Perceptual Learning: How Experience Shapes Our Senses
Perceptual learning refers to how experience can change the way we perceive sights, sounds, smells, tastes, and touch. We often take perceptual learning for granted, but it has a profound impact on how we perceive the world.
Examples abound: music training improves our ability to discern tones; experience with food and wines can refine our pallet. With years of training radiologists learn to save lives by discerning subtle details of images that escape the notice of untrained viewers.
A characteristic of experts in any domain is that they selectively pick up relevant information, discover important patterns, and see key structure in new cases or situations. Expertise and perceptual learning are tightly interwoven. Perceptual learning refers to changes in the way we pick up information as a result of experience or practice and is achieved through exposure to varied examples. Your abilities to correctly identify new cats, new songs by your favorite band, and your best friend’s voice on the phone depends on prior perceptual learning.
Our brain is good at this kind of learning, if given the right kinds of input. It had been thought that this kind of learning could not be systematically taught and could only be slowly acquired through experience. At Insight Learning Technology, they are introducing the first learning software of its kind-products that apply perceptual (and adaptive) learning to some of the toughest subjects in many domains. Based on years of research in cognitive science, they have developed learning technology that accelerates perceptual learning.
Perceptual Learning Modules (PALMs)
PALMs targeting crucial domains in mathematics and science have been shown to have powerful, long-lasting effects on students' learning, and can be generalized for solving many kinds of problems. Their PALMs, as well as stand-alone Adaptive Learning Modules for factual or procedural learning, utilize their patented adaptive learning technology to tailor interactive learning events to each individual learner.
Here are some notable studies and publications related to perceptual learning:
- Rimoin, L., Altieri, L., Craft, N., Krasne, S., & Kellman, P. J. (2015). Training pattern recognition of skin lesion morphology, configuration, and distribution.
- Bufford, C. A., Mettler, E., Geller, E. H., & Kellman, P. J. (2014). The psychophysics of algebra expertise: Mathematics perceptual learning interventions produce durable encoding changes. In P. Bellow, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 272-277).
- Mettler, E., & Kellman, P. J. (2014). Adaptive response-time-based category sequencing in perceptual learning.
- Krasne, S., Hillman, J. D., Kellman, P. J. & Drake, T. A. (2013). Applying perceptual and adaptive learning techniques for teaching introductory histopathology.
- Kellman, P. J. (2013). Adaptive and perceptual learning technologies in medical education and training.
- Kellman, P. J., & Massey, C. M. (2013) Perceptual learning, cognition, and expertise. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 58, pp. 117-165).
- Wise, J., & Kellman, P. J. (2011).
- Thai, K., Mettler, E., & Kellman, P. J. (2011). Basic information processing effects from perceptual learning in complex, real-world domains. In L. Carlson, C. Holscher, & T Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 555-560).
- Kellman, P. J., Massey, C. M., & Son, J. Y. (2010). Perceptual learning modules in mathematics: Enhancing students' pattern recognition, structure extraction, and fluency. [Special issue on perceptual learning].
The Five Senses and Perceptual Learning Styles
Our senses allow us to perceive the world around us. This process can be thought of as perceptual learning, or the taking in of information from the environment through the body's senses. There are seven specific methods through which people learn, with each relying on one sense more than the others.
We all know that we have five senses: sight, hearing, touch, taste, and smell.
Let's imagine a teacher, Mrs. Hesher, is considering using perceptual learning methods in her history class. If Mrs. Hesher wants to accommodate all perceptual learning modes in her unit on the Roman Empire, how could she do it? Let's investigate each one.
Visual Learners
There are actually two types of perceptual learners that depend on their eyes: visual learners and print-oriented learners. About 65% of the entire population learn best through perceiving information through their eyes.
Visual learners are those that need to see something to learn it. In math, for instance, they might need to watch an example be worked for them before they're able to comprehend a math problem. Basically, visual learners need to see and observe in order to learn.
Mrs. Hesher should consider playing a movie depicting Ancient Rome and the Roman Empire, or showing lots of pictures of the Roman Empire to her class to accommodate the visual learners.
Aural Learners
The bulk of education is presented verbally with an instructor at the front of the classroom and students sitting in chairs listening to the information. Even though this scenario seems to be a standard form of educating, only about 30% of the population actually learn best through listening.
Aural learners take in information best when they hear it. To support aural learners, Mrs. Heshner could give a traditional lecture on the Roman Empire.
Kinesthetic and Haptic Learners
Once again, there are two distinct learning styles that rely on our body's sense of touch. Approximately 5% of the population can be considered to be either kinesthetic or haptic learners. While this doesn't seem like a large number, remember that in a school with 1,000 students, 50 will fit into this category. Those 50 students deserve to have their specific means of learning accommodated just like the rest of students.
Haptic Learners
Haptic learners are experiential learners; they need to experience something to really learn it. They need to be able to hold, touch, and manipulate it to incorporate the information into their long-term memory. They might need to use blocks to experience math concepts before understanding how to add and subtract, or use sand writing techniques to begin the writing process.
Interactive Learners
While taste isn't specific to our forms of learning, using the mouth by way of talking is specific to a form of perceptual learning. Interactive learners need to be able to talk about what they're learning. They need to be able to express ideas or predictions, hear and respond to other's ideas, and share feelings about what they're learning. Without this time of reflection on the concepts, interactive learners have a difficult time retaining information.
