Mechanisms Of Perceptual Learning Research Articles

A Generalized Mechanism for Perception of Pitch Patterns

Surviving in a complex and changeable environment relies on the ability to extract probable recurring patterns. Here we report a neurophysiological mechanism for rapid probabilistic learning of a new system of music. Participants listened to different combinations of tones from a previously unheard system of pitches based on the Bohlen-Pierce scale, with chord progressions that form 3:1 ratios in frequency, notably different from 2:1 frequency ratios in existing musical systems. Event-related brain potentials elicited by improbable sounds in the new music system showed emergence over a 1 h period of physiological signatures known to index sound expectation in standard Western music. These indices of expectation learning were eliminated when sound patterns were played equiprobably, and covaried with individual behavioral differences in learning. These results demonstrate that humans use a generalized probability-based perceptual learning mechanism to process novel sound patterns in music.

Functions and mechanisms of perceptual learning

Functions and mechanisms of perceptual learning

Reverse hierarchies and sensory learning

Revealing the relationships between perceptual representations in the brain and mechanisms of adult perceptual learning is of great importance, potentially leading to significantly improved training techniques both for improving skills in the general population and for ameliorating deficits in special populations. In this review, we summarize the essentials of reverse hierarchy theory for perceptual learning in the visual and auditory modalities and describe the theory's implications for designing improved training procedures, for a variety of goals and populations.

Flattening generalization gradients, context, and perceptual learning

The present research investigated the effects of physical context change and perceptual learning on generalization. In a video game, participants learned to suppress their mouse-clicking behavior in the presence of one stimulus (AX). Generalization was observed between the AX stimulus and another stimulus (BX) that was designed to be similar. When testing was conducted in a context different from that in which AX was used in training, responding to AX was attenuated, and responding to BX was enhanced. That is, the generalization gradient flattened. The latter effect was only evident in groups for which generalization had been reduced through a preexposure manipulation believed to produce perceptual learning. Experiment 2 demonstrated that the increase in generalization observed in the first experiment was due to the context change between the preexposure and test rather than to a change between the conditioning and test contexts. Implications for flattening generalization gradients and mechanisms of perceptual learning are discussed.

Defining a Link between Perceptual Learning and Attention

Experts show amazingly high perceptual skills. Experienced jewelers routinely classify diamonds that appear very similar to the uninitiated into different grades with high precision. Within a few seconds, airport baggage security officers can detect forbidden inconspicuous materials through x-ray images. Such feats are possible because the experts' “eyes” are trained through practice and experience. Long after most aspects of brain development have ceased, repeated exposures or trainings improve our perceptual/sensory abilities, and cause neural reorganizations in the brain. Such experience-induced improvement, called perceptual learning [1], and the accompanying neural changes, called neural plasticity [2–6], underlie not only our ability to master a trade but operate at a more fundamental level to help us make sense of the world. We are constantly exposed to an overwhelming amount of sensory signals, most of which are not noteworthy. To function normally in the world, we must react quickly and precisely to the important signals, while ignoring or discounting the less important, just as organisms must do in the natural environment to survive. By directing attention only to important signals or being repeatedly exposed to signals in an important context, our sensory systems learn to process important signals more efficiently than the less-important signals. Reflecting this fundamental role of perceptual learning, studies have been conducted to examine mechanisms of perceptual learning and neural plasticity with various kinds of tasks and stimuli by using behavioral measurements [2,3] and neurophysiological [7,8] and brain imaging techniques [9,10]. Perceptual learning and neural plasticity have also been studied in all the sensory modalities including vision, hearing [11], and touch perception [12].

Social Psychological Face Perception: Why Appearance Matters.

We form first impressions from faces despite warnings not to do so. Moreover, there is considerable agreement in our impressions, which carry significant social outcomes. Appearance matters because some facial qualities are so useful in guiding adaptive behavior that even a trace of those qualities can create an impression. Specifically, the qualities revealed by facial cues that characterize low fitness, babies, emotion, and identity are overgeneralized to people whose facial appearance resembles the unfit (anomalous face overgeneralization), babies (babyface overgeneralization), a particular emotion (emotion face overgeneralization), or a particular identity (familiar face overgeneralization). We review studies that support the overgeneralization hypotheses and recommend research that incorporates additional tenets of the ecological theory from which these hypotheses are derived: the contribution of dynamic and multi-modal stimulus information to face perception; bidirectional relationships between behavior and face perception; perceptual learning mechanisms and social goals that sensitize perceivers to particular information in faces.

