Learning to see the difference specifically alters the most informative V4 neurons

Cholinergic Neuromodulation of Prefrontal Attractor Dynamics Controls Performance in Spatial Working Memory

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the nucleus basalis (NB) of Meynert have been recently examined in two male monkeys (Qi et al., 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in the PFC.

Category representation in primary visual cortex after visual perceptual learning

The visual perceptual learning (VPL) leads to long-term enhancement of visual task performance. The subjects are often trained to link different visual stimuli to several options, such as the widely used two-alternative forced choice (2AFC) task, which involves an implicit categorical decision. The enhancement of performance has been related to the specific changes of neural activities, but few studies investigate the effects of categorical responding on the changes of neural activities. Here we investigated whether the neural activities would exhibit the categorical characteristics if the subjects are requested to respond visual stimuli in a categorical manner during VPL. We analyzed the neural activities of two monkeys in a contour detection VPL. We found that the neural activities in primary visual cortex (V1) converge to one pattern if the contour can be detected by monkey and another pattern if the contour cannot be detected, exhibiting a kind of category learning that the neural representations of detectable contour become less selective for number of bars forming contour and diverge from the representations of undetectable contour.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09926-8.

Cholinergic neuromodulation of prefrontal attractor dynamics controls performance in spatial working memory

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the Nucleus Basalis of Meynert (NB) have been recently examined (Qi et al. 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in PFC.

Improving Visual Working Memory with Cholinergic Deep Brain Stimulation

Acetylcholine is a critical modulatory neurotransmitter for cognitive function. Cholinergic drugs improve cognitive performance and enhance neuronal activity in the sensory and association cortices. An alternative means of improving cognitive function is through the use of deep brain stimulation. Prior animal studies have demonstrated that stimulation of the nucleus basalis of Meynert through DBS improves cognitive performance on a visual working memory task to the same degree as cholinesterase inhibitors. Additionally, unlike current pharmacological treatments for neurocognitive disorders, DBS does not lose efficacy over time and adverse effects are rare. These findings suggest that DBS may be a promising alternative for treating cognitive impairments in neurodegenerative disorders such as Alzheimer's disease. Thus, further research and human trials should be considered to assess the potential of DBS as a therapeutic treatment for these disorders.

Perceptual learning: Breaking specificity by variability

A new psychophysical study in humans suggests that increasing the variability of task-irrelevant features during training enhances the generalization of visual perceptual learning to untrained stimuli and locations.

Many Roads Lead to Rome: Differential Learning Processes for the Same Perceptual Improvement

Repeatedly exercising a perceptual ability usually leads to improvement, yet it is unclear whether the mechanisms supporting the same perceptual learning could be flexibly adjusted according to the training settings. Here, we trained adult observers in an orientation-discrimination task at either a single (focused) retinal location or multiple (distributed) retinal locations. We examined the observers' discriminability (N = 52) and bias (N = 20) in orientation perception at the trained and untrained locations. The focused and distributed training enhanced orientation discriminability by the same amount and induced a bias in perceived orientation at the trained locations. Nevertheless, the distributed training promoted location generalization of both practice effects, whereas the focused training resulted in specificity. The two training tactics also differed in long-term retention of the training effects. Our results suggest that, depending on the training settings of the same task, the same discrimination learning could differentially engage location-specific and location-invariant representations of the learned stimulus feature.

Neural fatigue by passive induction: repeated stimulus exposure results in cognitive fatigue and altered representations in task-relevant networks

Cognitive fatigue is defined by a reduced capacity to perform mental tasks. Despite its pervasiveness, the underlying neural mechanisms remain elusive. Specifically, it is unclear whether prolonged effort affects performance through alterations in over-worked task-relevant neuronal assemblies. Our paradigm based on repeated passive visual stimulation discerns fatigue effects from the influence of motivation, skill and boredom. We induced performance loss and observed parallel alterations in the neural blueprint of the task, by mirroring behavioral performance with multivariate neuroimaging techniques (MVPA) that afford a subject-specific approach. Crucially, functional areas that responded the most to repeated stimulation were also the most affected. Finally, univariate analysis revealed clusters displaying significant disruption within the extrastriate visual cortex. In sum, here we show that repeated stimulation impacts the implicated brain areas' activity and causes tangible behavioral repercussions, providing evidence that cognitive fatigue can result from local, functional, disruptions in the neural signal induced by protracted recruitment.

Minimum perturbation theory of deep perceptual learning

Perceptual learning (PL) involves long-lasting improvement in perceptual tasks following extensive training and is accompanied by modified neuronal responses in sensory cortical areas in the brain. Understanding the dynamics of PL and the resultant synaptic changes is important for causally connecting PL to the observed neural plasticity. This is theoretically challenging because learning-related changes are distributed across many stages of the sensory hierarchy. In this paper, we modeled the sensory hierarchy as a deep nonlinear neural network and studied PL of fine discrimination, a common and well-studied paradigm of PL. Using tools from statistical physics, we developed a mean-field theory of the network in the limit of a large number of neurons and large number of examples. Our theory suggests that, in this thermodynamic limit, the input-output function of the network can be exactly mapped to that of a deep linear network, allowing us to characterize the space of solutions for the task. Surprisingly, we found that modifying synaptic weights in the first layer of the hierarchy is both sufficient and necessary for PL. To address the degeneracy of the space of solutions, we postulate that PL dynamics are constrained by a normative minimum perturbation (MP) principle, which favors weight matrices with minimal changes relative to their prelearning values. Interestingly, MP plasticity induces changes to weights and neural representations in all layers of the network, except for the readout weight vector. While weight changes in higher layers are not necessary for learning, they help reduce overall perturbation to the network. In addition, such plasticity can be learned simply through slow learning. We further elucidate the properties of MP changes and compare them against experimental findings. Overall, our statistical mechanics theory of PL provides mechanistic and normative understanding of several important empirical findings of PL.

Task Context Modulates Feature-Selective Responses in Area V4

Feature selectivity of visual cortical responses measured during passive fixation provides only a partial view of selectivity because it does not account for the influence of cognitive factors. Here we focus on primate area V4 and ask how neuronal tuning is modulated by task engagement. We investigated whether responses to colored shapes during active shape discrimination are simple, stimulus-agnostic, scaled versions of responses during passive fixation, akin to results from attentional studies. Alternatively, responses could be subject to stimulus-specific scaling, that is, responses to different stimuli are modulated differently, resulting in changes in underlying shape/color selectivity. Among 83 well-isolated V4 neurons in two male macaques, only a minority (16 of 83), which were weakly tuned to both shape and color, displayed responses during fixation and discrimination tasks that could be related by stimulus-agnostic scaling. The majority (67 of 83), which were strongly tuned to shape, color, or both, displayed stimulus-dependent response changes during discrimination. For some of these neurons (39 of 83), the shape or color of the stimulus dictated the magnitude of the change, and for others (28 of 83) it was the combination of stimulus shape and color. Importantly, for neurons with one strong and one weak tuning dimension, stimulus-dependent response changes during discrimination were associated with a relative increase in selectivity along the stronger tuning dimension, without changes in tuning peak. These results reveal that more strongly tuned V4 neurons may also be more flexible in their selectivity, and imbalances in selectivity are amplified during active task contexts.SIGNIFICANCE STATEMENT Tuning for stimulus features is typically characterized by recording responses during passive fixation, but cognitive factors, including attention, influence responses in visual cortex. To determine how behavioral engagement influences neuronal responses in area V4, we compared responses to colored shapes during passive fixation and active behavior. For a large fraction of neurons, differences in responses between passive fixation and active behavior depended on the identity of the visual stimulus. For a subgroup of strongly feature-selective neurons, this response modulation was associated with enhanced selectivity for one feature at the expense of selectivity for the other. Such flexibility in tuning strength could improve performance in tasks requiring active judgment of stimuli.

