Neural correlates tracking different aspects of the emerging representation of novel visual categories.

Current studies investigating electroencephalogram correlates associated with categorization of sensory stimuli (P300 event-related potential, alpha event-related desynchronization, theta event-related synchronization) typically use an oddball paradigm with few, familiar, highly distinct stimuli providing limited insight about the aspects of categorization (e.g. difficulty, membership, uncertainty) that the correlates are linked to. Using a more complex task, we investigated whether such more specific links could be established between correlates and learning and how these links change during the emergence of new categories. In our study, participants learned to categorize novel stimuli varying continuously on multiple integral feature dimensions, while electroencephalogram was recorded from the beginning of the learning process. While there was no significant P300 event-related potential modulation, both alpha event-related desynchronization and theta event-related synchronization followed a characteristic trajectory in proportion with the gradual acquisition of the two categories. Moreover, the two correlates were modulated by different aspects of categorization, alpha event-related desynchronization by the difficulty of the task, whereas the magnitude of theta -related synchronization by the identity and possibly the strength of category membership. Thus, neural signals commonly related to categorization are appropriate for tracking both the dynamic emergence of internal representation of categories, and different meaningful aspects of the categorization process.

Eye movements reflect active statistical learning.

What is the link between eye movements and sensory learning? Although some theories have argued for an automatic interaction between what we know and where we look that continuously modulates human information gathering behavior during both implicit and explicit learning, there exists limited experimental evidence supporting such an ongoing interplay. To address this issue, we used a visual statistical learning paradigm combined with a gaze-contingent stimulus presentation and manipulated the explicitness of the task to explore how learning and eye movements interact. During both implicit exploration and explicit visual learning of unknown composite visual scenes, spatial eye movement patterns systematically and gradually changed in accordance with the underlying statistical structure of the scenes. Moreover, the degree of change was directly correlated with the amount and type of knowledge the observers acquired. This suggests that eye movements are potential indicators of active learning, a process where long-term knowledge, current visual stimuli and an inherent tendency to reduce uncertainty about the visual environment jointly determine where we look.

Different mechanisms underlie implicit visual statistical learning in honey bees and humans.

The ability of developing complex internal representations of the environment is considered a crucial antecedent to the emergence of humans' higher cognitive functions. Yet it is an open question whether there is any fundamental difference in how humans and other good visual learner species naturally encode aspects of novel visual scenes. Using the same modified visual statistical learning paradigm and multielement stimuli, we investigated how human adults and honey bees (Apis mellifera) encode spontaneously, without dedicated training, various statistical properties of novel visual scenes. We found that, similarly to humans, honey bees automatically develop a complex internal representation of their visual environment that evolves with accumulation of new evidence even without a targeted reinforcement. In particular, with more experience, they shift from being sensitive to statistics of only elemental features of the scenes to relying on co-occurrence frequencies of elements while losing their sensitivity to elemental frequencies, but they never encode automatically the predictivity of elements. In contrast, humans involuntarily develop an internal representation that includes single-element and co-occurrence statistics, as well as information about the predictivity between elements. Importantly, capturing human visual learning results requires a probabilistic chunk-learning model, whereas a simple fragment-based memory-trace model that counts occurrence summary statistics is sufficient to replicate honey bees' learning behavior. Thus, humans' sophisticated encoding of sensory stimuli that provides intrinsic sensitivity to predictive information might be one of the fundamental prerequisites of developing higher cognitive abilities.

The effect of interference, offline sleep, and wake on spatial statistical learning.