Small-group discussions are a great way for Mrs. Hesher to support her interactive learners while offering all students another avenue to investigate and explore the concepts of the Roman Empire.
Olfactory Learners
Olfactory learners are those that associate learned information with smells present at the time of learning. Research shows that the sense of smell and memory are strongly tied together, so being able to connect a smell with learned information can be a key to learning for some people.
To support olfactory learners, Mrs. Hesher might use an oil diffuser to add a specific Italian-themed scent to the room during information presentation and during any testing; the smell will trigger the information.
Specificity of Perceptual Learning
Perceptual learning can be highly specific to the trained conditions. Several studies have demonstrated this specificity across different dimensions.
Orientation Specificity
Learning in one orientation does not necessarily transfer to other orientations. For example, eleven observers practiced vernier discriminations with a stimulus slanted by 5 degrees relative to the vertical. Performance improved within 1 hour of training. However, when the stimulus was rotated by 10 degrees to a slant of 5 degrees in the opposite direction, performance dropped to pretraining levels. The first orientation was retested at the end of the experiment.
Figure 1. No transfer of improvement through learning after stimulus rotation by 10 deg. Eleven observers practiced vernier discriminations with a stimulus slanted by 5 deg relative to the vertical. Performance (i.e., the percentage of correct responses) improved within 1 hr of training. On the next day, a single block of presentations at the old orientation (point immediately left of vertical red line) proved that performance remained constant over night. When the stimulus was rotated by 10 deg to a slant of 5 deg in the opposite direction, performance dropped to pretraining levels (first point to the right of vertical line). The first orientation was retested at the end of the experiment. Means and SEs of 11 observers (after Fahle, 1998).
Visual Field Position Specificity
Learning is often specific to the visual field position trained. Eight observers practiced vernier discriminations sequentially at 8 positions at 10° distance from the fovea. At each position, their mean performance improved during the 1 hr of training at each position by, on average, 7% (with one exception: position 4). But when proceeding to the next visual field position, performance dropped by roughly the same amount. Hence, improvement did not transfer between different visual field positions.
Figure 2. Specificity of perceptual learning for the visual field position trained. Eight observers practiced vernier discriminations sequentially at 8 positions at 10° distance from the fovea. At each position, their mean performance improved during the 1 hr of training at each position by, on average, 7% (with one exception: position 4). But when proceeding to the next visual field position, performance dropped by roughly the same amount. Hence, improvement did not transfer between different visual field positions (after Fahle, Edelman & Poggio, 1995).
Eye Specificity
Perceptual learning can also be specific to the eye used during training. Half of observers practiced vernier discrimination with the left eye patched, whereas the right eye was patched for the second half of observers. After 5 days of training for 1 h daily, the contralateral eye was patched during training. Thresholds had improved significantly over the first 5 days, but increased with an overshoot when the patch was moved to the contralateral eye.
Figure 3. Specificity of perceptual learning for the eye used during training. Half of observers practiced vernier discrimination with the left eye patched, whereas the right eye was patched for the second half of observers. After 5 days of training for 1 h daily, the contralateral eye was patched during training. Thresholds had improved significantly over the first 5 days, but increased with an overshoot when the patch was moved to the contralateral eye (after block 22). Means and SEs of six observers (after Fahle in Fahle & Poggio, 2002).
Task Specificity
Failure to transfer perceptual improvement between virtually identical stimuli can occur due to task difference. Half of the observers started with a three-dot vernier discrimination task; the other half of the observers started with practicing a three-dot bisection task for about 1 hr. The next day observers exchanged tasks. There was no transfer of improvement between the tasks, though the stimuli were virtually identical.
Figure 7. Failure to transfer perceptual improvement between virtually identical stimuli, due to task difference (after Fahle & Morgan, 1996). Half of the observers started with a three-dot vernier discrimination task; the other half of the observers started with practicing a three-dot bisection task for about 1 hr. The transition between tasks is indicated by the thin vertical lines. The next day observers exchanged tasks. There was no transfer of improvement between the tasks, though the stimuli were virtually identical. The nearest data point to the left of the vertical line (21st block) was recorded on the second day.
Neuronal Basis of Visual Hyperacuity
A simple hypothesis about the neuronal basis of visual hyperacuity with vernier stimuli postulates that improvement through training may lead to narrower receptive field centers. Neurons in the visual cortex have receptive fields with antagonistic center-surround characteristics. Narrowing of the field center means that the neurons are better able to discriminate between different stimulus orientations and between offset directions.
Figure 9. A simple hypothesis about the neuronal basis of visual hyperacuity with vernier stimuli, postulating that improvement through training may be leading to narrower receptive field centers. Neurons in the visual cortex have receptive fields with antagonistic center-surround characteristics. Neurons are optimally activated by stimuli restricted to their receptive field centers without activating the surround. Narrowing of the field center means that the neurons are better able to discriminate between different stimulus orientations, and between offset directions. Most models use orientational mechanisms rotated by several 10s of degrees relative to the target orientation (off-center mechanisms (cf., left and right parts of figure; Findlay, 1973; Mussap & Levi, 1996; see also Morgan, 1986).