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.
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 (www.ultrasonic-ringtones.com) 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. http://doi.org/10.1007/s10648-013-9246-y
Gray, L. (n.d.). Chapter 12: Auditory system: Structure and function. Neuroscience online: An electronic textbook for the neurosciences. Retrieved from http://neuroscience.uth.tmc.edu/s2/chapter12.html
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539. http://doi.org/10.1007/s10648-007-9054-3
Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390, 279–281. http://doi.org/10.1038/36846
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. http://doi.org/10.1016/j.tics.2013.06.006
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52. http://doi.org/10.1207/S15326985EP3801_6
Noise Help (n.d.). The teen buzz “ultrasonic” ringtones. Retrieved from http://www.noisehelp.com/ultrasonic-ringtones.html
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4. http://doi.org/10.1207/S15326985EP3801_1
Schneegans, S., & Bays, P. M. (2016). No fixed item limit in visuospatial working memory. Cortex, 83, 181–193. http://doi.org/10.1016/j.cortex.2016.07.021
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. http://doi.org/10.3758/s13428-017-0886-6