Selective enhancement of neural coding in V1 underlies fine-discrimination learning in tree shrew

Visual discrimination improves with training, a phenomenon that is thought to reflect plastic changes in the responses of neurons in primary visual cortex (V1). However, the identity of the neurons that undergo change, the nature of the changes, and the consequences of these changes for other visual behaviors remain unclear. We used chronic in vivo 2-photon calcium imaging to monitor the responses of neurons in the V1 of tree shrews learning a Go/No-Go fine orientation discrimination task. We observed increases in neural population measures of discriminability for task-relevant stimuli that correlate with performance and depend on a select subset of neurons with preferred orientations that include the rewarded stimulus and nearby orientations biased away from the non-rewarded stimulus. Learning is accompanied by selective enhancement in the response of these neurons to the rewarded stimulus that further increases their ability to discriminate the task stimuli. These changes persist outside of the trained task and predict observed enhancement and impairment in performance of other discriminations, providing evidence for selective and persistent learning-induced plasticity in the V1, with significant consequences for perception.

Visual neuroscience: A shrewd look at perceptual learning

A new study provides insight into the neuronal mechanisms that underlie visual learning in the tree shrew, revealing how improved coding for trained stimuli in visual cortex can negatively affect the perception of other stimuli.

Mechanisms of Surround Suppression Effect on the Contrast Sensitivity of V1 Neurons in Cats

Surround suppression (SS) is a phenomenon that a neuron’s response to visual stimuli within the classical receptive field (cRF) is suppressed by a concurrent stimulation in the surrounding receptive field (sRF) beyond the cRF. Studies show that SS affects neuronal response contrast sensitivity in the primary visual cortex (V1). However, the underlying mechanisms remain unclear. Here, we examined SS effect on the contrast sensitivity of cats’ V1 neurons with different preferred SFs using external noise-masked visual stimuli and perceptual template model (PTM) analysis at the system level. The contrast sensitivity was evaluated by the inverted threshold contrast of neurons in response to circular gratings of different contrasts in the cRF with or without an annular grating in the sRF. Our results showed that SS significantly reduced the contrast sensitivity of cats’ V1 neurons. The SS-induced reduction of contrast sensitivity was not correlated with SS strength but was dependent on neuron’s preferred SF, with a larger reduction for neurons with low preferred SFs than those with high preferred SFs. PTM analysis of threshold versus external noise contrast (TvC) functions indicated that SS decreased contrast sensitivity by increasing both the internal additive noise and impact of external noise for neurons with low preferred SFs, but improving only internal additive noise for neurons with high preferred SFs. Furthermore, the SS effect on the contrast-response function of low- and high-SF neurons also exhibited different mechanisms in contrast gain and response gain. Collectively, these results suggest that the mechanisms of SS effect on neuronal contrast sensitivity may depend on neuronal populations with different SFs.

Stimulation of the ventral tegmental area induces visual cortical plasticity at the neuronal level

fMRI studies have shown that pairing a task-irrelevant visual feature with electrical micro-stimulation of the ventral tegmental area (VTA-EM) is sufficient to increase the sensory cortical representation of the paired feature and to improve perceptual performance. However, since fMRI provides an indirect measure of neural activity, the neural response changes underlying the fMRI activations are unknown. Here, we pair a task-irrelevant grating orientation with VTA-EM while attention is directed to a difficult orthogonal task. We examine the changes in neural response properties in macaques by recording spiking activity in the posterior inferior temporal cortex, the locus of fMRI-defined plasticity in previous studies. We observe a relative increase in mean spike rate and preference for the VTA-EM paired orientation compared to an unpaired orientation, which is unrelated to attention. These results demonstrate that VTA-EM-stimulus pairing is sufficient to induce sensory cortical plasticity at the spiking level in nonhuman primates.

Perceptual learning as a result of concerted changes in prefrontal and visual cortex

Our perceptual ability remarkably improves with training. Some studies on visual perceptual learning have shown refined neural representation of the trained stimulus in the visual cortex, whereas others have exclusively argued for improved readout and decision-making processes in the frontoparietal cortex. The mixed results have rendered the underlying neural mechanisms puzzling and hotly debated. By simultaneously recording from monkey visual area V4 and ventrolateral prefrontal cortex (PFC) implanted with microelectrode arrays, we dissected learning-induced cortical changes over the course of training the monkeys in a global form detection task. Decoding analysis dissociated two distinct components of neuronal population codes that were progressively and markedly enhanced in both V4 and PFC. One component was closely related to the target stimulus feature and was subject to task-dependent top-down modulation; it emerged earlier in V4 than PFC and its enhancement was specific to the trained configuration of the target stimulus. The other component of the neural code was entirely related to the animal's behavioral choice; it emerged earlier in PFC than V4 and its enhancement completely generalized to an untrained stimulus configuration. These results implicate two concurrent and synergistic learning processes: a perceptual process that is specific to the details of the trained stimulus feature and a cognitive process that is dependent on the total amount of learning experience in the trained task. When combined, these two learning processes were well predictive of the animal's learning behavior.

Nucleus basalis stimulation enhances working memory by stabilizing stimulus representations in primate prefrontal cortical activity

Acetylcholine plays a critical role in the neocortex. Cholinergic agonists and acetylcholinesterase inhibitors can enhance cognitive functioning, as does intermittent electrical stimulation of the cortical source of acetylcholine, the nucleus basalis (NB) of Meynert. Here we show in two male monkeys how NB stimulation affects working memory and alters its neural code. NB stimulation increases dorsolateral prefrontal activity during the delay period of spatial working memory tasks and broadens selectivity for stimuli but does not strengthen phasic responses to each neuron's optimal visual stimulus. Paradoxically, despite this decrease in neuronal selectivity, performance improves in many task conditions, likely indicating increased delay period stability. Performance under NB stimulation does decline if distractors similar to the target are presented, consistent with reduced prefrontal selectivity. Our results indicate that stimulation of the cholinergic forebrain increases prefrontal neural activity, and this neuromodulatory tone can improve cognitive performance, subject to a stability-accuracy tradeoff.

