Perceptual learning for a pattern discrimination task

Learning spatial frequency identification through reweighted decoding

Perceptual learning, the improvement of perceptual judgments with practice, occurs in many visual tasks. There are, however, relatively fewer studies examining perceptual learning in spatial frequency judgments. In addition, perceptual learning has generally been studied in two-alternative tasks, occasionally in n-alternative tasks, and infrequently in identification. Recently, perceptual learning was found in an orientation identification task (eight-alternatives) and was well accounted for by a new identification integrated reweighting theory (I-IRT) (Liu et al., submitted). Here, we examined perceptual learning in a similar eight-alternative spatial frequency absolute identification task in two different training protocols, finding learning in the majority but not all observers. We fit the I-IRT to the spatial frequency learning data and discuss possible model explanations for variations in learning.

Current directions in visual perceptual learning

The visual expertise of adult humans is jointly determined by evolution, visual development, and visual perceptual learning. Perceptual learning refers to performance improvements in perceptual tasks after practice or training in the task. It occurs in almost all visual tasks, ranging from simple feature detection to complex scene analysis. In this Review, we focus on key behavioral aspects of visual perceptual learning. We begin by describing visual perceptual learning tasks and manipulations that influence the magnitude of learning, and then discuss specificity of learning. Next, we present theories and computational models of learning and specificity. We then review applications of visual perceptual learning in visual rehabilitation. Finally, we summarize the general principles of visual perceptual learning, discuss the tension between plasticity and stability, and conclude with new research directions.

The experience of stereoblindness does not improve use of texture for slant perception

Stereopsis is an important depth cue for normal people, but a subset of people suffer from stereoblindness and cannot use binocular disparity as a cue to depth. Does this experience of stereoblindness modulate use of other depth cues? We investigated this question by comparing perception of 3D slant from texture for stereoblind people and stereo-normal people. Subjects performed slant discrimination and slant estimation tasks using both monocular and binocular stimuli. We found that two groups had comparable ability to discriminate slant from texture information and showed similar mappings between texture information and slant perception (biased perception toward frontal surface with texture information indicating low slants). The results suggest that the experience of stereoblindness did not change the use of texture information for slant perception. In addition, we found that stereoblind people benefitted from binocular viewing in the slant estimation task, despite their inability to use binocular disparity information. These findings are generally consistent with the optimal cue combination model of slant perception.

Disentangling locus of perceptual learning in the visual hierarchy of motion processing

Visual perceptual learning (VPL) can lead to long-lasting perceptual improvements. One of the central topics in VPL studies is the locus of plasticity in the visual processing hierarchy. Here, we tackled this question in the context of motion processing. We took advantage of an established transition from component-dependent representations at the earliest level to pattern-dependent representations at the middle-level of cortical motion processing. Two groups of participants were trained on the same motion direction identification task using either grating or plaid stimuli. A set of pre- and post-training tests was used to determine the degree of learning specificity and generalizability. This approach allowed us to disentangle contributions from different levels of processing stages to behavioral improvements. We observed a complete bi-directional transfer of learning between component and pattern stimuli that moved to the same directions, indicating learning-induced plasticity associated with intermediate levels of motion processing. Moreover, we found that motion VPL is specific to the trained stimulus direction, speed, size, and contrast, diminishing the possibility of non-sensory decision-level enhancements. Taken together, these results indicate that, at least for the type of stimuli and the task used here, motion VPL most likely alters visual computation associated with signals at the middle stage of motion processing.

Learning to see again: biological constraints on cortical plasticity and the implications for sight restoration technologies

The 'bionic eye'-so long a dream of the future-is finally becoming a reality with retinal prostheses available to patients in both the US and Europe. However, clinical experience with these implants has made it apparent that the visual information provided by these devices differs substantially from normal sight. Consequently, the ability of patients to learn to make use of this abnormal retinal input plays a critical role in whether or not some functional vision is successfully regained. The goal of the present review is to summarize the vast basic science literature on developmental and adult cortical plasticity with an emphasis on how this literature might relate to the field of prosthetic vision. We begin with describing the distortion and information loss likely to be experienced by visual prosthesis users. We then define cortical plasticity and perceptual learning, and describe what is known, and what is unknown, about visual plasticity across the hierarchy of brain regions involved in visual processing, and across different stages of life. We close by discussing what is known about brain plasticity in sight restoration patients and discuss biological mechanisms that might eventually be harnessed to improve visual learning in these patients.

