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The term perceptual learning refers to the process of long lasting improvement in performing perceptual (visual, auditory, tactile, olfactory or taste) tasks as a function of experience and practice. These improvements contribute to learned expertise in many domains, ranging from simple sensory discriminations (e.g., orientation discrimination in the visual modality, frequency discrimination in the auditory modality) to complex categorizations of spatial and temporal patterns relevant to real-world expertise (e.g., reading, seeing relations among chess pieces, knowing whether or not an X-ray image shows a tumor).

Perceptual learning forms important foundations of complex cognitive processes (i.e., language) and interacts with other kinds of learning to produce perceptual expertise. Underlying perceptual learning are changes in the neural circuitry. The ability for perceptual learning is retained throughout life.

Basic sensory discriminations
Laboratory studies reported many examples of dramatic improvements in sensitivities from appropriately structured perceptual learning tasks. In Vernier acuity tasks, observers judge whether one line is displaced above or above a second line. Untrained observers are already remarkably precise with Vernier acuity (people can generally detect misalignments of less than 10 arc seconds). Yet, training in this task can produce impressive improvements, as much as a 6-fold decrease in perceptual |threshold. Similar improvements have been found for motion discrimination and orientation sensitivity

In visual search tasks, people are asked to find a target object hidden among distractors or in noise. Studies of perceptual learning with visual search show that experience lead to great gains in sensitivity and speed. In one study by Karni and Sagi, the amount of time needed to reliably search for an oblique line in a field of horizontal lines decreased from about 200 ms on session 1 to about 50 ms on session 15. Visual search can also become automatic with practice, so that people can search effectively regardless of how many other items there are in the search field. .

In the natural world
Perceptual learning occurs continuously in everyday life. As our perceptual system adapts to the natural world, we become better at distinguishing between different stimuli when they come from different categories than when they come from the same category. There has also been evidence that we become less sensitive to the differences between two instances of the same category. These effects are described as categorical perception effects, and they do not transfer across domains.

Very young infants show sensitivity to differences between speech sounds that they lose by the age of 10 months, but only if the different sounds belong to the same phonetic category in their native language.

In chess, expert chess players encode larger chunks of positions and relations on the board and require fewer exposures to fully recreate a chess board. This is due not to superior visual skills, but to perceptual learning effects that experts have advanced skills extracting structural patterns specific to chess. .

In reading, extensive practice with reading leads to extraction and rapid processing of structural regularities characteristics of English spelling patterns. The word superiority effect demonstrates this -- people are faster at recognizing words than individual letters.

In speech phonemes, observers who listen to a continuum of equally spaced consonant-vowel syllables going from /be/ to /de/ are much quicker to indicate that two syllables are different when they belonged to different phonemic categories than when they were two variants of the same phoneme, even when physical differences were equated between each pair of syllables.

Other examples of perceptual learning in the natural world are the ability to distinguish between relative pitches in music, identifying tumors in x-rays , sorting day-old chicks by gender , tasting the subtle differences between beers and wines , identifying faces as belonging to different races , detecting the features that distinguish among familiar faces , discriminating between bird species such as between “great blue crown heron” and “chipping sparrow” , and attending selectively to hue, chroma and value that comprise color.

Abstract perceptual learning
Perceptual learning effects can also be seen with sensitivity to high-level, abstract relations. In music, we can easily recognize a melody as the same even if if has been transposed to a different key. In shape perception, seeing a miniature giraffe as a giraffe requires that we encode the abstract relations or proportions of the body, neck and legs that can be applied to novel instances of giraffes even when size or constituent element changes occur. The Gestalt psychologists called this phenomena “transposition”.