Statistical learning, the ability of the human brain to uncover patterns organized according to probabilistic relationships between elements and events of the environment, is a powerful learning mechanism underlying many cognitive processes. Here we examined how memory for statistical learning of probabilistic spatial configurations is impacted by interference at the time of initial exposure and varying degrees of wakefulness and sleep during subsequent offline processing. We manipulated levels of interference at learning by varying the time between exposures of different spatial configurations. During the subsequent offline period, participants either remained awake (active wake or quiet wake) or took a nap comprised of either non-rapid eye movement (NREM) sleep only or NREM and rapid eye movement (REM) sleep. Recognition of the trained spatial configurations, as well as a novel configuration exposed after the offline period, was tested approximately 6-7 h after initial exposure. We found that the sleep conditions did not provide any additional memory benefit compared to wakefulness for spatial statistical learning with low interference. For high interference, we found some evidence that memory may be impaired following quiet wake and NREM sleep only, but not active wake or combined NREM and REM sleep. These results indicate that learning conditions may interact with offline brain states to influence the long-term retention of spatial statistical learning.

Development of Cross-Orientation Suppression and Size Tuning and the Role of Experience.

Many sensory neural circuits exhibit response normalization, which occurs when the response of a neuron to a combination of multiple stimuli is less than the sum of the responses to the individual stimuli presented alone. In the visual cortex, normalization takes the forms of cross-orientation suppression and surround suppression. At the onset of visual experience, visual circuits are partially developed and exhibit some mature features such as orientation selectivity, but it is unknown whether cross-orientation suppression is present at the onset of visual experience or requires visual experience for its emergence. We characterized the development of normalization and its dependence on visual experience in female ferrets. Visual experience was varied across the following three conditions: typical rearing, dark rearing, and dark rearing with daily exposure to simple sinusoidal gratings (14-16 h total). Cross-orientation suppression and surround suppression were noted in the earliest observations, and did not vary considerably with experience. We also observed evidence of continued maturation of receptive field properties in the second month of visual experience: substantial length summation was observed only in the oldest animals (postnatal day 90); evoked firing rates were greatly increased in older animals; and direction selectivity required experience, but declined slightly in older animals. These results constrain the space of possible circuit implementations of these features.SIGNIFICANCE STATEMENT The development of the brain depends on both nature-factors that are independent of the experience of an individual animal-and nurture-factors that depend on experience. While orientation selectivity, one of the major response properties of neurons in visual cortex, is already present at the onset of visual experience, it is unknown whether response properties that depend on interactions among multiple stimuli develop without experience. We find that the properties of cross-orientation suppression and surround suppression are present at eye opening, and do not depend on visual experience. Our results are consistent with the idea that a majority of the basic properties of sensory neurons in primary visual cortex are derived independent of the experience of an individual animal.

The relationship between initial threshold, learning, and generalization in perceptual learning.

We investigated the origin of two previously reported general rules of perceptual learning. First, the initial discrimination thresholds and the amount of learning were found to be related through a Weber-like law. Second, increased training length negatively influenced the observer's ability to generalize the obtained knowledge to a new context. Using a five-day training protocol, separate groups of observers were trained to perform discrimination around two different reference values of either contrast (73% and 30%) or orientation (25° and 0°). In line with previous research, we found a Weber-like law between initial performance and the amount of learning, regardless of whether the tested attribute was contrast or orientation. However, we also showed that this relationship directly reflected observers' perceptual scaling function relating physical intensities to perceptual magnitudes, suggesting that participants learned equally on their internal perceptual space in all conditions. In addition, we found that with the typical five-day training period, the extent of generalization was proportional to the amount of learning, seemingly contradicting the previously reported diminishing generalization with practice. This result suggests that the negative link between generalization and the length of training found in earlier studies might have been due to overfitting after longer training and not directly due to the amount of learning per se.

Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes.