Spatial Frequency Training Modulates Neural Face Processing: Learning Transfers from Low- to High-Level Visual Features

Perception of visual stimuli improves with training, but improvements are specific for trained stimuli rendering the development of generic training programs challenging. It remains unknown to which extent training of low-level visual features transfers to high-level visual perception, and whether this is accompanied by neuroplastic changes. The current event-related potential (ERP) study showed that training-induced increased sensitivity to a low-level feature, namely low spatial frequency (LSF), alters neural processing of this feature in high-level visual stimuli. Specifically, neural activity related to face processing (N170), was decreased for low (trained) but not high (untrained) SF content in faces following LSF training. These novel results suggest that: (1) SF discrimination learning transfers from simple stimuli to complex objects; and that (2) training the use of specific SF information affects neural processing of facial information. These findings may open up a new avenue to improve face recognition skills in individuals with atypical SF processing, such as in cataract or Autism Spectrum Disorder (ASD).

Chromatic Perceptual Learning but No Category Effects without Linguistic Input

Perceptual learning involves an improvement in perceptual judgment with practice, which is often specific to stimulus or task factors. Perceptual learning has been shown on a range of visual tasks but very little research has explored chromatic perceptual learning. Here, we use two low level perceptual threshold tasks and a supra-threshold target detection task to assess chromatic perceptual learning and category effects. Experiment 1 investigates whether chromatic thresholds reduce as a result of training and at what level of analysis learning effects occur. Experiment 2 explores the effect of category training on chromatic thresholds, whether training of this nature is category specific and whether it can induce categorical responding. Experiment 3 investigates the effect of category training on a higher level, lateralized target detection task, previously found to be sensitive to category effects. The findings indicate that performance on a perceptual threshold task improves following training but improvements do not transfer across retinal location or hue. Therefore, chromatic perceptual learning is category specific and can occur at relatively early stages of visual analysis. Additionally, category training does not induce category effects on a low level perceptual threshold task, as indicated by comparable discrimination thresholds at the newly learned hue boundary and adjacent test points. However, category training does induce emerging category effects on a supra-threshold target detection task. Whilst chromatic perceptual learning is possible, learnt category effects appear to be a product of left hemisphere processing, and may require the input of higher level linguistic coding processes in order to manifest.

The Functional Form of Performance Improvements in Perceptual Learning

The functional form of performance improvements has been extensively studied in speeded cognitive and motor tasks; in such tasks, reductions in response times have been characterized by the ubiquitous power law of learning or by a simpler exponential form. Performance improvements in perceptual capacities are also important in expertise, but their functional form is unknown. This study investigated the functional form of perceptual learning. For individual observers, reductions in thresholds were best described by an exponential function, rather than a power or compound exponential and power (apex) function. Learning was specific to orientation, a result that supports the perceptual locus of the learning, and was decoupled in high and low external noise, a result that reflects separable learning mechanisms in the two conditions. The simple exponential form of learning implies a constant relative rate of learning throughout practice; there was no evidence supporting multilevel hypotheses, such as serial reverse hierarchical and parallel-learning models, that posit multiple processes of learning characterized by different rates.

Neuronal basis of perceptual learning in striate cortex

It is well known that, in humans, contrast sensitivity training at high spatial frequency (SF) not only leads to contrast sensitivity improvement, but also results in an improvement in visual acuity as assessed with gratings (direct effect) or letters (transfer effect). However, the underlying neural mechanisms of this high spatial frequency training improvement remain to be elucidated. In the present study, we examined four properties of neurons in primary visual cortex (area 17) of adult cats that exhibited significantly improved acuity after contrast sensitivity training with a high spatial frequency grating and those of untrained control cats. We found no difference in neuronal contrast sensitivity or tuning width (Width) between the trained and untrained cats. However, the trained cats showed a displacement of the cells’ optimal spatial frequency (OSF) to higher spatial frequencies as well as a larger neuronal signal-to-noise ratio (SNR). Furthermore, both the neuronal differences in OSF and SNR were significantly correlated with the improvement of acuity measured behaviorally. These results suggest that striate neurons might mediate the perceptual learning-induced improvement for high spatial frequency stimuli by an alteration in their spatial frequency representation and by an increased SNR.