Early Visual Processing and Perception Processes in Object Discrimination Learning

A brief image presentation is sufficient to discriminate and individuate objects of expertise. Although perceptual expertise is acquired through extensive practice that increases the resolution of representations and reduces the latency of image decoding and coarse and fine information extraction, it is not known how the stages of visual processing impact object discrimination learning (ODL). Here, we compared object discrimination with brief (100 ms) and long (1,000 ms) perceptual encoding times to test if the early and late visual processes are required for ODL. Moreover, we evaluated whether encoding time and discrimination practice shape perception and recognition memory processes during ODL. During practice of a sequential matching task with initially unfamiliar complex stimuli, we find greater discrimination with greater encoding times regardless of the extent of practice, suggesting that the fine information extraction during late visual processing is necessary for discrimination. Interestingly, the overall discrimination learning was similar for brief and long stimuli, suggesting that early stages of visual processing are sufficient for ODL. In addition, discrimination practice enhances perceive and know for brief and long stimuli and both processes are associated with performance, suggesting that early stage information extraction is sufficient for modulating the perceptual processes, likely reflecting an increase in the resolution of the representations and an early availability of information. Conversely, practice elicited an increase of familiarity which was not associated with discrimination sensitivity, revealing the acquisition of a general recognition memory. Finally, the recall is likely enhanced by practice and is associated with discrimination sensitivity for long encoding times, suggesting the engagement of recognition memory in a practice independent manner. These findings contribute to unveiling the function of early stages of visual processing in ODL, and provide evidence on the modulation of the perception and recognition memory processes during discrimination practice and its relationship with ODL and perceptual expertise acquisition.

Recurrent Processing Drives Perceptual Plasticity

Learning and experience are critical for translating ambiguous sensory information from our environments to perceptual decisions. Yet evidence on how training molds the adult human brain remains controversial, as fMRI at standard resolution does not allow us to discern the finer scale mechanisms that underlie sensory plasticity. Here, we combine ultra-high-field (7T) functional imaging at sub-millimeter resolution with orientation discrimination training to interrogate experience-dependent plasticity across cortical depths that are known to support dissociable brain computations. We demonstrate that learning alters orientation-specific representations in superficial rather than middle or deeper V1 layers, consistent with recurrent plasticity mechanisms via horizontal connections. Further, learning increases feedforward rather than feedback layer-to-layer connectivity in occipito-parietal regions, suggesting that sensory plasticity gates perceptual decisions. Our findings reveal finer scale plasticity mechanisms that re-weight sensory signals to inform improved decisions, bridging the gap between micro- and macro-circuits of experience-dependent plasticity.

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Discrimination training alters orientation representations in superficial V1 layers

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Orientation-specific V1 plasticity is independent of task context

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Discrimination training alters orientation representations in middle IPS layers

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Learning enhances feedforward connectivity from visual to parietal cortex

Roving: The causes of interference and re-enabled learning in multi-task visual training

People routinely perform multiple visual judgments in the real world, yet, intermixing tasks or task variants during training can damage or even prevent learning. This paper explores why. We challenged theories of visual perceptual learning focused on plastic retuning of low-level retinotopic cortical representations by placing different task variants in different retinal locations, and tested theories of perceptual learning through reweighting (changes in readout) by varying task similarity. Discriminating different (but equivalent) and similar orientations in separate retinal locations interfered with learning, whereas training either with identical orientations or sufficiently different ones in different locations released rapid learning. This location crosstalk during learning renders it unlikely that the primary substrate of learning is retuning in early retinotopic visual areas; instead, learning likely involves reweighting from location-independent representations to a decision. We developed an Integrated Reweighting Theory (IRT), which has both V1-like location-specific representations and higher level (V4/IT or higher) location-invariant representations, and learns via reweighting the readout to decision, to predict the order of learning rates in different conditions. This model with suitable parameters successfully fit the behavioral data, as well as some microstructure of learning performance in a new trial-by-trial analysis.

Improved fidelity of orientation perception: a learning effect dissociable from enhanced discriminability

Visual perception can be influenced by stimulus context, selective attention, and prior experience. Many previous studies have shown complex interactions among these influencing factors, but it remains unclear whether context-induced illusions could be reduced by perceptual training and whether such a change in perceptual fidelity is linked to improved perceptual discriminability. To address this question, we introduced a context-induced tilt illusion into an orientation discrimination training paradigm. This resulted in parallel and long-term improvements in the discriminability and fidelity of orientation perception. The improved discriminability was specific to the task-relevant target stimulus but nonspecific to the task-irrelevant context. By contrast, the improved perceptual fidelity was specific to the task-irrelevant contextual stimulus that induced the illusion, but not specific to the task-relevant target stimulus or task performed on one of its features. These results indicate two dissociable learning effects associated with the same training procedure. Such a dissociation was further supported by the observation that the sizes of the two learning effects were uncorrelated across the subjects. Our findings suggest two parallel learning processes: a task-dependent process giving rise to enhanced discriminability for the task-relevant stimulus attribute, and a context-dependent process leading to improved perceptual fidelity for the attended stimuli.

A new format of perceptual learning based on evidence abstraction from multiple stimuli

Perceptual learning, which improves stimulus discrimination, typically results from training with a single stimulus condition. Two major learning mechanisms, early cortical neural plasticity and response reweighting, have been proposed. Here we report a new format of perceptual learning that by design may have bypassed these mechanisms. Instead, it is more likely based on abstracted stimulus evidence from multiple stimulus conditions. Specifically, we had observers practice orientation discrimination with Gabors or symmetric dot patterns at up to 47 random or rotating location × orientation conditions. Although each condition received sparse trials (12 trials/session), the practice produced significant orientation learning. Learning also transferred to a Gabor at a single untrained condition with two- to three-times lower orientation thresholds. Moreover, practicing a single stimulus condition with matched trial frequency (12 trials/session) failed to produce significant learning. These results suggest that learning with multiple stimulus conditions may not come from early cortical plasticity or response reweighting with each particular condition. Rather, it may materialize through a new format of perceptual learning, in which orientation evidence invariant to particular orientations and locations is first abstracted from multiple stimulus conditions and then reweighted by later learning mechanisms. The coarse-to-fine transfer of orientation learning from multiple Gabors or symmetric dot patterns to a single Gabor also suggest the involvement of orientation concept learning by the learning mechanisms.

Adaptive heading performance during self-motion perception

Previous studies have documented that the perception of self-motion direction can be extracted from the patterns of image motion on the retina (also termed optic flow). Self-motion perception remains stable even when the optic-flow information is distorted by concurrent gaze shifts from body/eye rotations. This has been interpreted that extraretinal signals-efference copies of eye/body movements-are involved in compensating for retinal distortions. Here, we tested an alternative hypothesis to the extraretinal interpretation. We hypothesized that accurate self-motion perception can be achieved from a purely optic-flow-based visual strategy acquired through experience, independent of extraretinal mechanism. To test this, we asked human subjects to perform a self-motion direction discrimination task under normal optic flow (fixation condition) or distorted optic flow resulted from either realistic (pursuit condition) or simulated (simulated condition) eye movements. The task was performed either without (pre- and posttraining) or with (during training) the feedback about the correct answer. We first replicated the previous observation that before training, direction perception was greatly impaired in the simulated condition where the optic flow was distorted and extraretinal eye movement signals were absent. We further showed that after a few training sessions, the initial impairment in direction perception was gradually improved. These results reveal that behavioral training can enforce the exploitation of retinal cues to compensate for the distortion, without the contribution from the extraretinal signals. Our results suggest that self-motion perception is a flexible and adaptive process which might depend on neural plasticity in relevant cortical areas.