Benefits of efficient consolidation: Short training enables long-term resistance to perceptual adaptation induced by intensive testing

Intensive training or testing reduces performance on perceptual and sensorimotor tasks. Here we show, for the visual texture discrimination task, that such adaptation-related performance decrements are practically eliminated following practice with a small number of trials and sleep. Thus, short training produces consolidation of an effective memory within the visual neural network, resistant to the performance decrements that are usually induced by intensive testing. We suggest a link between perceptual adaptation and learning: resistance is achieved by sleep dependent consolidation of distributed changes in network connectivity before saturated due to over-training. This link between memory generation, perceptual adaptation and memory consolidation may have an essential role in the underlying mechanisms of perceptual and motor learning. Therefore, intensive training yielding performance decrements in other modalities, such as the sensorimotor system, may be viewed in the context of the mechanisms suggested here.

Different Dynamics of Performance and Brain Activation in the Time Course of Perceptual Learning

Perceptual learning is regarded as a manifestation of experience-dependent plasticity in the sensory systems, yet the underlying neural mechanisms remain unclear. We measured the dynamics of performance on a visual task and brain activation in the human primary visual cortex (V1) across the time course of perceptual learning. Within the first few weeks of training, brain activation in a V1 subregion corresponding to the trained visual field quadrant and task performance both increased. However, while performance levels then saturated and were maintained at a constant level, brain activation in the corresponding areas decreased to the level observed before training. These findings indicate that there are distinct temporal phases in the time course of perceptual learning, related to differential dynamics of BOLD activity in visual cortex.

Learning to Link Visual Contours

In complex visual scenes, linking related contour elements is important for object recognition. This process, thought to be stimulus driven and hard wired, has substrates in primary visual cortex (V1). Here, however, we find contour integration in V1 to depend strongly on perceptual learning and top-down influences that are specific to contour detection. In naive monkeys, the information about contours embedded in complex backgrounds is absent in V1 neuronal responses and is independent of the locus of spatial attention. Training animals to find embedded contours induces strong contour-related responses specific to the trained retinotopic region. These responses are most robust when animals perform the contour detection task but disappear under anesthesia. Our findings suggest that top-down influences dynamically adapt neural circuits according to specific perceptual tasks. This may serve as a general neuronal mechanism of perceptual learning and reflect top-down mediated changes in cortical states.

Reading normal and degraded words: Contribution of the dorsal and ventral visual pathways

Fast, parallel word recognition, in expert readers, relies on sectors of the left ventral occipito-temporal pathway collectively known as the visual word form area. This expertise is thought to arise from perceptual learning mechanisms that extract informative features from the input strings. The perceptual expertise hypothesis leads to two predictions: (1) parallel word recognition, based on the ventral visual system, should be limited to words displayed in a familiar format (foveal horizontal words with normally spaced letters); (2) words displayed in formats outside this field of expertise should be read serially, under supervision of dorsal parietal attention systems. We presented adult readers with words that were progressively degraded in three different ways (word rotation, letter spacing, and displacement to the visual periphery). Behaviorally, we identified degradation thresholds above which reading difficulty increased non-linearly, with the concomitant emergence of a word length effect on reading latencies reflecting serial reading strategies. fMRI activations were correlated with reading difficulty in bilateral occipito-temporal and parietal regions, reflecting the strategies required to identify degraded words. A core region of the intraparietal cortex was engaged in all modes of degradation. Furthermore, in the ventral pathway, word degradation led to an amplification of activation in the posterior visual word form area, at a level thought to encode single letters. We also found an effect of word length restricted to highly degraded words in bilateral occipitoparietal regions. Those results clarify when and how the ventral parallel visual word form system needs to be supplemented by the deployment of dorsal serial reading strategies.

Effect of spatial distance to the task stimulus on task-irrelevant perceptual learning of static Gabors

It was previously shown that sensitivity improvements to a task-irrelevant motion direction can be obtained when it is presented in concurrence with observers' performance of an attended task (A. R. Seitz & T. Watanabe, 2003; T. Watanabe, J. E. Náñez, & Y. Sasaki, 2001). To test whether this task-irrelevant perceptual learning (TIPL) is specific for motion and to clarify the relationships between the observer's task and the resultant TIPL, we investigated the spatial profile of the sensitivity enhancement for a static task-irrelevant feature. During the training period, participants performed an attentionally demanding character identification task at one location, whereas subthreshold, static, Gabor patches, which were masked in noise, were presented at different locations in the visual field. Subjects' sensitivity to the Gabors was compared between the pre- and posttraining tests. First, we found that TIPL extends to learning of static visual stimuli. Thus, TIPL is not a specialized process to motion stimuli. As to the effect of spatial location, the largest improvement was found for the Gabors presented in closest proximity to the task. These data indicate that the learning of the task-irrelevant visual feature significantly depends on the task location, with a gradual attenuation according to the spatial distance between them. These findings give further insights into the mechanism of perceptual learning.

