Perceptual Learning: Use-Dependent Cortical Plasticity

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1 ANNUAL REVIEWS Further Click here to view this article's online features: Download figures as PPT slides Navigate linked references Download citations Explore related articles Search keywords Annu. Rev. Vis. Sci : First published online as a Review in Advance on July 18, 2016 The Annual Review of Vision Science is online at vision.annualreviews.org This article s doi: /annurev-vision Copyright c 2016 by Annual Reviews. All rights reserved Perceptual Learning: Use-Dependent Cortical Plasticity Wu Li 1,2 1 State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing , China; liwu@bnu.edu.cn 2 IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing , China Keywords perceptual learning, cortical plasticity, top-down influence, visual cortex, learning specificity, learning transfer Abstract Our perceptual abilities significantly improve with practice. This phenomenon, known as perceptual learning, offers an ideal window for understanding use-dependent changes in the adult brain. Different experimental approaches have revealed a diversity of behavioral and cortical changes associated with perceptual learning, and different interpretations have been given with respect to the cortical loci and neural processes responsible for the learning. Accumulated evidence has begun to put together a coherent picture of the neural substrates underlying perceptual learning. The emerging view is that perceptual learning results from a complex interplay between bottom-up and top-down processes, causing a global reorganization across cortical areas specialized for sensory processing, engaged in top-down attentional control, and involved in perceptual decision making. Future studies should focus on the interactions among cortical areas for a better understanding of the general rules and mechanisms underlying various forms of skill learning. 109

2 INTRODUCTION The brain and its inherent functions undergo experience-dependent changes throughout life, allowing adaptation to new environments and acquisition of new skills. Adaptive changes are seen even in basic perceptual functions. In particular, repeated practice of a discrimination or detection task usually leads to a remarkable enhancement of the trained perceptual ability, a process referred to as perceptual learning. That practice makes perfect is evident in all sensory modalities. For instance, expert radiologists are able to identify a subtle pathological change in X-ray films; piano tuners can distinguish a minute difference in pitch between two musical tones; professional wine tasters possess super senses of taste and smell to differentiate a great diversity of wines. Perceptual learning, like other types of skill learning, is usually associated with the formation of an implicit or nondeclarative form of long-term memory whose retrieval usually does not reach our conscious awareness (Squire & Zola 1996). This is in contrast with explicit or declarative learning and memory (i.e., conscious memory of facts and events) that rely on a unified memory system in the brain the medial temporal lobe (MTL) (Miyashita 2004, Squire et al. 2004). Amnesic patients with the memory system damaged are still able to acquire and retain new perceptual and motor skills (such as the famous patient H.M.; see Squire 2009), and they can show typical perceptual learning effects that are comparable to those of normal observers (Fahle & Daum 2002). Psychophysical studies have revealed a rich repertoire of characteristics of perceptual learning that may implicate the underlying processes, and physiological studies have begun to elucidate the adaptive changes in the brain. This review focuses on the neurophysiological mechanisms of visual perceptual learning and compares related findings in the other sensory modalities. IMPLICATIONS FROM BEHAVIORAL STUDIES Early behavioral studies by experimental psychologists discovered a prominent characteristic of learning specificity. The improvement in a certain brain function is usually restricted to the training settings such as the specific learning materials and tasks; the amount of learning effect that is transferable from one setting to another depends largely on the degree of similarity between them. This rule of identical elements governing learning transfer applies to simple discrimination tasks such as estimating the lengths of line segments and the areas of geometric shapes (Thorndike & Woodworth 1901a,b). The specificity of learning implicates specific modifications to the neural processes by training. Stimulus Specificity of Perceptual Learning Two distinctive properties of visual perceptual learning have been under the spotlight within the last several decades: location and orientation specificities. For example, in an orientation discrimination task, the smallest amount of change in orientation of a line that can be reliably detected by human observers becomes significantly smaller with training (Vogels & Orban 1985). However, this improvement is restricted to the trained location in the visual field and to the trained orientation of the stimulus; the learning effect disappears when the same stimulus is placed at a new visual-field location or is simply rotated (Schoups et al. 1995, Shiu & Pashler 1992). Similar location and orientation specificities are generally present in training for a variety of visual tasks, such as discrimination of grating waveforms (Berardi & Fiorentini 1987, Fiorentini & Berardi 1980, 1981); the vernier task to judge whether two end-to-end parallel lines are misaligned (Fahle et al. 1995, Poggio et al. 1992); the bisection task of determining whether three side-by-side parallel lines are equidistant (Crist et al. 1997); grating contrast discrimination (Yu et al. 2004); motion 110 Li

3 direction discrimination (Ball & Sekuler 1982, 1987); stereoscopic perception (Ramachandran & Braddick 1973) and disparity (depth) discrimination (Westheimer & Truong 1988); texture discrimination (Karni & Sagi 1991); and odd-ball target detection (Ahissar & Hochstein 1996, Sigman & Gilbert 2000). The striking specificities of perceptual learning to stimulus location and orientation lead to a speculation that learning-induced changes take place in the early visual cortex. The rationale is based on the functional architecture of the visual system. In early cortical areas such as the primary visual cortex (V1), the visual field is topographically represented on a fine map, the retinotopic map, and the neurons are selectively responsive to local, basic stimulus attributes such as orientations of line segments (Hubel & Wiesel 1959). Therefore, training at a specific visual-field location and stimulus orientation might specifically exercise only a subset of early cortical neurons, giving rise to the specific behavioral effects. Supporting such a hypothesis that learning-induced changes occur in the early visual cortex is the accumulation of a large body of psychophysical evidence on learning specificity to stimulus location and orientation, and also to other basic stimulus attributes (for a recent review, see Sagi 2011). However, some studies suggest that whether the learning effects are specific or transferable depends on a number of factors for instance, task difficulty (Ahissar & Hochstein 1997, Hung & Seitz 2014, Liu & Weinshall 2000), precision demand of the task ( Jeter et al. 2009), and intensity (duration) of training ( Jeter et al. 2010). Recent studies have shown that some of the previously documented stimulus specificities of perceptual learning can be eliminated by using a double-training procedure (Wang et al. 2012, Xiao et al. 2008). For example, learning to discriminate grating orientations around a given axis does not transfer to the orthogonal orientation; however, if this first training stage is followed by another stage in which the observer practices a different task (e.g., contrast discrimination) on gratings at the orthogonal orientation, the enhanced orientation discriminability acquired in the first training stage completely generalizes to the orthogonal orientation (Zhang et al. 2010). Most strikingly, vernier discrimination training, when paired with orientation or motion direction discrimination at the same peripheral location, is generalizable to the opposite, untrained visual-field quadrant (Wang et al. 2014). The learning effect of orientation or motion direction discrimination can even transfer between different types of stimuli encoded by separate neural mechanisms (R. Wang et al. 2016). These findings bring into question the validity of simply using stimulus specificities to infer the cortical loci of perceptual learning; they also suggest that some general rules could be learned by retuning attention and decision processes (R. Wang et al. 2016, Zhang et al. 2010). It has been proposed that the practice effects, as well as their specificities, could result from specific interactions between stimulus-driven bottom-up processes and goal-directed top-down influences (Zhang et al. 2013). In regard to the learning transfer, it could be mediated by remapping of the well-tuned top-down signals to affect the processing of untrained stimuli (Zhang et al. 2015). Another line of evidence against changes in early retinotopic areas alone as the neural basis of perceptual learning and learning specificity comes from the findings of nonretinotopic location specificity of the learning effects (Otto et al. 2010, Zhang & Li 2010, Zhang et al. 2013). In a task to discriminate a fine difference in motion direction (Zhang & Li 2010) or orientation (Zhang et al. 2013) between two successively displayed stimuli, by introducing a gaze shift in between, the relative location of these two stimuli in space (i.e., in the spatiotopic frame of reference) can be altered without changing their locations on the retina (i.e., in the retinotopic frame of reference). Under such a training condition, the improved discriminability is specific not only to the trained retinal locations but also to the trained spatial relation of the two stimuli. This spatiotopic learning effect could be due to specific interactions between retinotopic processing and attentional remapping mechanisms (Zhang et al. 2013). Perceptual Learning: Physiology 111