Relation between gamma oscillations and neuronal plasticity in the visual cortex

Learning is thought to be mediated by activity-dependent modification of neuronal interactions. To avoid maladaptive modifications of synaptic transmission by spurious activity, synaptic plasticity has to be gated. In the case of supervised learning, these gating functions are accomplished by reinforcement through value-assigning systems. Here we show that the dynamic state of local circuits correlates with the occurrence of activity-dependent long-term changes in neuronal response properties. We find that repeated visual stimuli induce long-term changes of orientation preference of neuronal populations in visual cortex if stimuli induce synchronized population responses oscillating at ɣ-frequencies. This suggests that neuronal plasticity is controlled by a hierarchy of gating systems and assigns critical gating functions to resonance properties of local circuits.

Use-dependent long-term changes of neuronal response properties must be gated to prevent irrelevant activity from inducing inappropriate modifications. Here we test the hypothesis that local network dynamics contribute to such gating. As synaptic modifications depend on temporal contiguity between presynaptic and postsynaptic activity, we examined the effect of synchronized gamma (ɣ) oscillations on stimulation-dependent modifications of orientation selectivity in adult cat visual cortex. Changes of orientation maps were induced by pairing visual stimulation with electrical activation of the mesencephalic reticular formation. Changes in orientation selectivity were assessed with optical recording of intrinsic signals and multiunit recordings. When conditioning stimuli were associated with strong ɣ-oscillations, orientation domains matching the orientation of the conditioning grating stimulus became more responsive and expanded, because neurons with preferences differing by less than 30° from the orientation of the conditioning grating shifted their orientation preference toward the conditioned orientation. When conditioning stimuli induced no or only weak ɣ-oscillations, responsiveness of neurons driven by the conditioning stimulus decreased. These differential effects depended on the power of oscillations in the low ɣ-band (20 Hz to 48 Hz) and not on differences in discharge rate of cortical neurons, because there was no correlation between the discharge rates during conditioning and the occurrence of changes in orientation preference. Thus, occurrence and polarity of use-dependent long-term changes of cortical response properties appear to depend on the occurrence of ɣ-oscillations during induction and hence on the degree of temporal coherence of the change-inducing network activity.

Interfering with a memory without erasing its trace

Previous research has shown that performance of a novice skill can be easily interfered with by subsequent training of another skill. We address the open questions whether extensively trained skills show the same vulnerability to interference as novice skills and which memory mechanism regulates interference between expert skills. We developed a recurrent neural network model of V1 able to learn from feedback experienced over the course of a long-term orientation discrimination experiment. After first exposing the model to one discrimination task for 3480 consecutive trials, we assessed how its performance was affected by subsequent training in a second, similar task. Training the second task strongly interfered with the first (highly trained) discrimination skill. The magnitude of interference depended on the relative amounts of training devoted to the different tasks. We used these and other model outcomes as predictions for a perceptual learning experiment in which human participants underwent the same training protocol as our model. Specifically, over the course of three months participants underwent baseline training in one orientation discrimination task for 15 sessions before being trained for 15 sessions on a similar task and finally undergoing another 15 sessions of training on the first task (to assess interference). Across all conditions, the pattern of interference observed empirically closely matched model predictions. According to our model, behavioral interference can be explained by antagonistic changes in neuronal tuning induced by the two tasks. Remarkably, this did not stem from erasing connections due to earlier learning but rather from a reweighting of lateral inhibition.

Ventral midbrain stimulation induces perceptual learning and cortical plasticity in primates

Practice improves perception and enhances neural representations of trained visual stimuli, a phenomenon known as visual perceptual learning (VPL). While attention to task-relevant stimuli plays an important role in such learning, Pavlovian stimulus-reinforcer associations are sufficient to drive VPL, even subconsciously. It has been proposed that reinforcement facilitates perceptual learning through the activation of neuromodulatory centers, but this has not been directly confirmed in primates. Here, we paired task-irrelevant visual stimuli with microstimulation of a dopaminergic center, the ventral tegmental area (VTA), in macaques. Pairing VTA microstimulation with a task-irrelevant visual stimulus increased fMRI activity and improved classification of fMRI activity patterns selectively for the microstimulation-paired stimulus. Moreover, pairing VTA microstimulation with a task-irrelevant visual stimulus improved the subject’s capacity to discriminate that stimulus. This is the first causal demonstration of the role of neuromodulatory centers in VPL in primates.

Practice can improve the perception of stimuli used to achieve a task (perceptual learning). Here, the authors show in monkeys that perceptual learning can be produced even for irrelevant stimuli if the stimuli are paired with stimulation of a dopaminergic centre, the ventral tegmental area (VTA).

Visual perceptual learning modulates decision network in the human brain: The evidence from psychophysics, modeling, and functional magnetic resonance imaging

Perceptual learning refers to improved perceptual performance after intensive training and was initially suggested to reflect long-term plasticity in early visual cortex. Recent behavioral and neurophysiological evidence further suggested that the plasticity in brain regions related to decision making could also contribute to the observed training effects. However, how perceptual learning modulates the responses of decision-related regions in the human brain remains largely unknown. In the present study, we combined psychophysics and functional magnetic resonance imaging (fMRI), and adopted a model-based approach to investigate this issue. We trained participants on a motion direction discrimination task and fitted their behavioral data using the linear ballistic accumulator model. The results from model fitting showed that behavioral improvement could be well explained by a specific improvement in sensory information accumulation. A critical model parameter, the drift rate of the information accumulation, was correlated with the fMRI responses derived from three spatial independent components: ventral premotor cortex (PMv), supplementary eye field (SEF), and the fronto-parietal network, including intraparietal sulcus (IPS) and frontal eye field (FEF). In this decision network, we found that the behavioral training effects were accompanied by signal enhancement specific to trained direction in PMv and FEF. Further, we also found direction-specific signal reduction in sensory areas (V3A and MT+), as well as the strengthened effective connectivity from V3A to PMv and from IPS to FEF. These findings provide evidence for the learning-induced decision refinement after perceptual learning and the brain regions that are involved in this process.