Treatment of amblyopia in the adult: insights from a new rodent model of visual perceptual learning

Amblyopia is the most common form of impairment of visual function affecting one eye, with a prevalence of about 1–5% of the total world population. Amblyopia usually derives from conditions of early functional imbalance between the two eyes, owing to anisometropia, strabismus, or congenital cataract, and results in a pronounced reduction of visual acuity and severe deficits in contrast sensitivity and stereopsis. It is widely accepted that, due to a lack of sufficient plasticity in the adult brain, amblyopia becomes untreatable after the closure of the critical period in the primary visual cortex. However, recent results obtained both in animal models and in clinical trials have challenged this view, unmasking a previously unsuspected potential for promoting recovery even in adulthood. In this context, non invasive procedures based on visual perceptual learning, i.e., the improvement in visual performance on a variety of simple visual tasks following practice, emerge as particularly promising to rescue discrimination abilities in adult amblyopic subjects. This review will survey recent work regarding the impact of visual perceptual learning on amblyopia, with a special focus on a new experimental model of perceptual learning in the amblyopic rat.

Optimization of perceptual learning: effects of task difficulty and external noise in older adults

Previous research has shown a wide array of age-related declines in vision. The current study examined the effects of perceptual learning (PL), external noise, and task difficulty in fine orientation discrimination with older individuals (mean age 71.73, range 65-91). Thirty-two older subjects participated in seven 1.5-h sessions conducted on separate days over a three-week period. A two-alternative forced choice procedure was used in discriminating the orientation of Gabor patches. Four training groups were examined in which the standard orientations for training were either easy or difficult and included either external noise (additive Gaussian noise) or no external noise. In addition, the transfer to an untrained orientation and noise levels were examined. An analysis of the four groups prior to training indicated no significant differences between the groups. An analysis of the change in performance post-training indicated that the degree of learning was related to task difficulty and the presence of external noise during training. In addition, measurements of pupil diameter indicated that changes in orientation discrimination were not associated with changes in retinal illuminance. These results suggest that task difficulty and training in noise are factors important for optimizing the effects of training among older individuals.

Brain activity underlying auditory perceptual learning during short period training: simultaneous fMRI and EEG recording

BACKGROUND

There is an accumulating body of evidence indicating that neuronal functional specificity to basic sensory stimulation is mutable and subject to experience. Although fMRI experiments have investigated changes in brain activity after relative to before perceptual learning, brain activity during perceptual learning has not been explored. This work investigated brain activity related to auditory frequency discrimination learning using a variational Bayesian approach for source localization, during simultaneous EEG and fMRI recording. We investigated whether the practice effects are determined solely by activity in stimulus-driven mechanisms or whether high-level attentional mechanisms, which are linked to the perceptual task, control the learning process.

RESULTS

The results of fMRI analyses revealed significant attention and learning related activity in left and right superior temporal gyrus STG as well as the left inferior frontal gyrus IFG. Current source localization of simultaneously recorded EEG data was estimated using a variational Bayesian method. Analysis of current localized to the left inferior frontal gyrus and the right superior temporal gyrus revealed gamma band activity correlated with behavioral performance.

CONCLUSIONS

Rapid improvement in task performance is accompanied by plastic changes in the sensory cortex as well as superior areas gated by selective attention. Together the fMRI and EEG results suggest that gamma band activity in the right STG and left IFG plays an important role during perceptual learning.

Learning to detect and combine the features of an object

To recognize an object, it is widely supposed that we first detect and then combine its features. Familiar objects are recognized effortlessly, but unfamiliar objects--like new faces or foreign-language letters--are hard to distinguish and must be learned through practice. Here, we describe a method that separates detection and combination and reveals how each improves as the observer learns. We dissociate the steps by two independent manipulations: For each step, we do or do not provide a bionic crutch that performs it optimally. Thus, the two steps may be performed solely by the human, solely by the crutches, or cooperatively, when the human takes one step and a crutch takes the other. The crutches reveal a double dissociation between detecting and combining. Relative to the two-step ideal, the human observer's overall efficiency for unconstrained identification equals the product of the efficiencies with which the human performs the steps separately. The two-step strategy is inefficient: Constraining the ideal to take two steps roughly halves its identification efficiency. In contrast, we find that humans constrained to take two steps perform just as well as when unconstrained, which suggests that they normally take two steps. Measuring threshold contrast (the faintness of a barely identifiable letter) as it improves with practice, we find that detection is inefficient and learned slowly. Combining is learned at a rate that is 4× higher and, after 1,000 trials, 7× more efficient. This difference explains much of the diversity of rates reported in perceptual learning studies, including effects of complexity and familiarity.