4 Top-Down Influences on Perceptual Learning It is worth emphasizing that although perceptual learning is transferable across stimulus conditions under certain circumstances (e.g., using the double-training regime mentioned above), the learning effect hardly generalizes from the trained task to an untrained one even though the visual stimuli are identical (Ahissar & Hochstein 1993, Saffell & Matthews 2003, Shiu & Pashler 1992). For example, when presented with an array of moving dots whose moving direction, speed, and luminance are varied independently in small steps, the observer s discriminability is enhanced only for the attended attribute; in particular, training on luminance discrimination has no effect on direction and speed discriminability (Saffell & Matthews 2003). Such task specificity, which is commonly seen in learning various skills, indicates that actively attending to the task-relevant information is required for the learning to take place. Selective attention is important for perceptual learning in that it not only enhances task-relevant stimulus features but also suppresses irrelevant ones (Vidnyánszky & Sohn 2005). However, studies from a research group have shown that perceptual learning can occur for a weak (near detection threshold) and task-irrelevant stimulus if its onset repetitively coincides with the onset of a taskrelevant target (Watanabe et al. 2001) or with the delivery of a reward (Seitz et al. 2009). This particular type of task-irrelevant learning could be enabled by diffusive reinforcement signals triggered by detection of the actively attended target or by reward stimulation (Watanabe & Sasaki 2015). Therefore, even in this case, top-down feedback is still indispensable. Although top-down attention is required for perceptual learning in various tasks, some basic sensory abilities (e.g., tactile acuity and visual sensitivity) can be enhanced or impaired after extended passive stimulation with temporal stimulus patterns mimicking those used for inducing synaptic plasticity. Such long-term sensitization or desensitization could be due to plastic changes in the corresponding sensory cortex induced by intensive bottom-up stimulation (for a recent review, see Beste & Dinse 2013). The significance of top-down control in perceptual learning is further demonstrated in mental imagery tasks. Repeatedly imagining a perceptual task of three-line bisection discrimination, lowcontrast grating detection, or motion direction discrimination without actual physical stimulation can improve behavioral performance on the real task (Tartaglia et al. 2009, 2012). Likewise, for identical stimuli that are impossible to tell apart, merely attempting to discern their difference in a certain attribute can enhance the ability to detect small deviations from the attended attribute, such as trying to differentiate a pitch difference between identical tones (Amitay et al. 2006) and trying three-line bisection discrimination on equidistant lines (Grzeczkowski et al. 2015). More surprisingly, mental imagery in any form, as long as it can repeatedly induce activation patterns in V1/V2 that are similar to those activated by real gratings, can specifically improve human observers orientation discriminability (Shibata et al. 2011). Another example of a top-down effect on perceptual learning is the role of external feedback. Informing human observers of their behavioral performance during training can facilitate learning (Herzog & Fahle 1997, Shiu & Pashler 1992), whereas random feedback signals that are uncorrelated with the behavioral responses render the learning impossible (Herzog & Fahle 1997). Moreover, when positive feedback is consistently delivered for behavioral choices of a nonexistent attribute in a featureless visual stimulus (an arbitrarily designated orientation signal in white noise) and error feedback is delivered for otherwise choices, after extended training with this make-believe visual attribute, human observers performance is significantly improved to detect real grating signals hidden within the noise at the designated orientation (Choi & Watanabe 2012). The dependence of perceptual learning on different top-down influencing factors, together with recent findings of learning transfer across stimulus conditions, suggests that perceptual 112 Li

5 learning, just like perception per se, is mediated by a series of stimulus- and behavior-driven processes that engage cortical areas specialized for sensory processing, attentional deployment, and decision making. Modifications to any related cortical areas or processes would alter the resulting percepts. CORTICAL PLASTICITY AND PERCEPTUAL LEARNING Experience-dependent changes in cortical structure and function were initially demonstrated in the primary visual cortex during early postnatal development. Deprivation of visual input from one eye (Hubel & Wiesel 1970, Wiesel & Hubel 1963) or a restriction on exposed visual features (Blakemore & Cooper 1970) within this critical period causes dramatic remodeling of V1. Reorganization of the sensory cortices has also been widely observed in adulthood in response to dramatic alterations of sensory experiences. Annu. Rev. Vis. Sci : Downloaded from Experience-Dependent Cortical Reorganization The presence of the critical period once led to a conjecture that the adult cortex is matured and thus hardly susceptible to environmental changes. However, subsequent studies in adult animals found pronounced changes in the somatosensory, auditory, and visual cortices associated with deafferentation of inputs from a region on the corresponding sensory surfaces (the skin, the cochlea, and the retina) (for a review, see Weinberger 1995). Using monkey V1 as an example, following binocular lesions of corresponding regions on the retinae, V1 neurons within the lesion projection zone can shift their receptive fields outside the lesion area (Abe et al. 2015, Gilbert & Wiesel 1992, Kaas et al. 1990). This reorganization process is mediated by remodeling of the lateral connections in V1 through axonal sprouting and pruning (Yamahachi et al. 2009). Reorganization of the V1 retinotopic map is also observed in humans through functional magnetic resonance imaging (fmri) in macular degeneration patients with bilateral central retinal lesions (Baker et al. 2005) and in stroke patients with partially disrupted input fibers projecting to V1 (Dilks et al. 2007). In these patients, the V1 regions deprived of visual input from the original visual-field areas regain responsiveness to neighboring areas. On the basis of the observations of experience-dependent reorganization in the adult sensory cortex, one may speculate that similar changes could happen during perceptual training. Learning-induced cortical changes were initially found in the somatosensory and auditory systems of monkeys. Training in a vibration frequency discrimination task using a finger results in an expansion of the territory within the primary somatosensory cortex (S1) representing the trained skin area (Recanzone et al. 1992a,b). Similarly, training to discriminate a difference in frequency between two tones drastically enlarges the regions in the primary auditory cortex (A1) responding to the trained frequencies (Recanzone et al. 1993). This process of cortical recruitment, which recruits a greater number of neurons to encode the trained stimuli, requires selective attention to the stimulus, as passive exposure has no effect. Learning-induced changes analogous to those seen in monkey S1 and A1 were also reported in human visual cortex (Vaina et al. 1998). After human observers are trained for several hundred trials in a coarse motion direction identification task (a small proportion of random dots coherently moving left or right among other randomly moving dots), improved behavioral performance is accompanied by an expansion of the cortical territory representing the trained stimuli in MT (middle temporal visual area involved in motion perception). It is controversial whether the cortical recruitment is directly responsible for the improved discriminability. The cortical map in cat s A1 is not altered by frequency discrimination training Perceptual Learning: Physiology 113

6 (Brown et al. 2004). For rats reared in a single-frequency tonal environment, although the frequencies around the exposed frequency are overrepresented in A1, the rat s ability to discriminate the overrepresented frequencies is actually impaired; in contrast, discrimination of the neighboring, underrepresented frequencies is improved (Han et al. 2007). For monkeys trained in the threeline bisection discrimination task for a few months, the retinotopic map in V1 remains unchanged (Crist et al. 2001), bringing into question the cortical recruitment as a general mechanism of perceptual learning. A recent study in rat s A1 indicates that cortical map plasticity itself is only a transient process (Reed et al. 2011). Repetitive pairing of a low-frequency tone with electric stimulation of the nucleus basalis leads to stimulus-specific map expansions in A1. This manipulation facilitates subsequent learning of low-frequency discrimination. However, further extended training resets the A1 map so that the trained frequencies are no longer overrepresented, but the behavioral learning effect is retained. Expansion and retraction processes are also reported in rat s primary motor cortex when learning a reaching task (Molina-Luna et al. 2008). These findings suggest that cortical recruitment is involved in encoding, but not storage, of the learned information. Enhanced Neuronal Selectivity for Task-Relevant Features Earlier psychophysical studies speculate modifications of retinotopic cortical areas by visual perceptual learning. Human imaging studies have indeed found changes in these areas that are correlated with improved behavioral performance. Some studies showed increased activities in the early visual cortex associated with training in tasks such as texture discrimination (Schwartz et al. 2002), detection of low-contrast gratings (Furmanski et al. 2004), and detection of camouflaged, lowsaliency shapes (Kourtzi et al. 2005, Sigman et al. 2005). However, decreased cortical activations have been reported in orientation (Schiltz et al. 1999) and contrast (Mukai et al. 2007) discrimination learning. These cortical changes are usually specific to the trained stimulus conditions, in agreement with the behavioral outcome. Recent fmri studies suggest that a change in activation strength of a cortical area itself, just like the cortical recruitment mentioned above, is not necessarily an indicator of the neural code for perceptual learning. In the texture discrimination task, human V1 responses are enhanced in the initial stage of training and then return to the pretraining levels (Yotsumoto et al. 2008). After orientation discrimination training, the overall activations in early retinotopic areas are not affected; instead, the ability of individual voxels to discriminate the trained and untrained stimulus orientations is enhanced ( Jehee et al. 2012). The involvement of V1 in orientation discrimination learning is further supported by a transcranial magnetic stimulation (TMS) study in humans (De Weerd et al. 2012): At the end of initial training sessions, applying TMS to the trained V1 region severely interferes with learning consolidation. As different stimulus attributes are selectively processed by different cortical areas and neurons, improved behavioral discrimination ability could be due to enhanced neuronal selectivity for the trained stimulus attribute. Physiological evidence for such a hypothesis came from monkeys trained in the orientation discrimination task. After several months of training at a fixed oblique orientation of gratings and a fixed visual-field location, the animal s thresholds for orientation discrimination drop by 7 12 times, reaching (Schoups et al. 2001). The improvement is orientation and location specific. Correlated with the behavioral result, a subset of V1 neurons within the trained retinotopic region become more sensitive to orientation changes of stimulus around the trained orientation (Figure 1). Similar and more pronounced effects were observed in V4 by the same research group (Raiguel et al. 2006). Studies from another research group did not find similar 114 Li