Behavioral evidence has shown that humans automatically develop internal representations adapted to the temporal and spatial statistics of the environment. Building on prior fMRI studies that have focused on statistical learning of temporal sequences, we investigated the neural substrates and mechanisms underlying statistical learning from scenes with a structured spatial layout. Our goals were twofold: (1) to determine discrete brain regions in which degree of learning (i.e., behavioral performance) was a significant predictor of neural activity during acquisition of spatial regularities and (2) to examine how connectivity between this set of areas and the rest of the brain changed over the course of learning. Univariate activity analyses indicated a diffuse set of dorsal striatal and occipitoparietal activations correlated with individual differences in participants' ability to acquire the underlying spatial structure of the scenes. In addition, bilateral medial-temporal activation was linked to participants' behavioral performance, suggesting that spatial statistical learning recruits additional resources from the limbic system. Connectivity analyses examined, across the time course of learning, psychophysiological interactions with peak regions defined by the initial univariate analysis. Generally, we find that task-based connectivity with these regions was significantly greater in early relative to later periods of learning. Moreover, in certain cases, decreased task-based connectivity between time points was predicted by overall posttest performance. Results suggest a narrowing mechanism whereby the brain, confronted with a novel structured environment, initially boosts overall functional integration and then reduces interregional coupling over time.

Optogenetic spatial and temporal control of cortical circuits on a columnar scale.

Many circuits in the mammalian brain are organized in a topographic or columnar manner. These circuits could be activated-in ways that reveal circuit function or restore function after disease-by an artificial stimulation system that is capable of independently driving local groups of neurons. Here we present a simple custom microscope called ProjectorScope 1 that incorporates off-the-shelf parts and a liquid crystal display (LCD) projector to stimulate surface brain regions that express channelrhodopsin-2 (ChR2). In principle, local optogenetic stimulation of the brain surface with optical projection systems might not produce local activation of a highly interconnected network like the cortex, because of potential stimulation of axons of passage or extended dendritic trees. However, here we demonstrate that the combination of virally mediated ChR2 expression levels and the light intensity of ProjectorScope 1 is capable of producing local spatial activation with a resolution of ∼200-300 µm. We use the system to examine the role of cortical activity in the experience-dependent emergence of motion selectivity in immature ferret visual cortex. We find that optogenetic cortical activation alone-without visual stimulation-is sufficient to produce increases in motion selectivity, suggesting the presence of a sharpening mechanism that does not require precise spatiotemporal activation of the visual system. These results demonstrate that optogenetic stimulation can sculpt the developing brain.

Prior implicit knowledge shapes human threshold for orientation noise.

Although orientation coding in the human visual system has been researched with simple stimuli, little is known about how orientation information is represented while viewing complex images. We show that, similar to findings with simple Gabor textures, the visual system involuntarily discounts orientation noise in a wide range of natural images, and that this discounting produces a dipper function in the sensitivity to orientation noise, with best sensitivity at intermediate levels of pedestal noise. However, the level of this discounting depends on the complexity and familiarity of the input image, resulting in an image-class-specific threshold that changes the shape and position of the dipper function according to image class. These findings do not fit a filter-based feed-forward view of orientation coding, but can be explained by a process that utilizes an experience-based perceptual prior of the expected local orientations and their noise. Thus, the visual system encodes orientation in a dynamic context by continuously combining sensory information with expectations derived from earlier experiences.

Suppression of cortical neural variability is stimulus- and state-dependent.

Internally generated, spontaneous activity is ubiquitous in the cortex, yet it does not appear to have a significant negative impact on sensory processing. Various studies have found that stimulus onset reduces the variability of cortical responses, but the characteristics of this suppression remained unexplored. By recording multiunit activity from awake and anesthetized rats, we investigated whether and how this noise suppression depends on properties of the stimulus and on the state of the cortex. In agreement with theoretical predictions, we found that the degree of noise suppression in awake rats has a nonmonotonic dependence on the temporal frequency of a flickering visual stimulus with an optimal frequency for noise suppression ~2 Hz. This effect cannot be explained by features of the power spectrum of the spontaneous neural activity. The nonmonotonic frequency dependence of the suppression of variability gradually disappears under increasing levels of anesthesia and shifts to a monotonic pattern of increasing suppression with decreasing frequency. Signal-to-noise ratios show a similar, although inverted, dependence on cortical state and frequency. These results suggest the existence of an active noise suppression mechanism in the awake cortical system that is tuned to support signal propagation and coding.