A Brief History
The fact that with huge amounts of practice individuals can reach impressive perceptual expertise, whether in wine tasting, fabric evaluation or musical preferences, was well acknowledged for centuries, along with the prevalent idiom that "practice makes perfect". The first documented report, in the middle of the 19th century, the earliest of tactile training aimed to decrease the minimal distance at which individuals can discriminate whether one or two points on their skin were touched. It was found that this distance (JND, Just Noticeable Difference) decreases dramatically with practice, and that this improvement is, at least partially, retained for subsequent days. Moreover, this improvement is at least partially specific to the trained skin area. A particularly dramatic improvement was found for skin positions at which initial discrimination was very crude (e.g. on the back), though training could not bring JND of initially crude areas to that of initially accurate ones (e.g. finger tips).

William James devoted a section in his Principles of Psychology (1890/1950) to "the improvement in discrimination by practice . He noted examples and emphasized the importance of perceptual learning for expertise. In 1918, Clark Hull, a noted mathematical learning theorist, performed an experiment on concept formation for his dissertation using slightly deformed Chinese characters. He trained human participants to learn 12 categories of characters using different instances that shared some invariant structural property. People learned to associate a sound as the name of each category using 6 instances of each categories, and more importantly, they were able to classify novel characters accurately . This ability to extract invariances from instances and apply them to classify new instances marks this study as a perceptual learning experiment.

It was not until 1969, however, that Eleanor Gibson published her seminal book The Principles of Perceptual learning and Development and defined the modern field of perceptual learning. She established the study of perceptual learning as an inquiry into the behavior and mechanism of perceptual change. By the mid-1970s, however, this area was in a state of dormancy due to a shift in focus to perceptual and cognitive development in infancy. Most of this research focused on characterizing basic perceptual capacities of young infants rather than on perceptual learning processes. (However, more research has directed specifically at perceptual learning in infancy).

Since the mid-1980s, there was a new wave of interest in perceptual learning due to findings of cortical plasticity at the lowest sensory levels of the sensory systems. Our increased understanding of physiology and anatomy of our cortical systems has been used to connect the behavioral improvement to the underlying cortical areas. Research in this period centered on basic sensory discriminations, where remarkable improvements were found on almost any sensory task by discrimination practice. Following training, subjects are tested with novel conditions and learning transfer is assessed. This focus differs from earlier work on perceptual learning which spans different tasks and levels.

A question still debated today is to what extent improvements from perceptual learning stems from peripheral modifications compared with improvement in higher-level readout stages. Early interpretations, such as that suggested by William James, attributed it to higher-level categorization mechanisms whereby initially blurred differences are gradually associated with distinctively different labels. The work focused on basic sensory discriminations, however, suggest that the effect of perceptual learning are those specific to changes in low-levels of the sensory nervous system (i.e., primary sensory cortices). More recently, research suggest that perceptual learning processes are multilevel and flexible. This brings back the earlier Gibsonian view that low-level learning effects are modulated by high-level factors, and suggest that improvement in information extraction may not involve only low-level sensory coding but also apprehension of relatively abstract structure and relations in time and space.

Within this past decade, Philip J. Kellman, Robert Goldstone, Alex Petrov, Barbara Ann Dosher and Zhong-Lin Lu are among the researchers who have sought for a more unified understanding of perceptual learning and worked to apply these principles to improve perceptual learning in applied domains.

Discovery and fluency effects
Perceptual learning effects can be organized into two categories: discovery and fluency effect. Discovery effects involve some change in the bases of response such as in selecting new information relevant for the task, amplifying relevant information or suppressing irrelevant information. Experts extract larger “chunks” of information and discover high-order relations and structures in their domains of expertise that are invisible to novices. Fluency effects involve changes in the ease of extraction. Experts process information with great speed and low |attentional load. Discovery and fluency effects work together so that as the discovery of structure becomes more automatic and fluent, attentional resources is conserved for discovery of new relations and for high-level thinking and problem-solving.

The role of attention
William James (Principles of Psychology, 1890) presented an extreme view asserting that "My experience is what I agree to attend to. Only those items which I notice shape my mind — without selective interest, experience is an utter chaos." His view was extreme, yet its gist was largely supported by subsequent behavioral and physiological studies. Mere exposure does not seem to suffice for acquiring expertise.