Mechanisms of perceptual learning of depth discrimination in random dot stereograms

Perceptual learning is a training induced improvement in performance. Mechanisms underlying the perceptual learning of depth discrimination in dynamic random dot stereograms were examined by assessing stereothresholds as a function of decorrelation. The inflection point of the decorrelation function was defined as the level of decorrelation corresponding to 1.4 times the threshold when decorrelation is 0%. In general, stereothresholds increased with increasing decorrelation. Following training, stereothresholds and standard errors of measurement decreased systematically for all tested decorrelation values. Post training decorrelation functions were reduced by a multiplicative constant (approximately 5), exhibiting changes in stereothresholds without changes in the inflection points. Disparity energy model simulations indicate that a post-training reduction in neuronal noise can sufficiently account for the perceptual learning effects. In two subjects, learning effects were retained over a period of six months, which may have application for training stereo deficient subjects.

An event-related potential study on perceptual learning in grating orientation discrimination

This study aims to investigate the neural mechanism of visual perceptual learning in grating orientation discrimination. We recorded event-related potentials of human adults when they were trained with a grating orientation discrimination task. Although psychophysical thresholds decreased significantly across training sessions, event-related potentials showed larger P2 and P3 amplitudes and smaller N1 amplitudes over the parietal/occipital areas with more practice. In line with the psychophysical thresholds, the training effect on the P2 and P3 were specific to stimulus orientation. The N1 effect, however, was generalized over differently oriented gratings. Our findings show that several stages of visual processing are involved in perceptual learning and provide an illustration of the temporal relationship between specific and generalized perceptual learning in human adults.

Parameter learning but not structure learning: A Bayesian network model of constraints on early perceptual learning

Visual scientists have shown that people are capable of perceptual learning in a large variety of circumstances. Are there constraints on such learning? We propose a new constraint on early perceptual learning, namely, that people are capable of parameter learning-they can modify their knowledge of the prior probabilities of scene variables or of the statistical relationships among scene and perceptual variables that are already considered to be potentially dependent-but they are not capable of structure learning-they cannot learn new relationships among variables that are not considered to be potentially dependent, even when placed in novel environments in which these variables are strongly related. These ideas are formalized using the notation of Bayesian networks. We report the results of five experiments that evaluate whether subjects can demonstrate cue acquisition, which means that they can learn that a sensory signal is a cue to a perceptual judgment. In Experiment 1, subjects were placed in a novel environment that resembled natural environments in the sense that it contained systematic relationships among scene and perceptual variables that which are normally dependent. In this case, cue acquisition requires parameter learning and, as predicted, subjects succeeded in learning a new cue. In Experiments 2-5, subjects were placed in novel environments that did not resemble natural environments-they contained systematic relationships among scene and perceptual variables that are not normally dependent. Cue acquisition requires structure learning in these cases. Consistent with our hypothesis, subjects failed to learn new cues in Experiments 2-5. Overall, the results suggest that the mechanisms of early perceptual learning are biased such that people can only learn new contingencies between scene and sensory variables that are considered to be potentially dependent.

Reverse feedback induces position and orientation specific changes

To investigate the mechanisms of perceptual learning, we recently introduced a paradigm in which incorrect, reverse feedback followed after some but not all vernier presentations. This feedback paradigm exerted a strong effect on performance that seemed to bias decisions rather than to yield perceptual learning. Here, we show that observers can develop independent decision biases for different stimulus orientations as well as for different visual field positions. Our results demonstrate that the effects of incorrect, reverse feedback are surprisingly specific.

Perceptual learning of motion direction discrimination in fovea: Separable mechanisms

Dosher and Lu (1998) [Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proceedings of the National Academy of Sciences of the United States of America, 95 (23), 13988-13993.] proposed three mechanisms of perceptual learning: stimulus enhancement, external noise exclusion, and multiplicative noise reduction. In this study, we used pre-training as a manipulation to evaluate the separability of these mechanisms as a key test of the theoretical framework. Observers were trained in identifying the motion direction of a moving sine-wave grating in fovea with varying amount of superimposed external noise across trials, after receiving no pre-training, pre-training in high external noise, or pre-training in zero external noise in the same task. We found: (1) Without pre-training, perceptual learning significantly reduced contrast thresholds by about the same amount across all the external noise levels. (2) Both types of pre-training significantly reduced contrast thresholds in the corresponding conditions. (3) Pre-training in high external noise greatly reduced subsequent learning in high external noise, accounting for 64.6% of the total (pre-training + subsequent) improvements in that condition. On the other hand, the amount of subsequent learning in low external noise conditions was essentially the same as the total (pre-training + subsequent) amount of improvements in high external noise, suggesting that pre-training in high external noise had mostly only improved performance in noisy displays. (4) Pre-training in zero external noise practically eliminated or left very little additional learning in all the external noise conditions. We concluded that the two mechanisms of perceptual learning, stimulus enhancement, and external noise exclusion, can be trained independently in motion direction discrimination in fovea; training in low noise suffices to improve observer performance over all the external noise conditions.