Learning motion: human vs. optimal Bayesian learner

We used the optimal perceptual learning paradigm (Eckstein, Abbey, Pham, & Shimozaki, 2004) to investigate the dynamics of human rapid learning processes in motion discrimination tasks and compare it to an optimal Bayesian learner. This paradigm consists of blocks of few trials defined by a set of target attributes, and it has been shown its ability to detect learning effects appearing as soon as after the first trial. In the present task a sequence consisting of four patches containing random-dot patterns is presented at four separate locations equidistant from a fixation point. On each trial, the random dots in three patches moved with a mean speed and the fourth, target patch, could move either with slower or faster mean speed. Observers' task was to indicate what speed, faster or slower, was present in the display. The mean direction of the target patch was kept invariant along a block of trials. Observers learned the target relevant motion direction through indirect feedback, leading to an improvement in speed identification performance ranging from 15% to 30% which is greater than previously studied contrast defined targets and faces. However, comparison to an ideal learner revealed incomplete or partial learning for the motion task which was lower than previously measured for contrast defined targets and faces. A sub-optimal model that included inefficiencies in the updating of motion direction weights due to memory effects could account for the human learning. Finally, the similarity of the rapid learning effect observed here for motion perception with that found for contrast defined targets for localization and identification tasks could be suggesting a general strategy for learning in the human visual system and some common limitations such as memory.

Hebbian Reweighting on Stable Representations in Perceptual Learning

Perceptual learning is the improvement in perceptual task performance with practice or training. The observation of specificity in perceptual learning has been widely associated with plasticity in early visual cortex representations. Here, we review the evidence supporting the plastic reweighting of readout from stable sensory representations, originally proposed by Dosher & Lu (1998), as an alternative explanation of perceptual learning. A task-analysis that identifies circumstances in which specificity supports representation enhancement and those in which it implies reweighting provides a framework for evaluating the literature; reweighting is broadly consistent with the behavioral results and almost all of the physiological reports. We also consider the evidence that the primary mode of perceptual learning is through augmented Hebbian learning of the reweighted associations, which has implications for the role and importance of feedback. Feedback is not necessary for perceptual learning, but can improve it in some circumstances, and in some cases block feedback is also helpful - all effects that are generally compatible with an augmented Hebbian model (Petrov, Dosher, & Lu, 2005). The two principles of perceptual learning through reweighting evidence from stable sensory representations and of augmented Hebbian learning provide a theoretical structure for the consideration of issues such as task difficulty, task roving, and cuing in perceptual learning.

MECHANISMS OF PERCEPTUAL LEARNING

What is learned in perceptual learning? How does perceptual learning change the perceptual system? We investigate these questions using a systems analysis of the perceptual system during the course of perceptual learning using psychophysical methods and models of the observer. Effects of perceptual learning on an observer's performance are characterized by external noise tests within the framework of noisy observer models. We find evidence that two independent mechanisms, external noise exclusion and stimulus enhancement support perceptual learning across a range of tasks. We suggest that both mechanisms may reflect re-weighting of stable early sensory representations.

Task precision at transfer determines specificity of perceptual learning

Perceptual learning, the improvement in performance with practice, reflects plasticity in the adult visual system. We challenge a standard claim that specificity of perceptual learning depends on task difficulty during training, instead showing that specificity, or conversely transfer, is primarily controlled by the precision demands (i.e., orientation difference) of the transfer task. Thus, for an orientation discrimination task, transfer of performance improvement is observed in low-precision transfer tasks, while specificity of performance improvement is observed in high-precision transfer tasks, regardless of the precision of initial training. The nature of specificity places important constraints on mechanisms of transfer in visual learning. These results contribute to understanding generalization of practiced improvements that may be key to the development of expertise and for applications in remediation.

Exercising your brain: a review of human brain plasticity and training-induced learning

Human beings have an amazing capacity to learn new skills and adapt to new environments. However, several obstacles remain to be overcome in designing paradigms to broadly improve quality of life. Arguably, the most notable impediment to this goal is that learning tends to be quite specific to the trained regimen and does not transfer to even qualitatively similar tasks. This severely limits the potential benefits of learning to daily life. This review discusses training regimens that lead to the acquisition of new knowledge and strategies that can be used flexibly across a range of tasks and contexts. Possible characteristics of training regimens are proposed that may be responsible for augmented learning, including the manner in which task difficulty is progressed, the motivational state of the learner, and the type of feedback the training provides. When maximally implemented in rehabilitative paradigms, these characteristics may greatly increase the efficacy of training.