Indeed, a relevant signal in a given behavioral condition may be considered as noise in another. For example, when presented with two similar stimuli, one may prefer to increase the difference between their representations and improve the ability to discriminate between them, or to attend the similarities and improve the ability to identify both as belonging to the same category. A particular difference between them will be considered as signal in the first case and as noise in the second case. Thus, as we adapt to tasks and environments, we pay increasingly more attention to the perceptual features that are relevant and important for the task at hand, and at the same time, less attention to the irrelevant features. This mechanism is called attentional weighting.

However, recent studies suggest that perceptual learning occurs without selective attention. Studies of such task-irrelevant perceptual learning (TIPL) show that degree of TIPL is similar to that found through direct training procedures. TIPL for a stimulus depends on the relationship between that stimulus and important task events or upon stimulus reward contingencies. It has thus been suggested that learning (of task irrelevant stimuli) is contingent upon spatially diffusive learning signals. Similar effects, but upon a shorter time scale, have been found for memory processes and in some cases is called attentional boosting. Thus, when an important (alerting) event occurs, learning may also affect concurrent, non-attended and non-salient stimuli.

Enrichment versus Differentiation
In some complex perceptual tasks all humans are experts. We are all experts at scene identification, face identification and speech perception. Traditional explanations attribute these expertises to some kind of holistic, somewhat specialized, mechanisms. 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. Whether any concurrence of features can gradually be chunked with practice or chunking can only be obtained with some pre-disposition (e.g. faces, phonological categories) is an open question. Current findings suggest that such expertises are correlated with a significant increase in the cortical volume involved in these processes. Thus, we all have somewhat specialized face areas, which may reveal an innate property, but we also develop somewhat specialized areas for written words as opposed to single letters or strings of letter-like symbols. Moreover, special experts in a given domain have larger cortical areas involved in that domain. Thus, expert musicians have larger auditory areas. These observations are in line with traditional theories of enrichment proposing that improved performance involves an increase in cortical representation. For these expertises, basic categorical identification may be based on enriched and detailed representations, located to some extent at specialized brain areas. Physiological evidence suggests that training for refined discrimination along basic dimensions (e.g. frequency in the auditory modality) also increases the representation of the trained parameters, though in these cases the increase may mainly involve lower-level sensory areas.

Clearly, increased representation areas cannot suffice for learning. The rest of the brain needs to learn to interpret these signals correctly.

Reverse hierarchy theory
The Reverse Hierarchy Theory (RHT), proposed by Ahissar & Hochstein, aims to link between learning dynamics and specificity and the underlying neuronal sites. RHT proposes that naïve performance is based on responses at high-level cortical areas, where crude, categorical level representations of the environment are represented. Hence initial learning stages involve understanding global aspects of the task. 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. Hence, subsequent learning and its specificity reflect the resolution of lower levels. RHT thus proposes that initial performance is limited by the high-level resolution whereas post-training performance is limited by the resolution at low levels. Since high-level representations of different individuals differ due to their prior experience, their initial learning patterns may differ. Several imaging studies are in line with this interpretation, finding that initial performance is correlated with average (BOLD) responses at higher-level areas whereas subsequent performance is more correlated with activity at lower-level areas. RHT proposes that modifications at low levels will occur only when the backward search (from high to low levels of processing) is successful. Such success requires that the backward search will "know" which neurons in the lower level are informative. 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. Recent studies found that mixing a broad range of stimuli may also yield effective learning if these stimuli are clearly perceived as different, or, are explicitly tagged as different. These findings further support the requirement for top-down guidance in order to obtain effective learning.

Receptive Field Modification
Research on basic sensory discriminations often show that perceptual learning effects are specific to the stimuli or the task trained Many researchers take this to suggest that perceptual learning may work by modifying the receptive fields of the cells that initially encode the stimulus. For example, individual cells could adapt to become more sensitive to important features, effectively recruiting more cells for a particular purpose, making some cells more specifically tuned for the task at hand. Evidence for receptive field change has been found using single-cell recording techniques in primates in both tactile and auditory domains.