7 Pretraining Posttraining Trained orientation Neuronal response Neuron 1 Neuron 2 Annu. Rev. Vis. Sci : Downloaded from Stimulus orientation Figure 1 Increased V1 orientation selectivity by orientation discrimination training. The changes are seen as steepening of the orientation tuning curves for neurons with preferred orientations away from the trained orientation. Figure adapted with permission from Schoups et al. (2001). changes in V1 and V2 (Ghose et al. 2002), but they found narrowing of orientation tuning curves in V4 (Yang & Maunsell 2004). Sharpening of neuronal tuning functions is generally thought to give rise to enhanced discriminability, but a theoretical modeling study raises a caveat (Series et al. 2004): Sharpening does not necessarily improve neural population code; depending on the mechanism of sharpening and the structure of correlated responses between neurons, narrowing of individual neurons tuning function may lead to a severe loss of information. In higher-order visual cortical areas, neurons are selective for complex shapes or objects. Training monkeys to discriminate different shapes (Kobatake et al. 1998, Logothetis et al. 1995) or to associate one shape with another (Messinger et al. 2005, Miyashita 1993) modifies neuronal selectivity in the inferior temporal cortex responsible for object recognition. Training humans to discriminate computer-generated novel objects selectively enhances subregions across the objectselective areas (Op de Beeck et al. 2006). These high-level forms of visual learning, which are distinct from typical implicit perceptual learning of simple visual features, are usually associated with explicit or declarative memories that engage the brain s memory system (Miyashita 2004, Takeda et al. 2015). However, visual statistical learning, another form of implicit learning whereby stimulus regularities distributed in space and time can be quickly captured even without intent or awareness, also involves the MTL memory system as well as the visual cortex (Goujon et al. 2015, Turk-Browne et al. 2009). Unlike discrimination of subtle changes in a stimulus feature, some visual tasks require detection of low-saliency targets. In these situations, enhancing the signal-to-noise ratio of neuronal response to the target would benefit behavioral detectability. Training cats to identify two orthogonal gratings using one eye at near-threshold luminance contrasts markedly increases V1 neurons contrast sensitivity, with the strongest practice effects seen in the trained eye and around the trained spatial frequency (Hua et al. 2010). Similarly, training monkeys to identify two orthogonal gratings masked by noise specifically enhances V4 neuronal discriminability between the two trained grating orientations, and both an increased difference of mean firing rates to the two stimuli and a decreased trial-by-trial response variance contribute to this enhancement (Adab & Vogels Perceptual Learning: Physiology 115

8 2011). Training in such a simple orientation identification task also improves the discriminability of neurons in the posterior inferior temporal cortex (PIT), which is hierarchically above V4; the training also recruits more PIT neurons to represent the trained orientations (Adab et al. 2014). Training monkeys to identify degraded natural images has also been shown to significantly increase V4 neurons responsiveness to the trained images as well as the amount of information conveyed by the neurons (Rainer et al. 2004). A visual stimulus is usually present within a certain stimulus context, which can influence neuronal responses to the stimulus and thus modify percepts (for a recent comprehensive review, see Spillmann et al. 2015). Extensively training monkeys to do the bisection discrimination task substantially changes the way contextual stimuli modulate responses of V1 neurons (Crist et al. 2001). Contextual influences mediated by regions outside the classical receptive fields of neurons play an important role in the processes of image grouping and segmentation. Contour integration in complex visual scenes, for example, requires proper grouping of contour segments belonging to the same object and segregating them from other image components. The primary visual cortex is intimately involved in this integration process (Li et al. 2006), which is strongly affected by perceptual learning (Figure 2). Before monkeys are trained to detect camouflaged contours within a cluttered background, V1 neurons carry little information about the global contours in a simple fixation task, even if the monkey s spatial attention is directed to the location where the contour stimulus is displayed. Training dramatically facilitates neuronal responses to the contour in a delayed response component, which is closely correlated with the animal s detection performance. Several important conclusions can be drawn from this study. First, top-down attention to the taskrelevant visual features is required for the learning-induced changes to take place in V1; repeated but passive exposure to the stimulus does not help. Second, both the behavioral and neuronal effects of practice are specific to the trained visual-field quadrants, as they are greatly diminished when the stimuli are moved to the opposite, untrained quadrants. Third, retrieval of the learning effect in V1 is also subject to top-down influences, as the learning-induced facilitation is significantly reduced when the trained animals perform a different task, and the contour-related responses are completely abolished under anesthesia when any forms of top-down influences are absent. Refined Neuronal Population Code Studies using single-electrode recording usually examine learning-induced cortical changes by comparing different neuronal samples before and after training, between the trained and untrained retinotopic regions, or even between the trained and untrained animals. These approaches are not able to longitudinally track dynamic cortical changes, and they cannot provide information about the neuronal population code for perceptual learning. Using implanted microelectrode arrays, a recent study recorded many V1 neurons over the entire course of training monkeys in the contour detection task (Yan et al. 2014). For contours of low perceptual saliencies, the monkey s detection performance is near the chance level at the training outset and is remarkably improved with daily practice. In the meantime, a push-pull response pattern evolves in V1 (Figure 3): Responses of neurons with receptive fields lying on the contour are progressively enhanced, and those on the noisy background are suppressed. The temporal properties of V1 neurons are modified by training as well: The latencies of contour facilitation and background suppression are gradually shortened by up to several tens of milliseconds. Using data from all of the simultaneously recorded V1 sites in response to the contour and noise patterns, decoding analyses reveal that the day-by-day changes in decoding accuracy parallel the practice effects in the animals. These results clearly demonstrate that perceptual training progressively 116 Li

9 a Firing rate (spikes/s) b Untrained (fixation task) 1-line 3-line 5-line 7-line 9-line c Trained (detection task) 0 Annu. Rev. Vis. Sci : Downloaded from Firing rate (spikes/s) d Trained (fixation task) e New location (fixation task) Time relative to stimulus onset (ms) Time relative to stimulus onset (ms) f Anesthesia Figure 2 Changes in monkey V1 induced by training in a contour detection task. (a) The detection task. A visual contour was formed by collinear line segments embedded in randomly oriented lines; the contour pattern was displayed simultaneously with another noise pattern without any embedded contour (the central dot represents the fixation point and the red circle represents the receptive field of a recorded neuron). Monkeys indicated the contour pattern by making a saccade to its location. (b f ) Averaged neuronal responses to visual contours consisting of 1-, 3-, 5-, 7-, and 9-collinear-line segments. Time 0 indicates stimulus onset. (b) Before training, V1 responses are independent of contour lengths. (c) After training, a late response component associated with the contour length emerges. (d )The contour-related responses are weakened at the trained location when the trained animals perform a fixation task irrelevant to contour detection, (e) are even much weaker at an untrained location, and ( f ) are completely abolished under anesthesia. Figure adapted with permission from Li et al. (2008). increases task-relevant information and reduces irrelevant external noise by refining V1 neural population code, and that training also speeds up the detection process in V1. In fmri studies, each voxel reflects the activities from a large population of neurons. A recent study has shown that training human observers in the orientation discrimination task significantly enhances the discriminability of individual voxels (measured as the d of activations between the trained and untrained orientations) at the trained retinotopic locations in V1, V2, V3, V3A, and hv4 ( Jehee et al. 2012). This improvement results from a combination of signal enhancement and noise reduction. Interestingly, training does not affect the overall activation strengths in these Perceptual Learning: Physiology 117