The best time to acquire new skills: age-related differences in implicit sequence learning across the human lifespan.

Implicit skill learning underlies obtaining not only motor, but also cognitive and social skills through the life of an individual. Yet, the ontogenetic changes in humans' implicit learning abilities have not yet been characterized, and, thus, their role in acquiring new knowledge efficiently during development is unknown. We investigated such learning across the lifespan, between 4 and 85 years of age with an implicit probabilistic sequence learning task, and we found that the difference in implicitly learning high- vs. low-probability events--measured by raw reaction time (RT)--exhibited a rapid decrement around age of 12. Accuracy and z-transformed data showed partially different developmental curves, suggesting a re-evaluation of analysis methods in developmental research. The decrement in raw RT differences supports an extension of the traditional two-stage lifespan skill acquisition model: in addition to a decline above the age 60 reported in earlier studies, sensitivity to raw probabilities and, therefore, acquiring new skills is significantly more effective until early adolescence than later in life. These results suggest that due to developmental changes in early adolescence, implicit skill learning processes undergo a marked shift in weighting raw probabilities vs. more complex interpretations of events, which, with appropriate timing, prove to be an optimal strategy for human skill learning.

Impact of simulated central scotomas on visual search in natural scenes.

PURPOSE: In performing search tasks, the visual system encodes information across the visual field at a resolution inversely related to eccentricity and deploys saccades to place visually interesting targets upon the fovea, where resolution is highest. The serial process of fixation, punctuated by saccadic eye movements, continues until the desired target has been located. Loss of central vision restricts the ability to resolve the high spatial information of a target, interfering with this visual search process. We investigate oculomotor adaptations to central visual field loss with gaze-contingent artificial scotomas. METHODS: Spatial distortions were placed at random locations in 25° square natural scenes. Gaze-contingent artificial central scotomas were updated at the screen rate (75 Hz) based on a 250 Hz eye tracker. Eight subjects searched the natural scene for the spatial distortion and indicated its location using a mouse-controlled cursor. RESULTS: As the central scotoma size increased, the mean search time increased [F(3,28) = 5.27, p = 0.05], and the spatial distribution of gaze points during fixation increased significantly along the x [F(3,28) = 6.33, p = 0.002] and y [F(3,28) = 3.32, p = 0.034] axes. Oculomotor patterns of fixation duration, saccade size, and saccade duration did not change significantly, regardless of scotoma size. CONCLUSIONS: There is limited automatic adaptation of the oculomotor system after simulated central vision loss.

Statistical Learning in Vision.

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety.

Adaptive erasure of spurious sequences in sensory cortical circuits.

Sequential activity reflecting previously experienced temporal sequences is considered a hallmark of learning across cortical areas. However, it is unknown how cortical circuits avoid the converse problem: producing spurious sequences that are not reflecting sequences in their inputs. We develop methods to quantify and study sequentiality in neural responses. We show that recurrent circuit responses generally include spurious sequences, which are specifically prevented in circuits that obey two widely known features of cortical microcircuit organization: Dale's law and Hebbian connectivity. In particular, spike-timing-dependent plasticity in excitation-inhibition networks leads to an adaptive erasure of spurious sequences. We tested our theory in multielectrode recordings from the visual cortex of awake ferrets. Although responses to natural stimuli were largely non-sequential, responses to artificial stimuli initially included spurious sequences, which diminished over extended exposure. These results reveal an unexpected role for Hebbian experience-dependent plasticity and Dale's law in sensory cortical circuits.

Right hemisphere dominance in visual statistical learning.