Neural Variability Limits Adolescent Skill Learning

Skill learning is fundamental to the acquisition of many complex behaviors that emerge during development. For example, years of practice give rise to perceptual improvements that contribute to mature speech and language skills. While fully honed learning skills might be thought to offer an advantage during the juvenile period, the ability to learn actually continues to develop through childhood and adolescence, suggesting that the neural mechanisms that support skill learning are slow to mature. To address this issue, we asked whether the rate and magnitude of perceptual learning varies as a function of age as male and female gerbils trained on an auditory task. Adolescents displayed a slower rate of perceptual learning compared with their young and mature counterparts. We recorded auditory cortical neuron activity from a subset of adolescent and adult gerbils as they underwent perceptual training. While training enhanced the sensitivity of most adult units, the sensitivity of many adolescent units remained unchanged, or even declined across training days. Therefore, the average rate of cortical improvement was significantly slower in adolescents compared with adults. Both smaller differences between sound-evoked response magnitudes and greater trial-to-trial response fluctuations contributed to the poorer sensitivity of individual adolescent neurons. Together, these findings suggest that elevated sensory neural variability limits adolescent skill learning.SIGNIFICANCE STATEMENT The ability to learn new skills emerges gradually as children age. This prolonged development, often lasting well into adolescence, suggests that children, teens, and adults may rely on distinct neural strategies to improve their sensory and motor capabilities. Here, we found that practice-based improvement on a sound detection task is slower in adolescent gerbils than in younger or older animals. Neural recordings made during training revealed that practice enhanced the sound sensitivity of adult cortical neurons, but had a weaker effect in adolescents. This latter finding was partially explained by the fact that adolescent neural responses were more variable than in adults. Our results suggest that one mechanistic basis of adult-like skill learning is a reduction in neural response variability.

The effect of LTP- and LTD-like visual stimulation on modulation of human orientation discrimination

Studies showing that repetitive visual stimulation protocols alter perception and induce cortical reorganization, as well-reported for the tactile domain, have been sparse. In this study, we investigated how “long-term potentiation [LTP]-like” and “long-term depression [LTD]-like” repetitive visual stimulation affects orientation discrimination ability in human observers. LTP-like stimulation with features most closely resembling the stimuli used during behavioral assessment evoked the largest improvement, while the effects were smaller in protocols that differed in shape or orientation features. This gradient suggests lower learning specificity than classical perceptual learning experiments, possibly because of an interplay of task- and feature-based factors. All modulatory effects of repetitive stimulation were superimposed on top of spontaneous task learning. Moreover, blockwise analysis revealed that LTP-like stimulation, in contrast to LTD-like or sham stimulation, prevented a loss of practice-related gain of orientation discrimination thresholds. This observation highlights a critical role of LTP-like stimulation for consolidation, typically observed during sleep.

Perceptual learning induces changes in early and late visual evoked potentials

Studies of visual cortical responses following visual perceptual learning (VPL) have produced diverse results, revealing neural changes in early and/or higher-level visual cortex as well as changes in regions responsible for higher cognitive processes such as attentional control. In this study, we investigated substrates of VPL in the human brain by recording visual evoked potentials with high-density electroencephalography (hdEEG) before (Session 1) and after (Session 2) training on a texture discrimination task (TDT), with two full nights of sleep between sessions. We studied the following event-related potential (ERP) components: C1 (early sensory processing), P1 and N1 (later sensory processing, modulated by top-down spatial attention), and P3 (cognitive processing). Our results showed a significant decrease in C1 amplitude at Session 2 relative to Session 1 that was positively correlated with the magnitude of improvement in behavioral performance. Although we observed no significant changes in P1 amplitude with VPL, both N1 amplitude and latency were significantly decreased in Session 2. Moreover, the difference in N1 latency between Session 1 and Session 2 was negatively correlated with behavioral improvement. We also found a significant increase in P3 amplitude following training. Our results suggest that VPL of the TDT task may be due to plasticity in early visual cortical areas as well as changes in top-down attentional control and cognitive processing.

GABA, not BOLD, reveals dissociable learning-dependent plasticity mechanisms in the human brain

Experience and training have been shown to facilitate our ability to extract and discriminate meaningful patterns from cluttered environments. Yet, the human brain mechanisms that mediate our ability to learn by suppressing noisy and irrelevant signals remain largely unknown. To test the role of suppression in perceptual learning, we combine fMRI with MR Spectroscopy measurements of GABA, as fMRI alone does not allow us to discern inhibitory vs. excitatory mechanisms. Our results demonstrate that task-dependent GABAergic inhibition relates to functional brain plasticity and behavioral improvement. Specifically, GABAergic inhibition in the occipito-temporal cortex relates to dissociable learning mechanisms: decreased GABA for noise filtering, while increased GABA for feature template retuning. Perturbing cortical excitability during training with tDCs alters performance in a task-specific manner, providing evidence for a direct link between suppression and behavioral improvement. Our findings propose dissociable GABAergic mechanisms that optimize our ability to make perceptual decisions through training.

When searching for a friend in the crowd or telling identical twins apart, your visual system must solve a complex puzzle. It must ignore all irrelevant information (e.g., unfamiliar faces in the crowd) and focus on key features (e.g., your friend’s familiar face) that will allow you to make a decision. We become better at solving complex visual discriminations with practice. But exactly how the brain achieves this improved performance is unclear.

To answer this question, Frangou et al. trained healthy volunteers on two such visual tasks. The first (target detection task) involved locating a target (e.g. circular shape made of dots among randomly distributed dots in the background), a task similar to identifying a friend in the crowd. The second (feature discrimination task) involved assigning highly alike shapes in two different categories, similar to telling apart identical twins. To solve this problem, volunteers had to identify distinct features that allowed them to distinguishthese shapes. During training on this task, they updated and refined the representation of these distinct features in their brain. This enabled them to make finer discriminations and assign each image correctly to one of the two categories.

While the volunteers trained on the tasks, Frangou et al. measured levels of a chemical called GABA in brain areas that process visual information. GABA is the brain's main inhibitory molecule and controls the activity of neurons. As the volunteers learned the two tasks, their brains showed opposite changes in GABA levels. In the first, target detection task, individuals did better if their GABA decreased during training. In the second, feature discrimination task, they achieved more if their GABA increased during training. To confirm these findings, Frangou et al. used a second technique to activate or suppress processing in visual areas of the brain. Activating visual areas enhanced performance on the target detection task. Suppressing them enhanced performance on the fine discrimination task. These changes are thus consistent with those seen in GABA levels.

As well as revealing how we learn to make decisions based on the information from our eyes, these findings suggest that adjusting brain activity could help patients regain skills lost as a result of eye-related or neurological conditions. Understanding the role of GABA in brain plasticity is also relevant to conditions like autism and psychosis, which have been shown to relate to changes in brain inhibition.

Perceptual learning of fine contrast discrimination changes neuronal tuning and population coding in macaque V4

Perceptual learning, the improvement in perceptual abilities with training, is thought to be mediated by an alteration of neuronal tuning. It remains poorly understood how tuning properties change as training progresses, whether improved stimulus tuning directly links to increased behavioural readout of sensory information, or how population coding mechanisms change with training. Here, we recorded continuously from multiple neuronal clusters in area V4 while macaque monkeys learned a fine contrast categorization task. Training increased neuronal coding abilities by shifting the steepest point of contrast response functions towards the categorization boundary. Population coding accuracy of difficult discriminations resulted largely from an increased information coding of individual channels, particularly for those channels that in early learning had larger ability for easy discriminations, but comparatively small encoding abilities for difficult discriminations. Population coding was also enhanced by specific changes in correlations. Neuronal activity became more indicative of upcoming choices with training.