10 a Proportion correct (%) c SVM Behavior d Contour Background Training time (days) Annu. Rev. Vis. Sci : Downloaded from b Pretraining Day 1: 65% Day 4: 75% Day 8: 87% Day 12: 90% Day 15: 89% Figure 3 Inhibition 1.5 Detectability (d') Facilitation 1.5 Perceptual learning refines neuronal population code in V1. (a)a contourstimuluswith an intermediate level of saliency. The circles illustrate some neuronal receptive fields covering the contour (red ) and background (blue) elements. The contour detection task is similar to that described in Figure 2. (b) Progressive enhancement of the signal-to-noise ratio over the neuronal populations during training. Different panels correspond to different time points, which are marked on the top along with the monkey s behavioral performance. Each circle or square represents the center of the receptive field recorded by an electrode; the sizes and hues of the circles and squares indicate the strengths and polarities of contour-induced modulatory effects (measured as the d values of neuronal responses to the contour and noise patterns). The thin black line illustrates the embedded contour. (c,d ) Decoding neural population responses in two monkeys using the support vector machine (SVM) classifier. The decoding accuracy is compared with the animal s behavioral performance on a daily basis. Figure adapted with permission from Yan et al. (2014). cortical areas, indicating that the learning-induced modulatory effects could be facilitatory for some voxels (or neuronal populations) but inhibitory for the others in response to the trained stimulus. These changes would generate a certain activation pattern in the visual cortex that is optimal for representing the learned information. In other words, activation patterns similar to the optimal one across stimulus repetitions would result in a better behavioral learning effect, as observed in face-selective cortical regions when human subjects are trained to discriminate (Bi et al. 2014) or memorize (Xue et al. 2010) faces. The visual signals encoded by V1 neurons are accessible to higher-order areas for readout and further processing, and two different mechanisms could account for improved neural code. One mechanism is input selection: Training selectively increases the amount of task-relevant information conveyed by neurons in early sensory cortex and thus facilitates subsequent readout, as indicated by physiological studies showing refined neural representations of the trained stimuli in the visual cortex. Another mechanism is readout reweighting: Sensory representations in a cortical area are not necessarily altered by perceptual training; instead, higher-order cortical areas 118 Li

11 can refine the readout process by assigning different weights to the sensory signals relevant and irrelevant to the task (Dosher & Lu 1998, Kahnt et al. 2011, Law & Gold 2008, Petrov et al. 2005), probably by selectively modifying the feedforward connectivities (Bejjanki et al. 2011, 2014; Law & Gold 2009). There has been an ongoing debate about which of these two coding strategies is used for perceptual learning. Recent studies suggest that these two mechanisms are not mutually exclusive but that they can actually operate synergistically. In the contour detection task, training can not only enhance V1 neural population code for the visual contour but also improve the readout strategy (Yan et al. 2014). In the early stage of training, a monkey s detection performance can be substantially below the decoding accuracy of the classifier (Figure 3d), implying that higher cortical areas cannot fully use the information that is already present in V1. With sufficient training, the animal s performance approaches that of the decoder, an indication of progressive optimization of the readout process. A recent fmri study examined cortical changes after training human observers in the motion direction discrimination task (Chen et al. 2015). Decoding and encoding analyses based on the activation patterns of voxels indicate enhanced neural selectivity in V3A for the trained motion direction; moreover, dynamic causal modeling reveals an increase in the effective connectivity from V3A to the intraparietal sulcus, which is involved in integration of motion signals. A combination of both factors accounts for the behavioral improvement. Theoretical analysis indicates that the structure of correlated responses between cortical neurons, such as the trial-by-trial covariation of neuronal firing rates (termed the noise correlation), may affect the amount of information conveyed by neuronal populations (Averbeck et al. 2006); perceptual learning could benefit from a decrease in noise correlation (Bejjanki et al. 2011). It has been shown that perceptual learning indeed causes a general reduction of the noise correlation, but this change has little impact on the amount of information conveyed by the neuronal population about the task-relevant stimulus features (Gu et al. 2011, Yan et al. 2014). Therefore, whether a change in noise correlation may contribute to perceptual learning is still inconclusive. Global Reorganization Induced by Perceptual Training Due to a close interaction between feedforward and feedback connections, modification of a cortical area or process by training could in turn affect the other interconnected areas or interdependent processes. Learning-induced changes in multiple visual areas have been reported in a number of human fmri studies, suggesting that perceptual training can cause a global reorganization of sensory representations. In the motion-direction identification task mentioned above, an increase of activation in MT to the trained stimulus is accompanied by reduced activities in the other extrastriate areas, suggesting a more focused representation of the task-relevant signals and removal of irrelevant information (Vaina et al. 1998). After human observers are trained for detection of contour shapes camouflaged in a cluttered background, neural sensitivities to the trained shapes are enhanced across early and higher visual areas along the ventral pathway, indicating that shape representation and learning involve distributed neural processes and plasticity (Kourtzi et al. 2005). In particular, multiple levels of the visual cortex, including the early retinotopic areas V1/V2 and the higher-order visual word form area (VWFA), show stronger activations to written words than scrambled words or other objects, most likely due to familiarity with word forms after long-term reading experience; this also suggests a link between reading expertise and basic perceptual learning (Szwed et al. 2011). Training-induced global cortical reorganization is also evident in a shift of stimulus representation from one visual area to another. For human subjects trained to identify a particular shape Perceptual Learning: Physiology 119

12 among similar distracters, processing of the familiar shape is largely devolved from the lateral occipital cortex (LO) to early retinotopic cortex V1/V2 (Sigman et al. 2005). For monkeys trained in a coarse binary depth identification task (whether a target plane formed by an array of moving dots is in front of or behind the fixation plane in the presence of a cloud of three-dimensional random dots), MT is crucial for this task because its inactivation by muscimol injection drastically impairs behavioral performance (Chowdhury & DeAngelis 2008); however, when the same monkeys are further trained to discriminate fine differences in relative depth between two surfaces forming a center-surround structure, inactivation of MT affects neither the newly learned fine discrimination task nor the previous coarse task. Interestingly, the disparity tuning functions of MT neurons are not significantly changed before and after the fine discrimination training. These findings indicate that the new learning experience has shifted the coarse disparity representation from MT to other cortical areas. Similar results are also observed in human subjects (Chang et al. 2014). Before any training, interference with the left posterior parietal cortex (PPC) by repetitive TMS impairs the coarse stereopsis task, and TMS of the LO has little effect. After the fine depth discrimination training, however, TMS of the PPC no longer affects the behavior, but interference with the LO impairs both the coarse and fine tasks. By combining TMS and fmri in human subjects, a recent study also found that the cortical locus for processing noisy motion signals is shifted from MT to V3A after perceptual training of motion direction discrimination (Chen et al. 2016). Accompanying changes in the visual cortex, learning-induced reduction in activation level is commonly observed in the frontoparietal areas responsible for attention and task control (Lewis et al. 2009, Mukai et al. 2007, Sigman et al. 2005). A recent event-related potential (ERP) study tracked the changes in human brain activities during training in a texture discrimination task (F. Wang et al. 2016). The amplitudes of a late ERP component (P ) recorded from the prefrontal areas shift gradually from positive toward negative (Figure 4). This change is correlated with behavioral improvement on a daily basis, suggesting that the attentional system is involved in and also shaped by perceptual training. Learning-induced changes are also reflected in complex modifications of functional connectivity between the visual cortical areas and the frontoparietal task networks (Lewis et al. 2009). Long-term intense training can even strengthen the structural, white-matter connectivity between the visual and frontal areas, as seen in experienced players of real-time strategy video games (Kim et al. 2015). Compared with simple perceptual learning that is usually specific to the trained, fixed task settings, long-term play of action video games has even much broader impacts on perceptual and cognitive functions (for a recent review, see Green & Bavelier 2015). The gamers surpass nongamers in attentional deployment, perceptual decision making, and multitasking; they also outperform nongamers during typical perceptual training experiments in terms of learning speed and transferability. Therefore, action video games endow the players with a flexible capability of learning to learn (Green & Bavelier 2015). The underlying cortical changes, which are not quite understood yet, must be more sophisticated than those in perceptual learning. Global reorganization in the brain caused by perceptual training is associated with improved sensory representations of the trained stimulus, refined cortical connectivities and readout strategy, and reduced effortfulness, leading to proficiency in the trained task. The global reorganization of cortical activities suggests that simple perceptual learning can cause complex changes in the brain. Multiple cortical areas and processes, as well as the interplay between them, must be taken into account in order to better understand the neural mechanisms underlying perceptual learning. THE ROLE OF TOP-DOWN FEEDBACK As aforementioned, both psychophysical and physiological studies indicate the importance of topdown influences in perceptual learning, and imaging and ERP studies implicate the involvement 120 Li