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.

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.

The dynamics of perceptual learning: an incremental reweighting model

The mechanisms of perceptual learning are analyzed theoretically, probed in an orientation-discrimination experiment involving a novel nonstationary context manipulation, and instantiated in a detailed computational model. Two hypotheses are examined: modification of early cortical representations versus task-specific selective reweighting. Representation modification seems neither functionally necessary nor implied by the available psychophysical and physiological evidence. Computer simulations and mathematical analyses demonstrate the functional and empirical adequacy of selective reweighting as a perceptual learning mechanism. The stimulus images are processed by standard orientation- and frequency-tuned representational units, divisively normalized. Learning occurs only in the "read-out" connections to a decision unit; the stimulus representations never change. An incremental Hebbian rule tracks the task-dependent predictive value of each unit, thereby improving the signal-to-noise ratio of their weighted combination. Each abrupt change in the environmental statistics induces a switch cost in the learning curves as the system temporarily works with suboptimal weights.

The nature of learned categorical perception effects: a psychophysical approach

Categorical perception is often cited as a striking example of cognitive influences on perception. However, some evidence suggests the term is a misnomer, with effects based on cognitive not perceptual processing. Here, using a psychophysical approach, we provide evidence consistent with a learned categorical perception effect that is dependent on analysis within the visual processing stream. An improvement in participants' discrimination between grating patterns that they had learned to place in different categories was 'tuned' around the orientation of the patterns experienced during category learning. Thus, here, categorical perception may result from attentionally modulated perceptual learning about diagnostic category features, based upon orientation-selective stages of analysis. This argues strongly that category learning can alter our perception of the world.

Perceptual learning of temporal structure

We investigated the extent to which the ability to perceive spatial form from temporal structure (TS) improves with practice. Observers trained monocularly for a number of consecutive days on a shape discrimination task, with one group of observers judging shape defined by luminance contrast between target and background elements and another group judging shape defined by correlated TS (synchronized changes in motion direction between target and background elements). Substantial learning was found for both shape tasks, with complete interocular transfer of training. Observers trained on TS showed no transfer of learning to the luminance condition, but observers trained using the luminance display with incidental synchronized changes did show transfer to the TS task. Possible underlying neural changes are discussed.

Perceptual learning of luminance contrast detection: specific for spatial frequency and retinal location but not orientation

Performance of a wide range of simple visual tasks improves with practice. Here we ask whether such learning occurs for the fundamental visual task of luminance contrast detection. In two experiments we find that contrast sensitivity increases following extensive practice at detecting briefly presented sinusoidal luminance gratings and that learning is maintained after six months. Learning is spatial frequency tuned, specific to retinal location and can be specific to one eye, but is not selective for orientation. The selectivity of learning implies that it is based on plasticity in early visual, as opposed to central cognitive, processing mechanisms.

Physiological correlates of perceptual learning in monkey V1 and V2

Performance in visual discrimination tasks improves with practice. Although the psychophysical parameters of these improvements have suggested the involvement of early areas in visual cortex, there has been little direct study of the physiological correlates of such perceptual learning at the level of individual neurons. To examine how neuronal response properties in the early visual system may change with practice, we trained monkeys for more than 6 mo in an orientation discrimination task in which behaviorally relevant stimuli were restricted to a particular retinal location and oriented around a specific orientation. During training the monkeys' discrimination thresholds gradually improved to much better than those of naive monkeys or humans. Although this improvement was specific to the trained orientation, it showed little retinotopic specificity. The receptive field properties of single neurons from regions representing the trained location and a location in the opposite visual hemifield were measured in V1 and V2. In most respects the receptive field properties in the representations of the trained and untrained regions were indistinguishable. However, in the regions of V1 and V2 representing the trained location, there were slightly fewer neurons whose optimal orientation was near the trained orientation. This resulted in a small but significant decrease in the V1 population response to the trained orientation at the trained location. Consequently, the observed neuronal populations did not exhibit any orientation-specific biases sufficient to explain the orientation specificity of the behavioral improvement. Pooling models suggest that the behavioral improvement was accomplished with a task-dependent and orientation-selective pooling of unaltered signals from early visual neurons. These data suggest that, even for training with stimuli suited to the selectivities found in early areas of visual cortex, behavioral improvements can occur in the absence of pronounced changes in the physiology of those areas.