Perceptual learning, the improvement in perceptual abilities with training, is thought to involve changes in neuronal 'tuning'. Here, the authors show that perceptual learning works by making neurons increasingly sensitive to task-relevant differences in stimuli, and by improving population coding mechanisms.

Cognition as a Window into Neuronal Population Space

Understanding how cognitive processes affect the responses of sensory neurons may clarify the relationship between neuronal population activity and behavior. However, tools for analyzing neuronal activity have not kept up with technological advances in recording from large neuronal populations. Here, we describe prevalent hypotheses of how cognitive processes affect sensory neurons, driven largely by a model based on the activity of single neurons or pools of neurons as the units of computation. We then use simple simulations to expand this model to a new conceptual framework that focuses on subspaces of population activity as the relevant units of computation, uses comparisons between brain areas or to behavior to guide analyses of these subspaces, and suggests that population activity is optimized to decode the large variety of stimuli and tasks that animals encounter in natural behavior. This framework provides new ways of understanding the ever-growing quantity of recorded population activity data.

Neural Mechanisms of High-Level Vision

The last three decades have seen major strides in our understanding of neural mechanisms of high-level vision, or visual cognition of the world around us. Vision has also served as a model system for the study of brain function. Several broad insights, as yet incomplete, have recently emerged. First, visual perception is best understood not as an end unto itself, but as a sensory process that subserves the animal's behavioral goal at hand. Visual perception is likely to be simply a side effect that reflects the readout of visual information processing that leads to behavior. Second, the brain is essentially a probabilistic computational system that produces behaviors by collectively evaluating, not necessarily consciously or always optimally, the available information about the outside world received from the senses, the behavioral goals, prior knowledge about the world, and possible risks and benefits of a given behavior. Vision plays a prominent role in the overall functioning of the brain providing the lion's share of information about the outside world. Third, the visual system does not function in isolation, but rather interacts actively and reciprocally with other brain systems, including other sensory faculties. Finally, various regions of the visual system process information not in a strict hierarchical manner, but as parts of various dynamic brain-wide networks, collectively referred to as the "connectome." Thus, a full understanding of vision will ultimately entail understanding, in granular, quantitative detail, various aspects of dynamic brain networks that use visual sensory information to produce behavior under real-world conditions. © 2017 American Physiological Society. Compr Physiol 8:903-953, 2018.

Deep Neural Networks for Modeling Visual Perceptual Learning

Understanding visual perceptual learning (VPL) has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. Although existing models aid our knowledge of critical aspects of VPL, the connections shown by these models between behavioral learning and plasticity across different brain areas are typically superficial. Most models explain VPL as readout from simple perceptual representations to decision areas and are not easily adaptable to explain new findings. Here, we show that a well -known instance of deep neural network (DNN), whereas not designed specifically for VPL, provides a computational model of VPL with enough complexity to be studied at many levels of analyses. After learning a Gabor orientation discrimination task, the DNN model reproduced key behavioral results, including increasing specificity with higher task precision, and also suggested that learning precise discriminations could transfer asymmetrically to coarse discriminations when the stimulus conditions varied. Consistent with the behavioral findings, the distribution of plasticity moved toward lower layers when task precision increased and this distribution was also modulated by tasks with different stimulus types. Furthermore, learning in the network units demonstrated close resemblance to extant electrophysiological recordings in monkey visual areas. Altogether, the DNN fulfilled predictions of existing theories regarding specificity and plasticity and reproduced findings of tuning changes in neurons of the primate visual areas. Although the comparisons were mostly qualitative, the DNN provides a new method of studying VPL, can serve as a test bed for theories, and assists in generating predictions for physiological investigations.SIGNIFICANCE STATEMENT Visual perceptual learning (VPL) has been found to cause changes at multiple stages of the visual hierarchy. We found that training a deep neural network (DNN) on an orientation discrimination task produced behavioral and physiological patterns similar to those found in human and monkey experiments. Unlike existing VPL models, the DNN was pre-trained on natural images to reach high performance in object recognition, but was not designed specifically for VPL; however, it fulfilled predictions of existing theories regarding specificity and plasticity and reproduced findings of tuning changes in neurons of the primate visual areas. When used with care, this unbiased and deep-hierarchical model can provide new ways of studying VPL from behavior to physiology.

Domain-general and domain-specific neural changes underlying visual expertise

Visual expertise induces changes in neural processing for many different domains of expertise. However, it is unclear how expertise effects for different domains of expertise are related. In the present fMRI study, we combine large-scale univariate and multi-voxel analyses to contrast the expertise-related neural changes associated with two different domains of expertise, bird expertise (ornithology) and mineral expertise (mineralogy). Results indicated distributed expertise-related neural changes, with effects for both domains of expertise in high-level visual cortex and effects for bird expertise even extending to low-level visual regions and the frontal lobe. Importantly, a multivariate generalization analysis showed that effects in high-level visual cortex were specific to the domain of expertise. In contrast, the neural changes in the frontal lobe relating to expertise showed significant generalization, signaling the presence of domain-independent expertise effects. In conclusion, expertise is related to a combination of domain-specific and domain-general changes in neural processing.

Towards a whole brain model of Perceptual Learning

A hallmark of modern Perceptual Learning (PL) is the extent to which learning is specific to the trained stimuli. Such specificity to orientation, spatial location and even eye of training has been used as psychophysical evidence of the neural basis of learning. This argument that specificity of PL implies regionalization of brain plasticity implicitly assumes that examination of a singular locus of PL is an appropriate approach to understand learning. However, recent research shows that learning effects once thought to be specific depend on subtleties of the training paradigm and that within even a simple training procedure there are multiple aspects of the task and stimuli that are learned simultaneously. Here, we suggest that learning on any task involves a broad network of brain regions undergoing changes in representations, read-out weights, decision rules, attention and feedback processes as well as oculomotor changes. However, importantly, the distribution of learning across the neural system depends upon the details of the training procedure and the characterstics of the individual being trained. We propose that to advance our understanding of PL, the field must move towards understanding how distributed brain processes jointly contribute to behavioral learning effects.