13 a Stimulus Mask SOA Annu. Rev. Vis. Sci : Downloaded from Amplitude (μv) Figure 4 b P Time relative to stimulus onset (ms) s1 s2 s3 s4 s5 s6 Test II Δ threshold (s6 s1, ms) c r = ΔP amplitude (s6 s1, μv) Perceptual learning progressively modifies prefrontal activities. (a) In the texture discrimination task, the stimulus consisted of three adjacent diagonal bars arranged horizontally or vertically within a background of horizontal bars, with a randomly oriented letter T or L as the fixation. After a brief exposure of the stimulus and a varying delay period [stimulus onset asynchrony (SOA)], a mask was presented to control the texture visibility. The observer was required to report the fixation letter and also to indicate the orientation of the three-bar texture. The texture discrimination threshold was measured as the SOA corresponding to 81.6% correct responses. Training significantly reduced this SOA. (b) Averaged event-related potentials at a frontal electrode in six training sessions (s1 to s6), and two weeks later (test II). (c) Comparison between the behavioral improvement and the amplitude change of the P component (delimited by the shaded area in panel b). Each data point represents a subject. Figure adapted with permission from F. Wang et al. (2016). of the attentional network. During perceptual training, the practice effect could be very small or negligible for some observers (e.g., Mukai et al. 2007, Saffell & Matthews 2003). In the grating contrast discrimination task, the nonlearners, compared with learners, show weaker functional connectivity between the frontoparietal area and the early visual cortex, which also points to the importance of top-down modulation (Mukai et al. 2007). Selective attention to task-relevant stimulus features is usually required for perceptual learning, and the learning effect is specific to the trained task that is, to a collection of cognitive settings that is specifically prepared for execution of the task on the stimulus. Task specificity of perceptual learning could be closely related to task-dependent changes in response properties of visual cortical neurons, as demonstrated in the following studies. In one study (Li et al. 2004), monkeys were trained to perform two different discrimination tasks on an identical set of stimulus patterns by focusing on different stimulus components (Figure 5). In either task, changes in the task-relevant stimulus components remarkably affect responses of V1 neurons, whereas changes in the task-irrelevant components have a significantly weaker effect. Information theoretic analysis indicates that V1 neurons convey more information when the stimulus components are task relevant than when they are irrelevant. Task-dependent changes in V1 were also observed in the contour detection task (McManus et al. 2011). Monkeys were cued, in a delayed match-to-sample task, to detect camouflaged visual Perceptual Learning: Physiology 121

14 Firing rate (spikes/s) a 45 b Task relevant Firing rate (spikes/s) Task irrelevant Annu. Rev. Vis. Sci : Downloaded from e2 Figure 5 s1 e1 s Position of s1 and s Position of e1 and e2 Task-dependent modulation in V1. The stimuli (inset at the lower-left corner) consisted of five lines: an optimally oriented line fixed in the receptive field ( gray square), with four additional flanking lines. In different trials, the position of the two side flankers (s1, s2) was randomly chosen from a set of five predefined positions, varying their relative distance from the fixed central line; the position of the two end flankers (e1, e2) was also randomly chosen from a set of five predefined positions with different lateral offsets relative to the central line. The animal was cued to perform either a bisection task (determining whether the central line was closer to s1 or s2) or a vernier task (determining whether the central line was misaligned with e1 and e2 to either side) on the same set of five-line stimuli. (a) Responses of a V1 cell as a function of the position of s1 and s2 in the bisection task, in which s1 and s2 were task relevant, or in the vernier task, in which the same s1 and s2 were task irrelevant. Trials with different end-flanker positions were pooled at each position of s1 and s2 (labeled from 2to+2, corresponding to the 5 insets). (b) Responses of a V1 cell as a function of the position of e1 and e2 in the vernier task, in which e1 and e2 were task relevant, or in the bisection task, in which the same e1 and e2 were task irrelevant. Trials with different side-flanker positions were pooled at each position of e1 and e2 (labeled from 2 to+2, corresponding to the 5 insets). Figure adapted with permission from Li et al. (2004). contours of a given shape (straight, circular, or wave-like). Depending on the cued shape, V1 neurons dynamically change their shape selectivity, showing stronger responses to contour shapes resembling the cued one. The above studies show that the task-dependent modulatory mechanisms endow the same neurons with flexible function-switching capability. However, if different tasks require the engagement of different cortical areas, the top-down influences may also selectively target and modify those areas that are closely involved. In an fmri study (Song et al. 2010), the same set of novel, artificial visual objects were used for training two groups of human subjects in two completely different tasks: one group to associate these objects with arbitrary words and the other group to discriminate their differences. After training, the activations in the VWFA (specialized in processing of visual word forms) and the LO (involved in object recognition) are respectively enhanced in these two groups of subjects in response to the same set of trained objects. The behavioral consequence is no learning transfer between the two different tasks. Top-down influences are mediated by feedback connections. In the contour detection task, feedback inputs to V1 are shown to selectively enhance the contour signals and suppress background elements (Chen et al. 2014, Piëch et al. 2013). Training may optimize the perceptual templates implemented in higher-level visual cortex (Kuai et al. 2013), which in turn could refine 122 Li

15 sensory processing in early cortical areas by feedback modulation. This conjecture is supported by a recent study combining two-photon calcium imaging with tracing and optogenetic approaches in mice trained in a visual association task (Makino & Komiyama 2015): Learning alters the balance between bottom-up and top-down inputs to V1, resulting in stronger feedback modulation and more behavior-relevant information conveyed by layer 2/3 excitatory neurons. Computational modeling also shows that orientation discrimination training can strengthen feedback inputs to V1, which in turn can sharpen orientation tuning function (Moldakarimov et al. 2014). It is worth pointing out that the modulatory signals seen in the visual cortex, such as V1 and V4, are not restricted to attentional modulation; they could be related to reward values (Baruni et al. 2015, Stănişor et al. 2013). All these behavior-relevant modulatory signals collectively contribute to improving neuronal representations and behavioral performance during learning (Poort et al. 2015, Roelfsema et al. 2010). The task-dependent top-down modulatory mechanisms, which are also present in the other sensory modalities (Fritz et al. 2005, Polley et al. 2006), could play an important role in perceptual learning that is highly specific to the trained task: Repeated execution of the same perceptual task, and therefore repetitive exertion of task-specific top-down influences on sensory processing, can potentiate the dynamic cortical changes useful for solving the specific task. SUMMARIES AND OUTLOOK Perceptual learning studies have been going through a long-lasting debate about the cortical loci and the neural codes for the practice effects. An unbiased point of view should take into account the nature of visual perception, which is an active and constructive process involving many interconnected cortical areas and brain functions. Different stimulus and task settings may employ different functional modules. In addition, currently available experimental approaches, all with their own limitations, are not able to fully unfold all of the changes in the brain that take place during training. Therefore, different studies may overemphasize or overlook different aspects of learning-induced changes. Nonetheless, when pieced together, available observations have begun to generate a global picture of the underlying neural mechanisms. The current understanding of the neurophysiological mechanisms of perceptual learning is largely contributed to by observations on neural correlates of the practice effects in individual cortical areas. Given the complexity of use-dependent cortical changes, this approach could lead to misinterpretations of the underlying processes. Taking advantage of fast-developing recording and interrogating techniques in animal studies such as various chronic microelectrode arrays, in vivo multiphoton microscopy, and genetic and optogenetic manipulation of specific neurons future studies should focus on learning-induced changes in spatiotemporal dynamics of the intra- and intercortical circuitry, dissecting the respective roles of feedforward, feedback, and intracortical connections. These approaches are rather mature in rodent models and should be applicable, in the near future, to behaving monkeys in which many perceptual learning paradigms used in humans can be examined at the synaptic, cellular, and circuitry levels. SUMMARY POINTS 1. Visual perceptual learning, as well as its specificity, is not necessarily caused by a specific change in hard-wired neural circuitry within the trained retinotopic cortical regions; rather, it could result from specific interactions between bottom-up and top-down processes. Perceptual Learning: Physiology 123

