[3] Laboratory studies reported many examples of dramatic improvements in sensitivities from appropriately structured perceptual learning tasks.
Studies of perceptual learning with visual search show that experience leads to great gains in sensitivity and speed.
[11][12][13][14] Practice with Braille reading and daily reliance on the sense of touch may underlie the enhancement in tactile spatial acuity of blind compared to sighted individuals.
[23] Other examples of perceptual learning in the natural world include the ability to distinguish between relative pitches in music,[24] identify tumors in x-rays,[25] sort day-old chicks by gender,[26] taste the subtle differences between beers or wines,[27] identify faces as belonging to different races,[28] detect the features that distinguish familiar faces,[29] discriminate between two bird species ("great blue crown heron" and "chipping sparrow"),[30] and attend selectively to the hue, saturation and brightness values that comprise a color definition.
[31] The prevalent idiom that “practice makes perfect” captures the essence of the ability to reach impressive perceptual expertise.
The first documented report, dating to the mid-19th century, is the earliest example of tactile training aimed at decreasing the minimal distance at which individuals can discriminate whether one or two points on their skin have been touched.
It was found that this distance (JND, Just Noticeable Difference) decreases dramatically with practice, and that this improvement is at least partially retained on subsequent days.
This trend began with earlier findings of Hubel and Wiesel that perceptual representations at sensory areas of the cortex are substantially modified during a short ("critical") period immediately following birth.
Experts extract larger "chunks" of information and discover high-order relations and structures in their domains of expertise that are invisible to novices.
[46] Research on basic sensory discriminations often show that perceptual learning effects are specific to the trained task or stimulus.
In human vision, not enough receptive field modification has been found in early visual areas to explain perceptual learning.
[53] The Reverse Hierarchy Theory (RHT), proposed by Ahissar & Hochstein, aims to link between learning dynamics and specificity and the underlying neuronal sites.
[54] RHT proposes that naïve performance is based on responses at high-level cortical areas, where crude, categorical level representations of the environment are represented.
Subsequent practice may yield better perceptual resolution as a consequence of accessing lower-level information via the feedback connections going from high to low levels.
Accessing the relevant low-level representations requires a backward search during which informative input populations of neurons in the low level are allocated.
This "knowledge" is gained by training repeatedly on a limited set of stimuli, such that the same lower-level neuronal populations are informative during several trials.
Perhaps such quick identifications are achieved by more specific and complex perceptual detectors which gradually "chunk" (i.e., unitize) features that tend to concur, making it easier to pull a whole set of information.
For this expertise, basic categorical identification may be based on enriched and detailed representations, located to some extent in specialized brain areas.
[56] In 2005, Petrov, Dosher and Lu pointed out that perceptual learning may be explained in terms of the selection of which analyzers best perform the classification, even in simple discrimination tasks.
Current studies suggest that sleep contributes to improved and durable learning effects, by further strengthening connections in the absence of continued practice.
Practice with comparison and contrast of instances that belong to the same or different categories allow for the pick-up of the distinguishing features—features that are important for the classification task—and the filter of the irrelevant features.
[61] By recording ERPs from human adults, Ding and Colleagues investigated the influence of task difficulty on the brain mechanisms of visual perceptual learning.
For instance, in tactile spatial acuity tasks, experiments suggest that the extent of learning is limited by fingertip surface area, which may constrain the underlying density of mechanoreceptors.
For example, the perceptual expertise of a baseball player at bat can detect early in the ball's flight whether the pitcher threw a curveball.
[1] In complex perceptual learning tasks (e.g., sorting of newborn chicks by sex, playing chess), experts are often unable to explain what stimulus relationships they are using in classification.
In people who have striatal damage, the need to ignore irrelevant information is more predictive of a rule-based category learning deficit.
An important characteristic is the functional increase in the size of the effective visual field (within which viewers can identify objects), which is trained in action games and transfers to new settings.
Like experimental procedures, other attempts to apply perceptual learning methods to basic and complex skills use training situations in which the learner receives many short classification trials.
In educational domains, recent efforts by Philip Kellman and colleagues showed that perceptual learning can be systematically produced and accelerated using specific, computer-based technology.
Similar results have also been replicated in other domains with PLMs, including anatomic recognition in medical and surgical training,[76] reading instrumental flight displays,[77] and apprehending molecular structures in chemistry.