this. Still, for example, we could hear the number 2 spoken and try to remember its tone or rhythm while visually processing the number 4. Then, during practice, we’d draw the numbers while also recalling the sound.[4]
C. Under Germane Load (GL):
(1) Redundant information. In teaching, redundancy should be removed. But in meditation, memorizing and visualizing extra information increases germane load. For instance, if we must remember numbers of different colors (e.g., 2 in yellow + 4 in red), then trying to retain both the meaning of the number (2 = two units) and its color creates more mental fatigue. This doesn’t even include cases where the shapes of numbers differ, adding yet another layer to recall.
(2) Familiarity vs. novelty of the task or information: After each problem we solve or new thing we learn, the information (called a schema[5]) is stored in long-term memory (subconscious or unconscious), forming our knowledge and experience. These schemas can then be instantly recalled into short-term memory without consuming much load. So, if we’re just solving familiar problems with familiar strategies[6] we won’t sink the conscious mind (where those restless monkeys dance around). We must constantly vary the problems or make them harder. With all the cognitive load factors above, I believe we have many tools to keep changing up my meditation “math/problem” exercises.
Other human factors—such as age, health, intellectual ability, vocal noise, and sleep[7] —can also affect WM’s performance (and of course, its breaking point). So, what is the -maximum load WM can handle before consciousness collapses? Research varies by author[8], but the consensus range is surprisingly low: only 3–4 to 7–9 information chunks.
(End of Part 4/8)
Notes:
[1] Collectively referred to as ‘chunks of information’. Xem Miller, 1956. The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychology Review 63(2), 81–97. https://en.wikipedia.org/wiki/George_Armitage_Miller
[2] See Bergson, 1911. Matter and memory. London. https://plato.stanford.edu/entries/bergson/
See Linde-Domingo, Treder, Kerren, and Wimber, 2019. Evidence that neural information flow is reversed between object perception and object reconstruction from memory. Nature Communications, 1–10. In this study, the authors investigated how the brain reconstructs images from memory. When perceiving an image, the brain first focuses on details (e.g., color, lines) before shifting to more general categories (e.g., object groups). But during recall, this process is reversed: general groupings are reconstructed first, followed by finer details.
[3] See Shen, Popov, Delahay, and Reder, 2018. Item strength affects working memory capacity. Memory & Cognition 46(2), 204–215.
[4] Sound may be useful in techniques that combine visualization with internal recitation (such as mantras, sacred phrases, or object names).
[5] https://en.wikipedia.org/wiki/Schema_(psychology); https://www.verywellmind.com/what-is-a-schema-2795873?print
[6] See Norris, Hall, and Gathercole, 2019. Can short-term memory be trained? Memory & Cognition 47, 1012–1023.
[7] See Xie, Berry, Lustig, Deldin, and Zhang, 2019. Poor sleep quality and compromised visual working memory capacity. Journal of the International Neuropsychological Society 25, 583–594. Boyer, Paubel, Ruiz, Yagoubi, and Daurat, 2018. Human voice as a measure of mental load level. Journal of Speech, Language, and Hearing Research 61, 2722–2734.
[8] See Miller, 1956. The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychology Review 63(2), 81–97. Cowan, 2001. The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences 24, 87–185.