16 2. Task-dependent top-down influences play a pivotal role in encoding and retrieval of the learned stimulus attributes, which is consistent with the task specificity of perceptual learning. 3. Perceptual training can cause a global reorganization of neural activities across cortical areas that are specialized for sensory processing and cognitive control. This process results in a refinement of the sensory representations and optimization of the cortical connectivities, leading to reduced demands for cognitive resources and automatization of the trained task. 4. Despite a possible chain of interconnected changes across multiple areas and processes, the early visual cortex contributes to the transformation of these changes into a more informative sensory representation of the learned stimulus, which in turn contributes to a more efficient and less effortful readout at subsequent processing stages. FUTURE ISSUES 1. Recent psychophysical findings of learning transfer across stimulus conditions suggest that implicit perceptual learning shares some high-level components with explicit cognitive learning (e.g., category learning) that is transferable to transformations (e.g., rotation, translation) and variations of the learned stimulus. This transferability is crucial for invariant perception of the same stimulus under diverse viewing conditions and also for categorization of diverse stimuli. More direct physiological evidence is needed to explain the transferability and specificity of perceptual learning under various training conditions. 2. Top-down modulation of sensory processing shares many features with perceptual learning, though they operate at different timescales. Future physiological studies need to clarify whether and how simple perceptual training alters the hard-wired neural circuitry in the sensory cortex. This will further help resolve the current debate on the neural mechanisms of perceptual learning; it will also contribute to the understanding of how the brain maintains a balance between pliability and stability in learning a new skill without affecting previously acquired skills and producing unwanted side effects. 3. Visual perceptual learning may share similar mechanisms with other forms of skill learning in spite of the involvement of different sensory (or motor) areas. Studies from different modalities should be closely compared in order to identify and understand the most critical neural processes responsible for learning-induced cortical and behavioral changes. This will further our understanding of use-dependent cortical plasticity in general. 4. Perceptual training under laboratory conditions is normally conducted for a relatively short period of time with relatively simple settings. This is quite different from the reallife situations, such as in some professions highly engaging the sensory modalities or in action video game players. The difference in cortical plasticity induced by these different training experiences remains unclear. The answer to this question may help develop effective training protocols with definable bases to enhance or restore brain functions and to minimize the limitations of learning specificity. 124 Li

17 DISCLOSURE STATEMENT The author is not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review. ACKNOWLEDGMENTS The author is supported by the National Natural Science Foundation of China ( ) and the National Key Basic Research Program of China (2014CB846101). Annu. Rev. Vis. Sci : Downloaded from LITERATURE CITED Abe H, McManus JNJ, Ramalingam N, Li W, Marik SA, et al Adult cortical plasticity studied with chronically implanted electrode arrays. J. Neurosci. 35: Adab HZ, Popivanov ID, Vanduffel W, Vogels R Perceptual learning of simple stimuli modifies stimulus representations in posterior inferior temporal cortex. J. Cogn. Neurosci. 26: Adab HZ, Vogels R Practicing coarse orientation discrimination improves orientation signals in macaque cortical area V4. Curr. Biol. 21: Ahissar M, Hochstein S Attentional control of early perceptual learning. PNAS 90: Ahissar M, Hochstein S Learning pop-out detection: specificities to stimulus characteristics. Vis. Res. 36: Ahissar M, Hochstein S Task difficulty and the specificity of perceptual learning. Nature 387:401 6 Amitay S, Irwin A, Moore DR Discrimination learning induced by training with identical stimuli. Nat. Neurosci. 9: Averbeck BB, Latham PE, Pouget A Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7: Baker CI, Peli E, Knouf N, Kanwisher NG Reorganization of visual processing in macular degeneration. J. Neurosci. 25: Ball K, Sekuler R A specific and enduring improvement in visual motion discrimination. Science 218: Ball K, Sekuler R Direction-specific improvement in motion discrimination. Vis. Res. 27: Baruni JK, Lau B, Salzman CD Reward expectation differentially modulates attentional behavior and activity in visual area V4. Nat. Neurosci. 18: Bejjanki VR, Beck JM, Lu Z-L, Pouget A Perceptual learning as improved probabilistic inference in early sensory areas. Nat. Neurosci. 14: Bejjanki VR, Zhang R, Li R, Pouget A, Green CS, et al Action video game play facilitates the development of better perceptual templates. PNAS 111: Berardi N, Fiorentini A Interhemispheric transfer of visual information in humans: spatial characteristics. J. Physiol. 384: Beste C, Dinse HR Learning without training. Curr. Biol. 23:R Bi T, Chen J, Zhou T, He Y, Fang F Function and structure of human left fusiform cortex are closely associated with perceptual learning of faces. Curr. Biol. 24: Blakemore C, Cooper GF Development of the brain depends on the visual environment. Nature 228: Brown M, Irvine DRF, Park VN Perceptual learning on an auditory frequency discrimination task by cats: association with changes in primary auditory cortex. Cereb. Cortex 14: Chang DHF, Mevorach C, Kourtzi Z, Welchman AE Training transfers the limits on perception from parietal to ventral cortex. Curr. Biol. 24: Chen M, Yan Y, Gong X, Gilbert CD, Liang H, Li W Incremental integration of global contours through interplay between visual cortical areas. Neuron 82: Chen N, Bi T, Zhou T, Li S, Liu Z, Fang F Sharpened cortical tuning and enhanced cortico-cortical communication contribute to the long-term neural mechanisms of visual motion perceptual learning. NeuroImage 115: Perceptual Learning: Physiology 125

18 Chen N, Cai P, Zhou T, Thompson B, Fang F Perceptual learning modifies the functional specializations of visual cortical areas. PNAS 113: Choi H, Watanabe T Perceptual learning solely induced by feedback. Vis. Res. 61:77 82 Chowdhury SA, DeAngelis GC Fine discrimination training alters the causal contribution of macaque area MT to depth perception. Neuron 60: Crist RE, Kapadia MK, Westheimer G, Gilbert CD Perceptual learning of spatial localization: specificity for orientation, position, and context. J. Neurophysiol. 78: Crist RE, Li W, Gilbert CD Learning to see: experience and attention in primary visual cortex. Nat. Neurosci. 4: De Weerd P, Reithler J, van de Ven V, Been M, Jacobs C, Sack AT Posttraining transcranial magnetic stimulation of striate cortex disrupts consolidation early in visual skill learning. J. Neurosci. 32: Dilks DD, Serences JT, Rosenau BJ, Yantis S, McCloskey M Human adult cortical reorganization and consequent visual distortion. J. Neurosci. 27: Dosher BA, Lu Z-L Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting. PNAS 95: Fahle M, Daum I Perceptual learning in amnesia. Neuropsychologia 40: Fahle M, Edelman S, Poggio T Fast perceptual learning in hyperacuity. Vis. Res. 35: Fiorentini A, Berardi N Perceptual learning specific for orientation and spatial frequency. Nature 287:43 44 Fiorentini A, Berardi N Learning in grating waveform discrimination: specificity for orientation and spatial frequency. Vis. Res. 21: Fritz J, Elhilali M, Shamma S Active listening: task-dependent plasticity of spectrotemporal receptive fields in primary auditory cortex. Hear. Res. 206: Furmanski CS, Schluppeck D, Engel SA Learning strengthens the response of primary visual cortex to simple patterns. Curr. Biol. 14: Ghose GM, Yang T, Maunsell JHR Physiological correlates of perceptual learning in monkey V1 and V2. J. Neurophysiol. 87: Gilbert CD, Wiesel TN Receptive field dynamics in adult primary visual cortex. Nature 356: Goujon A, Didierjean A, Thorpe S Investigating implicit statistical learning mechanisms through contextual cueing. Trends Cogn. Sci. 19: Green CS, Bavelier D Action video game training for cognitive enhancement. Curr. Opin. Behav. Sci. 4:103 8 Grzeczkowski L, Tartaglia EM, Mast FW, Herzog MH Linking perceptual learning with identical stimuli to imagery perceptual learning. J. Vis. 15(10):13 Gu Y, Liu S, Fetsch CR, Yang Y, Fok S, et al Perceptual learning reduces interneuronal correlations in macaque visual cortex. Neuron 71: Han YK, Köver H, Insanally MN, Semerdjian JH, Bao S Early experience impairs perceptual discrimination. Nat. Neurosci. 10: Herzog MH, Fahle M The role of feedback in learning a vernier discrimination task. Vis. Res. 37: Hua T, Bao P, Huang C-B, Wang Z, Xu J, et al Perceptual learning improves contrast sensitivity of V1 neurons in cats. Curr. Biol. 20: Hubel DH, Wiesel TN Receptive fields of single neurones in the cat s striate cortex. J. Physiol. 148: Hubel DH, Wiesel TN Period of susceptibility to physiological effects of unilateral eye closure in kittens. J. Physiol. 206: Hung S-C, Seitz AR Prolonged training at threshold promotes robust retinotopic specificity in perceptual learning. J. Neurosci. 34: Jehee JFM, Ling S, Swisher JD, van Bergen RS, Tong F Perceptual learning selectively refines orientation representations in early visual cortex. J. Neurosci. 32: Jeter PE, Dosher BA, Liu S-H, Lu Z-L Specificity of perceptual learning increases with increased training. Vis. Res. 50: Jeter PE, Dosher BA, Petrov A, Lu Z-L Task precision at transfer determines specificity of perceptual learning. J. Vis. 9(3):1 126 Li