Perceptual learning and abstraction in machine learning: an application to autonomous robotics

This paper deals with the possible benefits of perceptual learning in artificial intelligence. On the one hand, perceptual learning is more and more studied in neurobiology and is now considered as an essential part of any living system. In fact, perceptual learning and cognitive learning are both necessary for learning and often depend on each other. On the other hand, many works in machine learning are concerned with in order to reduce the amount of complexity related to some learning tasks. In the abstraction framework, perceptual learning can be seen as a specific process that learns how to transform the data before the traditional learning task itself takes place. In this paper, we argue that biologically inspired perceptual learning mechanisms could be used to build efficient low-level abstraction operators that deal with real-world data. To illustrate this, we present an application where perceptual-learning-inspired metaoperators are used to perform an abstraction on an autonomous robot visual perception. The goal of this work is to enable the robot to learn how to identify objects it encounters in its environment.

Level and mechanisms of perceptual learning: Learning first-order luminance and second-order texture objects

Perceptual learning is an improvement in perceptual task performance reflecting plasticity in the perceptual system. Practice effects were studied in two object orientation tasks: a first order, luminance object task and a second-order, texture object task. Perceptual learning was small or absent in the first-order task, but consistently occurred for the second-order (texture) task, where it was limited to improvements in low external noise conditions, or stimulus enhancement [Dosher, B., & Lu, Z. -L. (1998). Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. Proceedings of the National Academy of Sciences of the United States of America, 95 (23) 13988–13993; Dosher, B., & Lu, Z. -L. (1999). Mechanisms of perceptual learning. Vision Research, 39 (19) 3197–3221], analogous to attention effects in first- and second-order motion processing [Lu, Z. -L., Liu, C. Q., & Dosher, B. (2000). Attention mechanisms for multi-location first- and second-order motion perception. Vision Research, 40 (2) 173–186]. Perceptual learning affected the later, post-rectification, stages of perceptual analysis, possibly localized at V2 or above. It serves to amplify the stimulus relative to limiting internal noise for intrinsically noisy representations of second-order stimuli.

Scene Perception, Gaze Behavior, and Perceptual Learning in Virtual Environments

More and more immersive environments are developed to provide support for learning or training purposes. Ecological validity of such environments is usually based on learning performance comparisons between virtual environments and their genuine counterparts. Little is known about learning processes occurring in immersive environments. A new technique is proposed for testing perceptual learning during virtual immersion. This methodology relies upon eye-tracking technologies to analyze gaze behavior recorded in relation to virtual objects' features and tasks' requirements. It is proposed that perceptual learning mechanisms engaged could be detected through eye movements. In this study, nine subjects performed perceptual learning tasks in virtual immersion. Results obtained indicated that perceptual learning influences gaze behavior dynamics. More precisely, analysis revealed that fixation number and variability in fixation duration varied with perceptual learning level. Such findings could contribute in shedding light on learning mechanisms as well as providing additional support for validating virtual learning environments.

Perceptual learning of Gabor orientation identification in visual periphery: Complete inter-ocular transfer of learning mechanisms

We combined the external noise paradigm, the Perceptual Template Model approach, and transfer tests to investigate the mechanisms and eye-specificity of perceptual learning of Gabor orientation in visual periphery. Coupled with a fixation task, discriminating a 5 from an S in a rapid small character string at fixation, contrast thresholds were estimated for each of eight external noise levels at two performance criteria using 3/1 and 2/1 staircases. Perceptual learning in one eye was measured over 10 practice sessions, followed by five sessions of practice in the new eye to assess transfer. We found that monocular learning improved performance (reduced contrast thresholds) with virtually equal magnitude across a wide range of external noise levels with no significant change in central task performance. Based on measurements of learning effects at two performance criterion levels, we identified a mixture of stimulus enhancement and external noise exclusion as the mechanism of perceptual learning underlying the observed improvements. Perceptual learning in the trained eye generalized completely to the untrained eye. We related the transfer patterns to known physiology and psychophysics on orientation direction coding.