However, not all perceptual learning tasks are specific to the trained stimuli or tasks. Sireteanu and Rettenback discussed discrimination learning effects that generalize across eyes, retinal locations and tasks. Ahissar and Hochstein used visual search to show that learning to detect a single line element hidden in an array of differently-oriented line segments could generalize to positions at which the target was never preented. Furthermore, in human vision, receptive field modification in early visual areas do not show enough changes to explain perceptual learning. Training that produce large behavioral changes such as improvements in discrimination do not produce changes in receptive fields (e.g., V1 and V2 cells). In studies where changes have been found, the changes are too small to explain changes in behavior

The Current View - Selective Reweighting
An alternative and more current approach for explaining perceptual learning is selective reweighting. 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 simply discrimination tasks. The specificity effects that we observe in perceptual learning may be due to the specificity of some part of the neural system responsible for particular decisions, rather than due to the low-level perceptual units that have specificity. In their model, encodings at the lowest level do not change. The changes that occur in perceptual learning arise from changes in higher-level, abstract representations of the relevant stimuli. Because specificity can come from differentially selecting information from units with specificity, this “selective reweighting theory” allow for learning of complex, abstract representation. This corresponds to Gibson’s earlier account of perceptual learning as selection and learning of distinguishing features. Selection may be the unifying principles of perceptual learning at all levels.

Training Conditions
Ivan Pavlov discovered conditioning. He found that when a stimulus (e.g. sound) is immediately followed by food for several times, the mere presentation of this stimulus would subsequently elicit saliva in a dog's mouth. He further found that when he used a differential protocol, by consistently presenting food after one stimulus while not presenting food after another stimulus, dogs were quickly conditioned to selectively salivate in response to the rewarded one. He then asked whether this protocol could be used to increase perceptual discrimination, by differentially rewarding two very similar stimuli (e.g. tones with similar frequency). However, he found that differential conditioning was not effective.

Pavlov's studies were followed by many training studies which found that an effective way to increase perceptual resolution is to begin with a large difference along the required dimension and gradually proceed to small differences along this dimension. This easy-to-difficult transfer was termed "transfer along a continuum".

These studies showed that the dynamics of learning depend on the training protocol, rather than on the total amount of practice. Moreover, it seems that the strategy implicitly chosen for learning is highly sensitive to the choice of the first few trials during which the system tries to identify the relevant cues.

Consolidation and sleep
Several studies asked whether learning takes place during the practice sessions or in between, for example, during subsequent sleep. The dynamics of learning is hard to evaluate since the directly measured parameter is performance, which is affected by both learning, inducing improvement, and fatigue, which hampers performance. Current studies suggest that sleep contributes to improved and durable learning effects, by further strengthening connections in the absence of continued practice. . Both slow-wave and REM (rapid eye movement) stages of sleep may contribute to this process, via not-yet-understood mechanisms.

Comparison/contrast
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 irrevelant features.

Task difficulty
Learning easy examples first may lead to better transfer and better learning of more difficult cases. .

Active classification and attention
Active classification effort and attention are often necessary to produce perceptual learning effects. However, in some cases, mere exposure to certain stimulus variations can produce improved discriminations.

Feedback
In many cases, perceptual learning does not require feedback (whether or not the classification is correct). Other studies suggest that block feedback - feedback only after a block of trials - produce more learning effects than no feedback at all. .

Declarative & procedural learning
In many domains of expertise in the real world, perceptual learning interacts with other forms of learning. |Declarative knowledge tends to occur with perceptual learning. As we learn to distinguish between an array of wine flavors, we also develop a wide range of vocabularies to describe the intricacy of each flavor.