19 Kaas JH, Krubitzer LA, Chino YM, Langston AL, Polley EH, Blair N Reorganization of retinotopic cortical maps in adult mammals after lesions of the retina. Science 248: Kahnt T, Grueschow M, Speck O, Haynes J-D Perceptual learning and decision-making in human medial frontal cortex. Neuron 70: Karni A, Sagi D Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. PNAS 88: Kim Y-H, Kang D-W, Kim D, Kim H-J, Sasaki Y, Watanabe T Real-time strategy video game experience and visual perceptual learning. J. Neurosci. 35: Kobatake E, Wang G, Tanaka K Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. J. Neurophysiol. 80: Kourtzi Z, Betts LR, Sarkheil P, Welchman AE Distributed neural plasticity for shape learning in the human visual cortex. PLOS Biol. 3: Kuai S-G, Levi D, Kourtzi Z Learning optimizes decision templates in the human visual cortex. Curr. Biol. 23: Law C-T, Gold JI Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11: Law C-T, Gold JI Reinforcement learning can account for associative and perceptual learning on a visual-decision task. Nat. Neurosci. 12: Lewis CM, Baldassarre A, Committeri G, Romani GL, Corbetta M Learning sculpts the spontaneous activity of the resting human brain. PNAS 106: Li W, Piëch V, Gilbert CD Perceptual learning and top-down influences in primary visual cortex. Nat. Neurosci. 7: Li W, Piëch V, Gilbert CD Contour saliency in primary visual cortex. Neuron 50: Li W, Piëch V, Gilbert CD Learning to link visual contours. Neuron 57: Liu Z, Weinshall D Mechanisms of generalization in perceptual learning. Vis. Res. 40: Logothetis NK, Pauls J, Poggio T Shape representation in the inferior temporal cortex of monkeys. Curr. Biol. 5: Makino H, Komiyama T Learning enhances the relative impact of top-down processing in the visual cortex. Nat. Neurosci. 18: McManus JNJ, Li W, Gilbert CD Adaptive shape processing in primary visual cortex. PNAS 108: Messinger A, Squire LR, Zola SM, Albright TD Neural correlates of knowledge: stable representation of stimulus associations across variations in behavioral performance. Neuron 48: Miyashita Y Inferior temporal cortex: where visual perception meets memory. Annu. Rev. Neurosci. 16: Miyashita Y Cognitive memory: cellular and network machineries and their top-down control. Science 306: Moldakarimov S, Bazhenov M, Sejnowski TJ Top-down inputs enhance orientation selectivity in neurons of the primary visual cortex during perceptual learning. PLOS Comput. Biol. 10:e Molina-Luna K, Hertler B, Buitrago MM, Luft AR Motor learning transiently changes cortical somatotopy. NeuroImage 40: Mukai I, Kim D, Fukunaga M, Japee S, Marrett S, Ungerleider LG Activations in visual and attentionrelated areas predict and correlate with the degree of perceptual learning. J. Neurosci. 27: Op de Beeck HP, Baker CI, DiCarlo JJ, Kanwisher NG Discrimination training alters object representations in human extrastriate cortex. J. Neurosci. 26: Otto TU, Öğmen H, Herzog MH Perceptual learning in a nonretinotopic frame of reference. Psychol. Sci. 21: Petrov AA, Dosher BA, Lu Z-L The dynamics of perceptual learning: an incremental reweighting model. Psychol. Rev. 112: Piëch V, Li W, Reeke GN, Gilbert CD Network model of top-down influences on local gain and contextual interactions in visual cortex. PNAS 110:E Poggio T, Fahle M, Edelman S Fast perceptual learning in visual hyperacuity. Science 256: Perceptual Learning: Physiology 127

20 Polley DB, Steinberg EE, Merzenich MM Perceptual learning directs auditory cortical map reorganization through top-down influences. J. Neurosci. 26: Poort J, Khan AG, Pachitariu M, Nemri A, Orsolic I, et al Learning enhances sensory and multiple non-sensory representations in primary visual cortex. Neuron 86: Raiguel S, Vogels R, Mysore SG, Orban GA Learning to see the difference specifically alters the most informative V4 neurons. J. Neurosci. 26: Rainer G, Lee H, Logothetis NK The effect of learning on the function of monkey extrastriate visual cortex. PLOS Biol. 2: Ramachandran VS, Braddick O Orientation-specific learning in stereopsis. Perception 2: Recanzone GH, Merzenich MM, Jenkins WM. 1992a. Frequency discrimination training engaging a restricted skin surface results in an emergence of a cutaneous response zone in cortical area 3a. J. Neurophysiol. 67: Recanzone GH, Merzenich MM, Jenkins WM, Grajski KA, Dinse HR. 1992b. Topographic reorganization of the hand representation in cortical area 3b of owl monkeys trained in a frequency-discrimination task. J. Neurophysiol. 67: Recanzone GH, Schreiner CE, Merzenich MM Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. J. Neurosci. 13: Reed A, Riley J, Carraway R, Carrasco A, Perez C, et al Cortical map plasticity improves learning but is not necessary for improved performance. Neuron 70: Roelfsema PR, van Ooyen A, Watanabe T Perceptual learning rules based on reinforcers and attention. Trends Cogn. Sci. 14:64 71 Saffell T, Matthews N Task-specific perceptual learning on speed and direction discrimination. Vis. Res. 43: Sagi D Perceptual learning in Vision Research. Vis. Res. 51: Schiltz C, Bodart JM, Dubois S, Dejardin S, Michel C, et al Neuronal mechanisms of perceptual learning: changes in human brain activity with training in orientation discrimination. NeuroImage 9:46 62 Schoups A, Vogels R, Orban GA Human perceptual learning in identifying the oblique orientation: retinotopy, orientation specificity and monocularity. J. Physiol. 483: Schoups A, Vogels R, Qian N, Orban G Practising orientation identification improves orientation coding in V1 neurons. Nature 412: Schwartz S, Maquet P, Frith C Neural correlates of perceptual learning: a functional MRI study of visual texture discrimination. PNAS 99: Seitz AR, Kim D, Watanabe T Rewards evoke learning of unconsciously processed visual stimuli in adult humans. Neuron 61:700 7 Series P, Latham PE, Pouget A Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations. Nat. Neurosci. 7: Shibata K, Watanabe T, Sasaki Y, Kawato M Perceptual learning incepted by decoded fmri neurofeedback without stimulus presentation. Science 334: Shiu L-P, Pashler H Improvement in line orientation discrimination is retinally local but dependent on cognitive set. Percept. Psychophys. 52: Sigman M, Gilbert CD Learning to find a shape. Nat. Neurosci. 3: Sigman M, Pan H, Yang Y, Stern E, Silbersweig D, Gilbert CD Top-down reorganization of activity in the visual pathway after learning a shape identification task. Neuron 46: Song Y, Hu S, Li X, Li W, Liu J The role of top-down task context in learning to perceive objects. J. Neurosci. 30: Spillmann L, Dresp-Langley B, Tseng C-H Beyond the classical receptive field: the effect of contextual stimuli. J. Vis. 15(9):7 Squire LR The legacy of patient H.M. for neuroscience. Neuron 61:6 9 Squire LR, Stark CEL, Clark RE The medial temporal lobe. Annu. Rev. Neurosci. 27: Squire LR, Zola SM Structure and function of declarative and nondeclarative memory systems. PNAS 93: Li