Several studies report a right hemisphere advantage for visuospatial integration and a left hemisphere advantage for inferring conceptual knowledge from patterns of covariation. The present study examined hemispheric asymmetry in the implicit learning of new visual feature combinations. A split-brain patient and normal control participants viewed multishape scenes presented in either the right or the left visual fields. Unbeknownst to the participants, the scenes were composed from a random combination of fixed pairs of shapes. Subsequent testing found that control participants could discriminate fixed-pair shapes from randomly combined shapes when presented in either visual field. The split-brain patient performed at chance except when both the practice and the test displays were presented in the left visual field (right hemisphere). These results suggest that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provide a prediction about how fMRI activation patterns might change during unsupervised statistical learning.

Bayesian learning of visual chunks by human observers.

Efficient and versatile processing of any hierarchically structured information requires a learning mechanism that combines lower-level features into higher-level chunks. We investigated this chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison that extracts and stores only those chunks of information that are minimally sufficient to encode a set of visual scenes. Our ideal Bayesian chunk learner not only reproduced the results of a large set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input.

A probabilistic hammer for nailing complex neural data analyses.

In this issue of Neuron, Mlynarski et al. (2021) provide a maxent-based normative method for flexible neural data analysis by combining data-driven and theory-driven approaches. The next challenge is identifying the right frameworks to use this method at its best.

Representations of uncertainty: where art thou?

Perception is often described as probabilistic inference requiring an internal representation of uncertainty. However, it is unknown whether uncertainty is represented in a task-dependent manner, solely at the level of decisions, or in a fully Bayesian manner, across the entire perceptual pathway. To address this question, we first codify and evaluate the possible strategies the brain might use to represent uncertainty, and highlight the normative advantages of fully Bayesian representations. In such representations, uncertainty information is explicitly represented at all stages of processing, including early sensory areas, allowing for flexible and efficient computations in a wide variety of situations. Next, we critically review neural and behavioral evidence about the representation of uncertainty in the brain agreeing with fully Bayesian representations. We argue that sufficient behavioral evidence for fully Bayesian representations is lacking and suggest experimental approaches for demonstrating the existence of multivariate posterior distributions along the perceptual pathway.

Statistically defined visual chunks engage object-based attention.

Although objects are the fundamental units of our representation interpreting the environment around us, it is still not clear how we handle and organize the incoming sensory information to form object representations. By utilizing previously well-documented advantages of within-object over across-object information processing, here we test whether learning involuntarily consistent visual statistical properties of stimuli that are free of any traditional segmentation cues might be sufficient to create object-like behavioral effects. Using a visual statistical learning paradigm and measuring efficiency of 3-AFC search and object-based attention, we find that statistically defined and implicitly learned visual chunks bias observers' behavior in subsequent search tasks the same way as objects defined by visual boundaries do. These results suggest that learning consistent statistical contingencies based on the sensory input contributes to the emergence of object representations.

Small modulation of ongoing cortical dynamics by sensory input during natural vision.

During vision, it is believed that neural activity in the primary visual cortex is predominantly driven by sensory input from the environment. However, visual cortical neurons respond to repeated presentations of the same stimulus with a high degree of variability. Although this variability has been considered to be noise owing to random spontaneous activity within the cortex, recent studies show that spontaneous activity has a highly coherent spatio-temporal structure. This raises the possibility that the pattern of this spontaneous activity may shape neural responses during natural viewing conditions to a larger extent than previously thought. Here, we examine the relationship between spontaneous activity and the response of primary visual cortical neurons to dynamic natural-scene and random-noise film images in awake, freely viewing ferrets from the time of eye opening to maturity. The correspondence between evoked neural activity and the structure of the input signal was weak in young animals, but systematically improved with age. This improvement was linked to a shift in the dynamics of spontaneous activity. At all ages including the mature animal, correlations in spontaneous neural firing were only slightly modified by visual stimulation, irrespective of the sensory input. These results suggest that in both the developing and mature visual cortex, sensory evoked neural activity represents the modulation and triggering of ongoing circuit dynamics by input signals, rather than directly reflecting the structure of the input signal itself.