Similarly, perceptual learning also interacts flexibly with procedural knowledge. For example, the perceptual expertise of a baseball batter can detect early in its flight that the pitch was a curveball. However, the perceptual differentiation of the feel of swinging the bar in various ways may also have been involved in learning the motor commands that produced the smooth swing.

Sensory-motor learning
Lab based experiments typically dissociate between the sensory and the motor aspects of perception. Typically, participants are asked to make a response that is not the natural response they are used to make to the stimuli. Furthermore, the term attention is typically associated with a covert motor response. Under natural conditions, performance is better understood in sensory-motor loops, rather than in two separate routes, bottom-up and top-down, which most lab-based studies refer to.

Perceptual and sensory-motor learning probably share mechanisms with other forms of learning, which were traditionally viewed as cognitive. This entry emphasized the important role of selective attention. But other aspects are also shared. For example, recent findings show quickly induced, long-lasting insights in perception. This phenomenon, traditionally thought to characterize only problem-solving tasks, suggests that part of the traditional segregation between perceptual and problem-solving learning results from the use of different experimental paradigms rather than from inherently different underlying mechanisms.

Implicit learning
Perceptual learning is often said to be implicit, such that learning occurs without awareness. It is not at all clear whether perceptual learning is always implicit. Changes in sensitivity that arise are often not conscious and do not involve conscious procedures, but perceptual information can be mapped onto various responses.

In complex perceptual learning tasks (e.g., sorting of newborn chicks by gender, chess playing), experts are often unable to explain what stimulus relationships they are using in classification. However, in less complex perceptual learning tasks, people can point out what information they’re using to make classifications.

Improving perceptual skills
An important potential application of perceptual learning is the acquisition of skill for practical purposes. Towards that goal it is important to understand whether training for increased resolution in lab conditions induces a general upgrade which transfers to other environmental contexts, or results from mechanisms which are context specific. In the 1940s, Eleanor J. Gibson made the claim that lab-based training did not improve pilots' skill when landing under difficult visual conditions. Indeed, improving complex skills is typically gained by training under complex simulation conditions rather than one component at a time. Recent lab-based training protocols with complex action computer games have shown that such practice indeed modifies visual skills in a general way, which transfers to new visual contexts. 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. Whether learning of simple discriminations, which are trained in separation, transfers to new stimulus contexts (e.g. complex stimulus conditions) is still an open question.

Like experimental procedures, attempts to apply perceptual learning methods to basic and complex skills use training situations in which the learner receives may short classification trials. Tallal, Merzenich and their colleagues have successfully adapted auditory discrimination paradigms to address speech and language difficulties .They reported improvements in language learning impaired children using specially enhanced and extended speech signal. The results applied not only to auditory discrimination performance but speech and language comprehension as well.

In 2010, Achtman, Green, and Bavelier reviewed the research on video games to train visual skills. They cite a previous review by Green & Bavelier (2006) on using video games to enhance perceptual and cognitive abilities. A variety of skills were able to be modified in video game players, including "improved hand-eye coordination, increased processing in the periphery , enhanced mental rotation skills , greater divided attention abilities , and faster reaction times , to name a few".

Technologies for perceptual learning
In educational domains, recent efforts by Kellman and colleagues showed that perceptual learning can be systematically produced and accelerated using specific, computer-based technology. Their approach to perceptual learning methods take the form of perceptual learning modules (PLMs): sets of short, interactive trials that develop, in a particular domain, learners’ pattern recognition, classification abilities, and their abilities to map across multiple representations. Their recent focus on mathematics learning showed dramatic gains in students’ structure recognition in fraction learning. They also demonstrated that when students practice classifying algebraic transformations using PLMs, the results show remarkable improvements in fluency at algebra problem solving. . These results suggests that perceptual learning can offer a needed complement to conceptual and procedural instructions often seen in classroom instruction.

Similar results of PLMs have also been replicated in other domains, including anatomic recognition in medical and surgical training, reading instrumental flight displays , apprehending molecular structures in chemistry