21 Stănişor L, van der Togt C, Pennartz CMA, Roelfsema PR A unified selection signal for attention and reward in primary visual cortex. PNAS 110: Szwed M, Dehaene S, Kleinschmidt A, Eger E, Valabrègue R, et al Specialization for written words over objects in the visual cortex. NeuroImage 56: Takeda M, Koyano KW, Hirabayashi T, Adachi Y, Miyashita Y Top-down regulation of laminar circuit via inter-area signal for successful object memory recall in monkey temporal cortex. Neuron 86: Tartaglia EM, Bamert L, Herzog MH, Mast FW Perceptual learning of motion discrimination by mental imagery. J. Vis. 12(6):14 Tartaglia EM, Bamert L, Mast FW, Herzog MH Human perceptual learning by mental imagery. Curr. Biol. 19: Thorndike EL, Woodworth RS. 1901a. The influence of improvement in one mental function upon the efficiency of other functions. (I). Psychol. Rev. 8: Thorndike EL, Woodworth RS. 1901b. The influence of improvement in one mental function upon the efficiency of other functions. II. The estimation of magnitudes. Psychol. Rev. 8: Turk-Browne NB, Scholl BJ, Chun MM, Johnson MK Neural evidence of statistical learning: efficient detection of visual regularities without awareness. J. Cogn. Neurosci. 21: Vaina LM, Belliveau JW, des Roziers EB, Zeffiro TA Neural systems underlying learning and representation of global motion. PNAS 95: Vidnyánszky Z, Sohn W Learning to suppress task-irrelevant visual stimuli with attention. Vis. Res. 45: Vogels R, Orban GA The effect of practice on the oblique effect in line orientation judgments. Vis. Res. 25: Wang F, Huang J, Lv Y, Ma X, Yang B, et al Predicting perceptual learning from higher-order cortical processing. NeuroImage 124: Wang R, Wang J, Zhang J-Y, Xie X-Y, Yang Y-X, et al Perceptual learning at a conceptual level. J. Neurosci. 36: Wang R, Zhang J-Y, Klein SA, Levi DM, Yu C Task relevancy and demand modulate double-training enabled transfer of perceptual learning. Vis. Res. 61:33 38 Wang R, Zhang J-Y, Klein SA, Levi DM, Yu C Vernier perceptual learning transfers to completely untrained retinal locations after double training: a piggybacking effect. J. Vis. 14(13):12 Watanabe T, Nanez JE, Sasaki Y Perceptual learning without perception. Nature 413: Watanabe T, Sasaki Y Perceptual learning: toward a comprehensive theory. Annu. Rev. Psychol. 66: Weinberger NM Dynamic regulation of receptive fields and maps in the adult sensory cortex. Annu. Rev. Neurosci. 18: Westheimer G, Truong TT Target crowding in foveal and peripheral stereoacuity. Am. J. Optom. Physiol. Opt. 65: Wiesel TN, Hubel DH Single-cell responses in striate cortex of kittens deprived of vision in one eye. J. Neurophysiol. 26: Xiao L-Q, Zhang J-Y, Wang R, Klein SA, Levi DM, Yu C Complete transfer of perceptual learning across retinal locations enabled by double training. Curr. Biol. 18: Xue G, Dong Q, Chen C, Lu Z, Mumford JA, Poldrack RA Greater neural pattern similarity across repetitions is associated with better memory. Science 330: Yamahachi H, Marik SA, McManus JNJ, Denk W, Gilbert CD Rapid axonal sprouting and pruning accompany functional reorganization in primary visual cortex. Neuron 64: Yan Y, Rasch MJ, Chen M, Xiang X, Huang M, et al Perceptual training continuously refines neuronal population codes in primary visual cortex. Nat. Neurosci. 17: Yang T, Maunsell JHR The effect of perceptual learning on neuronal responses in monkey visual area V4. J. Neurosci. 24: Yotsumoto Y, Watanabe T, Sasaki Y Different dynamics of performance and brain activation in the time course of perceptual learning. Neuron 57: Yu C, Klein SA, Levi DM Perceptual learning in contrast discrimination and the (minimal) role of context. J. Vis. 4(3): Perceptual Learning: Physiology 129

22 Zhang E, Li W Perceptual learning beyond retinotopic reference frame. PNAS 107: Zhang E, Zhang G-L, Li W Spatiotopic perceptual learning mediated by retinotopic processing and attentional remapping. Eur. J. Neurosci. 38: Zhang G-L, Li H, Song Y, Yu C ERP C1 is top-down modulated by orientation perceptual learning. J. Vis. 15(10):8 Zhang J-Y, Zhang G-L, Xiao L-Q, Klein SA, Levi DM, Yu C Rule-based learning explains visual perceptual learning and its specificity and transfer. J. Neurosci. 30: Li

23 ANNUAL REVIEWS Connect With Our Experts New From Annual Reviews: Annual Review of Cancer Biology cancerbio.annualreviews.org Volume 1 March 2017 ONLINE NOW! Annu. Rev. Vis. Sci : Downloaded from Co-Editors: Tyler Jacks, Massachusetts Institute of Technology Charles L. Sawyers, Memorial Sloan Kettering Cancer Center The Annual Review of Cancer Biology reviews a range of subjects representing important and emerging areas in the field of cancer research. The Annual Review of Cancer Biology includes three broad themes: Cancer Cell Biology, Tumorigenesis and Cancer Progression, and Translational Cancer Science. TABLE OF CONTENTS FOR VOLUME 1: How Tumor Virology Evolved into Cancer Biology and Transformed Oncology, Harold Varmus The Role of Autophagy in Cancer, Naiara Santana-Codina, Joseph D. Mancias, Alec C. Kimmelman Cell Cycle Targeted Cancer Therapies, Charles J. Sherr, Jiri Bartek Ubiquitin in Cell-Cycle Regulation and Dysregulation in Cancer, Natalie A. Borg, Vishva M. Dixit The Two Faces of Reactive Oxygen Species in Cancer, Colleen R. Reczek, Navdeep S. Chandel Analyzing Tumor Metabolism In Vivo, Brandon Faubert, Ralph J. DeBerardinis Stress-Induced Mutagenesis: Implications in Cancer and Drug Resistance, Devon M. Fitzgerald, P.J. Hastings, Susan M. Rosenberg Synthetic Lethality in Cancer Therapeutics, Roderick L. Beijersbergen, Lodewyk F.A. Wessels, René Bernards Noncoding RNAs in Cancer Development, Chao-Po Lin, Lin He p53: Multiple Facets of a Rubik s Cube, Yun Zhang, Guillermina Lozano Resisting Resistance, Ivana Bozic, Martin A. Nowak Deciphering Genetic Intratumor Heterogeneity and Its Impact on Cancer Evolution, Rachel Rosenthal, Nicholas McGranahan, Javier Herrero, Charles Swanton Immune-Suppressing Cellular Elements of the Tumor Microenvironment, Douglas T. Fearon Overcoming On-Target Resistance to Tyrosine Kinase Inhibitors in Lung Cancer, Ibiayi Dagogo-Jack, Jeffrey A. Engelman, Alice T. Shaw Apoptosis and Cancer, Anthony Letai Chemical Carcinogenesis Models of Cancer: Back to the Future, Melissa Q. McCreery, Allan Balmain Extracellular Matrix Remodeling and Stiffening Modulate Tumor Phenotype and Treatment Response, Jennifer L. Leight, Allison P. Drain, Valerie M. Weaver Aneuploidy in Cancer: Seq-ing Answers to Old Questions, Kristin A. Knouse, Teresa Davoli, Stephen J. Elledge, Angelika Amon The Role of Chromatin-Associated Proteins in Cancer, Kristian Helin, Saverio Minucci Targeted Differentiation Therapy with Mutant IDH Inhibitors: Early Experiences and Parallels with Other Differentiation Agents, Eytan Stein, Katharine Yen Determinants of Organotropic Metastasis, Heath A. Smith, Yibin Kang Multiple Roles for the MLL/COMPASS Family in the Epigenetic Regulation of Gene Expression and in Cancer, Joshua J. Meeks, Ali Shilatifard Chimeric Antigen Receptors: A Paradigm Shift in Immunotherapy, Michel Sadelain ANNUAL REVIEWS CONNECT WITH OUR EXPERTS / (us/can) service@annualreviews.org

24 Contents Annual Review of Vision Science Volume 2, 2016 The Road to Certainty and Back Gerald Westheimer 1 Annu. Rev. Vis. Sci : Downloaded from Experience-Dependent Structural Plasticity in the Visual System Kalen P. Berry and Elly Nedivi 17 Strabismus and the Oculomotor System: Insights from Macaque Models Vallabh E. Das 37 Corollary Discharge and Oculomotor Proprioception: Cortical Mechanisms for Spatially Accurate Vision Linus D. Sun and Michael E. Goldberg 61 Mechanisms of Orientation Selectivity in the Primary Visual Cortex Nicholas J. Priebe 85 Perceptual Learning: Use-Dependent Cortical Plasticity Wu Li 109 Early Visual Cortex as a Multiscale Cognitive Blackboard Pieter R. Roelfsema and Floris P. de Lange 131 Ocular Photoreception for Circadian Rhythm Entrainment in Mammals Russell N. Van Gelder and Ethan D. Buhr 153 Probing Human Visual Deficits with Functional Magnetic Resonance Imaging Stelios M. Smirnakis 171 Retinoids and Retinal Diseases Philip D. Kiser and Krzysztof Palczewski 197 Understanding Glaucomatous Optic Neuropathy: The Synergy Between Clinical Observation and Investigation Harry A. Quigley 235 Vision and Aging Cynthia Owsley 255 Electrical Stimulation of the Retina to Produce Artificial Vision James D. Weiland, Steven T. Walston, and Mark S. Humayun 273 v