Perceptual Learning


Perceptual learning is the process of learning improved skills of perception from simple sensory discriminations (e.g., distinguishing two musical tones from one another) 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).

In visual Vernier acuity tasks, observers judge whether one line is displaced above or below a second line. Untrained observers are often already very good with this task, but after training, observers’ threshold has been shown to improve as much as six fold. Similar improvements have been found for visual motion discrimination and orientation sensitivity.

In visual search tasks, observers are asked to find a target object hidden among distractors or in noise. With appropriate practice, visual search can become automatic and very efficient, such that observers do not need more time to search when there are more items present on the search field. Tactile perceptual learning has been demonstrated on spatial acuity tasks such as tactile grating orientation discrimination, and on vibrotactile perceptual tasks such as frequency discrimination; tactile learning on these tasks has been found to transfer from trained to untrained fingers.

Perceptual learning is prevalent and occurs continuously in everyday life. As our perceptual system adapts to the natural world, we become better at discriminating between different stimuli when they belong to different categories than when they belong to the same category. We also tend to become less sensitive to the differences between two instances of the same category. These effects are described as the result of categorical perception. Categorical perception effects do not transfer across domains. Other examples of perceptual learning in the natural world include the ability to distinguish between relative pitches in music, identify tumors in x-rays, sort day-old chicks by gender, taste the subtle differences between beers or wines, identify faces as belonging to different races, detect the features that distinguish familiar faces, discriminate between two bird species, and attend selectively to the hue, saturation and brightness values that comprise a color definition.

Infants, when different sounds belong to the same phonetic category in their native language, tend to lose sensitivity to differences between speech sounds by ten months of age. They learn to pay attention to salient differences between native phonetic categories, and ignore the less language-relevant ones. 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 not due to their possessing superior visual skill, but rather to their advanced extraction of structural patterns specific to chess. Extensive practice reading in English leads to extraction and rapid processing of the structural regularities of English spelling patterns. The ‘word superiority effect’ demonstrates this—people are often much faster at recognizing words than individual letters.

The fact that with huge amounts of practice, individuals can reach impressive perceptual expertise, whether in wine tasting, fabric evaluation or musical preference, has been well acknowledged for centuries, along with the prevalent idiom that ‘practice makes perfect.’ 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. 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 the JND of initially crude areas down to that of initially accurate ones (e.g. finger tips).

William James, the father of American Psychology, devoted a section in his ‘Principles of Psychology’ (1890) to ‘the improvement in discrimination by practice.’ In 1918, Clark L. Hull, a noted learning theorist, trained human participants to categorize deformed Chinese characters. For each category, he used six instances that shared some invariant structural property. People learned to associate a sound as the name of each category, 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 marked this study as a perceptual learning experiment. It was not until 1969, however, that psychologist 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. Much of the scientific community tended to underestimate the impact of learning compared with innate mechanisms. Thus, most of this research focused on characterizing basic perceptual capacities of young infants rather than on perceptual learning processes.

Since the mid-1980s, there has been a new wave of interest in perceptual learning due to findings of cortical plasticity (functional flexibility of brain areas) at the lowest sensory levels of sensory systems. Our increased understanding of the physiology and anatomy of our cortical systems has been used to connect the behavioral improvement to the underlying cortical areas. This trend began with earlier findings that perceptual representations at sensory areas of the cortex are substantially modified during a short (‘critical’) period immediately following birth. Though neuroplasticity is diminished, it is not eliminated when the critical period ends. Thus, when the external pattern of stimulation is substantially modified, neuronal representations in lower-level (e.g. primary) sensory areas are also modified. Research in this period centered on basic sensory discriminations, where remarkable improvements were found on almost any sensory task through discrimination practice. Following training, subjects were tested with novel conditions and learning transfer was assessed. This work departed from earlier work on perceptual learning, which spanned different tasks and levels.

Perceptual learning effects can be organized into two broad categories: ‘discovery’ effects and ‘fluency’ effects. The former 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. The latter involve changes in the ease of extraction. Not only can experts process high-order information, they do so with great speed and low attentional load. Discovery and fluency effects work together so that as the discovery structures becomes more automatic, attentional resources are conserved for discovery of new relations and for high-level thinking and problem-solving.

William James examined the role of attention in perceptual attention, ultimately concluding: ‘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 noise in another. For example, when presented with two similar stimuli, one might endeavor to study the differences between their representations in order to improve one’s ability to discriminate between them, or one may instead concentrate on the similarities to improve one’s ability to identify both as belonging to the same category. A specific difference between them could be considered ‘signal’ in the first case and ‘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 the 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.

The time course of perceptual learning varies from one participant to another. Perceptual learning occurs not only within the first training session but also between sessions. Fast learning (i.e., within-first-session learning) and slow learning (i.e., between-session learning) involves different changes in the human adult brain. While the fast learning effects can only be retained for a short term of several days, the slow learning effects can be preserved for a long term over several months.

In some complex perceptual tasks, all humans are experts. Humans are very sophisticated, but not infallible at scene identification, face identification and speech perception. Traditional explanations attribute this expertise to some holistic, somewhat specialized, mechanisms, assuming that 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 expertise is correlated with a significant increase in the cortical volume involved in these processes. Thus, all people have somewhat specialized face areas, which may reveal an innate property, but can 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 this expertise, basic categorical identification may be based on enriched and detailed representations, located to some extent in 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.

Ivan Pavlov discovered conditioning. He found that when a stimulus (e.g. sound) is immediately followed by food 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.

In many domains of expertise in the real world, perceptual learning interacts with other forms of learning such as ‘declarative knowledge’ (knowledge expressed in declarative sentences or indicative propositions). 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 (knowledge exercised in the performance of some task; i.e. ‘know-how’). 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. However, the perceptual differentiation of the feel of swinging the bat in various ways may also have been involved in learning the motor commands that produce the required swing.

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, playing chess), 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.

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. A variety of skills were upgraded 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.’ 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, other attempts to apply perceptual learning methods to basic and complex skills use training situations in which the learner receives many short classification trials. One study adapted auditory discrimination paradigms to address speech and language difficulties. They reported improvements in language learning-impaired children using specially enhanced and extended speech signals. The results applied not only to auditory discrimination performance but speech and language comprehension as well.

In educational domains, recent efforts by UCLA cognitive scientist Philip 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. As a result of practice with mapping across transformations (e.g., algebra, fractions) and across multiple representations (e.g., graphs, equations, and word problems), students show dramatic gains in their structure recognition in fraction learning and algebra. 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 in the classroom. Similar results have also been replicated in other domains with PLMs, including anatomic recognition in medical and surgical training, reading instrumental flight displays, and apprehending molecular structures in chemistry.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.