Auditory perceptual learning and changes in the conceptualization of auditory cortex

Perceptual learning, improvement in discriminative ability as a consequence of training, is one of the forms of sensory system plasticity that has driven profound changes in our conceptualization of sensory cortical function. Psychophysical and neurophysiological studies of auditory perceptual learning have indicated that the characteristics of the learning, and by implication the nature of the underlying neural changes, are highly task specific. Some studies in animals have indicated that recruitment of neurons to the population responding to the training stimuli, and hence an increase in the so-called cortical "area of representation" of those stimuli, is the substrate of improved performance, but such changes have not been observed in other studies. A possible reconciliation of these conflicting results is provided by evidence that changes in area of representation constitute a transient stage in the processes underlying perceptual learning. This expansion - renormalization hypothesis is supported by evidence from studies of the learning of motor skills, another form of procedural learning, but leaves open the nature of the permanent neural substrate of improved performance. Other studies have suggested that the substrate might be reduced response variability - a decrease in internal noise. Neuroimaging studies in humans have also provided compelling evidence that training results in long-term changes in auditory cortical function and in the auditory brainstem frequency-following response. Musical training provides a valuable model, but the evidence it provides is qualified by the fact that most such training is multimodal and sensorimotor, and that few of the studies are experimental and allow control over confounding variables. More generally, the overwhelming majority of experimental studies of the various forms of auditory perceptual learning have established the co-occurrence of neural and perceptual changes, but have not established that the former are causally related to the latter. Important forms of perceptual learning in humans are those involved in language acquisition and in the improvement in speech perception performance of post-lingually deaf cochlear implantees over the months following implantation. The development of a range of auditory training programs has focused interest on the factors determining the extent to which perceptual learning is specific or generalises to tasks other than those used in training. The context specificity demonstrated in a number of studies of perceptual learning suggests a multiplexing model, in which learning relating to a particular stimulus attribute depends on a subset of the diverse inputs to a given cortical neuron being strengthened, and different subsets being gated by top-down influences. This hypothesis avoids the difficulty of balancing system stability with plasticity, which is a problem for recruitment hypotheses. The characteristics of auditory perceptual learning reflect the fact that auditory cortex forms part of distributed networks that integrate the representation of auditory stimuli with attention, decision, and reward processes.

A new framework of design and continuous evaluation to improve brain training

Effective teaching critically relies upon effective evaluation because evaluation is required to gain understanding of existing abilities and, in turn, determine learning outcomes. Methods of effective evaluation are surprisingly elusive in many fields and this limits our understanding of which training methods are truly effective. In the present article, issues of effective evaluation are discussed in the context of "brain training," an exciting but much criticized field. Problems in test validity in the field of brain training, parallel those in many other fields; such as deficiencies in test reliability, teaching to the test, expectation effects, as well as statistical rigor. Here we review these issues and discuss how commonalities between the goals of evaluation and adaptive training procedures suggest a new paradigm that synthesizes evaluation and training. We suggest that continuous evaluation, where testing is integrated into the training, may provide a path towards greater reliability of skill evaluation, through longitudinal sampling, and validity, through better alignment of evaluation activities in respect to learning objectives.

Neuronal Correlations in MT and MST Impair Population Decoding of Opposite Directions of Random Dot Motion

The study of neuronal responses to random-dot motion patterns has provided some of the most valuable insights into how the activity of neurons is related to perception. In the opposite directions of motion paradigm, the motion signal strength is decreased by manipulating the coherence of random dot patterns to examine how well the activity of single neurons represents the direction of motion. To extend this paradigm to populations of neurons, studies have used modelling based on data from pairs of neurons, but several important questions require further investigation with larger neuronal datasets. We recorded neuronal populations in the middle temporal (MT) and medial superior temporal (MST) areas of anaesthetized marmosets with electrode arrays, while varying the coherence of random dot patterns in two opposite directions of motion (left and right). Using the spike rates of simultaneously recorded neurons, we decoded the direction of motion at each level of coherence with linear classifiers. We found that the presence of correlations had a detrimental effect to decoding performance, but that learning the correlation structure produced better decoding performance compared to decoders that ignored the correlation structure. We also found that reducing motion coherence increased neuronal correlations, but decoders did not need to be optimized for each coherence level. Finally, we showed that decoder weights depend of left-right selectivity at 100% coherence, rather than the preferred direction. These results have implications for understanding how the information encoded by populations of neurons is affected by correlations in spiking activity.

Feature reliability determines specificity and transfer of perceptual learning in orientation search

Training can modify the visual system to produce a substantial improvement on perceptual tasks and therefore has applications for treating visual deficits. Visual perceptual learning (VPL) is often specific to the trained feature, which gives insight into processes underlying brain plasticity, but limits VPL’s effectiveness in rehabilitation. Under what circumstances VPL transfers to untrained stimuli is poorly understood. Here we report a qualitatively new phenomenon: intrinsic variation in the representation of features determines the transfer of VPL. Orientations around cardinal are represented more reliably than orientations around oblique in V1, which has been linked to behavioral consequences such as visual search asymmetries. We studied VPL for visual search of near-cardinal or oblique targets among distractors of the other orientation while controlling for other display and task attributes, including task precision, task difficulty, and stimulus exposure. Learning was the same in all training conditions; however, transfer depended on the orientation of the target, with full transfer of learning from near-cardinal to oblique targets but not the reverse. To evaluate the idea that representational reliability was the key difference between the orientations in determining VPL transfer, we created a model that combined orientation-dependent reliability, improvement of reliability with learning, and an optimal search strategy. Modeling suggested that not only search asymmetries but also the asymmetric transfer of VPL depended on preexisting differences between the reliability of near-cardinal and oblique representations. Transfer asymmetries in model behavior also depended on having different learning rates for targets and distractors, such that greater learning for low-reliability distractors facilitated transfer. These findings suggest that training on sensory features with intrinsically low reliability may maximize the generalizability of learning in complex visual environments.

Training can modify the visual system to produce improvements on perceptual tasks (visual perceptual learning), which is associated with adult brain plasticity. Visual perceptual learning has important clinical applications: it improves the vision of adults with visual deficits, e.g. amblyopia and cortical blindness, and even presbyopia (aging eye). A critical issue in visual perceptual learning is its specificity to the trained stimulus. Specificity gives insight into the processes underling experience-dependent plasticity but can be an obstacle in the development of efficient rehabilitation protocols. Under what circumstances visual perceptual learning transfers to untrained stimuli is poorly understood. Here we report a qualitatively new phenomenon: specificity in visual search depends on intrinsic variations in the reliability of feature representations; e.g., vertically oriented lines are represented in V1 with greater reliability than tilted lines. Our data and computational model suggest that training on sensory features with intrinsically low reliability can maximize the generalizability of learning, particularly in complex natural environments in which task performance is limited by low-reliability features. Our study has possible implications for the development of efficient clinical applications of perceptual learning.

Effects of regular aerobic exercise on visual perceptual learning

This study investigated the influence of five days of moderate intensity aerobic exercise on the acquisition and consolidation of visual perceptual learning using a motion direction discrimination (MDD) task. The timing of exercise relative to learning was manipulated by administering exercise either before or after perceptual training. Within a matched-subjects design, twenty-seven healthy participants (n = 9 per group) completed five consecutive days of perceptual training on a MDD task under one of three interventions: no exercise, exercise before the MDD task, or exercise after the MDD task. MDD task accuracy improved in all groups over the five-day period, but there was a trend for impaired learning when exercise was performed before visual perceptual training. MDD task accuracy (mean ± SD) increased in exercise before by 4.5 ± 6.5%; exercise after by 11.8 ± 6.4%; and no exercise by 11.3 ± 7.2%. All intervention groups displayed similar MDD threshold reductions for the trained and untrained motion axes after training. These findings suggest that moderate daily exercise does not enhance the rate of visual perceptual learning for an MDD task or the transfer of learning to an untrained motion axis. Furthermore, exercise performed immediately prior to a visual perceptual learning task may impair learning. Further research with larger groups is required in order to better understand these effects.

Visual Perceptual Learning and Models

Visual perceptual learning through practice or training can significantly improve performance on visual tasks. Originally seen as a manifestation of plasticity in the primary visual cortex, perceptual learning is more readily understood as improvements in the function of brain networks that integrate processes, including sensory representations, decision, attention, and reward, and balance plasticity with system stability. This review considers the primary phenomena of perceptual learning, theories of perceptual learning, and perceptual learning's effect on signal and noise in visual processing and decision. Models, especially computational models, play a key role in behavioral and physiological investigations of the mechanisms of perceptual learning and for understanding, predicting, and optimizing human perceptual processes, learning, and performance. Performance improvements resulting from reweighting or readout of sensory inputs to decision provide a strong theoretical framework for interpreting perceptual learning and transfer that may prove useful in optimizing learning in real-world applications.

Top-down modulation of sensory cortex gates perceptual learning

Practice sharpens our perceptual judgments, a process known as perceptual learning. Although several brain regions and neural mechanisms have been proposed to support perceptual learning, formal tests of causality are lacking. Furthermore, the temporal relationship between neural and behavioral plasticity remains uncertain. To address these issues, we recorded the activity of auditory cortical neurons as gerbils trained on a sound detection task. Training led to improvements in cortical and behavioral sensitivity that were closely matched in terms of magnitude and time course. Surprisingly, the degree of neural improvement was behaviorally gated. During task performance, cortical improvements were large and predicted behavioral outcomes. In contrast, during nontask listening sessions, cortical improvements were weak and uncorrelated with perceptual performance. Targeted reduction of auditory cortical activity during training diminished perceptual learning while leaving psychometric performance largely unaffected. Collectively, our findings suggest that training facilitates perceptual learning by strengthening both bottom-up sensory encoding and top-down modulation of auditory cortex.

Tuned by experience: How orientation probability modulates early perceptual processing

Probable stimuli are more often and more quickly detected. While stimulus probability is known to affect decision-making, it can also be explained as a perceptual phenomenon. Using spatial gratings, we have previously shown that probable orientations are also more precisely estimated, even while participants remained naive to the manipulation. We conducted an electrophysiological study to investigate the effect that probability has on perception and visual-evoked potentials. In line with previous studies on oddballs and stimulus prevalence, low-probability orientations were associated with a greater late positive 'P300' component which might be related to either surprise or decision-making. However, the early 'C1' component, thought to reflect V1 processing, was dampened for high-probability orientations while later P1 and N1 components were unaffected. Exploratory analyses revealed a participant-level correlation between C1 and P300 amplitudes, suggesting a link between perceptual processing and decision-making. We discuss how these probability effects could be indicative of sharpening of neurons preferring the probable orientations, due either to perceptual learning, or to feature-based attention.

The Contribution of Area MT to Visual Motion Perception Depends on Training

Perceptual decisions require the transformation of raw sensory inputs into cortical representations suitable for stimulus discrimination. One of the best-known examples of this transformation involves the middle temporal area (MT) of the primate visual cortex. Area MT provides a robust representation of stimulus motion, and previous work has shown that it contributes causally to performance on motion discrimination tasks. Here we report that the strength of this contribution can be highly plastic: depending on the recent training history, pharmacological inactivation of MT can severely impair motion discrimination, or it can have little detectable influence. Further analysis of neural and behavioral data suggests that training moves the readout of motion information between MT and lower-level cortical areas. These results show that the contribution of individual brain regions to conscious perception can shift flexibly depending on sensory experience.

Learning Enhances Sensory Processing in Mouse V1 before Improving Behavior

A fundamental property of visual cortex is to enhance the representation of those stimuli that are relevant for behavior, but it remains poorly understood how such enhanced representations arise during learning. Using classical conditioning in adult mice of either sex, we show that orientation discrimination is learned in a sequence of distinct behavioral stages, in which animals first rely on stimulus appearance before exploiting its orientation to guide behavior. After confirming that orientation discrimination under classical conditioning requires primary visual cortex (V1), we measured, during learning, response properties of V1 neurons. Learning improved neural discriminability, sharpened orientation tuning, and led to higher contrast sensitivity. Remarkably, these learning-related improvements in the V1 representation were fully expressed before successful orientation discrimination was evident in the animals' behavior. We propose that V1 plays a key role early in discrimination learning to enhance behaviorally relevant sensory information.SIGNIFICANCE STATEMENT Decades of research have documented that responses of neurons in visual cortex can reflect the behavioral relevance of visual information. The behavioral relevance of any stimulus needs to be learned, though, and little is known how visual sensory processing changes, as the significance of a stimulus becomes clear. Here, we trained mice to discriminate two visual stimuli, precisely quantified when learning happened, and measured, during learning, the neural representation of these stimuli in V1. We observed learning-related improvements in V1 processing, which were fully expressed before discrimination was evident in the animals' behavior. These findings indicate that sensory and behavioral improvements can follow different time courses and point toward a key role of V1 at early stages in discrimination learning.

Fluoxetine Does Not Enhance Visual Perceptual Learning and Triazolam Specifically Impairs Learning Transfer

The selective serotonin reuptake inhibitor fluoxetine significantly enhances adult visual cortex plasticity within the rat. This effect is related to decreased gamma-aminobutyric acid (GABA) mediated inhibition and identifies fluoxetine as a potential agent for enhancing plasticity in the adult human brain. We tested the hypothesis that fluoxetine would enhance visual perceptual learning of a motion direction discrimination (MDD) task in humans. We also investigated (1) the effect of fluoxetine on visual and motor cortex excitability and (2) the impact of increased GABA mediated inhibition following a single dose of triazolam on post-training MDD task performance. Within a double blind, placebo controlled design, 20 healthy adult participants completed a 19-day course of fluoxetine (n = 10, 20 mg per day) or placebo (n = 10). Participants were trained on the MDD task over the final 5 days of fluoxetine administration. Accuracy for the trained MDD stimulus and an untrained MDD stimulus configuration was assessed before and after training, after triazolam and 1 week after triazolam. Motor and visual cortex excitability were measured using transcranial magnetic stimulation. Fluoxetine did not enhance the magnitude or rate of perceptual learning and full transfer of learning to the untrained stimulus was observed for both groups. After training was complete, trazolam had no effect on trained task performance but significantly impaired untrained task performance. No consistent effects of fluoxetine on cortical excitability were observed. The results do not support the hypothesis that fluoxetine can enhance learning in humans. However, the specific effect of triazolam on MDD task performance for the untrained stimulus suggests that learning and learning transfer rely on dissociable neural mechanisms.